Assessing Nighttime DMSPOLS Data for Detection of Human Settlements in the Brazilian Amazon
微型睡眠评估(MSA)
微型睡眠评估(MSA)微型睡眠评估（MSA）微型睡眠评估（Micro Sleep Assessment, MSA）是一种用于评估个人是否存在微睡眠情况的方法。
微睡眠是指无意识地在清醒状态下发生的短暂睡眠。
这种状态容易发生在长时间疲劳或缺乏睡眠的人身上，给个人的注意力和反应能力带来危险。
通过MSA，我们可以了解一个人是否在日常生活中容易发生微睡眠，并采取相应的措施来预防潜在的危险。
MSA的实施方法MSA通常通过以下步骤进行：1. 设定合适的时间段：MSA通常在一段时间内进行，例如连续24小时。
2. 监测活动水平：使用各种传感器记录被测人员的身体活动水平。
3. 记录睡眠情况：同时记录被测人员的睡眠情况，包括入睡时间、觉醒时间和睡眠时长。
4. 分析数据：通过对收集的数据进行分析，确定是否存在微睡眠的迹象。
5. 结果解读：根据分析结果，判断被测人员是否容易发生微睡眠。
MSA的应用MSA的应用主要用于以下方面：1. 驾驶员安全：驾驶中的微睡眠可能导致交通事故，通过对驾驶员进行MSA评估，可以提前发现可能存在的微睡眠风险，并采取相应的措施，如休息、换班等。
2. 工作场所安全：对那些需要集中注意力和反应能力的岗位，如操作机器、监控设备等，进行MSA评估可以保障员工和工作场所的安全。
3. 研究和医学领域：MSA可以用于睡眠相关的研究和医学实践，帮助研究人员和医生了解睡眠问题对人体健康的影响。
总结微型睡眠评估（MSA）是一种用于评估微睡眠情况的方法。
通过监测活动水平和睡眠情况，并对数据进行分析，MSA可以提供有关个人是否存在微睡眠风险的信息。
MSA的应用涵盖了驾驶安全、工作场所安全以及研究和医学领域。
通过采取相应措施，可以减少微睡眠对个人的危险和对社会的威胁。
对奥德赛岁月进行调查并提出建议英语作文
An Investigation into the Odyssey Years and Suggestions for EnhancementIntroduction:The Odyssey years, often likened to a mid-life crisis or a phase of self-discovery and reinvention, are a period in an individual's life typically occurring between the ages of 45 and 65. This phase is characterized by a quest for new meanings, passions, and often, a reevaluation of life's purpose. Through an investigation into this unique period, this essay aims to explore the common experiences and challenges faced during the Odyssey years and propose suggestions for enhancing this transformative phase.Investigation:The Odyssey years are marked by several key experiences. Many individuals find themselves reassessing their careers, relationships, and personal identities. They may grapple with feelings of inadequacy stemming from unfulfilled dreams or a sense of stagnation in their professional or personal lives. This period often prompts a desire for change, leading to mid-life career shifts, the pursuit of new hobbies, or even relocation to a different environment.One of the primary challenges during these years is the fear of failure and the uncertainty that accompanies significant life changes. Society's expectations and the pressure to conform can exacerbate these fears, making it difficult for individuals to embrace their Odyssey years fully. Additionally, financial constraints and responsibilities towards family members can limit the ability to pursue new paths without reservation.Suggestions for Enhancement:1.Encourage Self-Reflection and Planning: Societies and communities should foster environments that encourage individuals to reflect on their lives and plan for their future during the Odyssey years. This can be achieved through workshops, seminars, or online resources that guide individuals in self-assessment and goal-setting.2.Provide Support Networks: Establishing support groups or mentorship programs can help individuals navigate the challenges of the Odyssey years. These platforms can offer guidance, shared experiences, and encouragement, reducing the feeling of isolation and enhancing resilience.3.Promote Lifelong Learning: Encouraging lifelong learning opportunities, such as access to adult education courses, online learning platforms, or vocational training, can empower individuals to acquire new skills and explore alternative career paths. This not only enhances personal growth but also contributes to societal development.4.Flexible Work Arrangements: Employers can play a crucial role by offering flexiblework arrangements, part-time positions, or remote work options to accommodate individuals transitioning through their Odyssey years. This flexibility allows for the pursuit of personal interests and goals without sacrificing financial stability.5.Cultural Shift in Perception: A broader cultural shift is needed to recognize and celebrate the Odyssey years as a positive and transformative phase of life. Media and public figures can contribute to this by sharing stories of successful transitions and reinventions, inspiring others to embrace their own journeys.Conclusion:The Odyssey years represent a unique and transformative period in an individual's life, filled with opportunities for growth, self-discovery, and reinvention. By addressing the challenges faced during this phase and implementing supportive measures, societies can foster an environment that encourages and enables individuals to embrace their Odyssey years with confidence and enthusiasm. Through self-reflection, community support, lifelong learning, flexible work arrangements, and a cultural shift in perception, we can enhance this period of life, transforming it into a time of profound personal and societal enrichment.。
巴黎睡眠质量指数问卷(附评分标准)
巴黎睡眠质量指数问卷(附评分标准)以下是巴黎睡眠质量指数问卷的完整版及其评分标准。
问卷内容1. 您入睡通常需要多长时间？- 0分：不到15分钟- 1分：15-30分钟- 2分：31-45分钟- 3分：45分钟以上2. 您一晚通常醒来多少次？- 0分：没有- 1分：1-2次- 2分：3-4次- 3分：5次及以上3. 当您早上醒来，您是否觉得精力充沛？- 0分：几乎每天都是- 1分：一半或更多天数是- 2分：不到一半的天数是- 3分：几乎每天都不是4. 您认为自己的睡眠质量如何？- 0分：非常好- 1分：好- 2分：较好- 3分：较差5. 您是否在白天容易打盹？- 0分：几乎从不- 1分：偶尔- 2分：经常- 3分：每天都打盹6. 您有困难入睡或保持睡眠吗？- 0分：几乎从不- 1分：偶尔- 2分：经常- 3分：每天都有7. 您认为自己的睡眠对日常生活的影响有多大？- 0分：没有问题- 1分：很小的问题- 2分：中等程度的问题- 3分：很大的问题评分标准根据巴黎睡眠质量指数问卷的得分，睡眠质量可以被分为以下几个等级：- 0-5分：优质睡眠- 6-10分：良好睡眠- 11-15分：中等睡眠- 16-20分：较差睡眠- 21-28分：非常差的睡眠根据您的得分，您可以初步了解自己的睡眠质量。
请注意，这只是一个评估工具，如果您有严重的睡眠问题，建议咨询专业医生或睡眠专家进行进一步的诊断和治疗。
以上是巴黎睡眠质量指数问卷的完整版及其评分标准。
希望这份问卷能帮助您评估自己的睡眠质量。
祝您有个良好的睡眠！*注意：本问卷仅供参考，具体应用时需结合个人实际情况，不能替代专业睡眠评估和医疗建议。
*。
SPSS-Clementine和KNIME数据挖掘入门
SPSS Clementine是Spss公司收购ISL获得的数据挖掘工具。
在Gartner的客户数据挖掘工具评估中，仅有两家厂商被列为领导者：SAS和SPSS。
SAS获得了最高ability to execute评分，代表着SAS在市场执行、推广、认知方面有最佳表现；而SPSS获得了最高的completeness of vision，表明SPSS在技术创新方面遥遥领先。
客户端基本界面SPSS Clementine（在此简称clementine）在安装好后会自动启用服务，服务端的管理需要使用SPSS Predictive Enterprise Manager，在服务端clementine没有复杂的管理工具，一般的数据挖掘人员通过客户端完成所有工作。
下面就是clementine客户端的界面。
一看到上面这个界面，我相信只要是使用过SSIS+SSAS部署数据挖掘模型的，应该已经明白了六、七分。
是否以跃跃欲试了呢，别急，精彩的还在后面^_’项目区顾名思义，是对项目的管理，提供了两种视图。
其中CRISP-DM （Cross Industry Standard Process for Data Mining，数据挖掘跨行业标准流程）是由SPSS、DaimlerChrysler（戴姆勒克莱斯勒，汽车公司）、NCR（就是那个拥有Teradata的公司）共同提出的。
Clementine里通过组织CRISP-DM的六个步骤完成项目。
在项目中可以加入流、节点、输出、模型等。
工具栏工具栏总包括了ETL、数据分析、挖掘模型工具，工具可以加入到数据流设计区中，跟SSIS中的数据流非常相似。
Clementine中有6类工具。
源工具（Sources）相当SSIS数据流中的源组件啦，clementine支持的数据源有数据库、平面文件、Excel、维度数据、SAS数据、用户输入等。
记录操作（Record Ops）和字段操作（Field Ops）相当于SSIS数据流的转换组件，Record Ops是对数据行转换，Field Ops是对列转换，有些类型SSIS的异步输出转换和同步输出转换（关于SSIS异步和同步输出的概念，详见拙作：）。
国际标准睡眠筛查量表
国际标准睡眠筛查量表
这里是国际标准睡眠筛查量表：
1. Epworth嗜睡量表（ESS）：用于评估白天嗜睡程度及日间睡眠障碍。
2. Pittsburgh睡眠质量指数量表（PSQI）：用于评估睡眠质量及失眠症状。
3. Berlin问卷：用于评估慢性阻塞性睡眠呼吸暂停的风险。
4. STOP-BANG问卷：用于评估睡眠呼吸暂停综合症的风险。
5. 阿尔伯塔嗜睡量表（ALBERTA SLEEP GRID）：用于评估夜间睡眠质量及日间嗜睡。
6. 睡眠差异症状问卷（SDSQ）：用于评估睡眠障碍的类型和严重程度。
7. 多维度失眠症状评估量表（MIDAS）：用于评估失眠症状的频率、严重程度和影响。
8. 汉密尔顿情绪量表（HAM-D）：用于评估患者情绪状态和心理健康。
9. 抑郁和焦虑量表（HADS）：用于评估患者抑郁和焦虑症状。
DMSPOLS数据应用研究综述
DMSPOLS数据应用研究综述一、本文概述随着遥感技术的快速发展，各种地球观测数据在各个领域得到了广泛应用。
其中，Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) 数据作为一种重要的夜间灯光遥感数据，因其独特的夜间探测能力和广泛的应用前景，受到了学者们的广泛关注。
DMSPOLS数据能够提供大范围的、连续的夜间灯光信息，对于城市扩张、人口分布、能源消耗等研究具有重要的价值。
本文旨在对DMSPOLS数据的应用研究进行综述，总结其在国内外的研究现状和发展趋势，以期为未来DMSPOLS数据的应用提供有益的参考和借鉴。
本文将介绍DMSPOLS数据的基本特点和优势，包括其覆盖范围、分辨率、数据连续性等方面的优势，为后续应用研究提供基础支撑。
本文将重点综述DMSPOLS数据在城市扩张、人口分布、能源消耗等领域的应用研究，分析其在不同领域的应用效果和应用前景。
本文还将对DMSPOLS数据的应用研究方法和技术进行介绍，包括数据处理、信息提取、模型构建等方面的技术方法和研究进展。
本文将探讨DMSPOLS 数据的应用研究面临的挑战和未来的发展方向，以期为相关领域的学者和研究人员提供有益的启示和借鉴。
通过对DMSPOLS数据应用研究的综述，本文旨在推动DMSPOLS数据在不同领域的应用研究，为相关领域的学术研究和实践应用提供有益的参考和借鉴。
本文也希望能够促进遥感技术的发展和应用，推动地球观测数据在各个领域的广泛应用和发展。
二、DMSPOLS数据获取与处理DMSPOLS（Defense Meteorological Satellite Program Operational Linescan System）数据是由美国国防气象卫星计划（DMSP）提供的重要遥感数据源之一，广泛应用于全球气候变化、海洋环境监测、灾害预警等领域。
FortiEDR产品概述说明书
