Modelling use rexperience with web sites-Usability, hedonic value, beauty and goodness
surrogate model 解释
英文回答:The proxy model is a mathematical model used to simulate the behaviour ofplex systems. It is designed to simplifyplex systems and make them easier to process. Proxy models are usually built on existing data or empirical knowledge, using mathematics, statistics or machine learning. It can replace the original system for forecasting, optimization or control, thus saving costs and time. Proxy models are widely used in modern science and engineering, for example in aircraft design using proxy modelsto simulate aerodynamics and in chemical engineering to optimize production processes.代理模型是一种数学模型,用于模拟复杂系统的行为。
其设计旨在简化复杂系统,使其易于处理。
代理模型通常基于已有数据或经验知识,利用数学、统计学或机器学习等方法建立起来。
它可以替代原系统进行预测、优化或控制,从而节约成本和时间。
代理模型在现代科学和工程领域有广泛应用,例如在飞行器设计中利用代理模型模拟空气动力学,在化工工程中利用代理模型优化生产过程。
The proxy model has many advantages. For example, it could simplify the originalplex system and make calculations more efficient. Some systems are inherentlyplex and requireconsiderableputing resources to analyse and optimize. But proxy models can extract key features and patterns and then solve problems with simple mathematical models, so they can produce results quickly. The proxy model could also supplement the original system data. Some systems may have little or no access to data, which makes analysis difficult. However, proxy models can predict missing data by using available data or empirical knowledge, thus making system—wide analysis moreplete.代理模型有很多好处。
nlp小模型蒸馏
nlp小模型蒸馏
模型蒸馏是一种将大型模型压缩为小型模型的技术,同时保持其性能。
在自然语言处理(NLP)中,模型蒸馏可以用于压缩语言模型,以便在资源受限的设备上运行。
模型蒸馏的基本思想是通过训练一个小型模型来模拟大型模型的输出。
这可以通过以下两种方法实现:
1. 知识蒸馏:在训练小型模型时,将大型模型的输出作为软目标,以指导小型模型的学习。
2. 模型压缩:通过减少模型参数的数量或使用更高效的架构来压缩模型。
模型蒸馏的过程通常包括以下步骤:
1. 训练大型模型:首先,需要训练一个大型的NLP 模型,例如Transformer 模型。
2. 收集大型模型的输出:在训练大型模型时,收集其在训练数据上的输出。
3. 训练小型模型:使用大型模型的输出作为软目标,训练一个小型模型。
4. 优化小型模型:通过调整小型模型的参数,使其尽可能地模拟大型模型的输出。
模型蒸馏可以有效地减少模型的大小和计算资源的使用,同时保持其性能。
这使得它成为在资源受限的设备上运行NLP 模型的一种有前途的方法。
Prompt Engineering Mastery - 10 Best Practices
What is prompt engineering?Prompt engineering is the craft of designing and refining inputs (prompts) to elicit the desired output from AI language models. It requires a blend of creativity, understanding of the model’s capabilities, and strategic structuring of the question or statement to guide the AI towards providing accurate, relevant, and useful responses. Prompt engineering improves communication between humans and machines, ensuring the resulting interaction is efficient and effective.Why is prompt engineering important?Prompt engineering is crucial because it influences the performance and utility of AI language models. The quality of the input determines the relevance and accuracy of the AI’s response, making prompt engineering a pivotal skill for anyone looking to harness the full potential of these powerful tools. Prompt engineering is not only for prompt engineers. By effectively communicating with AI, anyone can unlock insights, generate ideas, and solve problems more efficiently.Here are several reasons why prompt engineering is important: •Improves accuracy: Well-crafted prompts lead to more precise answers, reducing the likelihood of misinterpretation orirrelevant responses from the AI.•Saves time: Prompt engineering streamlines interactions with the AI by getting the desired information in fewer attempts,saving valuable time for users.•Facilitates complex tasks: Complex tasks require complex understanding; good prompts translate intricate questions intoa form that AI can process effectively.•Improves user experience: A user’s experience with an AI system can greatly improve when the prompts lead to clear,concise, and contextually appropriate answers.•Enables better outcomes: In areas such as coding, content creation, and data analysis, well-engineered prompts can leadto higher-quality outcomes by leveraging AI’s capabilities tothe fullest.•Drives innovation: As we better understand how tocommunicate with AI, we can push the boundaries of what’spossible, leading to innovative applications and solutions.10 Prompt engineering best practicesCrafting effective prompts for AI can improve the quality and relevance of the responses you receive. This expertise requires a nuanced understanding of how AI interprets and processes natural language inputs. Ahead, we explore ten AI prompt engineering best practices to help you communicate with AI more effectively:1. Be as specific as possibleSpecificity is key to obtaining the most accurate and relevant information from an AI when writing prompts. A specific prompt minimizes ambiguity, allowing the AI to understand the request’s context and nuance, preventing it from providing overly broad or unrelated responses. To achieve this, include as many relevant details as possible without overloading the AI with superfluous information. This balance ensures that the AI has just enough guidance to produce the specific outcome you’re aiming for.When creating the best prompts for an AI, ask for the following specifics:•Detailed context: Provide the AI with enough background information to understand the scenario you’re inquiring about.This includes the subject matter, scope, and any relevantconstraints.•Desired format: Clearly specify the format in which you want the information to be presented, whether it’s a list, a detailedreport, bullet points, or a summary. Mention any structuralpreferences, such as headings, subheadings, or paragraphlimits.•Output length: Detail how long you want the AI’s response, whether “3paragraphs” or “250words.”•Level of detail: Indicate the level of detail required for the response, from high-level overviews to in-depth analysis, toensure the model’s output matches your informational needs. •Tone and style: Request the preferred tone and style, whether it’s formal, conversational, persuasive, or informational, tomake sure the output aligns with your intended audience orpurpose.•Examples and comparisons: Ask the AI to include examples, analogies, or comparisons to clarify complex concepts or make the information more relatable and easily understood.Prompt Example:Please provide an outline for a comprehensive report that analyzes the current trends in social media marketing for technology companies, focusing on the developments from 2020 onward.The outline should include an introduction, three main sections addressing different aspects of social media trends, and a conclusion summarizing the findings. Please suggest the types of graphs that could illustrate user engagement trends and list bullet points that summarize key marketing strategies in each section. 2. Supply the AI with examplesIncorporating examples into your prompts is a powerful technique to steer the AI’s responses in the desired direction. By providing examples as you write prompts, you set a precedent for the type of information or response you expect. This practice is particularly useful for complex tasks where the desired output might be ambiguous or for creative tasks with more than one correct answer. When you supply the AI with examples, ensure they represent the quality and style of your desired result. This strategy clarifies your expectations and helps the AI model its responses after the examples provided, leading to more accurate and tailored outputs. Here are some example types you could provide to an AI to help guide it toward generating the best response possible:•Sample texts: Share excerpts reflecting the style, tone, and content you want the AI to replicate.•Data formats: To guide the AI’s output, provide specific data structures, such as table layouts or spreadsheet formats.•Templates for documents: Offer templates to ensure the AI’s response follows a desired structure and format.•Code snippets: Provide code examples if you need help with programming tasks to ensure correct syntax and logic. •Graphs and charts examples: If you’re asking the AI to create similar graphics, share samples of visual data representation.•Marketing copy: If you’re crafting marketing content, present ad copy that aligns with your brand’s voice for the AI to mimic. Prompt Example:Create a comparison table for two project management tools, Tool A and Tool B.Include the following categories: Price, Key Features, User Reviews, and Support Options. For instance, under Key Features, list things like ‘Task Assignment’,‘Time Tracking’, and ‘File Sharing’.The format should mirror something like this:Please ensure the table is concise and suitable for inclusion in a business report.3. Get better answers by providing dataIncorporating specific and relevant data into your prompts significantly enhances the quality of AI-generated responses, providing a solid foundation for the AI to understand the context and craft precise answers. Providing data that includes numerical values, dates, or categories, organized in a clear and structured way, allows for detailed analysis and decision-making. It’s essential to give context to the data and, when possible, to cite its source, lending credibility and clarity to the specific task, whether for quantitative analysis or comparisons.To ensure the AI delivers the most relevant and insightful answers, always use updated and well-organized information, and if comparisons are needed, establish clear parameters. Supplying the AI with concrete, contextualized data transforms raw figures into intelligible and actionable insights. Data-driven prompts are particularly valuable in tasks requiring a deep dive into numbers, trends, or patterns, enabling the AI to generate outputs that can effectively inform business strategies or research conclusions.Prompt Example:Please analyze the sales data from the first quarter of 2024 provided in the attached PDF document. I need a summary that identifies our best-selling product, the overall sales trend, and any notable patterns in customer purchases.The PDF contains detailed monthly sales units for three products: Product A, Product B, and Product C. After reviewing the data, summarize your findings in a concise paragraph that is suitable for a business meeting. Highlight significant increases or decreases in sales and offer insights into potential factors driving these trends. 4. Specify your desired outputWhen engaging with AI, articulate the precise format and structure you expect in the response. Specify whether you require a detailedreport, a summary, bullet points, or a narrative form to ensure the AI tailors its output to your needs.Indicate any preferences such as tone, style, and the inclusion of certain elements like headings or subheadings. By clearly defining your desired output, you guide the AI to deliver information that aligns seamlessly with your intended use.Prompt Example:Create a comprehensive overview of the key milestones in the history of software development. The output should be structured as a timeline with bullet points, each bullet including the year, the milestone event, and a brief description of its significance. Start from the 1980s. The tone should be educational. Please limit the overview to ten major milestones to maintain conciseness.5. Provide instructions on what to do instead of what not to doWhen constructing prompts for AI, it’s more effective to direct the system toward the desired action rather than detailing what it should avoid. This positive instruction approach reduces ambiguity and focuses the AI’s processing power on generating constructive outcomes.Negative instructions often require the AI to interpret and invert them, increasing the cognitive load and potential for misunderstanding. By clearly stating the intended actions, you enable the AI to apply its capabilities directly to fulfilling the task at hand, improving the efficiency and accuracy of the response.Prompt Examples:•Avoid: “Don’t write too much detail. → Use Instead: “Please provide a concise summary.”•Avoid: “Avoid using technical jargon.”→ Use Instead: “Use clear and simple language accessible to a general audience.”•Avoid: “Don’t give examples from before the year 2000.”→ Use Instead: “Provide examples from the year 2000 onwards.”6. Give the model a persona or frame of referenceAssigning a persona or a specific frame of reference to an AI model can significantly enhance the relevance and precision of its output. By doing so, you get more relevant responses, aligned with a particular perspective or expertise, ensuring that the information provided meets the unique requirements of your query.This approach is especially beneficial in business contexts where domain-specific knowledge is pivotal, as it guides the AI to utilize a tone and terminology appropriate for the given scenario. The persona also helps set the right expectations and can make interactions with the AI more relatable and engaging for the end user.Prompt Example:Imagine you are a seasoned marketing consultant. Please draft an email to a new startup client outlining three digital marketing strategies tailored for their upcoming product launch (see attached PDF for details).Include key performance indicators (KPIs) for each strategy that will help track their campaign’s success. Ensure the tone is encouraging and professional, imparting confidence in your expertise.7. Try chain of thought promptingChain of thought prompting is a technique that elicits a more deliberate and explanatory response from an AI by specifically asking it to detail the reasoning behind its answer. By prompting the AI to articulate the steps it takes to reach a conclusion, users can better understand the logic employed and the reliability of the response.This approach is particularly useful when tackling complex problems or when the reasoning process itself is as important as the answer. It ensures a deeper level of problem-solving and provides a learning opportunity for the user to see a modeled approach to reasoning.Prompt Example:Imagine you are a software engineer tasked with optimizing this piece of software for performance:[Insert code block]Use the following chain of thought to guide your approach:•Performance profiling: Start with how you would profile the software to identify current performance bottlenecks.•Optimization techniques: Discuss the specific techniques you would consider to address the identified bottlenecks, such asalgorithm optimization, code refactoring, or hardwareacceleration.•Testing and validation: Describe your method for testing the optimized software to ensure that the changes have had thedesired effect and have not introduced new issues.•Implementation strategy: Finally, outline how you would safely implement the optimized code into the production environment, ensuring minimal disruption.Conclude with a summary of the key steps in the optimization process and how you would document and maintain the improvements over time.8. Split complex tasks into simpler onesWhen dealing with complex tasks, breaking them into simpler, more manageable components can make them more approachable for an AI. Using step by step instructions helps prevent the AI frombecoming overwhelmed and ensures that each part of the task is handled with attention to detail.Additionally, this approach allows for easier monitoring and adjustment of each step, facilitating better quality control throughout the process. By compartmentalizing tasks, the AI can also use its resources more efficiently, allocating the necessary attention where it’s most needed, resulting in a more effective problem-solving strategy.Prompt Example:Avoid a single broad prompt:•“Write a 1500-word article on the impact of AI on remote work.”Try an initial prompt and follow-up prompts instead: •“Develop a detailed outline for a 1500-word article titled ‘Revolutionizing Remote Work: The Role of AI for TechProfessionals.’ The outline should include an engagingintroduction, three main sections titled ‘Enhancing Productivity with AI Tools,’‘AI-Driven Communication Optimization,’ and‘Advanced Project Management through AI,’ plus a conclusion that offers a perspective on future developments.”•“Compose a detailed introduction for the article‘Revolutio nizing Remote Work: The Role of AI for TechProfessionals.’ The introduction should be 150-200 words,setting the stage for how AI is changing the game for remoteworkers in the tech industry, and providing a hook that willentice tech professionals to continue reading.”9. Understand the model’s shortcomingsIn crafting prompts for an AI, recognize the model’s limitations to set realistic expectations. Prompting AI to perform tasks it’s not designed for, such as interacting with external databases or providing real-time updates, will lead to ineffective and potentially misleading outputs called AI hallucinations.Here are some known shortcomings of AI models:•Lack of real-time data processing, as the knowledge is up-to-date only until the last training cut-off.•Inability to access or retrieve personal data unless it has been shared during the interaction.•No direct interaction with external software, databases, or live web content.•Potential bias in the data, as AI models can inadvertently learn and replicate biases present in their training data.•Limited understanding of context can lead to less nuanced responses in complex or ambiguous situations.•The absence of personal experiences or emotions means the AI cannot form genuine, empathetic connections or offerpersonal anecdotes.10. Take an experimental approach to promptingPrompt engineering is an emergent field that necessitates an experimental mindset. As you navigate this new territory, use an iterative process to test various prompts, paying careful attention to how slight modifications can significantly alter the AI’s responses. You’ll only learn how models respond by testing them.While maintaining a commitment to AI privacy and ethical standards is key, don’t hesitate to explore diverse phrasings and structures to discover the most effective prompts. This trial-and-error process can yield better results and contribute to a broader understanding of how large language models interpret and act on different types of instructions.。
