A Genetic-Algorithm Based Mobile Sensor Network Deployment Algorithm EE382C Embedded Softwa
人工智能 遗传算法
人工智能遗传算法英文回答：Genetic Algorithms for Artificial Intelligence.Genetic algorithms (GAs) are a class of evolutionary algorithms that are inspired by the process of natural selection. They are used to solve optimization problems by iteratively improving a population of candidate solutions.How GAs Work.GAs work by simulating the process of natural selection. In each iteration, the fittest individuals in thepopulation are selected to reproduce. Their offspring are then combined and mutated to create a new population. This process is repeated until a satisfactory solution is found.Components of a GA.A GA consists of the following components:Population: A set of candidate solutions.Fitness function: A function that evaluates thequality of each candidate solution.Selection: The process of choosing the fittest individuals to reproduce.Reproduction: The process of creating new individuals from the selected parents.Mutation: The process of introducing random changes into the new individuals.Applications of GAs.GAs have been used to solve a wide variety of problems, including:Optimization problems.Machine learning.Scheduling.Design.Robotics.Advantages of GAs.GAs offer several advantages over traditional optimization methods, including:They can find near-optimal solutions to complex problems.They are not easily trapped in local optima.They can be used to solve problems with multiple objectives.Disadvantages of GAs.GAs also have some disadvantages, including:They can be computationally expensive.They can be sensitive to the choice of parameters.They can be difficult to terminate.中文回答：人工智能中的遗传算法。
Genetic Algorithms(遗传算法)PPT课件
Encoding
{0,1}L
(representation)
010001001
011101001 Decoding (inverse representation)
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms
Holland’s original GA is now known as the simple genetic algorithm (SGA)
Other GAs use different:
– Representations – Mutations – Crossovers – Selection mechanisms
probability pc , otherwise copy parents 4. For each offspring apply mutation (bit-flip with
probability pm independently for each bit) 5. Replace the whole population with the resulting
Main idea: better individuals get higher chance
– Chances proportional to fitness
– Implementation: roulette wheel technique
– many variants, e.g., reproduction models, operators
A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Genetic Algorithms
备战高考英语名校模拟真题速递(江苏专用)专题06 阅读理解之说明文10篇(第六期)(含解析)
备战高考英语名校模拟真题速递(江苏专用)第六期专题06 阅读理解之说明文10篇（2024·江苏南通·模拟预测）Mark Temple, a medical molecular (分子的) biologist, used to spend a lot of time in his lab researching new drugs for cancer treatments. He would extract DNA from cells and then add a drug to see where it was binding (结合) along the chemical sequence(序列). Before he introduced the drug, he’d look at DNA combination on a screen to see what might work best for the experiment, but the visual readout of the sequences was often unimaginably large.So Temple wondered if there was an easier way to detect favorable patterns. I realized I wanted to hear the sequence,” says Temple, who is also a musician. He started his own system of assigning notes to the different elements of DNA — human DNA is made of four distinct bases, so it was easy to start off with four notes — and made a little tune out of his materials. This trick indeed helped him better spot patterns in the sequences, which allowed him to make better choices about which DNA combinations to use.Temple isn’t the first person to turn scientific data into sound. In the past 40 years, researchers have gone from exploring this trick as a fun way to spot patterns in their studies tousing it as a guide to discovery. And the scientific community has come to realize that there’s some long-term value in this type of work. Temple, who from that first experiment has created his own algorithmic software to turn data into sound, believes the resulting music can be used to improve research and science communication.So Temple decided to add layers of sound to make the sonification (可听化) into songs. He sees a clear difference between “sonification” and “musification”. Using sound to represent data is scientific, but very different from using creative input to make songs. The musical notes from DNA may be melodic to the human ear, but they don’t sound like a song you’d listen to on the radio. So when he tried to sonify the virus, he added layers of drums and guitar, and had some musician friends add their own music to turn the virus into a full-blown post-rock song.Temple sees this work as an effective communication tool that will help a general audience understand complex systems in biology. He has performed his songs in public at concert halls in Australia.1．What is Mark Temple’s purpose in turning DNA data into sound?A．To help him fight boredom.B．To develop his creative ability.C．To make his drug more powerful.D．To aid the process of his experiments.2．What can we learn about Temple’s system?A．Its effect remains to be seen.B．It failed to work as expected.C．It is too complicated to operate.D．It has produced satisfying results.3．Why did Temple try to make the virus sound like real music when sonifying it?A．