Enable Secure Guest Experiences and Safeguard Against Cyberattacks With FortiEDR Executive SummaryCyber criminals consider retail operations attractive targets. Vulnerable internet-connected point-of-sale (POS) systems and devices provide a path to valuable information, including customer payment card data, personal information, and corporate intelligence. Often facing cybersecurity staffing shortages, many retailers struggle to cover security gaps. And this situation is exacerbated by the proliferation of point security products and the advanced threat landscape.Real-time, automated endpoint protection that includes orchestrated incident response across any communication device can help defend retailers against cyberattacks. FortiEDR helps retailers predict, prevent, and detect threats. It also helps them respond and recover from attacks for complete protection across the entire kill chain.Cyberattacks Risk Positive Shopping ExperiencesSome retailers may feel caught between protecting the business against security threats and providing the type of seamless, responsive shopping experience that customers expect. First, there’s the task of managing risk and compliance. Payment data must be protected across distributed and complex networks while adheringto growing data privacy requirements. The threat landscape has also grownmore sophisticated, and data stored on POS systems, e-commerce, and big data platforms are a common target of cyber criminals.Speed is the key to breaking the Cyber Kill Chain, and first-generation endpoint detection and response (EDR) tools simply cannot keep up. They require manual triage and responses that are slow and generate many alerts. These first-generation EDR solutions can drive up the cost of security operations and slow incident response processes, which can lead to production shutdowns and disruption for system users. Retailers need an EDR solution that can solve these challenges and is easy to use that offers a low total cost of ownership (TCO).Fortinet Delivers Advanced, Automated EDRBuilt from the ground up for end-to-end security, FortiEDR provides transparent visibility across all endpoints, including POS systems. It has an intuitive user interface that gives retailers the ability to manage endpoint policies and remediate infections quickly and easily. In a single agent, FortiEDR combines next-generation antivirus (NGAV) protection, application communication control, virtual patching, and automated EDR for real-time blocking, threat hunting, and incident response.nn Protection. FortiEDR offers proactive, real-time, automated endpoint protection with orchestrated incident response across platforms. It stops breaches with real-time post-infection blocking to protect data from exfiltration and ransomware encryption. nn Management. A single unified console features an intuitive interface to access and manage FortiEDR. The cloud-managed platform closes the loop and automates mundane endpoint security tasks, freeing your staff to tackle other assignments.Connected environments to meet customer demand. More than 40% of retailers point to network complexity as the top barrier to data security. And 66% believe that newly deployed technology is very secure.1Retailers adapt to new markets.Retailers are accelerating merchandise cycles, moving supply chains closer to the consumer, and deploying advanced technologies.2 COVID-19 has compounded problems with margins becoming even more compressed as consumers shift online.3From bricks to clicks. 96% of retailers use sensitive data on digitally transformative technologies and 62% report they have been breached at some point in their history.4SOLUTION BRIEF1SOLUTION BRIEF | Enable Secure Guest Experiences and Safeguard Against Cyberattacks With FortiEDRCopyright © 2021 Fortinet, Inc. All rights reserved. Fortinet ®, FortiGate ®, FortiCare ® and FortiGuard ®, and certain other marks are registered trademarks of Fortinet, Inc., and other Fortinet names herein may also be registered and/or common law trademarks of Fortinet. All other product or company names may be trademarks of their respective owners. Performance and other metrics contained herein were attained in internal lab tests under ideal conditions, and actual performance and other results may vary. Network variables, different network environments and other conditions may affect performance results. Nothing herein represents any binding commitment by Fortinet, and Fortinet disclaims all warranties, whether express or implied, except to the extent Fortinet enters a binding written contract, signed by Fortinet’s General Counsel, with a purchaser that expressly warrants that the identified product will perform according to certain expressly-identified performance metrics and, in such event, only the specific performance metrics expressly identified in such binding written contract shall be binding on Fortinet. For absolute clarity, any such warranty will be limited to performance in the same ideal conditions as in Fortinet’s internal lab tests. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable.May 12, 2021 1:32 AMD:\Fortinet\2021 Rebranded templates\Solution Briefs\May\Retail EDR\sb-FA-retail-edr-5122021\sb-FA-retail-edr-51220211022667-0-0-EN n n Scalability. With a native cloud infrastructure and a small footprint, FortiEDR can bedeployed quickly and scale up to protect hundreds of thousands of endpoints.n n Flexibility. Endpoints are protected both online and offline, and you can addressan array of scenarios, from on-premises in an air-gapped environment to asecure cloud instance.n n Cost. With a low, predictable cost, and capped TCO, FortiEDR can help eliminatepost-breach operational expenses and breach damage to the organization.Pre- and Post-infection Real-time Endpoint Threat ProtectionFortiEDR can help protect retail organizations from constant threats, whetherattackers are trying to steal customer financial data or sabotage operations. Itscapabilities include:n n Proactive attack surface risk mitigation. FortiEDR delivers advanced automated attack surface policy control with vulnerability assessments andInternet-of-Things (IoT) security.In 2020, there were over a hundred thousand incidents investigated and around 3,900 confirmed breaches with over 400 confirmed breaches taking place in retail alone. 61% of all breaches occurred via web application exploits, POS attacks, or the use of crimeware.5FortiGuard Labs extracts threat intelligence from over 100 billion security events daily.6n n Malware prevention. A machine-learning (ML) antivirus engine helps stop malware pre-execution. This cross-operatingsystem NGAV capability is configurable and comes built into the single, lightweight agent, so users can assign anti-malware protection to any endpoint group without requiring additional installation.n n Real-time automated breach protection. FortiEDR detects suspicious process flows and behaviors and immediatelydefuses potential threats by blocking outbound communications and access to the file system from suspicious processes, which prevents data exfiltration, command-and-control communications, file tampering, and ransomware encryption. At the same time, the FortiEDR back end continues to gather additional evidence, enrich event data, and classify the incidents. The solution surgically stops data breach and ransomware damage in real time to automatically keep the business running even on devices that have been compromised.n n Customizable incident response. With FortiEDR, you can orchestrate incident response operations using tailor-made playbooks with cross-environment insights. You can streamline incident response and remediation processes and manually or automatically roll back malicious changes done by already contained threats on a single device or on devices across the environment.n n Guided interface with data enrichment. FortiEDR automatically enriches data with detailed information on malware both pre- and post-infection to conduct forensics on infiltrated endpoints. This unique interface provides helpful guidance, recommends best practices, and suggests the next logical steps for security analysts.Retailers that use FortiEDR also benefit from the Fortinet Security Fabric architecture and integration with other components of the Security Fabric, including FortiGate, FortiSandbox, and FortiSIEM.Detection and Remediation Without the AggravationWith FortiEDR, retail organizations can strategically reduce the complexity and cost associated with the detection andremediation of advanced malware across POS and other endpoints. In addition, FortiEDR minimizes incident response timepressures and alert fatigue, while preventing the exploitation of vulnerable endpoints that commonly leads to data breaches and disruption from cyberattacks.1 “2019 Thales Data Threat Report—Retail Edition ,” Thales, September 24, 2019.2 “2019 Retail Industry Outlook ,” Deloitte, 2019.3 “2021 Retail Industry Outlook ,” Deloitte, 2020.4 “2019 Thales Data Threat Report—Retail Edition ,” Thales, September 24, 2019.5 “2020 Data Breach Investigations Report ,” Verizon, 2020.6“FortiGuard Security Services ,” Fortinet, September 2020.。
revisiting time series outlier detection 参考文献