汽车主动悬架与ABS系统联合控制研究
第29卷第6期 2006年6月合肥工业大学学报(自然科学版)J OU RNAL OF H EFEI UN IV ERSIT Y OF TECHNOLO GYVol.29No.6 J un.2006 收稿日期:2006202221作者简介:杨柳青(1971-),男,安徽巢湖人,合肥工业大学硕士生;陈无畏(1951-),男,湖南攸县人,博士,合肥工业大学教授,博士生导师.汽车主动悬架与ABS 系统联合控制研究杨柳青, 陈无畏, 初长宝(合肥工业大学机械与汽车工程学院,安徽合肥 230009)摘 要:文章建立了7自由度的半车模型、液压制动模型及白噪声路面模型,基于实用的PID 控制器,将汽车主动悬架与ABS 系统进行了联合控制。
悬架控制系统既以改善悬架性能为调节目标,又以车轮滑移率在最优时车轮法向反力达最优为调节目标;ABS 系统以车轮滑移率达最优,制动性能提高为调节目标。
仿真实验表明,在联合控制情况下,汽车悬架的性能指标、制动性能较之两系统单独控制的情况均有明显改善与提高。
关键词:主动悬架;防抱制动系统;联合控制;PID 控制器中图分类号:U463.33;U463.5;TP373.3 文献标识码:A 文章编号:100325060(2006)0620767205R esearch on the conjoint control of the vehicle ’s active suspension and anti 2lock braking systemYAN G Liu 2qing , C H EN Wu 2wei , C HU Chang 2bao(School of Machinery and Automobile Engineering ,Hefei University of Technology ,Hefei 230009,China )Abstract :In t he paper ,t he semi 2vehicle model of seven degrees of f reedom ,t he hydraulic braking model and t he white 2noise road surface model are founded ,and based on t he practical PID cont roller ,t he con 2joint co nt rol of t he vehicle ’s active 2suspension and anti 2lock braking system (ABS )is achieved.The adjusting of t he suspension cont rol system aims at imp roving suspension performance as well as get 2ting t he optimal normal counter force of t he wheel as t he sliding rate of t he wheel is at it s best.The adjusting of t he ABS aims at getting t he optimal sliding rate of t he wheel and advancing braking per 2formance.Simulation experiment s show t hat under t he conjoint cont rol ,bot h t he suspension perform 2ance indexes and t he braking performance of t he vehicle have evident improvement s in compariso n wit h t he single control of t he two systems.K ey w ords :active suspension ;anti 2lock braking sysgem ;conjoint cont rol ;PID conlroller0 引 言汽车由多个系统组成,而且各系统相互协作、相互影响,因而整车性能的提高依赖于各系统的协调工作。
写旅游的优美句子英文单词
写旅游的优美句⼦英⽂单词1. 描写风景的英语优美句⼦⼀条条⼩河宛如蓝⾊的缎带缠绕着⼀望⽆际的绿⾊⽥野,远处⼀座座造型古朴、⾊彩和谐的⼩屋,⼀派美丽动⼈的⽥园风光!A creek twines the vast green field just like the blue color satin ribbon, a distant place modelling is being plain, color harmonious hut, a school of beautiful moving rural scenery!⼀座座古⽼的风车,风车的风叶像张开的翅膀,迎风转动,与绿草、野花构成了独特的景致更为这童话般世界增添神奇⾊彩!An ancient windmill, windmill's wind leaf opens likely the wing, rotates against the wind, with the green grass, the wild flower constituted the unique view this fairy tale world addition mysterious color!⼀对对⾊彩鲜艳、精致绚丽,象征着甜蜜爱情的⽊鞋!还有那华丽的郁⾦⾹倾倒⽆数情⼈……郁⾦⾹飘⾹的季节,娇艳妩媚的⼥孩的笑容如花传芬芳……幽幽湖边,风车,绿草,⼩房,多惬意红红⽕⽕郁⾦⾹花⽥,阵阵芳⾹,风车悠悠转!这是梦吗?A right color bright, fine gorgeous, is drafting the happy love sabot likely! Also has that magnificent tulip to fall the season which innumerable sweetheart 。
the tulip smells as sweet, the tender and beautiful charming girl's smiling face like flower passes on fragrantly 。
simulation modeling practice
Simulation Modeling PracticeSimulation modeling is an essential skill that many professionals use in their work. It involves creating a virtual representation of a system or process, allowing us to test and experiment with different scenarios and outcomes. In this article, we will explore the importance of simulation modeling, how to practice it, and some tips for success.The Benefits of Simulation ModelingSimulation modeling has many benefits, including:* Accuracy: By simulating a system or process, we can eliminate human error and guesswork, ensuring a more accurate representation of reality.* Efficiency: Simulation modeling allows us to test different scenarios and outcomes quickly and efficiently, saving time and resources.* Portability: Simulation models can be easily transferred between different systems or organizations, making them a valuable asset in cross-functional teams.How to Practice Simulation ModelingPracticing simulation modeling requires a combination of knowledge, skills, and resources. Here are some tips for success:* Identify a topic: Start by choosing a system or process that you are interested in simulating. This could be anything from a business process to a mechanical device.* Research resources: Find online resources that can help you learn more about simulation modeling. This could include tutorials, courses, or online communities.* Practice with a partner: Pair up with someone who is also learning simulation modeling and work together on simulating different scenarios. This will help you identify areas where you need more practice and provide feedback on your progress.* Use simulation software: There are many simulation software packages available that allow you to create virtual representations of systems or processes. Practice using these software packages to develop your skills.* Be patient: Learning new skills takes time and practice, so be patient with yourself and your progress.ConclusionSimulation modeling is an essential skill that many professionals use in their work. By practicing simulation modeling, you can improve your skills and become more effective in your job. The benefits of simulation modeling include accuracy, efficiency, and portability. To practice simulation modeling, identify a topic, research resources, practice with a partner, use simulation software, and be patient with yourself and your progress. With these tips in mind, you can become a more effective simulation modeler and achieve success in your career.。
simulation modelling practice
simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。
Geometric Modeling
Geometric ModelingGeometric modeling is a crucial aspect of computer graphics and design, playing a significant role in various fields such as engineering, architecture, animation, and gaming. It involves the creation and manipulation of geometric shapes and structures in a digital environment, allowing for the visualization and representation of complex objects and scenes. However, despite its importance, geometric modeling presents several challenges and limitations that need to be addressed in order to improve its efficiency and effectiveness. One of the primary issues in geometric modeling is the complexity of representing real-world objects and environments in a digital format. The process of converting physical objects into digital models involves capturing and processing a vast amount of data, which can be time-consuming and resource-intensive. This is particularly challenging when dealing with intricate and irregular shapes, as it requires advanced techniques such as surface reconstruction and mesh generation to accurately capture the details of the object. As a result, geometric modeling often requires a balance between precision and efficiency, as the level of detail in the model directly impacts its computational cost and performance. Another challenge in geometric modeling is the need for seamless integration with other design and simulation tools. In many applications, geometric models are used as a basis for further analysis and manipulation, such as finite element analysis in engineering or physics-based simulations in animation. Therefore, it is essential for geometric modeling software to be compatible with other software and data formats, allowing for the transfer and utilization of geometric models across different platforms. This interoperability is crucial for streamlining the design and production process, as it enables seamless collaboration and data exchange between different teams and disciplines. Furthermore, geometric modeling also faces challenges related to the representation and manipulation of geometric data. Traditional modeling techniques, such as boundary representation (B-rep) and constructive solid geometry (CSG), have limitations in representing complex and organic shapes, often leading to issues such as geometric inaccuracies and topological errors. To address this, advanced modeling techniques such as non-uniform rational B-splines (NURBS) and subdivision surfaces have been developed toprovide more flexible and accurate representations of geometric shapes. However, these techniques also come with their own set of challenges, such as increased computational complexity and difficulty in controlling the shape of the model. In addition to technical challenges, geometric modeling also raises ethical and societal considerations, particularly in the context of digital representation and manipulation. As the boundary between physical and digital reality becomes increasingly blurred, issues such as intellectual property rights, privacy, and authenticity of digital models have become more prominent. For example, the unauthorized use and reproduction of digital models can lead to copyright infringement and legal disputes, highlighting the need for robust mechanisms to protect the intellectual property of digital content creators. Similarly, the rise of deepfakes and digital forgeries has raised concerns about the potential misuse of geometric modeling technology for malicious purposes, such as misinformation and identity theft. It is crucial for the industry to address these ethical concerns and develop standards and regulations to ensure the responsible use of geometric modeling technology. Despite these challenges, the field of geometric modeling continues to evolve and advance, driven by the growing demand forrealistic and interactive digital experiences. Recent developments in machine learning and artificial intelligence have shown promise in addressing some of the technical limitations of geometric modeling, such as automated feature recognition and shape optimization. Furthermore, the increasing availability of powerful hardware and software tools has enabled more efficient and accessible geometric modeling workflows, empowering designers and artists to create intricate and immersive digital content. With ongoing research and innovation, it is likely that many of the current challenges in geometric modeling will be overcome, leading to more sophisticated and versatile tools for digital design and visualization. In conclusion, geometric modeling is a critical component of modern digital design and visualization, enabling the creation and manipulation of complex geometric shapes and structures. However, the field faces several challenges related to the representation, integration, and ethical implications of geometric models. By addressing these challenges through technological innovation and ethical considerations, the industry can continue to push the boundaries of what ispossible in digital design and create more immersive and impactful experiences for users.。
Alias自动化工作流程教程说明书
TR473677Procedural and Automated Workflows in Alias for AutomotiveMichael Günther-GeffersAutodeskDescriptionIn this class we will go over some of the new features in Alias software that can help inprocedural modeling techniques, and tools that you can create with scripts to be driven through Dynamo software. Dynamo is a visual programming platform that you can use to create custom algorithms to process data and generate geometry. Since version 2019, we have had anintegration of Dynamo in our Alias line of products (Concept, Surface, and AutoStudio). With the latest release of 2021, we have included Dynamo player, which enables anyone to run scripts and capitalize on the power of these tools to improve workflows and processes to save time and effort.SpeakerWhile Michael Günther-Geffers achieved his diploma inmathematics and computers in 2006, he has already been intouch with CAD programs for the automotive industry since theyear 2000.He started as a quality assurance engineer for ICEM Surf, andlater also tested its integration into Catia V5 (ICEM ShapeDesign at that time, later renamed to ICEM Catia).In 2010 he joined Virtual Shape Research (VSR) as a QA,support and content creator for a rendering and class A pluginfor Rhinoceros 3D.Joining Autodesk with the acquisition of VSR in 2013, he thenworked as a UX designer for SpeedForm and later Alias.2018 he transitioned back to the QA role, becoming the technical lead for the testing of Alias. Since 2019 he is the QA manager for all automotive products (Alias, VRED, SketchBook, Shotgun), and became a free time enthusiast in using Dynamo and writing scripts to solve problems for Alias users. Learning Objectives • Understand in which areas Dynamo and the Dynamo Player can be utilized• Learn how to make a Dynamo Script work for the Dynamo PlayerApplication areas for DynamoDynamo and the Dynamo Player can be used in much more areas than probably most people are aware of. This class goes over several categories of possible appliances and shows you example scripts for each of them.Alias 2021.2 comes with 14 Dynamo Player sample scripts, which demonstrate how Dynamo can be used to create your own tools. Each of these scripts could have been written by anyone who understands Dynamo. There is no dependency of them to the Autodesk development team. You can find those scripts on t he What’s New Shelf, which is accessible under “Help –What’s New –What’s New Shelf”:TemplatesWhenever you have a reoccurring task of creating objects of a certain shape and/or structure, or modifying them in a way, chances are not too bad that this approach could be captured in a Dynamo script. If that is the case, you can create a Dynamo Player tool, which can then be saved onto your shelf in Alias. This allows you to easily skip over the reoccurring work, e.g. like creating a base shape of certain dimensions, as Dynamo does this for you. You simply start using your tailored tool in Alias. When doing so, you don’t have to go to Dynamo, or even know that Dynamo is utilized in the background. This allows each user to optimize his or her workflow, saving precious time which can be used to create more and better models.Nurbs TemplatesA typical reoccurring shape in the automotive contextis a tire. While it is true that the shape of a tire can beeasily created by rotating a profile curve by 360degrees, having a Dynamo Player tool available forthis shape comes with several advantages. You candirectly enter “real life” parameters like the rim size orthe width of the tire. At the same time, you can justsimply move sliders to adjust the global shape, toachieve the wanted result very quickly.The tire script takes construction points as input, tothen create the Nurbs tire(s) at the wantedposition(s).Subdiv TemplatesSubdiv templates can be even morepowerful than Nurbs, as Dynamooffers a lot of notes to create andmodify subdiv geometry. A goodexample for this is the Wheel Archtool. As a lot of car designers startwith the wheel arches and the sideshape of a car, the Wheel Arch tool provides an easy way to create two wheel arches with specified radius and amount of faces. Both wheel arches are bridged together, to form the side shape of a car, again with user defined values like distance and amount of faces. This template can save the first 10-15 minutes of modeling in the beginning of each new subdiv car design.Similar tools could be created for other objects which have a defined shape every time, like steering wheels, seats, and such.Templates using geometry inputProbably most templates would take defined numerical values as input, e.g. the radius of the wheel arch, or the width of a tire. But you should also be aware that it sometimes might make sense to drive the geometry created by your templates by Alias geometry. This is for example the case when working visually, like when you want to create a 3D model from a 2D sketch. In this case, you don’t have technical data like the length of the car. Instead you would like to drive your base shape by simply dragging your mouse. One way to achieve this is letting the user create the needed geometry, and then select it for the Dynamo Player script. Another way is to provide the user with a wire file, which already has the needed geometry input in place.The wheel arch tool has a version which does exactly that. Instead of using the Dynamo Player, you can import a sample wire file. This file already has the template geometry created (in this case, the bridged wheel arches creating the side shape of a car). The dimension of this shape is controlled by Alias geometry, in this case Nurbs curves. By that, the user can simply move the curves to the needed positions, and move e.g. CVs as needed, looking at a displayed background canvas, to fit the wanted dimensions.To check out thi s workflow, click on the “2021” tab of the What’s New Shelf, and click on the icon all on the right. A file browser opens, pointing at the location “C:\ProgramFiles\Autodesk\AliasAutoStudio2021.2\Dynamo\Sample Files”. Navigate into the “Subdiv Wheel Arch” folder, and double click on the wire file Subdiv-Wheel-Arch-2021.2.4.wire to import it.Similar as in this wire file, every user who wants to drive his template object by Alias geometry, can simply save a wire file, which has the wanted Dynamo script referenced. Since Alias 2021, dragging and dropping the “import wire file” tool onto a shelf remembers the last path being used. This way, with very few