To get rid of public fear of the virus.B．To show h1s talent in producing music.C．To facilitate people’s understanding of science.D．To remind people or the roe or Science in art creation.4．What does the text mainly talk about?A．Why scientists are turning molecules into music.B．How scientists help the public understand science.C．Why music can be the best way to present science.D．How music helps scientists conduct their research.（2024·江苏南通·模拟预测）Phonics, which involves sounding out words syllable (音节) by syllable, is the best way to teach children to read. But in many classrooms, this can be a dirty word. So much so that some teachers have had to take phonics teaching materials secretly into the classroom. Most American children are taught to read in a way that study after study has found to be wrong.The consequences of this are striking. Less than half of all American adults were efficient readers in 2017. American fourth graders rank 15th on the Progress in International Literacy Study, an international exam.America is stuck in a debate about teaching children to read that has been going on for decades. Some advocate teaching symbol sound relationships (the sound k can be spelled as c, k, ck, or ch) known as phonics Others support an immersive approach (using pictures of cat to learn the word cat), known as “whole language”. Most teachers today, almost three out of four according to a survey by EdWeek Research Centre in 2019, use a mix of the two methods called “balanced literacy”.“A little phonics is far from enough.” says Tenette Smith, executive director of elementary education and reding at Mississippi’s education department. “It has to be systematic and explicitly taught.”Mississippi, often behind in social policy, has set an example here. In a state once blamed for its low reading scores, the Mississippi state legislature passed new literacy standards in 2013.Since then Mississippi has seen remarkable gains., Its fourth graders have moved from 49th (out of 50 states) to 20th on the National assessment of Educational Progress, a nationwide exam.Mississippi’s success is attributed to application of reading methods supported by a body of research known as the science of reading. In 1997 experts from the Department of Education ended the “reading war” and summed up the evidence. They found that phonics, along with explicit instruction in phonemic (音位的) awareness,fluency and comprehension, worked best.Yet over two decades on, “balanced literacy” is still being taught in classrooms. But advances in statistics and brain imaging have disproved the whole-language method. To the teacher who is an efficient reader, literacy seem like a natural process that requires educated guessing, rather than the deliberate process emphasized by phonics. Teachers can imagine that they learned to read through osmosis(潜移默化) when they were children. Without proper training, they bring this to classrooms.5．What do we learn about phonics in many American classrooms?A．It is ill reputed.B．It is mostly misapplied.C．It is totally ignored.D．It is seemingly contradictory.6．What has America been witnessing?A．A burning passion for improving teaching methods.B．A lasting debate over how to teach children to read.C．An increasing concern with children’s inadequacy in literacy.D．A forceful advocacy of a combined method for teaching reading.7．What’s Tenette Smith’s attitude towards “balanced literacy”?A．Tolerant.B．Enthusiastic.C．Unclear.D．Disapproving.8．According to the author what contributed to Mississippi’s success?A．Focusing on the natural process rather than deliberate training.B．Obtaining support from other states to upgrade teaching methods.C．Adopting scientifically grounded approaches to teaching reading.D．Placing sufficient emphasis upon both fluency and comprehension.（2024·江苏泰州·一模）A satellite is an object in space that orbits around another. It has two kinds — natural satellites and artificial satellites. The moon is a natural satellite that moves around the earth while artificial satellites are those made by man.Despite their widespread impact on daily life, artificial satellites mainly depend on different complicated makeups. On the outside, they may look like a wheel, equipped with solar panels or sails. Inside, the satellites contain mission-specific scientific instruments, which include whatever tools the satellites need to perform their work. Among them, high-resolution cameras and communication electronics are typical ones. Besides, the part that carries the load and holds all the parts together is called the bus.Artificial satellites operate in a systematic way just like humans. Computers function as the satellite’s brain, which receive information, interpret it, and send messages back to the earth. Advanced digital cameras serve asthe satellite’s eyes. Sensors are other important parts that not only recognize light, heat, and gases, but also record changes in what is being observed. Radios on the satellite send information back to the earth. Solar panels provide electrical power for the computers and other equipment, as well as the power to move the satellite forward.Artificial satellites use gravity to stay in their orbits. Earth’s gravity pulls everything toward the center of the planet. To stay in the earth’s orbit, the speed of a satellite must adjust to the tiniest changes in the pull of gravity. The satellite’s speed works against earth’s gravity just enough so that it doesn’t go speeding into space or falling back to the earth.Rockets carry satellites to different types and heights of orbits, based on the tasks they need to perform. Satellites closer to the earth are in low-earth orbit, which can be 200-500 miles high. The closer to the earth, the stronger the gravity is. Therefore, these satellites must travel at about 17,000 miles per hour to keep from falling back to the earth, while higher-orbiting satellites can travel more slowly.9．