Revisiting Time Series Outlier DetectionIntroductionIn the field of time series analysis, outlier detection plays a crucial role in identifying abnormal patterns or data points that deviate significantly from the expected behavior. Detecting outliers in time series data is essential for various applications, such as anomaly detection, fault diagnosis, and fraud detection. This article revisits the topic of time series outlier detection, discussing its importance, challenges, and recent advancements.Challenges in Time Series Outlier DetectionDetecting outliers in time series data presents several challenges due to the sequential nature of the data. Some of the key challenges include:1.Temporal Dependencies: Time series data often exhibits temporaldependencies, where the current value depends on previous values.Detecting outliers requires considering both the current datapoint and its relationship with past observations.2.Seasonality and Trends: Time series data often contain seasonalpatterns or long-term trends. These underlying patterns can affect the detection of outliers, as they may be misinterpreted asabnormal behavior.3.Varying Magnitudes: Outliers in time series data can have varyingmagnitudes. Some outliers might be moderate deviations from theexpected behavior, while others can be extreme anomalies. A robust outlier detection technique should be able to capture outliers of different magnitudes.4.Data Imbalance: Time series data is typically imbalanced, with alarge number of normal instances and only a few outliers. Thismakes it challenging to build accurate models that can effectively identify outliers.Classical Approaches to Time Series Outlier DetectionSeveral classical approaches have been proposed for time series outlier detection. These approaches can be broadly classified into three categories:Statistical MethodsStatistical methods are widely used for detecting outliers in time series data. These methods rely on statistical properties, such as mean, standard deviation, or confidence intervals, to identify data pointsthat deviate significantly from the expected behavior. Some commonly used statistical techniques include:•Z-Score: This method measures the number of standard deviations an observation is away from the mean. Data points with a high z-score are considered outliers.•Grubbs’ Test:Grubbs’ Test is based on the hypothesis that the maximum or minimum value in a univariate dataset is an outlier.The test calculates a test statistic and determines whether it is significant enough to reject the null hypothesis.Distance-Based MethodsDistance-based methods treat time series data as high-dimensional vectors and measure the similarity or dissimilarity between data points. Outliers are identified based on their distance from the remaining data points. Some commonly used distance-based techniques include:•k-Nearest Neighbors: This method calculates the average distance to the k nearest neighbors. Observations with a significantlyhigher average distance are considered outliers.•Local Outlier Factor (LOF): LOF measures the density deviation ofa data point compared to its neighbors. Outliers have asignificantly lower density compared to their neighbors.Model-Based MethodsModel-based methods assume that the time series data follows a certain model, and outliers are defined as data points that do not conform tothe model. Some commonly used model-based techniques include:•ARIMA Models: Autoregressive Integrated Moving Average (ARIMA) models are widely used for time series forecasting. Outliers aredetected based on the residuals obtained from fitting an ARIMAmodel to the data.•Hidden Markov Models: Hidden Markov Models (HMMs) areprobabilistic models that assume the underlying system hasunobserved states. Outliers are identified based on the likelihood of the observed sequence given the HMM.Recent Advancements in Time Series Outlier DetectionRecent advancements in time series outlier detection have focused on improving the accuracy and robustness of existing methods. Some notable advancements include:1.Deep Learning-Based Approaches: Deep learning models, such asrecurrent neural networks (RNNs) and convolutional neural networks (CNNs), have shown promising results in time series outlierdetection. These models can capture temporal dependencies andlearn complex patterns in the data, improving the detection ofoutliers.2.Ensemble Methods: Ensemble methods combine multiple outlierdetection techniques to improve performance. By aggregating theoutputs of different methods, ensemble approaches can provide more reliable outlier detection results.3.Unsupervised Learning Techniques: Unsupervised learningtechniques, such as clustering algorithms and self-organizing maps, have been applied to time series outlier detection. These methodsleverage the inherent clustering structure in the data to identify outliers.4.Domain-Specific Approaches: Some recent advancements cater tospecific domains, such as financial data or sensor data. Thesedomain-specific approaches incorporate domain knowledge to improve outlier detection accuracy.Evaluation Metrics for Time Series Outlier DetectionTo evaluate the performance of time series outlier detection techniques, several metrics are commonly used. These include:1.Precision: Precision measures the proportion of true outliersamong the detected outliers. A higher precision indicates fewerfalse positives.2.Recall: Recall measures the proportion of true outliers that arecorrectly detected. A higher recall indicates fewer falsenegatives.3.F1 Score: The F1 score is the harmonic mean of precision andrecall. It provides a balance between precision and recall, withhigher values indicating better performance.4.Area Under the Receiver Operating Characteristic (ROC) Curve: TheROC curve plots the true positive rate against the false positive rate for different threshold values. The area under the ROC curve (AUC) provides an overall measure of a model’s performance, with larger values indicating better performance.ConclusionTime series outlier detection remains an important research area with significant practical implications. Classical approaches based on statistical, distance-based, and model-based methods have been widely used but have their limitations. Recent advancements in deep learning, ensemble methods, unsupervised learning, and domain-specific approaches have improved the accuracy and robustness of outlier detection techniques. Selecting appropriate evaluation metrics is crucial to assess the performance of these methods accurately. As time series data continues to grow in complexity and volume, further research is needed to develop more advanced techniques for detecting outliers effectively.。
探讨DMSP-OLS夜间灯光数据的校正
探讨DMSP-OLS夜间灯光数据的校正作者：李梦真来源：《大经贸》 2019年第7期李梦真【摘要】随着夜间灯光数据的广泛应用，夜间灯光数据的长时间序列应用逐渐受到人们关注。
但应用最广的DMSP/OLS数据，具有数据之间的不连续性以及像元饱和问题，所以需要对这种数据进行校正。
本文介绍了一种基于不变区域的夜间灯光数据校正方法，能够合理的解决DMSP/OLS数据所存在的问题。
【关键词】夜间灯光DMSP/OLS 像元饱和校正美国国防气象卫星（Defense Meteorological Satellite Program, DMSP）搭载的业务型线扫描传感器（Operational Linescan System, OLS）最初主要为气象监测而设计，用于探测月光照射下的云，后来由于其独特的光电放大能力使其能在夜间探测到地表微弱的近红外辐射，因此，该传感器获取的夜间灯光影像被越来越多的用来研究人类活动，目前主要应用于社会经济参数估算、城市化监测与评估、人口发展与估算以及重大事件评估。
一、存在的缺陷DMSP/OLS 夜间灯光影像本身存在缺陷，该数据集包括由多个DMSP卫星传感器获取的自1992-2012年共33期影像，其中存在由不同传感器获取的同一年度的影像。
因为卫星传感器在获取地表数据的过程受到多种因素的影响，所以不同传感器获取的同一年度的影像之间是有差异的。
同时，不同的OLS传感器在获取影像时并没有进行星上辐射校正，造成了同一个卫星传感器获取的连续不同年度的影像间相同位置的亮值像元DN值之间的异常波动。
所以长时间序列的DMSP/OLS夜间灯光影像数据集主要存在2个问题需在校正中解决：（1）原始影像数据集中的影像是非连续性的；（2）数据集中的每一期影像都存在着像元DN值饱和的现象。
所以针对这些问题前人做了很多研究，目前已经能较好的解决长时间序列的DMSP/OLS夜间灯光影像数据的非连续性问题以及像元DN值饱和现象。
Assessing data sets
利用DMSPOLS夜间灯光影像解析中美两国“锈带”空间特征
第35卷第6期2020年12月遥感信息Remote Sensing InformationVol.35,No.6Dec.,2020利用DMSP/OLS夜间灯光影像解析中美两国“锈带”空间特征刘艺炫123,刘涛123，周亮123，孙钦珂123(1.兰州交通大学测绘与地理信息学院，兰州730070；2.地理国情监测技术应用国家地方联合工程研究中心，兰州730070;3.甘肃省地理国情监测工程实验室，兰州730070)摘要：针对较少有研究基于夜间灯光数据探究''锈带”的空间特征的现状，选取1992—2012年的夜间灯光数据对中美两国''锈带”的空间特征进行分析研究。
通过波段融合产生多时相地图，并计算空间光基尼系数、平均灯光亮度值增长速率等指标，结合灯光重心演变和GDP数据进行分析讨论。
结果表明：①中国东北地区区域中心城市与其余城市两极分化极为严重，灯光重心始终在哈大线上移动，该线上的城市在东北地区发展中起主导作用，但也因此造成两极分化严重，导致东北地区整体发展相对滞后；②美国“锈带”地区各大城市分散分布，区域间协调发展，新泽西州因其人口密度极高而显示出不同于其余各州的异常值，纽约州的实际GDP增长率增加最多，结合各州的GDP数据，发现纽约州在“锈带”地区较为突出：③借鉴美国“锈带”地区城市分布，东北地区应加强中心城市对周边小城市的带动作用，同时积极发展中小城市，促进区域协调发展。