clicks, you can import a wire file to drive your template creation with geometry instead of numerical parameters. You can see this workflow in the following video:https:///watch?v=nOXOC0qkO3kSubdiv tools using the T-Spline library in DynamoThe T-Spline library is a library which is often used in the background for a lot of Alias subdiv operations. While the resulting object type of T-Spline nodes in Dynamo is a T-Spline, it will be converted to a subdiv when it is sent to Alias.T-Spline is a more powerful type of geometry than a subdivision object. As this comes with a cost of performance, and as having two different types of subdivision objects would have been likely a confusing user experience, Alias introduced only subdivision surfaces 1.5 years ago. Nevertheless, the T-Spline library is very powerful. In Dynamo, a lot of nodes allow you to create and modify T-Spline objects, to then send them to Alias as subdivision objects.In Dynamo, the T-Spline nod es can be found in the tree on the left under “Geometry – TS pline”. In the beginning, probably the nodes creating complete bodies are most useful to you. Those nodes can be found in the sub section “TSplineSurface”.Primitive toolsUtilizing the T-Spline library in Dynamo, it was veryeasy to add the functionality to create subdivcones, spheres, quad balls and torus as DynamoPlayer tools in Alias. Depending on your needs andpreferences, you might want to add differentversions of those tools. For example, the currentsubdiv box creation tool in Alias only allows you tochoose the spans for the X, Y and Z direction, thedimensions are controlled dynamically. If youprefer to define width, length and height of the boxnumerically instead, you could simply use theDynamo node TSplineSurface.ByBoxLengths, andcreate a Dynamo Player tool from it. An examplelike this will be shown in the second section of this handout.General toolsThe T-Spline library in Dynamo also offers morecomplex subdiv generation nodes, like the Sweep,the Revolve and the Pipe tool. While the Sweepand Revolve tool work rather straight forward andcan be seen as the subdiv pendants of thecorresponding Nurbs tools, the pipe tool deservesa second look. When the input curves aredisconnected, it will simply create a pipe for eachcurve, as expected. But if the curves areconnected, it will take care of the subdiv topologyat the meeting points, creating one closed subdivbody out of all connected curves.Creating distributed geometryOf course, Dynamo can also be used in its well-known area of creating and placing a lot of objects in an easy fashion. With the “Nurbs-Hexagon-Pattern” sample script, people can try different versions of a hexagonal pattern, which will be distributed over the input surface the user chooses:It is worth pointing out that you can also apply easier, self-written scripts with great visual effects. The following script has been created after 2021.2 was shipped, so it’s not part of the sample scripts of this release. It simply distributes a closed subdiv body along a curve, in a controllable nonlinear way. It also allows a staggered rotation of that body along the guide curve. This creates a nice visual effect, and saved a good amount of modeling time, while the script itself was written in about an hour:Ease of useThe Dynamo Player allows you to tailor your tools, as you are now in control about which parameters to expose, how to name them, and what the default parameter range is. E.g. you can now create your own circle tool, which takes the radius of the circle as a numerical input value. This way, you don’t have to create the circle, and then afterwards scale it in the information window, to achieve the wanted size. You can directly enter the wanted value. Modifying existing geometryAnother area of applying Dynamo scripts which might be easily overlooked is the ability to write your own “modification” tools. The quotation marks must be used, because Dynamo can’t directly work on the input geometry. But you can take the input, create a copy, and modify the copy in the needed way. When the original input is then deleted afterwards, you have effectively (in a way) modified your input.It is e.g. possible to write a script to align subdiv geometry perpendicularly to reference geometry, as you can see in this video:https://youtu.be/Ge5PInMRHs8?t=26Another way to use this is the sample morph script “Subdiv-Morph-between-2-Objects”, which is part of the What’s New Shelf. It takes two differently shaped su bdiv’s as an input, and then creates a third copy in between them, which is an intermediate shape of the two input bodies. With one single slider, you can then control if the new object shall be closer to the shape of the first, or the second input body, as you can see here:https://youtu.be/Jx531XHWFAg?t=76Be aware that both input bodies must have the same number of CVs, to allow the script to work. Ideally, they should be created via copy and paste, before they received their different shape. Import and Export operationsDynamo is also able to import and export several types of file formats, such as images, excel files, T-Spline formats (.tss and .tsm), text and CSV files. A Dynamo Player sample script which utilizes this is the “Export-to-tsm” tool. It allows the user to select a subdiv body, and then write it to disk, with a user given file path and file name. This is useful if you want to send Alias subdiv geometry to Fusion (Fusion can import the .tsm file format).Alias does not support the .tsm export functionality natively yet. In fact, this script has been written for an Autodesk employee, who needed a quick support of this data exchange. This is therefore a good example on how it is now possible to solve issues directly, without having to wait for a next Alias release.Making a Dynamo script work for the Dynamo PlayerIn general, each Dynamo script can become a Dynamo Player script, and therefore utilized as being a tool in Alias. Let’s create a new one from scratch. Start Alias and start the “normal” Dynamo tool. It can be found in the Palette, in the bottom right of the “Transform” tab. Keep the left mouse button pressed to expand the tools, and choose the “Dynamo” tool on the ri ght: After Dynamo has come up, click on “New”. We want to create a Dynamo Player tool for a circle, which allows the user to specify the radius directly on creation. So, the first thing we need to do, is to add a “Circle” no de. Right click into the grap hics area of Dynamo, and type “circle” into the upcoming search window:Choose the option ByCenterPointRadius. A node named Circle.ByCenterPointRadius appears. The needed inputs for this node are displayed on the left and named centerPoint and radius. The output of this node on the right is named Circle.Next thing we need are the nodes we want to gather the needed input from Alias. Right click in the graphics area of Dynamo, and type in “select”:Pick the “Select from Alias” option on the top of the lis t. A nod e named “Select from Alias” appears. To connect this node with the Circle node we created before, left mouse button click on the text Geometry, and then left mouse button click on the text centerPoint of the “Circle” node. As only points are suitab le inputs for a center point of the circle to be created, let’s limit the Alias selection to that object type. To do so, click on Unspecified in the “Select from Alias” note, and select the entry Point. This will set the selection filter of the Dynamo Player tool accordingly, when this script is used.Our last remaining needed input is a value for the radius. Right click again into the graphics area of Dynamo, and type in “slider”. Pick the “Number Slider” entry of the upcoming list. To connect this node wi th our “Circle” no de, left click on the >symbol on the right of the “Number Slider” no de, then left click on the text radius of the “Circle” no de.As we want this slider to show up in the Dynamo Player tool, we need to mark it accordingly. For that, right click on the “Number Slider”, and activate the option Is Input.Note that th is option is set by default for the “Select from Alias” no de, as the solely purpose of this node is to select something from Alias, so it always will be an input.Once the circle creation is complete, we want to send the geometry back to Alias. For this, right click into the graphics area from Dynamo, and type in “send”. Pick the “Send to Alias” entry. Left click on the text Circle of the “Circle” no de, then left click on the >icon on the left of the “Send to Alias” no de.A Dynamo script which sends geometry to Alias needs to be saved on disk before it can work. Reason for that is, that the Alias model needs to store the path to the Dynamo script. This can only happen, if the script exists somewhere on disk.Once you have saved your file, you can use it in the Dynamo Player. To do so, open the Dynamo Player in Alias. It’s also in the Transform tab of the Palette, in the bottom right, called dynply.Browse to your script which you have just saved, using the “…” icon in the Dynamo Player tool window:Once you have done that, you will notice that your Dynamo Player window has changed. It now shows the name of your saved script in the title, as well as the needed inputs (a point to select, and a radius to be given) in the lower section of the window:Let’s do some fine tuning, before saving this new tool to a shelf. Click on the “Edit in Dynamo” button, to edit the currently loaded script. We want the text “Select from Alias” to be “Select Center Point” instead. For this, in Dynamo, right mouse button click on the “Select from Alias” node, and pick “Rename node…”. In the upcoming window, change the name to “Select Center Point”.In addition to that, we want to change the text “Number Slider”, too. Right mouse button click on the number slider in Dynamo, pick “Rename node…”, and change the title to “Radius”.Save the script, and you will immediately see that the Dynamo Player tool window has adapted the texts.If you now drag and drop the Dynamo Player icon onto your own user defined shelf, you have created your own circle tool, which directly allows you to enter a radius open circle creation. For a more detailed description, and sample videos, check out the Alias online help on this topic:https:///view/ALIAS/2021/ENU/?guid=GUID-1E1BBB04-060B-4AC4-AD06-0CA8B539FE16。
Modelling of microturbine systems
MODELLING OF MICROTURBINE SYSTEMSStaffan HaugwitzDepartment of Automatic Control,Lund Institute of TechnologyBox118,SE-22100Lund,Swedenstaffan.haugwitz@control.lth.seKeywords:gas turbine,modelling,Modelica,ThermoFluid AbstractThe paper describes the development of a dynamic model of a microturbine system.The paper was done in close cooperation with the gas turbine manufacturer Turbec AB and the model was tuned and verified against their microturbine system T100. The microturbine unit consists of a compressor and a turbine connected on a single shaft to a high-speed generator.The model includes all the main thermodynamic and mechanical components of the microturbine.Possible applications for the model are development of control strategies,dynamic performance verification,operator training and control software/hardware verification.The emphasis has been on the functionality and accuracy of the system model and not of the component models.The model is written in the Modelica modelling language and uses,when available,com-ponents from the standard libraries or from previous work as [11]and[6].Steady-state verification has been done with good results.When the microturbine runs at full load of100kW, there is an average error of0.6%for the13most important thermodynamic variables compared to reference values.Dy-namic verification in three different experiments has been done and the model shows a goodfit to the measured data from the real microturbine.1Introduction and problem formulationFor control system design,process knowledge is essential.One way to obtain better process understanding is through models. The microturbine T100from Turbec AB is constantly being upgraded.In order to predict the effects of explicit changes in the control system or in the hardware configuration of the microturbine system,a dynamic model saves time and money. The safety issue is also a strong incitement,where mistakes and deficiencies can be revealed in a simulator instead of in the real microturbine.The objective with this project was to develop a dynamic model,which should capture the main characteristics of the microturbine system,but should also be easy to use in the ev-ery day work for the control engineers of the company.The model should be component-oriented and all developed com-ponent models should be easy to reuse in new microturbine system models.This paper is based on[8],which is a continuation of[6].The Figure1:The thermodynamic components of the T100micro-turbinework of Tummescheit in[11],has been the main inspiration. For confidentiality,the axes of somefigures are removed.2The T100microturbine systemThe microturbine T100from Turbec AB is a combined heat and power(CHP)generation system,originally described in[9].It is based on a small gas turbine engine directly connected via a rotating shaft to a high-speed generator without any intermedi-ate gear box.There is only one moving part,the rotating shaft, which minimizes the friction.The T100is a microturbine for two reasons,the physical size and the power output.The compressor and the turbine are0.15 m in diameter and the entire enclosure of the system is only 2.92x1.90x0.87m.This makes installation very easy e.g. in canfit in a normal basement.The power output of100kW is chosen tofit a special market demand,which corresponds to hotels,green houses,sport facilities and wastewater treatment plants.In Figure1,a scheme of the thermodynamic stages of the T100 is shown.The microturbine’s combustor normally runs on nat-ural gas,but can be modified to accept various fuels such as diesel,ethanol and bio-gas.A recuperator regenerates heat from the exhaust gases to the air going into the combustor,in-creasing the electric efficiency to30%.After the recuperator,a gas/water heat exchanger uses the last heat of the exhaust gases to heat water,giving the T100a total efficiency of80%.The electricity created by the high-speed generator is converted into AC voltage with a constant frequency using power electronics, which is not modelled in this paper.2.1The control system and its operation modesThe reference input to the controller is a power reference,the amount of electric power the generator should produce. main control signal is the fuel rate.The measurementfor the feedback controller are speed,power from the the ator and turbine outlet temperature(TOT).The control has two different modes,parallel mode and stand aloneIn the parallel mode,the T100produces power in parallel an existing external power grid,which serves as a powerIn the stand alone mode,the external grid is disconnected the microturbine should provide all the power needed in a power grid.The differences in the control system modes elaborated in more detail in[8].3Simulation ToolsThe model code is written in the Modelica language.If sible,complete components have been used from the standard Modelica libraries,especially the ThermoFluid library in[11] or from[6].To simulate the models,the program Dymola has been used.Dymola,Dynamic Modeling Laboratory,is a simulation pro-gram developed by Dynasim AB in Lund.It consists of a graphical user interface,compilers,numerical solvers and plot functions.For more information on Dymola,see[4]. Modelica is an object-oriented language designed to allow con-venient,component-oriented modelling of complex heteroge-neous physical systems.Important parts of Modelica are the object-oriented structure,the equation based modelling ap-proach,multiple inheritance and the support for graphical rep-resentations for each component.Constructing a model with components from the standard libraries is very fast and easy, since it is just to drag and drop.For more detailed information see[10].The ThermoFluid library has been developed at the department of Automatic Control,Lund Institute of Technology.The main purpose is to provide a general framework and basic building blocks for modelling thermo-hydraulic systems,written in the Modelica language.From this library,components as pipes, reservoirs,sensor and compressible medium models were used. More information about the library can be found in[11].4Theory and ModellingThe thermodynamic theory of turbomachinery and heat ex-changers,based on the laws of physics,can be found in[1], [2]and[3].The original models of the compressor,turbine and combustion chamber are taken from[6]and[7].4.1The compressorFrom the conservation of energy,an equation can be derived that describes the specific work required to achieve a certain pressure ratio over the compressor at a given temperature.TheFigure2:Compressor model(dashed)and the data(solid) complete derivation can be found in[8].The specific work w can be computed as:w comp1κ1RT1p2κ11(1)where the isentropic efficiencyηis has been introduced to com-pensate for the non-adiabatic and non-reversible compression. The variableκis the ratio of the specific heats,R is the uni-versal gas constant,T is the temperature,p is the pressure and the subscript1and2refers to the inlet and outlet position.The work is negative since work is done on the gas by the compres-sor.The consumed power P can then be calculated as:P compτcompω˙mw comp(2)whereωis the angular velocity of