What is Paragraph 2 of the text mainly about?A．The appearance of artificial satellites.B．The components of artificial satellites.C．The basic function of artificial satellites.D．The specific mission of artificial satellites.10．What is the role of computers in artificial satellites?A．Providing electrical power.B．Recording changes observed.C．Monitoring space environment.D．Processing information received.11．How do artificial satellites stay in their orbits?A．By relying on powerful rockets to get out of gravity.B．By orbiting at a fixed speed regardless of gravity’s pull.C．By changing speed constantly based on the pull of gravity.D．By resisting the pull of gravity with advanced technologies.12．Why do satellites in higher-earth orbit travel more slowly?A．They are more affected by earth’s gravity.B．They take advantage of rockets more effectively.C．They have weaker pull of gravity in higher orbits.D．They are equipped with more advanced instruments.（2024·江苏泰州·一模）The human body possesses an efficient defense system to battle with flu viruses. The immune system protects against the attack of harmful microbes (微生物) by producing chemicals called antibodies, which are programmed to destroy a specific type of microbe. They travel in the blood and search the body for invaders (入侵者). When they find an invasive microbe, antibodies attack and destroy any cell thatcontains the virus. However, flu viruses can be a terrible enemy. Even if your body successfully fights against the viruses, with their ability to evolve rapidly, your body may have no protection or immunity from the new ones.Your body produces white blood cells to protect you against infectious diseases. Your body can detect invading microbes in your bloodstream because they carry antigens in their proteins. White blood cells in your immune system, such as T cells, can sense antigens in the viruses in your cells. Once your body finds an antigen, it takes immediate action in many different ways. For example, T cells produce more antibodies, call in cells that eat microbes, and destroy cells that are infected with a virus.One of the best things about the immune system is that it will always remember a microbe it has fought before and know just how to fight it again in the future. Your body can learn to fight so well that your immune system can completely destroy a virus before you feel sick at all.However, even the most cautious people can become infected. Fortunately, medical scientists have developed vaccines (疫苗), which are weakened or dead flu viruses that enter a person’s body before the person gets sick. These viruses cause the body to produce antibodies to attack and destroy the strong viruses that may invade during flu season.13．Why does flu pose a threat to the immune system?A．Microbes contain large quantities of viruses.B．Antibodies are too weak to attack flu viruses.C．The body has few effective ways to tackle flu.D．It’s hard to keep pace with the evolution of viruses.14．What does the underlined word “antigens” refer to in Paragraph 2?A．The cell protecting your body from viruses.B．The matter serving as the indicator of viruses.C．The antibodies helping to fight against viruses.D．The substance destroying cells infected with viruses.15．How do vaccines defend the body against the flu viruses?A．They strengthen the body’s immune system.B．They battle against weakened or dead viruses.C．They help produce antibodies to wipe out viruses.D．They expose the body to viruses during flu season.16．Which of the following is a suitable title for the text?A．Antibodies Save Our Health.B．Vaccines Are Of Great Necessity.C．Infectious Flu Viruses Are Around.D．Human Body Fights Against Flu Viruses.（23-24高三下·江苏扬州·开学考试）A recent study, led by Professor Andrew Barron, Dr. HaDi MaBouDi, and Professor James Marshall, illustrates how evolution has fine-tuned honey bees to make quick judgments while minimizing danger.