关键词:DMSP/OLS；夜间灯光；空间特征；“锈带”；区域发展doi：10.3969/j.issn.1000-3177.2020.06.016中图分类号:TP79文献标志码:A文章编号：1000-3177(2020)06-0105-10Analysis on Spatial Features of“Rust Belts”in Chinaand the United States by Using DMSP/OLSNighttime Lighting ImagesLIU Yixuan123,LIU Tao1，，,ZHOU Liang123,SUN Qinke123(1.Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou730070,China;2.犖ationallocal Joint Engineering Research Center of Technologiss andApplications frr National Geographic State Monitoring^Lanzhou730070,China；3.Gansu Provincial Engineering Laboratory for National Geographic State Monitoring^Lanzhou730070China)Abstract:Aiming at the actuality that few studies have explored the spatial characteristics of the“rust belt”based on night light data,the night light data from1992to2012are chosen to analyze and study the spatial characteristics of the “rustbelt”inChinaandtheUnitedStates Multi-temporalmapsaregeneratedthroughbandfusion，theginicoe f icientof spatial light,the growth rate of average light brightness value and other indicators are calculated,and the light center evolution and GDP data are analyzed and discussed.The results show that:①The polarization between the regional central收稿日期：2020-02-07修订日期：2020-03-12基金项目：国家重点研发计划课题项目(2016YFC0803106)；国家自然科学基金项目(41761088)；兰州交通大学优秀平台支持项目(201806)。
ICI-Q-SF评分失眠评估
ICI-Q-SF评分失眠评估概述本文档旨在介绍ICI-Q-SF评分失眠评估工具的使用方法和背景知识。
ICI-Q-SF（即Insomnia Severity Index--Short Form）是一种常用的问卷调查工具，用于评估个体的失眠症状及其严重程度。
该评估工具简洁易用，适用于临床实践和研究领域。
ICI-Q-SF评分失眠评估工具背景知识失眠是一种常见的睡眠障碍，表现为入睡困难、睡眠质量差或早醒等症状。
ICI-Q-SF评分失眠评估工具是由Buysse等人在2006年开发的，旨在快速、简便地评估个体的失眠症状。
该工具由七个问题组成，包括睡眠困难、睡眠时间缩短、日间功能障碍等方面。
评分方法ICI-Q-SF评分失眠评估工具总分范围为0-28分，分数越高表示失眠症状越严重。
根据总分，可以将个体的失眠程度分为轻度失眠、中度失眠和重度失眠三个级别。
具体的评分指导如下：- 总分0-7分：表示轻度失眠- 总分8-14分：表示中度失眠- 总分15-28分：表示重度失眠使用注意事项在使用ICI-Q-SF评分失眠评估工具时，需要注意以下几点：1. 评估前请向被测者解释工具的含义和使用方法，并确保他们能够理解问题的意思。
2. 被测者应根据自己的实际情况回答问题，回答过程中不要求完全准确，但要尽量真实反映自己的感受。
3. 评估结果仅作为参考，不能作为诊断依据。
如对结果有疑问，建议咨询专业医生或心理咨询师。
参考文献1. Buysse DJ, Reynolds CF 3rd, Monk TH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213.3. Morin CM. Insomnia: Psychological Assessment and Management. Guilford Press; 1993.。
睡眠障碍的诊断工具深入了解匹兹堡睡眠质量指数量表
睡眠障碍的诊断工具深入了解匹兹堡睡眠质量指数量表睡眠障碍的诊断工具-深入了解匹兹堡睡眠质量指数量表睡眠是人类维持健康的重要组成部分，它对我们的身体和心理状态有着深远的影响。
然而，有时候我们可能会面临睡眠障碍的问题，比如失眠、睡眠过度、睡眠呼吸暂停等。
为了准确评估睡眠问题，医学界发展了多项诊断工具，其中最常用和流行的是匹兹堡睡眠质量指数量表（Pittsburgh Sleep Quality Index，简称PSQI）。
本文将详细介绍并深入了解PSQI作为睡眠障碍诊断工具的特点和使用方法。
一、匹兹堡睡眠质量指数量表简介匹兹堡睡眠质量指数量表是由匹兹堡大学医学院的睡眠研究中心开发的，被广泛视为睡眠障碍领域的“金标准”。
该问卷致力于全面评估睡眠质量及相关的睡眠问题，包括入睡质量、睡眠延迟、睡眠时间、睡眠效率、睡眠质量、睡眠障碍和用药情况。
二、匹兹堡睡眠质量指数量表的结构匹兹堡睡眠质量指数量表由19个项目组成，分为七个主要分量：主观质量分量、睡眠延迟分量、睡眠时间分量、睡眠效率分量、睡眠质量分量、睡眠障碍分量和用药情况分量。
1. 主观质量分量：通过一个项目来评估参与者对自己睡眠质量的整体评估。
2. 睡眠延迟分量：通过评估参与者入睡需要花费的时间。
3. 睡眠时间分量：通过评估参与者实际睡眠的时间。
4. 睡眠效率分量：通过评估参与者睡觉和清醒的时间比例。
5. 睡眠质量分量：通过评估参与者睡眠中的难度和质量。
6. 睡眠障碍分量：通过评估参与者出现的睡眠障碍种类和频率。
7. 用药情况分量：通过评估参与者使用药物来改善睡眠的情况。
三、匹兹堡睡眠质量指数量表的使用方法匹兹堡睡眠质量指数量表是一种自评量表，可以由被试者自行填写。
通常情况下，参与者会在一段时间内进行连续7天的睡眠监测，然后根据填写的问卷来量化睡眠质量。
为了提高问卷的准确性，填写者需要参照特定的说明进行操作。
填写时应该回忆前一个月的睡眠情况，并根据每个项目的评分标准选择最符合自己实际情况的选项。
(完整版)罗马睡眠质量指数量表(RPSQI)
(完整版)罗马睡眠质量指数量表(RPSQI)
简介
本文档提供了罗马睡眠质量指数量表(RPSQI)的完整版，内容详细介绍了该量表的用途、评估方法和计分方式等。
罗马睡眠质量指数量表(RPSQI)
罗马睡眠质量指数量表(Rome Sleep Quality Index, RPSQI)是一种经过验证的睡眠质量评估工具。
它被广泛用于医学研究和临床实践中，用于评估个体的睡眠质量。
用途
RPSQI用于测量睡眠质量，通过评估不同因素对个体睡眠的影响程度，从而得出一个综合的睡眠质量指数。
该量表包含了多个方面的评估项目，包括睡眠延迟、睡眠时间、睡眠质量、睡眠效率、睡眠障碍、用药和白天功能等。
评估方法
RPSQI的评估方法相对简单，需要被评估者回答一系列与睡眠相关的问题。
这些问题涵盖了睡眠的各个方面，并要求被评估者根
据自己的睡眠情况进行打分。
评估者根据被评估者的回答计算得出最终的睡眠质量指数。
计分方式
RPSQI的计分方式是根据被评估者的回答，将每个评估项目的得分加总得出总分。
得分越高表示睡眠质量越差，得分越低表示睡眠质量越好。
根据总分的不同范围，可以将睡眠质量分为好、一般和差三个等级。
结论
罗马睡眠质量指数量表(RPSQI)是一种简单有效的评估睡眠质量的工具。
使用该量表可以客观评估个体的睡眠情况，并提供合理的参考来改善睡眠质量。
在科研和临床实践中，RPSQI被广泛使用并得到了广泛认可。
美敦力ADVISA?起搏器的滞后与睡眠功能
美敦力ADVISA™起搏器的滞后与睡眠功能
美敦力
7.11.4睡眠功能的编程
选择“参数Params”图标
AdditionalFeatures...（附加功能）
Sleep...（睡眠）
Sleep（睡眠<开启>）
SleepRate（睡眠频率）
BedTime（上床时间）
WakeTime（觉醒时间）
7.11.5对睡眠功能的评估
心室率直方图显示低于下限频率但高于睡眠频率的心率，所占时间百分比与睡眠期相关。
心脏纵览报告显示了白天和夜间的平均心室率，这应该表明该设备允许夜间心率较慢。
7.11.5.1打印心室率直方图报告
选择数据Data图标
ClinicalDiagnostics（临床诊断）
RateHistograms（ReportOnly）频率直方图（仅限报告）
7.11.5.2打印心脏纵览趋势报告
选择数据Data图标
ClinicalDiagnostics（临床诊断）
CardiacCompassTrends（ReportOnly）心脏纵览趋势（仅限报告）
211
临床医生手册。
睡眠评定量表-DHI。DARS
睡眠评定量表-DHI。
DARS
简介
睡眠评定量表是用于评估个体的睡眠质量和问题的工具。
其中
两个常用的睡眠评定量表是DHI (Daytime Sleepiness。
Hypnotics。
and Insomnia) 和DARS (Duke Activity Status Index)。
DHI（白天嗜睡、催眠药和失眠）量表
DHI量表旨在评估个体的白天嗜睡、使用催眠药和失眠问题的
程度。
该量表由一系列问题组成，涉及个体在白天的嗜睡程度、使
用催眠药的频率和种类，以及失眠的频率和严重程度。
参与者需要
回答一系列问题，根据问题的程度选择适当的答案。
DARS（Duke活动状况指数）量表
DARS量表用于评估个体的日常活动状况对其睡眠质量的影响。
该量表衡量了个体在进行一系列日常活动时在身体功能、体力和心
理方面的限制。
参与者需要回答一系列问题，根据问题的程度选择适当的答案。
使用方法
这两个量表可以通过向参与者提供问卷并让他们回答相应的问题来使用。
根据参与者的回答，可以计算出他们的睡眠评定得分，并据此评估其睡眠质量和问题的程度。
结论
DHI和DARS量表是评估个体睡眠质量和问题的有用工具。
通过使用这些量表，可以了解参与者的睡眠情况，并采取相应的措施来改善其睡眠质量和解决睡眠问题。
以上是关于睡眠评定量表___和___的简要介绍，请参考。
时序分析与数据检索功能
时序分析与数据检索功能时序分析是一种常见的数据分析方法，它可以对时间序列数据进行统计、模式识别和预测等操作。
在许多领域，如金融、气象和生物医学等，时序数据都起到关键的作用。
而数据检索功能则是一种帮助用户从大量数据中快速准确地检索所需信息的功能。
时序分析功能的实现需要借助特定的算法和工具。
其中，最常用的时序分析方法包括：平滑技术、分解方法、时间序列模型、时间序列聚类和时间序列预测等。
平滑技术可以用来去除时间序列中的季节性和趋势性变化，从而更好地分析数据的周期性变化。
分解方法可以将时间序列分解为趋势、季节和随机成分，便于对各个成分进行独立分析。
时间序列模型则用于对时间序列的未来趋势进行预测。
时间序列聚类可以帮助用户对大量时间序列数据进行分组，找出具有相似趋势或模式的数据集。
时序分析的结果可以通过图形化展示，如趋势图、周期图和相关图等，便于用户理解和发现数据的规律。
数据检索功能则是为了方便用户快速有效地从庞大的数据集中找到所需的信息。
数据检索通常包括以下几个关键步骤：首先，用户需要明确自己需要检索的数据类型和特定的查询条件。
然后，根据这些条件，系统会自动筛选出满足要求的数据子集。
接下来，系统会根据用户的选择，将数据按照一定的规则进行排序和分类，以便用户更方便地查找。
最后，系统会向用户展示检索结果，并提供进一步操作，如导出、保存或进一步分析。
为了提高数据检索的效率，一些技术手段也常常被应用，如索引技术、数据压缩和数据分片等。
时序分析与数据检索功能在许多实际应用中有着广泛的应用。
举例来说，金融领域中的股票市场分析就需要对股票价格进行时序分析，以便预测未来趋势。
气象领域中的天气预报需要对过去的气象数据进行时序分析，以便预测未来的天气状况。
生物医学领域中的疾病预测需要对患者的生理数据进行时序分析，以便辅助临床决策。
在这些应用中，数据的准确性和及时性是非常重要的，时序分析和数据检索功能能够为用户提供精确的数据和快速的分析结果。
ADDT包：加速破坏性坏化测试数据分析的标准方法说明书
Package‘ADDT’October12,2022Type PackageTitle Analysis of Accelerated Destructive Degradation Test DataVersion2.0Date2016-10-08Author Yili Hong,Yimeng Xie,Zhongnan Jin,and Caleb KingMaintainer Yili Hong<***************>Description Accelerated destructive degradation tests(ADDT)are often used to collect neces-sary data for assessing the long-term properties of polymeric materials.Based on the col-lected data,a thermal index(TI)is estimated.The TI can be useful for material rating and com-parison.This package implements the traditional method based on the least-squares method,the parametric method based on maximum likelihood estimation,and the semi-parametric method based on spline methods,and the corresponding methods for estimat-ing TI for polymeric materials.The traditional approach is a two-step approach that is cur-rently used in industrial standards,while the parametric method is widely used in the statisti-cal literature.The semiparametric method is newly developed.Both the parametric and semi-parametric approaches allow one to do statistical inference such as quantifying uncertain-ties in estimation,hypothesis testing,and predictions.Publicly available datasets are provided il-lustrations.More details can be found in Jin et al.(2017).License GPL-2Depends nlme,Matrix,coneprojRoxygenNote5.0.1.9000NeedsCompilation noRepository CRANDate/Publication2016-11-0320:12:52R topics documented:ADDT-package (2)addt.confint.ti.mle (3)addt.fit (4)addt.mean.summary (6)addt.predint.ybar.mle (7)12ADDT-package AdhesiveBondB (8)AdhesiveFormulationK (9)plot.addt.fit (9)PolymerY (10)SealStrength (10)summary.addt.fit (11)Index13 ADDT-package Accelerated Destructive Degradation TestingDescriptionAccelerated destructive degradation tests(ADDT)are often used to collect necessary data for as-sessing the long-term properties of polymeric materials.Based on the collected data,a thermal index(TI)is estimated.The TI can be useful for material rating and comparisons.This package implements the traditional method based on the least-squares method,the parametric method based on maximum likelihood estimation,and the semiparametric method based on spline methods,and their corresponding methods for estimating TI for polymeric materials.The traditional approach is a two-step approach that is currently used in industrial standards,while the parametric method is widely used in the statistical literature.The semiparametric method is newly developed.The parametric and semiparametric approaches allow one to do statistical inference such as quantify-ing uncertainties in estimation,hypothesis testing,and predictions.Publicly available datasets are provided for illustrations.More details can be found in Jin et al.