the shaft,˙m is the massflow rate andτis the torque,the compressor needs.There are some properties of a compressor that cannot be eas-ily calculated analytically,e.g.the isentropic efficiency and the massflow through the compressor.These must instead come from measured data,which is given in the form of a compres-sor map.In the map,the massflow and efficiency are listed for different values of speed and pressure ratio.In order to re-duce the number of variables needed to represent the map,the non-dimensional variables pressure ratio(pr),corrected speed (n corr)and corrected massflow(˙m corr)are used.The variables are normalized with the inlet temperature and inlet/outlet pres-sures during the experiments.pr p2T1corrected mass flow, mdotcorr=mdot sqrt(T 1)/p1p r e s s u r e r a t i o , p 2/p1A single ellipsoid curve and the surge line absurge line(x,y)Figure 3:Ellipsoid curve for one particular speed of rotation 74000rpm.The area enclosed between the surge line and the choke line is the normal operating range for the compressor.For a given pressure ratio and inlet temperature,the compres-sor model should give a unique mass flow using data from the map.The method used in this paper was to fit continuous ellipsoid curves to the different speed curves and then parameterize them so that the whole map can be continuously represented,see [7].The curves in Figure 2can be approximated as ellipsoid curves and can be represented with an ellipsoid equation:xbzc (3)When the parameters a ,b ,c and z are varied,the form of the ellipsoid curve can be adjusted to fit any speed curve from Fig-ure 2.The parameter a corresponds to the corrected mass flow at pressure ratio one (where the curve would cross the x-axis).Similarly the parameter b represents the pressure ratio at zero mass flow (where the curve would cross the y-axis).The pa-rameter c is a constant usually taken to one.The parameter z represents the curvature of the curve.For each speed curve,one set of these parameters are calculated,see Figure 3.To make the model continuous in respect to speed based on these discrete sets of parameters,the parameters are fitted as polyno-mial functions of speed.The compressor model is calculated in the following way:For each speed,the value of the parameter a is taken directly from the map,i.e.the mass flow value at choking conditions.The parameter z is at first set to an arbitrarily value,e.g. 5.One data point (x ,y )is taken from the map,at which the ellipsoid curve and data will be identical ing this point,the ellipsoid equation is solved for b .The curve is plotted and com-pared to data from the map.By visual inspection,the parame-ters a and z are modified to ensure a better fit for this specific speed inside the operating range.The procedure is repeated for each speed of rotation,which results in a set of parametersforFigure 4:Compressor efficiency,map data (solid)and model (dashed)each speed.The values of the each parameter are fitted in Mat-lab into polynomial functions as a function of speed to achieve a continuous model.The mass flow is now continuously given by speed and pressure ratio.The compressor efficiency was modelled as a parabolic degra-dation function depending on mass flow rate.Both the com-pressor map model and the efficiency model originate from and are in detail described in [7].For a given rotational speed,the maximum efficiency is well-known,i.e.the top value of the curves seen in Figure 4.Then the efficiency map can be modelled as parabolic degradation curves.The amount of curvature/degradation is denoted d and is fitted based on numerous data to get parameterization for all speeds.The following equation is taken from [7]and valid for one speed.ηcompηcomp maxd ˙m ˙m max e f f2(4)where ηcomp max is the maximum efficiency at that speed and˙m maxe f f is the corresponding mass flow for this maximum effi-ciency.To make to model continuous,these variables are fitted to polynomial functions of speed.As can be seen in Figure 4,the solid curves are not exactly symmetrical.Near choking conditions (to the right),the effi-ciency is decreased rapidly.Another difficult part in this case is that the curvature changes a factor of 20from the lowest to the highest speed.Due to the control system,the microturbine often operates near optimum conditions and there the efficiency model is accurate.4.2The turbineThe equation for the specific work done by the gas on the tur-bine is almost the same as for the compressor,see Equation 1,except for a sign change and that the isentropic efficiency isI s e n t r o p i c e f f i c i e n c yTurbine map for various speedsPressure ratio p in/poutC o r r e c t e d m a s s f l o wFigure 5:Turbine map for different speedsincluded in the numerator:w turbηisκp 1κT 1κR 1κ1κ1(7)The variable A thr is the smallest nozzlethroat area (the cross-section area)at the inlet to the turbine.The variable M is the Mach number,defined as the ratio of flow speed inside the tur-bine and the speed of sound.The efficiency data from the turbine map in Figure 5,is used in a bilinear (2-dimensional)interpolation method to model the efficiency of the turbine,see [8].5Other components of the microturbine modelThe recuperator and the gas/water heat exchanger are both of mainly counter flow type and are therefore modelled in the same way,except for the medium and the geometry.From [3],standard 1-dimensional convection and conduction heat trans-fer laws are used.Figure 6:The complete model with graphical representation of each componentThe combustion chamber is modelled with chemical and en-ergy conservation equations.For a given flow rate of the fuel and the compressed air,the theoretically released energy can be computed and the temperature of the exhaust gases is increased accordingly.For the complete equations and the chemical re-actions,see [6].The electric power to be generated is set by the user of the model.Knowing the rotational speed,the power is converted to a torque,which is subtracted from the common shaft of the compressor and the turbine.6Simulations and VerificationWith the overall model,it is open for the user to simulate the microturbine under any circumstances the user chooses.For each simulation case,there are numerous parameters that are to be set,in total there are about 6000equations and 130states and the simulation time is about a minute.These numbers can vary depending on the number of discretizations used in the heat exchangers and the accuracy of the medium models.The model can be linearized in Dymola for theoretical analysis and with appropriate model reduction,used in controller design.It is also possible to include the Modelica model in Simulink as a separate block.In Figure 6,the system model is presented.Each component model has a graphical representation,to allow drag and drop actions to quickly build new system models.Each component (e.g.a heat exchanger)can itself be constructed,either graph-ically or by code,using other sub components (e.g.pipes and wall models).0100200300400500050100150P o w e r (k W )Step response at parallel modeF u e l (k W )Time (s)T O T (K )Figure 7:Step response in the electric power reference in par-allel mode,measured data (solid)and model data (dashed)6.1Steady-state verificationThe steady-state results of the model were verified with data provided by Turbec AB.The simulations were done with the control system in parallel mode.The model was verified at three different loads,100kW,70kW and 50kW of elec-tric power.The 13most important thermodynamic variables were used in the verification process and a percentage error of the model output compared to the provided data was cal-culated.The thermodynamic variables used in the verification were e.g.speed,fuel rate,temperatures,efficiencies,pressure drops and pressure ratios for the compressor.The average error was 0.61%,1.2%and 1.1%for the 100kW,70kW and 50kW case respectively.The complete result can be found in [8].The largest errors were in the compressor’s pressure ratio (3.7%)and the pressure drop between the compressor and the turbine (3.5%).The first error is caused by errors in the el-lipsoid curves that model the compressor mass flow,which is tightly coupled to the pressure ratio.The second error was due to too simple pressure drop models.6.2Dynamic verificationThree dynamic experiments were investigated and the data from the real microturbine was plotted and compared with the model output from simulations of the same experiments.The first case is a simple step response experiment shown in Figure 7.The microturbine runs at full load,100kW of electric power and is in steady state.The control system is in parallel mode.The power reference (dashed-dot)is,at a given time t ,set to 30kW.After approximately 200seconds,the reference is set again to 100kW.In Figure 7,the trajectories of the power output,consumed fuel and TOT (turbine outlet temperature)are shown from the measured data (solid)and model output (dashed).Figure 8:Step response in the stand-alone mode,measured data (solid)and model data (dashed)The model output show large similarity to the measured values.At t =320s,there is a difference in the TOT signal,probably due to unmodelled dynamics and differences in the model of the control system.The second case is also a step response experiment,but now the control system is in stand-alone mode using a speed controller.The power reference is set to 5kW load,i.e.some kind of idle load.The speed is kept constant at 63000rpm.At a given time t the power reference (dashed-dot)is set to 59.2kW and is then after another 45seconds set back to 5kW.In Figure 8the trajectories of the power output,speed,consumed fuel,TOT and TIT (turbine inlet temperature)are shown,from the measured data (solid)and model output (dashed).When the load is suddenly increased,the speed drops quickly,but the controller reacts and increases the fuel flow to the max-imum value,100%.When the load disappears,the control system cuts down the fuel rate to a minimum and the fuel rate should then rise to the original level.Due to problems in the log file,neither measured data or model output is plotted after t =220s.The difference between the model and the measured data in TOT and TIT,right after the step at 220seconds,is probably caused by changes in the time constants of the tem-perature sensors due to varying flow rate.The third case is a brake test verification.The microturbine is run at part load.At a given time t ,the electric power is disconnected to simulate a power circuit failure or a sudden decrease in load.Then the fuel valves are closed immediately down to a minimum.To prevent the microturbine from over speed,i.e.speeds over 70000rpm,a brake chopper is switched on.The brake chopper consists of resistors,which brake and dissipate the kinetic energy of the machine via the generator.The data from the model in Figure 9demonstrates great sim-ilarity with the measured data down to speeds around 35000rpm,the lowest part of the valid range of the model.The er-S p e e d (r p m )Brake chopper testTime (s)Figure 9:Brakechopper testror comes from a difference in the simple brake chopper model (the power it dissipates)and the control system model.The difference increases for lower speeds.7ApplicationsThe model has at first been used at Turbec AB,to study braking strategies and the choice of brake choppers.The objective is to prevent over speed and depending on size,duration of the brake action and also the ambient temperature,the response from the microturbine will be different.The model can also be used to simulate the microturbine be-haviour at internal and external variable changes,e.g.variable load,ambient temperature and control system settings.Other fields of use can be dynamic performance verification,operator training and control software/hardware verification.8ConclusionsA dynamic model of a microturbine has been developed and thoroughly verified against a real microturbine from the indus-try.The steady-state results are very good with an average er-ror of 0.6%for the 13most important variables compared to reference values.The dynamic simulation results have demon-strated that the model captures the characteristics of a real mi-croturbine very well in three different situations.The model was mainly constructed by complete components from [11]and [6]and when not available,new component models were developed.A large effort has been made to accu-rately model the compressor and turbine map.Even though this model describes a microturbine,the model can be adjusted to a gas turbine of any size,by changing the geometrical properties and the special characteristics of the compressor and the tur-bine.This is achieved by the multiple inheritance,the object-oriented structure of the Modelica language and its support forgraphical representation of each component,see Figure 6.9Future workTo model the compressor and the turbine accurately,a large manual effort was made in order to tune the model parameters as much as possible.It is desirable that the models are modi-fied,such that more of the work is done automatically.To make the system model more general,a model of the power electronics can be developed.It might also be interesting to have a model of the auxiliary system,to make the system model more complete.10AcknowledgmentsThe project has been made largely at Turbec AB under the su-pervision of Anders Åberg,which has been a great help and mentor.At the department,Hubertus Tummescheit has been the expert on modelling in general and on ThermoFluid in par-ticular.References[1]Y .Cengel and M.Boles.Thermodynamics,An Engineering Ap-proach .WCB/McGraw-Hill,New York,3edition,1998.[2]G.Cohen,H.Rogers and H.Saravanamuttoo.Gas Turbine The-ory .Longman Group Ltd,London,4edition,1996.[3] D.DeWitt and F.Incropera.Fundamentals of Heat and MassTransfer .Wiley,New York,4edition,1996.[4]Dynasim AB,Lund,www.dynasim.se.Dymola,Dynamic Mod-eling Laboratory User’s Manual ,2001.[5]R.Fox and A.McDonald.Introduction to Fluid Mechanics .Wi-ley,New York,5edition,1998.[6] A.A.Gómez Pérez.“Modelling of a gas turbine with mod-elica.”Technical Report Masters thesis ISRN LUTFD2/TFRT--5668--SE,Department of Automatic Control,Lund Institute of Technology,Sweden,May 2001.[7]J.Gustafsson.“Static and dynamic modelling of gas turbinein advanced cycles.”Technical Report Licentiate Thesis ISRN LUTMDN/TMVK--98/7030--SE,Lund Institute of Technol-ogy,Lund,Sweden,1998.[8]S.Haugwitz.“Modelling of microturbine systems.”Techni-cal Report Masters thesis ISRN LUTFD2/TFRT--5687--SE,De-partment of Automatic Control,Lund Institute of Technology,Lund,Sweden,May 2002.[9] A.Malmquist.Analysis of a Gas Turbine Driven Hybrid DriveSystem for Heavy Vehicles .PhD thesis ISSN --1102--0172,The Royal Institute of Technology,Sweden,June 1999.[10]Modelica Association,.Modelica -A Uni-fied Object-Oriented Language ,2000.[11]H.Tummescheit.Design and Implementation of Object-Oriented Model Libraries using Modelica .PhD thesis ISRN LUTFD2/TFRT--1063--SE,Department of Automatic Control,Lund Institute of Technology,Sweden,August 2002.。
simulation modelling practice and theory
simulation modelling practice andtheorySimulation modelling practice and theory refer to the techniques and methods used to represent the behaviour of dynamic systems across disciplines including business, engineering, social and physical sciences. Through the use of computer-based simulations, researchers and developers can explore potential outcomes in a cost-effective and safe way. Simulation modelling has become an increasingly important engineering tool to analyse complex systems in the presence of uncertainty. In addition, it can help users gain insights into system operation, test hypotheses, and explore the cause and effects of changes in system design.As its focus increases, simulation modelling has been extended to meet the demands of increasingly complex systems. Simulation modelling becomes more important when systems become bigger and require more analysis before any decisions can be made. Modelers are now able to create and modify more detailed, realistic models that accurately represent the actual system. For example, the addition of large data sets, the inclusion of nonlinear parameters, and parameter uncertainty analysis allow for more accurate representation of system behavior.Simulation modelling is a powerful tool for understanding complex systems and for assessing the effects of change. It is a technique used to gain insight into the behavior of a system or process under a range of operatingconditions or scenarios. Simulation models provide a starting point for further analysis and decision-making. Simulation can provide a prediction of system performance before any actual changes are made, or they can be used to analyze the potential effects of a proposed change.Simulation modelling is an area with both practice and theory. As the technology of modelling matures, theory must be developed to continue the development of these models. Simulation modelling will enable better efficiency and precision in the process of decision making and better flexibility in terms of alternative solution evaluation. The development and application of modelling will continue to be an important part of decision making and problem solving.。
Mall of the future NEW(未来的购物商城)
When “Discovery” issues are prevalent, shoppers spend less time and money in the mall. For example, shoppers who felt there was “nothing unique in the mall”
It’s size can be as small as an iphone or as big as a 3 storey building.