“Animal lives are full of decisions,” says Professor Barron. “A honey bee has a brain smaller than a sesame (芝麻) seed. And yet it can make decisions faster and more accurately than’ we can. A robot programmed to do a bee’s job would need the backup of a supercomputer.”Bees need to work quickly and efficiently. They need to make decisions. Which flower will have a sweet liquid? While they’re flying, they face threats from the air. While landing, they’re vulnerable to potential hunter, some of which pretend to look like flowers.Researchers trained 20 bees to associate each of the five different colored “flower disks” with their visit history of reward and punishment. Blue flowers always had sugar juice. Green flowers always had a type of liquid with a bitter taste for bees. Other colors sometimes had glucose (葡萄糖). “Then we introduced each bee to a ‘garden’ with artificial ‘flowers’. We filmed each bee and timed their decision-making process,” says Dr. MaBouDi. “If the bees were confident that a flower would have food, they quickly decided to land on it, taking an average of 0.6 seconds. If they were confident that a flower wouldn’t have food, they made a decision just as quickly. If unsure, they took on average 1.4 seconds, and the time reflected the probability that a flower had food.”The team then built a computer model mirroring the bees’ decision-making process. They found the structure of the model looked very similar to the physical layout of a bee brain. “AI researchers can learn much from bees and other ‘simple’ animals. Millions of years of evolution has led to incredibly efficient brains with very low power requirements,” says Professor Marshall who co-founded a company that uses insect brain patterns to enable machines to move autonomously, like nature.17．Why does Professor Andrew Barron mention “a supercomputer”?A．To illustrate how a honey bee’s brain resemble each other.B．To explain how animals arrive at informed decisions fast.C．To demonstrate how a robot could finish a honey bee’s job.D．To emphasize how honey bees make decisions remarkably.18．Which of the following can best replace “vulnerable to” underlined in paragraph 3?A．Easily harmed by.B．Highly sensitive to.C．Deeply critical to.D．Closely followed by.19．What influenced the speed of trained bees in making decisions?A．Their judgments about reward and punishment.B．Their preference for the colors of flower disks.C．Their confirmation of food’s presence and absence.D．Their ability to tell real flowers from artificial ones.20．What message does Professor James Marshall want to give us?A．The power of bee brains is underestimated.B．Biology can inspire future AI.C．Autonomous machines are changing nature.D．AI should be far more efficient.（23-24高三下·江苏扬州·开学考试）Are you frequently overwhelmed by the feeling that life is leaving you behind, particularly when you look through social media sites and see all the exciting things your friends are up to? If so, you are not alone.FOMO, or Fear of Missing Out, refers to the perception that other people’s lives are superior to our own, whether this concerns socializing, accomplishing professional goals or generally having a more deeply fulfilling life. It shows itself as a deep sense of envy, and constant exposure to it can have a weakening effect on our self-respect. The feeling that we are always being left out of fundamentally important events, or that our lives are not living up to the image pictured by others, can have long-term damaging psychological consequences.While feelings of envy and inadequacy seem to be naturally human, social media seems to have added fuel to the fire in several ways. The reason why social media has such a triggering effect is tied to the appeal of social media in the first place: these are platforms which allow us to share only the most glowing presentations of our accomplishments, while leaving out the boring aspects of life. While this kind of misrepresentation could be characterized as dishonest, it is what the polished atmosphere of social media seems to demand.So how do we avoid falling into the trap of our own insecurities? Firstly, consider your own social media posts. Have you ever chosen photos or quotes which lead others to the rosiest conclusions about your life? Well, so have others and what they’ve left hidden is the fact that loneliness and boredom are unavoidably a part of everyone’s day-to-day life, and you are not the only one feeling left out. Secondly, learn to appreciate the positives. You may not be a regular at exciting parties or a climber of dizzying peaks, but you have your health, a place to live, and real friends who appreciate your presence in their lives. Last of all, learn to shake things off. We are all bombarded daily with images of other people’s perfection, but really, what does it matter? They are probably no more real than the most ridiculous reality TV shows.21．What can frequently experiencing FOMO lead to?A．Harm to one’s feeling of self-value.B．A more satisfying and fulfilling social life.C．Damage to one’s work productivity.D．Less likelihood of professional success.22．What does the author suggest in the third paragraph?A．The primary reason for FOMO is deeply rooted in social media.B．Our own social media posts help us feel much more confident.C．People who don’t share posts on social media are more bored.D．Social media’s nature enhances envious feelings and self-doubt.23．Why does the author mention reality TV shows in the last paragraph?A．To emphasize how false what we see on social media can be.B．To indicate how complicated social media has turned to.C．To figure out how popular and useful social media has been.D．To point out how educational value reality TV shows reflect.24．Which is the best title for the text?A．Myths and misconceptions about FOMO B．FOMO: what it is and how to overcome itC．How FOMO is changing human relationships D．