(2017).DetailsPackage:ADDTType:PackageVersion: 2.0Date:2016-10-08License:GPL-2Author(s)Yili Hong,Yimeng Xie,Zhongnan Jin,and Caleb KingMaintainer:Yili Hong<***************>ReferencesC.B.King,Y.Xie,Y.Hong,J.H.Van Mullekom,S.P.DeHart,and P.A.DeFeo,“A compari-son of traditional and maximum likelihood approaches to estimating thermal indices for polymeric materials,”Journal of Quality Technology,in press,2016.addt.confint.ti.mle3L.A.Escobar,W.Q.Meeker,D.L.Kugler,and L.L.Kramer,“Accelerated destructive degradation tests:Data,models,and analysis,”in Mathematical and Statistical Methods in Reliability,B.H.Lindqvist and K.A.Doksum,Eds.River Edge,NJ:World Scientific Publishing Company,2003, ch.21.M.Li and N.Doganaksoy,“Batch variability in accelerated-degradation testing,”Journal of Quality Technology,vol.46,pp.171-180,2014.Y.Xie,C.B.King,Y.Hong,and Q.Yang,“Semi-parametric models for accelerated destructive degradation test data analysis,”Preprint:arXiv:1512.03036,2015.Y.Xie,Z.Jin,Y.Hong,and J.H.Van Mullekom,“Statistical methods for thermal index estimation based on accelerated destructive degradation test data,”in Statistical Modeling for Degradation Data,D.G.Chen,Y.L.Lio,H.K.T.Ng,and T.R.Tsai,Eds.NY:New York:Springer,2017,ch.12.Z.Jin,Y.Xie,Y.Hong,and J.H.Van Mullekom,“ADDT:An R package for analysis of accelerated destructive degradation test data,”in Statistical Modeling for Degradation Data,D.G.Chen,Y.L.Lio,H.K.T.Ng,and T.R.Tsai,Eds.NY:New York:Springer,2017,ch.14.addt.confint.ti.mle Confidence Interval for Thermal Index(TI).DescriptionComputes a confidence interval for the TI.Usageaddt.confint.ti.mle(obj,conflevel)Argumentsobj An addt.fit object.conflevel Confidence level in decimal form(i.e.,95%is0.95)ValueReturns a vector containing the estimated TI,standard error,and lower and upper confidence limits.NoteThis currently only implements the CI procedure for ML approach.See Alsoaddt.fitaddt.fit ADDT Model FittingDescriptionFits degradation data using the least-squares,maximum likelihood,and semiparametric methodsand estimates the thermal indices(TI).Usageaddt.fit(formula,data,initial.val=100,proc="All",failure.threshold,time.rti=1e+05,method="Nelder-Mead",subset,na.action,starts=NULL,fail.thres.vec=c(70,80),semi.control=list(cor=F,...),...)Argumentsformula A formula of type Response~Time+Temperature.The order of Time and Tem-perature cannot be changed.data A data frame contains the ADDT data to be analyzed.initial.val We need response measurements at time point0to compute the initial degrada-tion level in the model.If the data does not contain that information,user mustsupply the initial.value.Otherwise,the function will give an error message.proc The type of analysis to be performed which can be"LS"(least squares),"ML"(maximum likelihood),"SemiPara"(semiparametric),or"All"(the least-squares,maximum likelihood and semiparametric methods).failure.thresholdThe value below which a soft failure occurs.Must be in the form of a percent ofthe initial value.time.rti The time associated with the thermal index(TI)or relative thermal index(RTI).Typically100,000hours.method An argument passed to optim specifying the optimization procedure.Default isNelder-Mead.subset An optional statement that allows the use of only part of the dataset.na.action Indicates the action required when data contain missing values.starts A vector of starting values for the maximum likelihood procedure.See fail.thres.vec for alternative.fail.thres.vec If the user does not specify“starts”argument,the user may specify a vectorof two different failure.thresholds.The least-squares procedure is then used forthe two different failure.thresholds to produce starting values for the maximumlikelihood procedure.semi.control list=(cor=F,...),control parameters related to the semiparametric method.If notspecified,default is no correlation.When cor=T in the list is specified,modelassumes a correlation term....Optional arguments.DetailsA thermal index(TI)or relative thermal index(RTI)is often used to evaluate long-term perfor-mance of polymeric materials.Accelerated destructive degradation testing(ADDT)is widely used to calculate the TI of certain polymeric materials.The dataset considered in addt.fit function contain repeated measurements of a response,say tensile strength,at some combinations of time and temperature.The least squares procedure aggregates data into the average of measurements at each combination of time and temperature.Then,polynominal regression is used to interpolate the failure time for each combination.A least squares line isfitted to the failure time data and the TI is then obtained byT I=beta1log10(time.rti)−beta0−273.16.It is important to note that observations are required after failure in order for this procedure to be successful.The maximum likelihood procedure assumes a degradation path dependent on time and temperature.An example of a parametric form for this path can be found in Vaca-Trigo and Meeker(2009)and is the form currently used here.The error term is assumed to follow a multivariate normal distribution.A TI can be directly estimated from the parameter estimates for the degradation path.The addt.fitfunction will be generalized to allow other parametric forms of the mean function,and/or other non-Guassian distribution in later versions.The semiparametric model assembles both parametric model like Arrhenius model for degradation variable extrapolation as well as non-parametric model in order to be more compatible for various materials.ValueAn object of class"addt.fit",which is a list containing:LS.obj If least-squares approach is used,a LS.obj will returned.It contains estimates of coefficients in the TI formula,estimated TI,a matrix contains the failure timeby polynomial interpolation.ML.obj A ML.obj object is returned if maximum likelihood approach is specified.SemiPara.obj A SemiPara.obj is returned if SemiPara approach is specified.dat The data set used in least square/maximum likelihood approaches.time.rti An argument stored to be used for functions related to"addt.fit"object.initial.val An argument stored to be used for functions related to"addt.fit"object.failure.thresholdAn argument stored to be used for functions related to"addt.fit"object. ReferencesY.Hong,C.B.King,Y.Xie,J.H.Van Mullekom,S.P.Dehart,and P.A.DeFeo(2014).“A Comparison of Least Squares and Maximum Likelihood Approaches to Estimating Thermal Indices for Polymeric Materials,”Journal of Quality Technology,in press,2016.6addt.mean.summaryI.Vaca-Trigoand,W.Q.Meeker,“A statistical model for linkingfield and laboratory exposureresults for a model coating,”in Service Life Prediction of Polymeric Materials,J.Martin,R.A.Ryntz,J.Chin,and R.A.Dickie,Eds.NY:New York:Springer,2009,ch.2.Y.Xie,C.B.King,Y.Hong,and Q.Yang,“Semi-parametric models for accelerated destructive degradation test data analysis,”Preprint:arXiv:1512.03036,2015.See Alsoplot.addt.fit,summary.addt.fitExamplesdata(AdhesiveBondB)##Least Squaresaddt.fit.lsa<-addt.fit(Response~TimeH+TempC,data=AdhesiveBondB,proc="LS",failure.threshold=70)##Maximum Likelihoodaddt.fit.mla<-addt.fit(Response~TimeH+TempC,data=AdhesiveBondB,proc="ML",failure.threshold=70)##Semiparametric##Not run:addt.fit.semi<-addt.fit(Response~TimeH+TempC,data=AdhesiveBondB,proc="SemiPara", failure.threshold=70)##End(Not run)##All LS,ML and Semi-Parametric procedures##Not run:addt.fit.all<-addt.fit(Response~TimeH+TempC,data=AdhesiveBondB,proc="All", failure.threshold=70)##End(Not run)summary(addt.fit.lsa)summary(addt.fit.mla)##Not run:summary(addt.fit.semi)##Not run:summary(addt.fit.all)##Not run:plot(addt.fit.all,type="data")##Not run:plot(addt.fit.all,type="LS")##Not run:plot(addt.fit.all,type="ML")##Not run:plot(addt.fit.semi,type="SEMI")##Not run:addt.confint.ti.mle(addt.fit.mla,conflevel=0.95)addt.mean.summary ADDT Batch Means SummaryDescriptionFunction that returns the averaged responses for each time-temperature batch.addt.predint.ybar.mle7Usageaddt.mean.summary(dat)Argumentsdat A dataframe contains the measurements from the ADDT.The dataframe con-tains temperature,time,and response from left to right.ValueReturns a dataframe giving the mean response for each time-temperature batch.ReferencesY.Hong,C.B.King,Y.Xie,J.H.Van Mullekom,S.P.Dehart,and P.A.DeFeo(2014).