COMFORT Is the mall clean, well maintained and safe? Are washrooms numerous, easy to find and sanitary?
NAVIGATION How simple is it to find the mall from the street or highway? Once inside, is the mall layout easy to navigate with clear signage? ACCESIBILITY Is parking ample and conveniently located?
You've seen it in the movies and on rides in amusement parks. Interactive experiences that seem to reach out and grab you. However, the practical applications of these cutting-edge technologies have been viewed as being in the distant future. Well, we're here to tell you that the technology of the future has arrived.
埃隆·马斯克的韧性和远见英语作文
埃隆·马斯克的韧性和远见英语作文全文共10篇示例,供读者参考篇1Elon Musk is like, super awesome, you guys! He's like areal-life superhero with his crazy resilience and amazing foresight. Let me tell you all about it!So, like, Elon Musk has had a lot of setbacks in his life but he never gives up. He's been through so many tough times, like when his first three companies failed and he had to borrow money to pay his rent. But he never lost hope and kept on working hard to achieve his dreams.And his dreams are like, totally out of this world! He's all about making the world a better place, and he's doing it with his cool inventions and ideas. Like, he's the dude behind Tesla, the electric car company that's making cars that don't use gas. How awesome is that? And he's also working on SpaceX, a company that's gonna take people to Mars one day. That's like,mind-blowing!Elon Musk is, like, always thinking ahead and coming up with new ways to make the world a better place. He's, like, a real-lifeTony Stark, you know? He's always pushing the boundaries of what's possible and inspiring others to do the same.So, yeah, Elon Musk is, like, the coolest dude ever. He's all about resilience and foresight, and he's, like, changing the world one invention at a time. He's definitely someone we can all look up to and learn from. Keep rocking, Elon Musk!篇2Elon Musk is like, super cool. He is like a real life superhero with his resilience and farsightedness. Have you guys heard of him? He's like the boss of Tesla, SpaceX, and other awesome companies.So, Elon Musk is like, really tough. He faced a lot of challenges when he was starting out, but he never gave up. He's like, "I'm gonna build electric cars and send people to Mars." And everyone was like, "Dude, that's impossible." But he's like, "Watch me." And now, Tesla is like, everywhere and SpaceX is sending rockets to space.And he's like, really smart too. He's always thinking ahead and coming up with new ideas. Like, he's building a super fast train called the Hyperloop and working on making self-drivingcars. He's even thinking about colonizing Mars! Like, how cool is that?And the best part is, he's like really generous. He's like, "I wanna save the planet and help people." So, he's like, making sustainable energy and donating money to good causes. He's like, a real life hero, you guys.So yeah, Elon Musk is like, the coolest guy ever. He's tough, smart, and caring. He's like, changing the world and making it a better place. I wanna be like him when I grow up!篇3Title: Elon Musk: A Super Cool and Smart DudeHey guys, do you know who Elon Musk is? He is like areal-life superhero! He is super smart and cool, and he does all kinds of amazing things. Let me tell you all about him!First of all, Elon Musk is really tough. He never gives up, even when things get really hard. He started a bunch of companies like Tesla and SpaceX, and some people thought he couldn't do it. But he worked really hard and never stopped believing in himself. Now his companies are super successful and everyone loves them!Not only is Elon tough, but he also has really cool ideas. He thinks about the future and how we can make it better. He wants to build cool things like electric cars and rockets that can go to space. How awesome is that?Elon Musk is also a great leader. He inspires all the people who work for him to do their best. He is always coming up with new ideas and pushing the boundaries of what is possible. He is like a real-life Tony Stark!In conclusion, Elon Musk is the coolest and smartest dude around. He never gives up, has amazing ideas, and inspires everyone around him. We can all learn a lot from him and try to be more like him. Thanks for being awesome, Elon Musk!篇4Elon Musk is like, so cool! He's like a superhero with superpowers! He's super resilient and has like, really awesome vision. Let me tell you all about him!So, Elon Musk is like this super smart dude who started companies like SpaceX, Tesla, and Neuralink. He's always thinking about the future and how to make the world a better place. He's always trying new things and taking risks, even when people say it's impossible. That's why he's so resilient!One time, SpaceX had a rocket explode and everyone was like, "Oh no, it's over!," but Elon Musk was like, "No way, we can do this!" And he kept going and now SpaceX is like sending rockets to space and landing them back on Earth. How cool is that?And then there's Tesla, the electric car company. People used to laugh at him for making electric cars, but now everyone wants one! He's helping save the planet and make cars super cool at the same time. He's like a real-life Tony Stark from Iron Man!Oh, and don't forget about Neuralink, the company that's making brain implants to help people with disabilities. Elon Musk is always thinking about how to make the world a better place for everyone, not just himself. That's why he's got such awesome vision!So yeah, Elon Musk is like the coolest dude ever. He's super resilient and has like, really awesome vision. He's like a superhero in real life! We should all be like Elon Musk and never give up on our dreams. Yay Elon Musk!篇5Elon Musk is a super duper cool dude who is like a superhero in real life. He is super duper smart and has big big dreams to make the world a better place. He is like a real life Iron Man!One of the coolest things about Elon Musk is his resilience. He never gives up, even when things are super tough. He has faced lots of challenges in his life, but he always finds a way to overcome them. He is like a ninja warrior who never backs down.Elon Musk also has super duper far-sighted vision. He can see things that other people can't see. He dreams of making electric cars and rockets that can go to Mars. He is like a space cowboy who is exploring the final frontier.One of the most amazing things Elon Musk has done is create Tesla, a company that makes super cool electric cars. He wants to save the planet by getting rid of polluting gas cars. He is like a superhero saving the Earth from destruction.Elon Musk is a real life superhero with incredible resilience and vision. He is making the world a better place with his super cool inventions. We can all learn from him and be inspired to never give up on our dreams. Elon Musk is the coolest dude ever!篇6Elon Musk is like a superhero in real life. He is super smart, super brave, and super cool. He's like Iron Man mixed with Tony Stark. Elon Musk is the founder of companies like SpaceX, Tesla, and Neuralink. He is always thinking of ways to make the world a better place.One thing that makes Elon Musk so awesome is his resilience. He never gives up, even when things get tough. Like that time when SpaceX had three failed rocket launches in a row. Instead of giving up, Elon Musk kept working hard and finally, the fourth launch was a success. He didn't let failure hold him back. That's what you call resilience, folks!Another cool thing about Elon Musk is his vision. He doesn't just think about what's happening now, he thinks about the future. Like when he started Tesla, everyone thought electric cars were a joke. But Elon Musk believed in his vision and now Tesla is one of the most successful car companies in the world. He is always thinking ahead and imagining what the world could be like in the future. That's what you call vision, folks!In conclusion, Elon Musk is a real-life superhero with his resilience and vision. He shows us that no matter how tough things get, we should never give up. And he teaches us to always think about the future and how we can make the world a betterplace. Elon Musk is a true inspiration to all of us, and we can all learn a lot from him.篇7Once upon a time, there was a super cool dude named Elon Musk. He was like a superhero with his awesome inventions and big dreams. He had so much resilience and vision that everyone looked up to him.Elon Musk was not your average guy. He didn't just sit around playing video games all day. No way! He was busy creating things like electric cars and spaceships. Can you believe that? He was like a real-life Tony Stark from Iron Man.But it wasn't always easy for Elon Musk. He faced a lot of challenges along the way. People doubted him and said his ideas were crazy. But you know what? He never gave up. He kept pushing forward and proving them all wrong.That's what resilience is all about. It's like having a superpower that helps you bounce back from tough times. Elon Musk knew how to roll with the punches and keep going no matter what.And his vision was out of this world. Literally! He wanted to colonize Mars and make life multiplanetary. How awesome is that? He wasn't satisfied with just staying on Earth. He wanted to explore the stars and push the boundaries of what was possible.So next time you feel like giving up on your dreams, just think of Elon Musk. Remember his resilience and vision. If he can do it, so can you. Aim for the stars and never stop believing in yourself. Who knows? Maybe one day you'll be the next Elon Musk, changing the world with your big ideas.篇8Elon Musk is like a superhero to me. He's super smart, super resilient, and super cool! I love reading about him and his amazing accomplishments. Let me tell you all about his resilience and foresight!First off, let's talk about his resilience. Elon Musk has faced so many challenges in his life, but he never gives up. When his companies were struggling, he worked even harder to turn things around. He's been through tough times, but he always bounces back stronger than ever. That's what I call real superhero power!And his foresight? Oh man, Elon Musk is like a time traveler from the future. He's always thinking of new ideas and inventions that will change the world. From electric cars to Mars colonization, he's always one step ahead of everyone else. I wish I could have half of his brilliant mind!One of the things I admire most about Elon Musk is how he never settles for mediocrity. He's always pushing the boundaries of what is possible and striving for excellence. He's not afraid to take risks and go after his dreams, no matter how crazy they may seem. That's the kind of attitude I want to have when I grow up.In conclusion, Elon Musk is my role model because of his resilience and foresight. He's a true inspiration to me and to everyone else who dreams of making a difference in the world. I can't wait to see what amazing things he will come up with next!篇9Elon Musk is like a superhero in real life! He's so cool and smart. When he was young, he read a lot of books and learned so much. He had a dream to make the world a better place, and now he's doing it!Elon Musk is really tough. He started lots of companies, like SpaceX and Tesla, even when people said it was impossible. Buthe never gave up! He worked super hard and made his dreams come true. He's like a real-life Iron Man!Elon Musk is also really smart. He invented awesome stuff like electric cars and rockets that can go to space. He's always thinking about the future and how to make the world better for everyone. He's like a genius inventor!Elon Musk is my hero because he's so strong and smart. He shows us that we can do anything if we work hard and never give up. I want to be like him when I grow up – a superhero saving the world with cool inventions!I love Elon Musk and all the amazing things he's doing. He's the coolest person ever! I can't wait to see what he does next. Thank you, Elon Musk, for being so awesome!篇10Elon Musk is super cool guy! He is like a superhero because he is super smart and has big dreams. He is the CEO of Tesla and SpaceX, and he is always thinking of new ways to make the world a better place.One thing that makes Elon Musk so awesome is his resilience. He never gives up, even when things get tough. For example,when his company SpaceX had three rocket failures in a row, instead of giving up, he worked even harder to make sure the next launch was successful. That takes a lot of courage and determination!Another thing that makes Elon Musk amazing is his vision. He has big ideas for the future, like building a city on Mars and creating a world where cars run on clean energy. He is always thinking ahead and coming up with new ways to make the world a better place.In conclusion, Elon Musk is a real-life superhero! His resilience and vision inspire us all to dream big and never give up. We can all learn a lot from him and strive to make the world a better place, just like he does. Keep dreaming big, just like Elon Musk!。
体验人工智能英语作文