“A Comparison of Least Squares and Maximum Likelihood Approaches to Estimating Thermal Indices for Polymeric Materials,”Journal of Quality Technology,in press,2016.Examplesdata(AdhesiveBondB)addt.mean.summary(AdhesiveBondB)addt.predint.ybar.mle Prediction of the mean of future observationsDescriptionGiven observations for one temperature level up to some time point,computes a prediction interval for the mean degradation level at some future time point.Usageaddt.predint.ybar.mle(obj,conflevel,num.fut.obs=5,temp,tt)Argumentsobj An addt.fit object.conflevel The confidence level of the prediction interval.This argument is in decimal form(i.e.,95%is0.95).num.fut.obs The number of future observations within a batch at the future time point.temp The temperature level at which predictions are to be made.tt The future time point where prediction is desired.ValueReturns a vector containing the lower and upper bounds of the prediction interval.8AdhesiveBondB NoteThis function only works with an object resulted from ML approach.ReferencesY.Hong,C.B.King,Y.Xie,J.H.Van Mullekom,S.P.Dehart,and P.A.DeFeo(2014).“A Comparison of Least Squares and Maximum Likelihood Approaches to Estimating Thermal Indices for Polymeric Materials,”Journal of Quality Technology,in press,2016.See Alsoaddt.fitAdhesiveBondB Adhesive Bond B datasetDescriptionA dataset from Escobar et al.(2003)containing the results of an accelerated destructive degradationtesting on the strength of an adhesive bond.Usagedata(AdhesiveBondB)FormatA data frame with82observations on the following3variables.TempC Temperature.TimeH Time in hours.Response Strength(Newtons).SourceL.A.Escobar,W.Q.Meeker,D.L.Kugler,and L.L.Kramer,“Accelerated destructive degradation tests:Data,models,and analysis,”in Mathematical and Statistical Methods in Reliability,B.H.Lindqvist and K.A.Doksum,Eds.River Edge,NJ:World Scientific Publishing Company,2003, ch.21.AdhesiveFormulationK9 AdhesiveFormulationK Adhesive Formulation K DataDescriptionA dataset from Xie al.(2015)Strength was tested at40,50,and60degree C.Usagedata(AdhesiveFormulationK)FormatA data frame with101observations on the following3variables.TempC Temperature.TimeH Time in hours.Response Strength(Newtons).SourceY.Xie,C.B.King,Y.Hong,and Q.Yang(2015).Semiparametric Models for Accelerated Destruc-tive Degradation Test Data Analysis.Preprint:arXiv:1512.03036.plot.addt.fit ADDT PlottingDescriptionProvides graphical tools for ADDT analysis.Usage##S3method for class addt.fitplot(x,type,...)Argumentsx An addt.fit object.type Type of plot("data","ML","LS","SEMI")to be generated.If type="data",a scatter plot of the dataset in addt.fit object is generated.If type="ML",a plotof data along with the results of ML approach is produced.If type="LS",thepolynominalfit to the data is plotted.If type="SEMI",the semi-parametricfit tothe data is plotted....Optional arguments.10SealStrengthSee Alsoaddt.fitPolymerY Polymeric MaterialDescriptionA dataset from Tsai et al.(2013)conducting a study on the tensile strength of a new type of polymermaterial.Usagedata(PolymerY)FormatA data frame with76observations on the following3variables.TempC Temperature.TimeH Time in hours.Response Strength(Newtons).SourceTsai,C.-C.,S.-T.Tseng,N.Balakrishnan,and C.-T.Lin(2013).Optimal design for accelerated destructive degradation tests.Quality Technology and Quantitative Management10,263-276. SealStrength Data for Seal StrengthDescriptionSeal Strength dataset presented in Li and Doganaksoy(2014).In the R package ADDT,Seal Strength dataset has minor modifications where the temperature at time point0is changed to200degree where in the original dataset,it is100degree.This is a computing trick that will not affect the model results.Usagedata(SealStrength)FormatA data frame with210observations on the following3variables.TempC Temperature.TimeH Time in hours.Response Strength(Newtons)DetailsSeal strength dataset is collected under an experiment of testing the strength of a new seal. SourceM.Li,and N.Doganaksoy(2014).Batch variability in accelerated-degradation testing.Journal of Quality Technology46,171-180.summary.addt.fit Summary of an"addt.fit"object.DescriptionProvides a brief summary of an addt.fit object.Usage##S3method for class addt.fitsummary(object,...)Argumentsobject An addt.fit object....Optional arguments.ValueReturns a list whose items vary depending on which procedure was used in addt.fit.If proc="ML", then the output has the following items:coef.mle.mat A matrix giving the estimated coefficients,their standard errors,and the lower and upper bounds of a95%confidence interval.ti.CI The estimated thermal index of the material,its standard error,and95%confi-dence interval.logLik The maximum log-likelihood value achieved.If proc="LS",the output has the following items:coefs A vector giving the estimated coefficients for the time-temperature line.TI The estimated thermal index of the material.interp.time A matrix contains the interpolated time at the failure.threshold specified for each temperature level.If proc="SemiPara",the output has the following items:betahat An estimate of our model coefficient under the users chosen setups.knots Knots used in the B-spline,these are the optimal knots if best.knots=TRUE.Loglik The maximum log-likelihood value achieved.aic aic value for thefinal model.aicc aicc value for thefinal model.If proc="All",the output will give the preceeding values for all the LS.obj,ML.obj and Semi-Para.obj.See Alsoaddt.fitIndex∗packageADDT-package,2ADDT(ADDT-package),2ADDT-package,2addt.confint.ti.mle,3addt.fit,3,4,5,8,10–12addt.mean.summary,6addt.predint.ybar.mle,7 AdhesiveBondB,8 AdhesiveFormulationK,9optim,4plot.addt.fit,6,9PolymerY,10SealStrength,10summary.addt.fit,6,1113。
vitasedds助眠测评
Vitasedds助眠测评1. 引言助眠是指通过各种方法来帮助人们更好地入睡和改善睡眠质量的过程。
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2. Vitasedds助眠测评系统介绍Vitasedds助眠测评系统是一款基于人工智能技术开发的助眠评估工具。
该系统通过收集用户的睡眠数据和生活习惯，并结合专业的助眠知识，为用户提供个性化的助眠建议和方案。
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< ♦ > Anais X SBSR, Foz do Iguaçu, 21-26 abril 2001, INPE, p. 895-903, Sessão Técnica OralAssessing Nighttime DMSP/OLS Data for Detection of Human Settlements inthe Brazilian Amazonia.S ILVANA A MARAL 1G ILBERTO CÂMARA 1A NTÔNIO M IGUEL V IEIRA M ONTEIRO1C HRIS D. E LVIDGE2J OSÉ A LBERTO Q UINTANILHA 31INPE - Instituto Nacional de Pesquisas EspaciaisCaixa Postal 515 - 12201-097 - São José dos Campos - SP, Brazilsilvana@dpi.inpe.br, gilberto@dpi.inpe.br, miguel@dpi.inpe.br2NOAA/NGDC - National Geophysical Data Center325 Broadway, Boulder, CO, USAcde@3Escola Politécnica da Universidade de São Paulo - POLI-USPAv. Prof. Almeida Prado, Trav. 2, n o 83, CEP 05508-900, São Paulo - SP, Braziljaquinta@usp.brAbstract. This paper describes an initial assessment of the DMSP/OLS night-light data as an instrument to investigate the human presence and activity over the Brazilian Amazonia. The work explores the potential of the sensor for detecting and monitoring urban area extensions and human presence and activity at a regional scale. Using DMSP/OLS night-lights imagery, the authors detected 248 towns from a total of 749 municípios presented in the Legal Amazonia. All the night-lights foci detected were related to human activities, including urban settlements and mining. A strong linear relation (R2= 0.8) was obtained between urban area extensions, detected by TM/Landsat, and DMSP night-lights foci. Our conclusion is that DMSP/OLS imagery can be used as an indication of human presence and activity in the Amazon region.Keywords: DMSP/OLS night-lights, human settlements, urban area, human activities, Urban Amazon, Spatial Analysis, Regional Scale.1 IntroductionThe Brazilian Amazonia supports the World's largest contiguous area of still untouched tropical forest. Land cover changes in the Amazon region contributes significantly to carbon budgets, and it is taken as a disturbance agent to the biodiversity conservation, to the hydrological cycle having serious impacts over the global climate change (Gash et al., 1996).During the last three decades, the region has experienced the biggest urban growth rates in Brazil. In 1970, urban population comprised 35.5% of the total population. This proportion increased to 44.6% in 1980, to 58% in 1991 and to 61% in the 19961. An increasing diversity in1 Population data for this paper was obtained from IBGE (Brazil’s Census Bureau) surveys.economic activities and the resulting population changes have restructured and reorganised the network of human settlements all over the region. As a result, the picture over the early 21st century shows patterns and spatial arrangements that reveals a different Amazonia. This new Amazonia emerges instead is a tropical forest with a complex urban tissue in the making, a perspective that has taken some researchers to put forward the claim for an “urbanized forest”(Becker, 1996).Currently, the mainstream research agenda for the Amazonia is focused on the physical geography aspects (such as land use and land cover change) and on global change issue. By contrast, a relatively small number of researchers are concentrating on the human aspects of the Amazonian occupation. These studies are showing that the growth in urban population has not came with an improvement on quality of life of local populations, as demonstrated by the low indices of health, education and income wages (Becker, (1996 and 1998), Browder and Godfrey (1997), Monte-Mór (1998)).The fragile ecological equilibrium of the Amazon forest, the population growth and its movements, the lack of basic infrastructure, and the conflicting driving forces around the region, make a project for sustainable development of the region the real issue to be dealt with and the actual challenge for world science.The objective of this study was to assess nighttime DMSP/OLS data to detect human settlements in the Brazilian Amazonia. To reach this goal, we developed a procedure to detect human activity on DMSP/OLS image composition and to integrate and analyse the data set in a spatial database. We investigated the correlation between DMSP/OLS night-lights and the human activity information obtained from ancillary data sources.2 A Review of DMSP/OLS data for Detection of Urban SettlementsThe U.S. Air Force Defense Meteorological Satellite Program (DMSP) operates since 1970s the Operational Linescan System (OLS), an oscillating scan radiometer capable of detecting the visible and thermal-infrared emissions, with nominal resolution of 2.7 km and ~3000 km of swath. The OLS sensor's primary mission was the observation of nighttime moonlit cloud cover for global meteorological forecasting for the Air Force. For this reason, the visible spectral band (VIS: 0,4-1,1 µm) signal, that includes the visible near infrared portion of the spectrum (VNIR: 0.5 to 0.9 µm), is intensified at night using a photomultiplier tube. This makes the sensor four orders of magnitude more sensitive, enabling to detect faint VNIR emission sources. With sunlight eliminated, the light intensification results in a unique data set in which city lights, gas flares, lightning illuminated clouds and fires can be observed (Elvidge et al., 1997).There are two spatial resolution modes in which data can be acquired. The full resolution data has a nominal spatial resolution of 0.56 km and it is referred to as "fine". The "smooth" resolution mode is generated towards averaging of five by five blocks of fine data onboard, with nominal spatial resolution of 2.8km.DMSP/OLS image was first presented as a potential urban mapping tool by Croft (1973, 1978). The author argued that high contrast between lighted and unlighted areas and the sensor's spatial resolution made it a useful tool to identify regions of significant human activity. Welch (1980), Foster (1983) and Welch and Zupko (1980) attempted to use DMSP/OLS imagery to map the distribution of human settlements and inventory the spatial distribution of human activities, such as energy consumption. These works showed promising results, but also exposedproblems in the use of single data acquisition, pixel saturation and blooming, cloud cover and presence of ephemeral light sources as lightning and fires.Sullivan (1989) produced a VNIR emission sources global map (10km resolution). Despite it was derived from single dates of DMSP/OLS film data imagery and mosaicked into a global product, many of the features presented, in some areas like Africa, were ephemeral VNIR emission from fires.The problem with ephemeral lights and cloud cover was eliminated with the methodology developed to generate a stable light data set by the NOAA/NGDC (National Oceanic and Atmospheric Administration's National Geoscience Data Center). This method includes the collection, rectification and aggregation of a large number of nighttime OLS image (Elvidge et al., 1997). The image time series analysis distinguishes stable lights produces by cities, towns and industrial facilities from ephemeral lights. This analysis is additionally used for cloud screening and ensures sufficient cloud-free observations for determining the location of all VNIR emissions.To convert this stable light data set into a map of "urban areas" in the United States mosaic, Imhoff et al. (1997) proposed a thresholding technique. The values reported in the original data set represent a percentage of lighted pixel, for each pixel extracted from 231 images of nighttime cloud screen orbits. Each pixel has a digital number ranging from 0 to 100, representing the ratio of lighted observations to total cloud free observation x 100. A digital number of 30 means that this pixel was lighted 30% of the times that it was observed under could-free conditions. A threshold of >89% removed ephemeral light sources and blooming of light onto water when adjacent to cities, while still leaving the dense urban core intact. Comparing with the urban areas from 1990 U.S. Census, the urban area from DMSP night-lights was only 5% smaller. Although this technique worked well in the United States, the authors emphasised the need to verify these relations in less-developed countries where the type of infrastructure development and its associated nighttime lighting may be different.Based on a series of DSMP/OLS images, Miranda (1999) generated a map of cities and urban settlements in the Amazon that identified 1300 urban sites comprising small towns, medium size towns and cities in the region. Using ancillary data from federal institutes (IBGE, FUNAI, DNPM) and non-governmental organisations, this number raised to up to 1500 sites of vila s and cities in the Amazon. Unfortunately, a scientific assessment of his work is not possible, since the author did not publish a description of his methodology.3 Data Integration and Analysis3.1 General DescriptionThe methodology for data integration and analysis used in this work included the following Array steps, as outlined in Figure 1:1.Building a spatial database consisting of DMSP/OLS data, TM/Landsat images andIBGE-derived geographic limits of municípios, urban centres and vilas. All the 749 municípios of the Legal Amazon had their boundary and urban centres spatialized from the IBGE (Instituto Brasileiro de Geografia e Estatística) data available for 1997. The geographical positions of the 256 vila s, computed from IBGE-1998 data, were alsospatialized. Both, cities and vila s, were defined as objects in the database in order toenable the spatial query over these features.Figure 1 - The data integration and analysis flow chart.2.Correction of the urban centre location of the original IBGE data set based onDMSP/OLS night-lights and urban polygons derived from TM/LANDSAT imagery.parison of DMSP/OLS night-lights and urban polygons derived fromTM/LANDSAT imagery to assess the potential of DMSP/OLS for detection of urban settlement extension.The spatial database and the analytical procedure used the SPRING GIS system, version 3.5 (SPRING, 2000).3.2 DMSP/OLS Data ProcessingAn image from DMSP/OLS covering the Amazon region was used. This image was generated by NOAA- National Geophysical Data Center, from 16 single DMSP/OLS orbit, from September 2nd to 18th, 1999. In order to have only stable light sources in the final image, ephemeral night-lights like clouds and fires were removed during the mosaic process, using the procedure described by Elvidge et al. (1997).The original image data were available in a "geographical" projection with cells that are 0.008333 degrees square, 1 km2 approximately. The conversion from "geographical" to Policonic projection was the unique geometrical correction applied over the image.Although the DMSP/OLS image used an 8-bits quantization (255 values), its histogram had frequencies only from 7 to 100, due to the fact that the digital numbers in the image represent the cumulative percentage of lighted pixels considering the nighttime cloud screen orbits available.The next processing step was to select a threshold for the pixel values that could detect urban areas, without overestimating larger cities. To preserve the city boundaries and to detect small towns for the continental US, Imhoff et