体验人工智能英语作文Experiencing Artificial IntelligenceIn today's rapidly evolving technological landscape, the emergence of artificial intelligence (AI) has transformed numerous aspects of our lives. From personalized recommendations on streaming platforms to autonomous vehicles navigating our roads, the influence of AI is undeniable. As an individual, I have had the opportunity to directly experience the power and potential of this revolutionary technology. Through my encounters with AI, I have gained a deeper appreciation for its capabilities and the profound implications it holds for the future.One of my most profound experiences with AI came through my interactions with virtual assistants. I remember the first time I conversed with Alexa, the AI-powered virtual assistant developed by Amazon. At first, I was hesitant, unsure of how to communicate with a non-human entity. However, as I began asking Alexa simple questions and giving it commands, I was amazed by the fluency and responsiveness of the interaction. Alexa was able to understand mynatural language, retrieve relevant information, and provide thoughtful responses. Whether I was inquiring about the weather, setting reminders, or even engaging in casual conversation, Alexa demonstrated an uncanny ability to comprehend and cater to my needs.As I continued to use Alexa, I was struck by the depth of its knowledge and the speed at which it could process information. I would often pose complex questions, ranging from historical facts to scientific concepts, and Alexa would provide detailed and well-researched answers. This experience highlighted the incredible advancements in natural language processing and knowledge representation that have enabled AI systems to engage in meaningful dialogues and share vast amounts of information.Beyond virtual assistants, I have also had the opportunity to interact with AI-powered chatbots in various customer service and support contexts. These chatbots, powered by advanced language models and machine learning algorithms, have demonstrated an impressive ability to understand and respond to user queries. I have encountered chatbots that can troubleshoot technical issues, provide product recommendations, and even offer emotional support. The seamless integration of these AI-driven chatbots into customer service platforms has significantly improved the efficiency and accessibility of support services, often providing faster and morepersonalized assistance than traditional human-based interactions.One particularly memorable experience with an AI chatbot occurred when I encountered a technical issue with a software application I was using. Instead of navigating through a frustrating phone tree or waiting for a human representative, I was able to engage with an AI-powered chatbot that guided me through the troubleshooting process step-by-step. The chatbot asked relevant questions, provided clear instructions, and even offered suggestions for preventive measures to avoid similar issues in the future. I was amazed by the chatbot's ability to understand the nuances of my problem and provide a tailored solution, all without the need for human intervention.The influence of AI extends beyond virtual assistants and chatbots, as I have also experienced its impact in more specialized domains. As a student, I have encountered AI-powered tools that have revolutionized the way I approach academic tasks. For instance, I have used AI-based writing assistants that can analyze my work, provide feedback on grammar, structure, and tone, and even offer suggestions for improving the overall quality of my writing. These tools have not only enhanced my writing skills but have also saved me countless hours of manual editing and proofreading.Moreover, I have witnessed the integration of AI in educationalplatforms, where intelligent tutoring systems can adapt to my learning style, identify my strengths and weaknesses, and provide personalized guidance and support. These AI-driven educational tools have the potential to democratize access to quality education, ensuring that learners of all backgrounds can receive the tailored instruction they need to succeed.Beyond the realm of education, I have also encountered AI in the context of healthcare. During a recent visit to the doctor, I was surprised to learn that the clinic had implemented an AI-powered system to assist with diagnostic procedures. The system was able to analyze medical images, such as X-rays and MRI scans, and provide preliminary insights and recommendations to the healthcare professionals. This integration of AI into the medical field has the potential to enhance the accuracy and speed of diagnoses, ultimately leading to more effective and timely treatments for patients.As I reflect on my experiences with AI, I am struck by the profound impact this technology has had on various aspects of my life. From the convenience and efficiency of virtual assistants to the transformative potential of AI in education and healthcare, I have witnessed firsthand the remarkable capabilities of this technology. However, with these advancements come important considerations and ethical questions that must be addressed.As AI systems become more sophisticated and integrated into our daily lives, there is a growing need to ensure that they are developed and deployed in a responsible and transparent manner. Issues of privacy, data security, and algorithmic bias must be carefully addressed to ensure that the benefits of AI are distributed equitably and that the technology does not perpetuate or exacerbate societal inequalities.Furthermore, as AI systems become more autonomous and capable of making decisions that impact human lives, there is a pressing need to establish clear ethical frameworks and governance structures to guide the development and deployment of these technologies. Policymakers, technologists, and the broader public must engage in thoughtful dialogues to determine the appropriate boundaries and safeguards for AI applications, ensuring that they align with our fundamental values and principles.In conclusion, my experiences with artificial intelligence have been both captivating and thought-provoking. I have witnessed the remarkable capabilities of AI in enhancing our daily lives, from streamlining customer service to revolutionizing education and healthcare. However, as this technology continues to evolve, it is essential that we approach its development and implementation with a critical and responsible mindset. By doing so, we can harness thetransformative power of AI while mitigating its potential risks and ensuring that it serves the greater good of humanity.。
中文 topic modelling -回复
中文topic modelling -回复关于中文主题建模的文章。
主题建模是一种文本分析技术,旨在从大量文本数据中发现和归纳隐藏的主题。
具体而言,它通过分析文章的关键词、上下文和其他信息,识别出文章所涉及的主题或话题。
本文将围绕中文主题建模展开讨论。
第一步,准备数据。
进行中文主题建模前,我们需要收集大量的中文文本数据。
这些数据可以来自各种渠道,如新闻报道、社交媒体、论坛帖子等。
收集到的文本数据需要进行清洗和预处理,包括去除停用词、标点符号等,以保证后续分析的准确性和可靠性。
第二步,构建词袋模型。
词袋模型是主题建模的基础,用于表示文本数据中的单词和其出现的频率。
在中文中,我们可以使用分词技术将文本拆分成单个的词语。
常用的中文分词工具有结巴分词、哈工大LTP等。
将分词后的结果转化为词袋模型,可以形成一个包含所有文本数据中所有词语及其频率的大矩阵。
第三步,选择主题建模算法。
常用的中文主题建模算法有潜在语义分析(LSA)、潜在狄利克雷分配(LDA)等。
潜在语义分析通过奇异值分解方法进行降维,从而发现文本数据中的潜在语义信息。
潜在狄利克雷分配是一种基于贝叶斯概率模型的主题建模方法,通过对词袋模型进行建模,推断出每个文本的主题分布。
第四步,模型训练和主题挖掘。
在选择好主题建模算法后,我们需要将清洗、预处理后的文本数据输入到算法中进行模型训练。
模型训练的过程中,算法会对文本数据进行学习和推断,识别出数据中的主题。
通过分析主题词和文本关联性,我们可以对每个主题进行解释和命名,形成可解释的结果。
第五步,结果可视化与分析。
主题建模算法得到的结果是一个包含不同主题及其相关的词语列表的矩阵。
为了更好地理解和分析结果,我们可以通过可视化技术展示主题间的关系和主题内部的词语分布。
常用的可视化工具有词云、主题河流图等。
通过观察主题之间的相似性和差异性,可以对文本数据进行更深入的分析和挖掘。
总结起来,中文主题建模是一种通过分析中文文本数据中的主题信息的技术。
以我的ai使用经历为主题英语作文
以我的ai使用经历为主题英语作文My Experience with AIArtificial intelligence (AI) has become an integral part of our daily lives, from voice assistants like Siri and Alexa to personalized recommendations on streaming services. As someone who has been an early adopter of AI technology, I have had a plethora of experiences that have shaped my understanding and appreciation of this revolutionary technology.One of the first AI applications I encountered was in the form of predictive text on my smartphone keyboard. At first, I was skeptical of the accuracy and usefulness of this feature. However, as I continued to use it, I noticed that the AI was able to predict my words with remarkable accuracy, saving me time and effort in typing out messages and emails. This experience opened my eyes to the potential of AI to enhance our daily tasks and make our lives more efficient.Another AI technology that has greatly impacted me is virtual assistants. I use Siri on my iPhone and Alexa in my smart home devices to set reminders, check the weather, play music, and more. The convenience of being able to simply ask aquestion or give a command without having to type or search for information has been truly transformative. I have also been impressed by the advancements in natural language processing that allow these virtual assistants to understand and respond to complex queries.In addition to everyday applications, I have also delved into using AI for more specialized tasks. For example, I have usedAI-powered language translation apps to communicate with people from different parts of the world. The accuracy and speed of these translations have been impressive and have enabled me to connect with a wider range of people and cultures.Furthermore, I have explored AI in the realm of healthcare through fitness trackers and health monitoring apps. These apps use AI algorithms to analyze data such as heart rate, sleep patterns, and exercise habits to provide personalized recommendations for improving health and overall well-being. By tracking my daily activities and receiving insights from these AI-powered apps, I have been able to make positive changes to my lifestyle and become more mindful of my health.Overall, my experiences with AI have been overwhelmingly positive. I have seen firsthand the transformative power of this technology in improving efficiency, convenience, andpersonalized experiences. As AI continues to evolve and be integrated into more aspects of our lives, I am excited to see the endless possibilities that lie ahead.AI has been controversial in some circles, with concerns about privacy, bias, and automation of jobs. While these are valid issues that need to be addressed through regulation and ethical practices, I believe that the benefits of AI far outweigh the risks. As long as we approach AI with a thoughtful and responsible mindset, we can harness its potential to create a better and more connected world.。
探索交互场景英文作文
探索交互场景英文作文英文回答:Interactive scenes are compelling experiences that bridge the gap between users and digital environments. By engaging users in a dynamic and immersive manner, they create a sense of presence and foster deeper connections with the content.Key elements of interactive scenes include:Responsiveness: The scene reacts to user input, such as mouse movements, clicks, or voice commands.Immersion: The scene envelops the user in a believable and engaging environment, often through the use of virtual reality (VR), augmented reality (AR), or 360-degree video.Agency: The user has a sense of control over their actions within the scene, influencing its outcome orprogression.Narrative: Interactive scenes may incorporate elements of storytelling or gamification, providing users with a sense of purpose and motivation.Benefits of Interactive Scenes:Enhanced Engagement: Users are more likely to retain information and feel invested in the experience, fostering a deeper connection with the content.Improved Learning: Interactive scenes can facilitate experiential learning, allowing users to explore complex concepts and simulations in a hands-on manner.Increased Accessibility: Interactive scenes can make content accessible to a wider audience, including those with disabilities or language barriers.Personalized Experiences: Users can tailor the scene to their preferences, leading to a more personalized andenjoyable experience.Challenges of Developing Interactive Scenes:Technical Complexity: Creating immersive and responsive interactive scenes requires advanced technical skills and specialized software.Content Creation: Developing engaging and meaningful content for interactive scenes is a complex and time-consuming process.Accessibility Considerations: Interactive scenes need to be designed with accessibility in mind, ensuring they are usable by people with a range of abilities.Scalability: Interactive scenes can be resource-intensive, and scaling them to large audiences or distributing them across multiple platforms can be challenging.Future of Interactive Scenes:Interactive scenes are poised to play an increasingly significant role in various domains, including:Education: Interactive simulations and virtual learning environments will enhance educational experiences.Entertainment: Immersive video games, VR experiences, and interactive movies will provide new levels of entertainment.Healthcare: VR and AR technologies will enable remote medical consultations, surgical simulations, and patient education.Communication: Interactive scenes will facilitate more engaging and immersive communication experiences, such as remote team collaboration and virtual conferences.中文回答:互动场景的探索。
大模型融合 日志解析
大模型融合日志解析
大模型融合通常是指在自然语言处理(NLP)领域中,将多个预训练的语言模型(例如BERT、GPT等)进行融合,以提高模型的性能和泛化能力。
日志解析则是指将应用程序或系统的日志文件进行解析,提取出有用的信息,以便进行监控、故障排查、性能优化等。
对于大模型融合,其主要的流程包括:
1. 收集预训练模型:从不同的来源收集多个预训练模型。
这些模型可能来自于不同的研究团队、不同的训练数据集、不同的超参数设置等。
2. 特征提取:使用每个预训练模型对新的输入数据进行特征提取。
每个模型都会生成一个特征向量,这些特征向量可以用来表示输入文本的语义信息。
3. 特征融合:将来自不同预训练模型的特征向量进行融合,生成一个统一的特征表示。
融合的方式有很多种,例如平均值、加权平均值、投票等。
4. 输出层:使用融合后的特征向量作为输入,训练一个新的输出层(例如分类器或生成器),以提高模型的性能和泛化能力。
对于日志解析,常见的日志文件格式包括文本文件、JSON、XML等。
解析这些日志文件的方法包括正则表达式、日志解析器、机器学习等。
解析后的数据可以存储在数据库中,也可以直接用于实时监控和报警系统。
总的来说,大模型融合和日志解析都是处理大量数据的重要技术,它们在数据科学、机器学习、自然语言处理等领域中有着广泛的应用。
exploration modeling essentials -回复
exploration modeling essentials -回复什么是探索建模? 为什么它对数据分析至关重要? 探索建模的基本要素是什么?探索建模是数据分析过程中的一个重要环节,用于揭示和发现数据背后的潜在模式、关联和见解。
它涉及处理和分析数据,以了解数据的特征、趋势和关系,并通过这些发现提供洞察力和决策支持。
探索建模主要应用于数据挖掘、商业智能、市场调研等领域,为组织和企业提供了更全面、更深入的数据理解,进而提高业务绩效和效率。
探索建模在数据分析中的重要性主要体现在以下几个方面:1. 发现新的模式和关联:探索建模能够帮助我们挖掘数据中隐藏的模式和关联。
通过对数据进行分析和可视化,我们可以发现以前未曾预料和察觉到的模式,从而提供新的见解和决策支持。
2. 理解数据特征和趋势:探索建模可以帮助我们更好地了解数据的特征和趋势。
通过对数据的统计和可视化分析,我们可以识别出数据中的异常值、趋势和分布,进而深入了解数据的特性和演变。
3. 获取洞察力和决策支持:探索建模可以帮助组织和企业获取更全面、更深入的洞察力。
通过探索建模,我们可以从数据中发现关键的见解和挖掘商业价值,从而为决策者提供更好的决策支持。
探索建模的基本要素包括数据收集和清洗、数据可视化和分析以及模式识别和关联发现。
1. 数据收集和清洗:首先,我们需要收集数据,可以寻找实时数据或从已有的数据集中获取。
然后,我们需要对数据进行清洗和预处理,包括去除重复值、填充缺失值、处理异常值等,以确保数据的质量和可靠性。
2. 数据可视化和分析:接下来,我们可以使用图表、统计指标、仪表板等工具对数据进行可视化和分析。
通过数据可视化,我们可以更直观地理解数据的分布、趋势和关系,从而快速获取数据的洞察力。
此外,统计分析和模型构建也是探索建模中的重要环节,用于对数据进行进一步深入的研究和发现。
3. 模式识别和关联发现:最后,我们可以运用数据挖掘算法和技术,如聚类、分类、关联规则挖掘等,来识别数据中的模式和关联。
nlp建模流程
nlp建模流程可以分为以下几个步骤:
1.数据收集与预处理:收集与任务相关的文本数据,包括爬取网
络数据、收集已有数据集等。
对数据进行预处理,包括文本清
洗(去除特殊字符、标点符号等),分词(将文本划分为词语)、
去除停用词(常见无意义词语)等。
2.特征工程:根据任务目标选择合适的特征。
常见的特征包括词
袋模型(将文本转化为向量表示)、TF-IDF(词频和逆文档频率)、词嵌入(将词语映射到低维向量空间)等。
还可以考虑使用其
他的文本特征,如词性标注、句法分析等。
3.建立模型:选择适合任务的模型进行建立。
常见的NLP模型包
括传统的机器学习模型(如朴素贝叶斯、支持向量机、随机森
林等)和深度学习模型(如循环神经网络(RNN)、长短时记忆
网络(LSTM)、卷积神经网络(CNN)等)。
根据具体任务的不
同,选择合适的模型进行建立。
4.模型训练与调优:使用已标注的数据对模型进行训练,并进行
参数调优,以提高模型的性能。
常见的训练方法包括梯度下降、
反向传播等。
可以使用交叉验证等技术来评估模型的性能。
5.模型评估与优化:使用测试集对训练好的模型进行评估,计算
模型的准确率、精确率、召回率、F1值等指标。
根据评估结果
进行模型优化,可能需要调整模型参数、改变特征工程等。
6.部署与应用:将训练好的模型部署到实际应用中,可以是一个
接口供其他系统调用,或者是一个可执行的应用程序。
在实际
应用中,可能需要进行模型的更新和迭代,以适应不断变化的数据和需求。