al., (1997) used a threshold value of 89%. However, their methodology was developed for a series of 236 images, whereas only 16 images were available for this work for a region with frequent cloud cover. By interactive visual analysis, we compared different threshold values at DMSP images with the location of small towns and big cities in the TM/Landsat image. We found that a threshold of 7% over estimated the city boundaries, and a threshold of 89% did not detect most of the small towns. As a compromise, a threshold of 30% was defined to generate a DMSP binary image.The DMSP binary image was then classified into night-light and background classes, so polygons of night-light were defined. These night-lights foci were used to compare with the spatial location of the cities and vila s available, and the extension of the urban areas.3.3 Integration of TM/Landsat Data with DMSP/OLSTM/Landsat images were used to verify the relations between DMSP night-light focus and the urban area of the cities. The spatial resolution of 30 m and the spectral bands of TM/Landsat sensor enable the detection of urban areas. Urban structures as houses, buildings, parking, civil constructions, roads, asphalt, etc., are easily distinguished in the TM3 band because of the intense spectral response of these targets at the red visible wavelength (0.63 µm a 0.69 µm).The intense cloud cover in the Amazon region and the large dimension of the area restricted the number of the TM/Landsat scenes available, so this analysis was limited to Mato Grosso State. To that end, we included 32 TM/Landsat band 3, from 1999 dry season, to our spatial database. A linear contrast was applied over these images to enhance the urban areas.For every município in the Mato Grosso state, the urban area was defined as polygons that had their total area calculated by the GIS. These urban area values were introduced in the database as an attribute in the table, to be further comparable with the night-light foci.3.4 Use of Ancillary DataThe image data available in the "Mosaico do Brasil" (www.dpi.inpe.br/mosaico) were used to locate urban sites and to identify the features pointed out by the night-light focus. Basically, TM/Landsat colour compositions (TM3-B, TM4-G, TM5-R) available for 1998 and 1999, and the JERS-1 SAR mosaic image of 1995 were used.4 Results4.1 Detecting Human Presence and ActivityBy visualising the DMSP night-lights foci with the city centres and vila s overlaid, it was possible to assess the ability in the DMSP night-lights to detect human activity. Some IBGE urban centres had their geographical position corrected, based on the DMSP night-lights, with confirmation obtained from TM/Landsat data.Considering the DMSP image threshold adopted (ND>30), 261 night-light foci were detected, while with ND>7, it was possible to identify 560 foci. Even without any specificgeometrical correction, the visual analysis verified a general good spatial correspondence between the night-lights foci and the IBGE urban centres, as presented at Figure 2.Figure 2 - DMSP/OLS image over the Amazonia with a detail of Belém-PA region.From the total of 261 night-light foci, 149 contained IBGE urban centres and 64 were less than 5 km from IBGE urban centres. 48 night-light foci were not related to any urban ing the ancillary data from "Mosaico do Brasil" these 48 foci were analysed.The results, summarised in the Table 1, indicate that the DMSP night-lights detection is always related to human activity. Even in places without resident population, the lights point out human presence that requires some type of infrastructure, such as mining or oil production.Analysing the 749 municípios from the Legal Amazon, 186 are inside the night-light foci and 62 are less than 5 km from the foci, a total of 248 cities detected by the DMSP/OLS data.Considering the total resident population (Table 2), DMSP/OLS imagery could, in some cases,detect the night-lights from municípios with 1.000-2.000 people.However, DMSP/OLS night-lights only detected all municípios in the population class of more than 500.000 residents. Municípios with population between 5.000 and 500.000 werepartially detected.Table 1 - DMSP night-light foci not associated with urban centres.Description - targets observed at "Mosaico do Brasil"N o FocusUrban settlements - small towns and vila s - missing at IBGE census9IBGE Vila s3Urban nuclei near to big cities4Vila s near to reservoirs2Mining3Oil and Gas production (URUCU-AM)1IBGE urban centres - inaccurate coordinates16Unable to check - TM/Landsat or JERS images not available7Out of Amazon Legal limits3 From this result, 501 towns were not detected with DMSP night-lights. Santa Luzia do Maranhão was the município with the highest population (53.287 people, 19.450 at urban area); in this case the lack of TM/Landsat image for this area suggests frequent cloud cover. Alta Floresta (MT) had the biggest urban population (35.053 people), and only was detected with the DMSP image threshold of DN=7, probably the fires and smokes, very intense in this region at that time of the year, attenuated the night-light signal. Only 25 of these 501 towns not detected by DMSP had urban population greater than 10.000 people. With exception of Alta Floresta and Rosário do Oeste, situated at Mato Grosso state, all of then are at north part of the region, at Acre, Amapá, Pará and Maranhão states where the cloud cover is very frequent.Table 2 - Municipal population and municípios in the Legal Amazon.Number of MunicípiosPopulation(IBGE-1996)Total DMSP light0 - 1000101.000 -2.0003112.000 - 5.00013555.000 -10.0001902810.000 -20.0002196720.000 -50.0001599650.000 - 100.0003734100.000 - 200.000107200.000 -500.00077500.000 - 1.000.00011> 1.000.00022Total792248Among the 248 towns detected by DMSP night-lights, Paço do Lumiar (MA) presented the smaller urban population (1095), but it is adjacent to São José do Ribamar and São Luís, the capital of Maranhão state, configuring a metropolitan region. The city detected by DMSP night-lights with the smallest urban population was Alto Alegre (RR), with 3.292 people.4.2 Locating and Measuring Urban AreasTo correlate DMSP night-light area with the urban area extracted from the TM/Landsat images, the total of DMSP night-lights pixels was calculated for every município having the municipalboundary as a spatial restriction. This area value was stored in the table of municípios in the database.From the urban area analysis, performed to Mato Grosso state, 56 towns from 118 municípios had its night-light detected by DMSP imagery. The Figure 2 presents the linear relation that can be obtained between urban area, identified from TM-3 Landsat and night-light foci.(a)(b)Figure 2 - Relation between urban area detected by TM/Landsat and DMSP night-lights: (a) considering all municípios, (b) without Cuiabá and Várzea Grande - metropolitan region.The urban area identified from TM/Landsat image was linearly related to the area defined from DMSP night-lights, with a R2=0.91, if we consider the capital and the metropolitan region, and R2=0.76, if we exclude this extreme values. The DMSP night-lights, corresponding to towns, are then a reference of urban infrastructure area, like streets, houses and civil constructions that requires night illumination.5 ConclusionThis work presented the first results from the analysis of DMSP/OLS nighttime image to detect human settlements in the Amazon region. With the data available, it was observed that the detection of cities and towns is limited by several factors like cloud cover, smoke, and the threshold definition to the DMSP image. Even with these limitations, DMSP night-light image is a potential data to identify human presence, not only urban regions, but also any human activity that requires illumination, as mining and others civil constructions.From 749 municípios, only 248 were directly associated to urban centres. Therefore, our results do not support the claims made by Miranda (1999) of the detection of 1300 towns and cities from DMPS/OLS. Further comparison with results from Miranda (1999) is hindered, since this author did not publish detailed information about his data sets and analysis procedures.A strong linear relation was obtained for the urban area identified from TM/Landsat image and from DMSP night-light. 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