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Modelling user experience with web sites:Usability,value,beauty and goodnessPaul van Schaik a,*,Jonathan Ling baPsychology Subject Group,School of Social Sciences and Law,University of Teesside,Middlesbrough,Cleveland TS13BA,UKbSchool of Psychology,Keele University,UKReceived 16November 2007;received in revised form 20February 2008;accepted 3March 2008Available online 13March 2008AbstractRecent research into user experience has identified the need for a theoretical model to build cumulative knowledge in research addressing how the overall quality or ‘goodness’of an interactive product is formed.An experiment tested and extended Hassen-zahl’s model of aesthetic experience.The study used a 2Â2Â(2)experimental design with three factors:principles of screen design,principles for organizing information on a web page and experience of using a web site.Dependent variables included hedonic perceptions and evaluations of a web site as well as measures of task performance,navigation behaviour and mental effort.Measures,except Beauty,were sensitive to manipulation of web design.Beauty was influenced by hedonic attributes (identification and stimulation),but Goodness by both hedonic and pragmatic (user-perceived usability)attributes as well as task performance and mental effort.Hedonic quality was more stable with experience of web-site use than pragmatic quality and Beauty was more stable than Goodness.Ó2008Elsevier B.V.All rights reserved.Keywords:User experience;Aesthetics;Hedonic quality;Usability;Web site;Modelling1.IntroductionAesthetics can be seen as an aspect of the broader con-cept of user experience (Hassenzahl and Tractinsky,2006),which can include usability,beauty,overall quality and hedonic,affective and experiential aspects of the use of technology.The aesthetics of web page design is a crucial but until recently,somewhat neglected area of empirical investigation.The visual impact of a web page can have a significant influence on user experience and has impor-tant implications for effective communication (Hoffman and Krauss,2004)and,in particular,the interaction users have with a site (Schenkman and Jo ¨nsson,2000).Indeed,poorly designed pages could rapidly turn users away,an irritation for a web site run by an academic,but a majorproblem for a company that may derive a significant pro-portion of its revenue from e-commerce (Ben-Bassat et al.,2006;Lindgaard et al.,2006).Conversely,a web site that participants feel is aesthetically pleasing may have a posi-tive effect on trust (Karvonen,2000).Nonetheless,usability remains a necessary,but not in all contexts a sufficient,condition for positive judgements of system quality (Nielsen,2007),and these judgements may also be related to a user’s goal in interacting with a specific site,depending on whether the goal is func-tional (e.g.booking a train ticket)versus hedonic (e.g.reading the latest entertainment news).When investigat-ing aspects of user experience that go beyond the instru-mental,arguably one of the most important questions to answer is ‘‘how the overall quality or the ‘goodness’of an interactive product is formed,given pragmatic and hedonic aspects and underlying needs ”(Hassenzahl and Tractinsky,2006,p.93).This question is addressed in this paper.0953-5438/$-see front matter Ó2008Elsevier B.V.All rights reserved.doi:10.1016/j.intcom.2008.03.001*Corresponding author.Tel.:+4401642342320;fax:+4401642342399.E-mail address:P.Van-Schaik@ (P.van Schaik)/locate/intcomAvailable online at Interacting with Computers 20(2008)419–4322.BackgroundTractinsky et al.(2006)distinguish two approaches to the identification of higher-level concepts in web-site design as a basis for making progress with design that aims to enhance user experience.First,a screen-design-based approach(e.g. Kim et al.,2003;Ngo,2001;Park et al.,2004)focuses on identifying design factors in the objects and their organiza-tion on a web page that impact on user experience.Although this approach could work to support design for usability and has led to design guidelines for usability(e.g.Galitz, 1997),Tractinsky(2004)raises doubts about its usefulness for designing aesthetics:this design will have to address a very large number of combinations of design options com-bined with a wide range of individual differences in users’preferences.Second,an information-processing approach focuses on users’processing of the attributes of artefacts (Hassenzahl,2004),including web pages(vie and Tractinsky,2004),at different levels of cognition(Norman, 2004).A theoretical model is required to build a cumulative body of knowledge,when addressing the question of how the overall quality or‘goodness’of an interactive product is formed(Hassenzahl,2004).There is a dearth of such mod-els,although Hassenzahl’s(2003,2004)model of user expe-rience provides a starting point.In Hassenzahl’s(2003,2004)model of user experience, users construct product attributes by combining a product’s features with personal expectations or standards.Product attributes,such as content,presentation,functionality and interaction style,influence users’perceptions of product attributes.Two main types of product attributes perceived by users are Pragmatic quality(user-perceived usability) and Hedonic quality(pleasure-producing product qualities), two forms of which are stimulation(personal)and identifica-tion(social).A product can be stimulating by offering nov-elty and challenge and can lead to identification by communicating important personal values to relevant oth-ers.A product character is‘‘a bundle of attributes...”which ‘‘can be understood as a cognitive structure that integrates product attributes and their covariation”(Hassenzahl, 2004,p.322).According to Hassenzahl(2004,p.322)‘‘using a product with a particular...character in a particular situa-tion will lead to consequences,such as emotions(e.g.satis-faction,pleasure),explicit evaluations(i.e.judgements of appeal,Beauty,Goodness),or overt behaviour(i.e. approach,avoidance)”.Perceptions influence evaluations, in particular judgements of Beauty and Goodness(overall quality of interaction),which Hassenzahl(2004)considers as high-level constructs(‘‘verdictive”,in the sense of‘‘expres-sion an authoritative judgement”,p.323)and which are dis-tinct from its low-level determinants(perceptions which are ‘‘substantive”,in the sense of‘‘relating to the essence or sub-stance”,p.323).Perceptions are separate from evaluations because a positive perception of pragmatic or hedonic prod-uct attributes can lead to a positive evaluation,though this is not automatic.In contrast to Hedonic quality,Pragmatic quality is changed through experience of using a system.Therefore,Beauty–which is only influenced by Hedonic qual-ity–is relatively stable,but Goodness is less stable as it is influ-enced by both Pragmatic quality and Hedonic quality.The current study is based on Hassenzahl’s theoretical approach.According to Hassenzahl(2003,2004),system design characteristics that make a design more usable should improve its users’experience in terms of Pragmatic quality and consequently Goodness(overall quality).Hedonic qual-ity and consequently overall Beauty should not be affected. In contrast,Tractinsky et al.(2000)predict that(more)beau-tiful products are perceived to be(more)usable.The current study investigates the effect of system design characteristics in web pages on user experience.Although Hassenzahl stud-ied the effect of experience on perceptions and evaluations, his work does not explicitly address human–system interac-tion in terms of efficiency,effectiveness and workload.In this paper,we therefore propose and explore a set of models based on the following principles:P1:System characteristics have an effect on system per-ceptions,for example clearer interfaces are associated with greater perceived ease of use(e.g.Ahuja and Webster,2001;Davis and Wiedenbeck,2001);P2:System perceptions have an effect on system evalua-tions.Systems which,before use,are viewed as possess-ing positive attributes are more likely to be evaluated positively by users(Hassenzahl,2003,2004);P3:System characteristics have an effect on actual usabil-ity(‘quality in use’).Usability can be measured by a variety of metrics including effectiveness,efficiency and satisfaction and manipulations of the system can have measurable consequences for usability(seee.g.MacLeod et al.,1997,for the effects of a redesignof a bank’s computers on efficiency);P4:Usability has an effect on system perceptions.Several papers have highlighted the interplay between usability and system evaluations,for example,Hassenzahl (2004)found more usable MP3player skins led to more positive assessments of systems than less usable ones.According to our model of aesthetic experience before(or without)using a system,system characteristics directly influence perceived quality and this in turn will have an effect on evaluations(see Fig.1a).When the system is used,then in an interaction-based model,system characteristics,such as the presentation and organization of information on-screen, will affect users’immediate interaction outcomes,such as correctness of task performance and mental effort.Out-comes of this interaction will have an effect on subsequent perceptions of product attributes and these perceptions have an effect on evaluations(see Fig.1b).Alternatively,a per-ception-based model specifies experimental manipulations before and during use,pre-use perceptions,pre-use evalua-tions and post-use perceptions as predictors of post-use evaluations(see Fig.1c).We conducted an experiment in which participants used different versions of an existing edu-cational intranet site to address the following aims:420P.van Schaik,J.Ling/Interacting with Computers20(2008)419–432(1)To test the effect of cognitive design principles on per-ceptions of product attributes(Pragmatic and Hedo-nic quality)and product evaluations(Beauty and Goodness)as well as task performance using a web site.Hypothesis1:Violation of design principles has a negative effect on task performance,Pragmatic qual-ity and Goodness,but no effect on Beauty(Hassen-zahl,2003,2004).In this study,the concepts of Goodness and Beauty are defined as degree of overall quality and aesthetic pleasure,respectively.(2)To test models of aesthetic experience in Hassenzahl(2003,2004)theoretical framework,including the effect of experience(before versus after use)on the relation among attributes and evaluations.Hypothesis2:Hedonic quality is a predictor of both Beauty and Goodness,but Pragmatic quality,task performance and mental effort are predictors of Goodness.Hypothesis3:Beauty(Hassenzahl,2004;Lindgaard et al.,2006;Tractinsky et al.,2006)and Hedonic quality(Hassenzahl,2004)are stable over time and are not influenced by human–system interaction;however,Pragmatic quality and Goodness are lessstable over time and are influenced by interaction (Hassenzahl,2004).Because Beauty is relatively sta-ble,the effects of pre-use perceptions and Beauty on post-use Beauty are partially mediated,but,because Goodness is relatively unstable,the effects of pre-use perceptions and Goodness on post-use Goodness are completely mediated.3.Methods3.1.Experimental designThe experiment used a2Â2Â(2)experimental design with three factors:presentation principles(principles of screen design either followed or violated),information-organization principles(principles for organizing informa-tion followed or violated)and experience of using an intranet web site(before use and after use).Thefirst two independent variables were between-subjects.A wide range of design principles have been found to influence user interactions in earlier research(see overview in van Schaik and Ling,2007).For thefirst independent variable, user interface design principles were complied with (Fig.2a and b)or violated(Fig.2c and d)on web pages.P.van Schaik,J.Ling/Interacting with Computers20(2008)419–432421These included preserving the context of information units,using higher-order information units,avoiding gra-tuitous animation,being consistent,using conventions for appearance and using colour contrast to enhance read-ability.For the second independent variable,we presented information using a topical organization scheme (Rosenfeld and Morville,2002)(see Fig.2a and c)or with-out systematically organizing the information(see Fig.2b and d).The third independent variable was used within-subjects to test the effect of experience on hedonic and aes-thetic value.Outcome measures included perceptions of product attributes,evaluations of web pages,mental effort and objective-performance and navigation-behaviour measures.3.2.ParticipantsThere were111undergraduate psychology students(84 females and27males),with a mean age of22years (SD=5.95).They took part in the experiment as a course requirement.All had used the Web.Mean years of experi-ence using the web was6.37(SD=3.13),mean years of confidence in using the web was4.66(SD=2.85),mean time per week spent using the web was13.02h(SD= 16.19)and mean frequency of web use per week was 13.18h(SD=10.69).3.3.Materials and equipmentAn existing intranet site,developed by a university psy-chology department for its previous cohorts of students was used.Four versions of the site were created by combin-ing the two levels of each of the two independent variables (presentation principles–followed or violated-and infor-mation-organization principles–followed or violated):pre-sentation followed–organization followed(Fig.2a), presentation followed–organization violated(Fig.2b), presentation violated–organization followed(Fig.2c) and presentation violated–organization violated (Fig.2d).The experiment ran on personal computers(Intel Pentium,2.8GHz,512MB RAM,Microsoft Windows XP operating system,15in.monitors).The screen dimensions were800Â600.Contrast and brightness were set to opti-mal levels.Participants gave responses to two questionnaires:the questionnaire used and validated by Hassenzahl(2004) to measure hedonic and aesthetic value and the Subject Mental Effort Questionnaire(SMEQ)(Zijlstra,1993). Thefirst questionnaire included21items to measure per-ceptions of three product attributes(Pragmatic quality, Hedonic quality-identification and Hedonic quality-stimu-lation–7items each)and two items to measure product evaluations(Beauty and Goodness–one item each);all Fig.2.Typical web pages used in the experiment.(a)Presentation principles followed,organization principles followed.(b)Presentation principles followed,organization principles violated.(c)Presentation principles violated,organization principles followed.(d)Presentation principles violated, organization principles violated.422P.van Schaik,J.Ling/Interacting with Computers20(2008)419–432items used a7-point semantic differential scale(see Appen-dix).The SMEQ consisted of a single-item visual analogue scale with graded categories and numerical values(range: 0–150).Factor analysis was conducted on the items for the perceived product quality of three attributes or groups (‘perceptions’):Pragmatic quality,Hedonic quality-iden-tification and Hedonic quality-stimulation.A three-fac-tor solution was found for both pre-use-and post-use scores after removing the following items:PQ1,HQI1, HQI4,HQI5,HQI6and HQS6,with Pragmatic quality (Factor1),Hedonic quality-stimulation(Factor2)and Hedonic quality-identification(Factor3)each constitut-ing one factor(see Table1).Using the items selected in the factor analysis,the scales for Pragmatic quality, Hedonic quality-identification and Hedonic quality-stim-ulation possessed high reliability both before and after System use(see Table2).Subsequently,overall scores–PQ,HQI and HQS,respectively–were calculated for each of the reliable scales by averaging item scores per scale(see Table2for descriptives and correlations). The correlation between Beauty and Goodness was.64 before use and.63after use,both p<.001(41%and 39%overlap in variance,respectively),indicating a strong association,but the two constructs were not identical.3.4.ProcedureThe experiment consisted of four parts:a viewing task (of typical intranet pages),followed by thefirst question-naire,an information retrieval task and the second larger questionnaire(that also included thefirst questionnaire). The experiment was run in a computer laboratory with groups of15–20participants who worked independently. The information retrieval task included typical tasks that users perform with educational intranet sites such as ‘What is PsycINFO?’and‘Who deals with requests for coursework extensions?’.In each trial,a question appeared at the top of the screen.Once participants had read the question,they had to click on a button labelled ‘Show web site’.The home page of the intranet site then appeared on the screen and,using the site,they had to find the answer to the question.Participants were told to take the most direct route possible to locate the answer.Having found this,they clicked on a button labelled‘Your answer’,which opened a dialog box at the bottom of the screen.Participants typed their answers into the box,clicked on‘OK’,completed the SMEQ for the task they had just performed and moved on to the next question.After three practice questions,the main phase consisting of10further questions followed.In addi-tion to the items from thefirst questionnaire,thefinal questionnaire included demographic questions.The exper-iment took approximately45minutes to complete.4.ResultsAnalysis of variance(ANOVA)was used to test the effect of presentation principles(Aim1).The effect of expe-rience and models of aesthetic experience(Aim2)were tested using tests of correlations and multiple regression analysis,respectively.4.1.The effect of presentation principles on outcomesA set of2(presentation principles)Â2(organization principles)ANOVAs was conducted.Table1Factor analysis of questionnaire itemsItem FactorPre-use Post-usePragmatic quality Hedonicquality-stimulationHedonicquality-identificationPragmaticqualityHedonicquality-stimulationHedonicquality-identificationPQ2.70.98PQ3.82.92PQ4.81.83PQ5.49.71PQ6.87.89PQ7.71.78HQS1.55.84HQS2.77.87HQS3.84.76HQS4.83.89HQS5.50.43.66HQS7.79.81HQI2.72À.59HQI3.80À.79HQI7.72À.52Eigenvalue 5.19 4.50 3.11 5.93 4.17 3.55Note:Extraction method:principal axis factoring;rotation method:direct oblimin.Figures per item are factor loadings from the pattern matrix.PQ, pragmatic quality;HQS,Hedonic quality–stimulation;HQI,Hedonic quality–identification.P.van Schaik,J.Ling/Interacting with Computers20(2008)419–4324234.1.1.Manipulation checkThe manipulation of presentation principles was effec-tive,as the following results demonstrate.More correct answers were given when presentation principles were fol-lowed and this was confirmed by a significant main effect of presentation principles on correctness (percentage of correct responses),F (1,107)=8.63,p <.01,e 2=.06,but there was also a significant interaction effect of presenta-tion principles by organization principles,F (1,107)=5.47,p =.02,e 2=.04(see also Table 3).The main effect of organization principles was not significant,F (1,107)=1.25,p =.27.Simple effect tests demonstrated that the effect of presentation principles was significant when orga-nization principles were followed,with more correct answers when presentation principles were followed,t (54)=3.45,p <.001,r =.43,but not when organization principles were violated,t <1.The main effect of presentation principles on mental effort was significant,with less effort when presentationprinciples were followed,F (1,107)=16.36,p <.001,e 2=.12,but the main effect of organization principles and the interaction effect were not significant,both F <1(see also Table 3).The main effect of presentation princi-ples on efficiency (number of links visited)for incorrect answers was significant,with higher efficiency when presen-tation principles were followed,F (1,102)=6.97,p <.01,e 2=.05,but neither the main effect of organization princi-ples nor the interaction effect was significant,both F <1(see also Table 3).In sum the manipulation was successful only for presentation principles,but not for organisation principles.This might be due to the strong effect of presen-tation principles,which may have occluded the smaller effect of organisation principles.4.1.2.The effect of experimental manipulations on perceptions and evaluationsOverall,the effect of experience,if any,was smaller than that of presentation principles,as the following results show.Perceptions:Pragmatic quality (PQ).The main effects of experience,F (1,107)=56.07,p <.001,e 2=.09,and pre-sentation principles,F (1,107)=64.92,p <.001,e 2=.27,were significant –with higher PQ when presentation prin-ciples were followed and before site use (see also Table 4).Hedonic quality-identification (HQI).The main effect of presentation principles,F (1,107)=36.55,p <.001,e 2=.21,was significant –with higher HQI when presenta-tion principles were followed (see also Table 4).Hedonic quality-stimulation (HQS).The main effect of presentation principles,F (1,107)=23.22,p <.001,e 2=.15,and the interaction effect of presentation principles and experience,F (1,107)=8.30,p <.001,e 2=.01,were significant (see also Table 4).Because of the significant interaction effect,simple effect tests with Bonferroni correction were con-ducted.A 2(presentation principles)Â2(organization principles)ANOVA showed that the effect of presentation principles was significant –with higher HQS when presen-Table 3Interaction outcomes as a function of experimental manipulations Organization principles Presentation principles Followed Violated Overall Correctness Followed 75.67(14.78)56.92(24.13)66.96(21.65)Violated 71.33(16.97)69.20(18.24)70.36(17.42)Overall75.67(14.78)56.92(24.13)66.96(21.65)Efficiency (incorrect answers)Followed 2.10(0.49) 2.47(1.03) 2.28(0.80)Violated 2.11(0.60) 2.58(1.05) 2.33(0.85)Overall 2.11(0.54) 2.52(1.03) 2.30(0.83)Mental effort Followed 26.04(12.48)39.11(19.84)32.11(17.44)Violated 28.09(14.43)39.81(17.43)33.42(16.78)Overall27.07(13.41)39.45(18.51)32.76(17.05)Note:Mean values are presented with standard deviations in brackets.Table 2Reliability,descriptives and correlationsICRMean SD HQI (1)HQS (1)PQ (1)B (1)G (1)HQI (2)HQS (2)PQ (2)B (2)HQI (1).83 3.74 1.42HQS (1).88 3.33 1.29.11PQ (1).90 4.94 1.32.48***À.47***B (1)NA 3.17 1.50.57***.47***.12G (1)NA 4.14 1.65.74***.12.49***.64***HQI (2).89 3.68 1.46.75***.11.41***.54***.64***HQS (2).92 3.27 1.25.14.83***À.33***.50***.19.28**PQ (2).95 4.03 1.57.48***À.24*.61***.16.47***.69***.02B (2)NA 2.97 1.34.57***.39***.17.72***.58***.68***.52***.41***G (2)NA3.761.68.56***.10.41***.42***.55***.76***.30**.76***.63***Note:ICR,internal consistency reliability (Cronbach’s alpha);mean,arithmetic mean;SD,standard deviation.Remaining figures are correlations among constructs.PQ,pragmatic quality;HQI,Hedonic quality-identification;HQS,Hedonic quality-stimulation;B,Beauty;G,Goodness.(1)Pre-use and (2)post-use.*p <.05.**p <.01.***p <.001.424P.van Schaik,J.Ling /Interacting with Computers 20(2008)419–432tation principles were violated –before use with a very large effect size,F (1,107)=31.18,p <.001,e 2=.22,and after use with a medium to large effect size,F (1,107)=13.04,p <.001,e 2=.10.In sum,adherence to presentation principles had a positive effect on PQ before use presumably because this made the web site appear more usable and after use because it was more usable.The opposite effect of presentation principles on HQI and HQS presumably reflects (a)users’appreciation of the more usual presentation format –when the principles were followed –of a web site that they would identify with for HQI and (b)users’experience of stimulation by an uncon-ventional presentation format –when the principles were violated -for HQS.Evaluations:Beauty .The effect of experience was signif-icant,F (1,107)=4.43,p <.05,e 2=.004-with a higher evaluation of Beauty before use (see also Table 4).Good-ness .Only the main effects of experience,F (1,107)=6.61,p <.05,e 2=.01,and presentation principles,F (1,107)=18.26,p <.001,e 2=.10,were significant –with a higher evaluation of Goodness when presentation princi-ples were followed and before use (see also Table 4).4.2.Effect of experience on the relation among attributes and evaluationsAssociations within perceptions and evaluations were analysed and comparisons were made between associations before and those after use.The positive correlation of measurements before use of the site with those after (see Table 2)was significant for all attributes and all evalua-tions,all p <.001.However,the correlation was signifi-cantly higher for HQS than for PQ,z =2.05,p <.01and the correlation was marginally significantly higher for HQI than for PQ,z =1.72,.05<p <.10.Furthermore,the correlation was significantly higher for Beauty than for Goodness,z =1.96,p <.05.These results confirm Has-senzahl’s hypothesis that (a)Pragmatic quality –because it is based more on experience –is less stable than Hedonic quality and that (b)Goodness –because it is more influenced by Pragmatic quality –is less stable than Beauty –because it is more influenced by stable Hedonic quality.These results are further strengthened by those of the model tests that follow.4.3.Testing models of aesthetic experienceAccording to Hassenzahl (2004),Hedonic quality is related to Beauty both before and after use.Pragmatic quality and Hedonic quality are related to Goodness before use,but only Pragmatic quality is related to Goodness after use.Tests of models of aesthetic experience are presented in support of Hassenzahl’s model.The models presented in Fig.1a–c were tested with multiple regression analysis,using mediator analysis (for a good introduction to multi-ple regression analysis and mediator analysis see Miles and Shevlin,2001).The goal of a mediator analysis is toT a b l e 4P e r c e p t i o n s o f q u a l i t y a n d e v a l u a t i o n sP r a g m a t i c q u a l i t y (P Q )H e d o n i c q u a l i t y -i d e n t i fic a t i o n (H Q I )H e d o n i c q u a l i t y -s t i m u l a t i o n (H Q S )B e a u t y G o o d n e s s T i m eT i m eT i m eT i m eT i m e P r e -u s e P o s t -u s e O v e r a l l P r e -u s eP o s t -u s e O v e r a l lP r e -u s e P o s t -u s e O v e r a l l P r e -u s e P o s t -u s e O v e r a l l P r e -u s e P o s t -u s e O v e r a l lP r e s e n t a t i o n p r i n c i p l e s F o l l o w e d 5.60(0.81)4.83(1.26)5.21(0.94)4.32(1.13)4.34(1.03)4.33(1.00)2.77(1.01)2.90(1.06)2.83(0.98)3.15(1.25)3.12(1.19)3.13(1.13)4.62(1.40)4.30(1.52)4.46(1.33)V i o l a t e d 4.16(1.38)3.08(1.37)3.62(1.13)3.06(1.44)2.91(1.51)2.98(1.35)3.99(1.28)3.72(1.33)3.85(1.24)3.20(1.77)2.80(1.48)3.00(1.52)3.57(1.76)3.12(1.64)3.34(1.40)O v e r a l l 4.94(1.32)4.03(1.57)4.48(1.30)3.74(1.42)3.68(1.46)3.71(1.35)3.33(1.29)3.27(1.25)3.30(1.22)3.17(1.50)2.97(1.34)3.07(1.32)4.14(1.65)3.76(1.68)3.95(1.47)N o t e :M e a n v a l u e s a r e p r e s e n t e d w i t h s t a n d a r d d e v i a t i o n s i n b r a c k e t s .P.van Schaik,J.Ling /Interacting with Computers 20(2008)419–432425establish if the effect of a predictor on an outcome is com-pletely or partially mediated by a third variable(the media-tor).Hierarchical regression can be used to establish if the third variable is a mediator,in other words if it is still a sig-nificant predictor of the outcome after the variability explained by the predictor.Hierarchical regression can also be used to establish the type of mediation:partial mediation occurs if the predictor still explains a statistically significant amount of variability in the outcome after the variability explained by the mediator;otherwise,mediation is complete. In the mediator analyses reported in this paper,predicted (pre-use or post-use)evaluation scores(e.g.pre-use Good-ness predicted from HQI,HQS and PQ)are used as a single overall measure of the corresponding set of(pre-use or post-use)perceptions(e.g.HQI,HQS and PQ).4.3.1.Evaluations before useBeauty(Fig.1a).Recall that presentation principles and organization principles had no effect on pre-use Beauty; furthermore,the partial correlation of pre-use PQ with Beauty was not significant.Therefore,a multiple regression analysis with Beauty as outcome and perceptions(HQI and HQS)as predictors was conducted,explaining a significant amount of variability,R2=.49,F(2,108)=51.52, p<.001,and both were significant predictors,b=.52, sr2=.27,t(108)=7.49for pre-use HQI and b=.41, sr2=.17,t(108)=5.95,for pre-use HQS,both p<.001–the higher HQI and HQS,the higher the evaluation of Beauty.Goodness(Fig.1a,Table5).A mediator analysis tested the effect of presentation principles(before use)on pre-use Goodness with pre-use perceptions as a mediator and demonstrated complete mediation.The indirect positive effect of presentation principles on pre-use Goodness after controlling for the positive effect of product attributes explained10%(%(.10–.001)Â100,from Table5)of variability.4.3.2.Evaluations after useBeauty(Fig.1c and Table6).Afirst mediator analysis (Table6a)tested the effect of pre-use perceptions on pre-dicted post-use Beauty with pre-use Beauty as a mediator and demonstrated partial mediation.The positive indirect effect of pre-use perceptions on predicted post-use Beauty after controlling for the positive effect of pre-use Beauty explained39%of variability(=(.57–.18)Â100,from Table 6a).A second mediator analysis(Table6b)tested the effect of pre-use perceptions and Beauty on post-use Beauty with post-use perceptions as a mediator and demonstrated par-tial mediation.The indirect positive effect of pre-use per-ceptions and Beauty on post-use Beauty after controlling for the positive effect of post-use perceptions explained 48%(=(.77–.09)Â100)of variability.Subsequent media-tor analyses(Table6c)demonstrated that post-use percep-tions were a complete mediator of pre-use perceptions and a partial mediator of pre-use Beauty.The indirect positive effect of pre-use perceptions on post-use Beauty after con-trolling for the positive effect of post-use perceptions explained42%(=(.43–.01)Â100)of variability.The indi-rect positive effect of pre-use Beauty on post-use Beauty after controlling for the positive effect of post-use percep-tions explained43%(=(.52–.09)Â100)variability.Goodness–interaction-based model(Fig.1b,Table7).A first mediator analysis(Table7a)tested the effect of presen-tation principles on mental effort with correctness of answers as a mediator and demonstrated complete mediation.The indirect negative effect of presentation principles on mental effort after controlling for the negative effect of correctness explained13%(=(.14–.01)Â100,from Table7a)of vari-ability.A second mediator analysis(Table7b)tested the effect of correctness on predicted post-use Goodness with mental effort as a mediator and demonstrated complete mediation.The indirect positive effect of correctness on pre-dicted post-use Goodness after controlling for the negative effect of mental effort explained11%(%(.11–.002)Â100) of variability.A third mediator analysis(Table7c)tested the effect of mental effort on post-use Goodness with post-use perceptions as a mediator and demonstrated complete mediation.The indirect negative effect of mental effort on post-use Goodness after controlling for the positive effect of post-use perceptions explained29%(%(.30–.004)Â100) of variability.Table5Mediator analysis of pre-use GoodnessOutcome R2F df1df2Predictor b sr2t Predicted pre-use Goodness.1824.01**1109Presentation principlesPre-use Goodness.1012.19**1109Presentation principles.4943.14**3106Pre-use PQPre-use HQIPre-use HQSPre-use Goodness.5932.36**3107Pre-use PQ.30.04 3.40**Pre-use HQI.58.217.36**Pre-use HQS.19.02 2.45* <.001<1Presentation principles*p<.05.**p<.001.426P.van Schaik,J.Ling/Interacting with Computers20(2008)419–432。