We’re now all in the power of “FOMO addiction”（23-24高三上·江苏泰州·阶段练习）While Huawei’s official website does not call Mate 60 Pro a 5G smartphone, the phone’s wideband capabilities are on par with other 5G smartphones, raising a related question: As a leader in 5G technology, has Huawei managed to develop a 5G smartphone on its own?The answer is not simple. Huawei, as a pioneer in global 5G communication equipment, has played a leading role in the commercialization of 5G technology, with its strong system design and fields such as baseband chips (基带芯片), baseband processors and 5G modems.However, basebands and modems are not the only aspects that define 5G wireless communication. The stability and high-quality signals of a 5G smartphone also depend on other critical components such as RF transceivers (射频收发器) and RF front ends and antennas (天线) . These components are largely dominated by four US high-tech giants—Qualcomm, Avago Technologies, Ansem and Qorvo—which account for a surprising global market share.Huawei has faced significant challenges in getting critical components because of the sanctions imposed by the United States which are primarily responsible for the inability of the Chinese company to launch 5G smartphones in the past three years. However, Mate 60 Pro, despite not being labeled a 5G device, exhibits mobile network speeds comparable to Apple’s latest 5G-enabled devices, offering a stable communication experience. This suggests Huawei has, over the past three years, overcome the 5G development and production limits due to the US sanctions by cooperating with domestic partners, and establishing an independent and controllable stable supply chain.Considering that Huawei has not explicitly marketed this device as a 5G smartphone, it is possible that it isyet to fully overcome some key core technological and componential shortcomings. For the time being, we can consider Huawei’s Mate 60 Pro as 4.99G. But when combined with the satellite communication capabilities of Mate 60 Pro, it is clear Huawei has been trying to find more advanced wireless communication solutions for smartphones and making significant progress in this attempt. This should be recognized as a remarkable endeavor, even a breakthrough.25．What do the underlined words “on par with” mean in Paragraph 1?A．as poor as.B．as good as.C．worse than.D．better than.26．Why was it tough for Huawei to develop a 5G smartphone three years ago?A．Its system design and fields needed to be updated.B．It only focused on the commercialization of 5G technology.C．It was unwilling to cooperate with high-tech giants in America.D．It lacked critical components mainly controlled by US high-tech giants.27．What does Paragraph 4 centre on?A．The US sanctions.B．Critical components.C．Apple’s latest 5G-enabled devices.D．Progress in Mate 60 Pro.28．What is the text mainly about?A．Huawei faced with significant challengesB．Huawei’s Mate 60 Pro—a 5G smartphoneC．Huawei’s Mate 60 Pro—a remarkable breakthroughD．Huawei leading in global 5G communication equipment（23-24高三上·江苏无锡·期末）Blue-light-filtering glasses (滤蓝光眼镜) have become an increasingly popular solution for protecting our eyes from electronic screens’ near-inescapable glow — light that is commonly associated with eyestrain (眼疲劳). In recent years they’ve even become fashion statements that are recognized by celebrities and ranked in style guides. But a recent review paper shows such glasses might not be as effective as people think.The paper, published last week in Cochrane Database of Systematic Reviews, analyzed data from previous trials that studied how blue-light-filtering glasses affect vision tiredness and eye health. The study’s authors found that wearing blue-light-filtering glasses does not reduce the eyestrain people feel after using computers.“It’s an excellent review,” says Mark Rosenfield, a professor at the State University of New York College of Optometry, who was not involved in the study. “The conclusions are no surprise at all. There have been a number of studies that have found exactly the same thing, that there’s just no evidence that blue-blocking glasses have anyeffect on eyestrain.” He adds that the new review reinforces the fact that there is virtually no evidence that blue-blocking glasses affect eyestrain despite them being specifically marketed for that purpose. As for using blue-light-filtering eyeglasses for eye health, for now, Rosenfield says, “there’s nothing to support people buying them”.The strain we may feel while staring at our phone or computer screen too long is likely to be caused by multiple factors, such as bad habits or underlying conditions, an associate professor of vision science at the University of Melbourne, Downie says. She argues that how we interact with digital devices contributes more to eyestrain than screens’ blue light does. Changing the frequency and duration of screen usage and distancing one’s eyes from the screens might be more important in reducing discomfort, Downie says. She adds that people who experience eyestrain should see a doctor to assess whether they have an underlying health issue such as far-sightedness or dry eye disease.29．What can we know about blue-light-filtering glasses from the text?A．They can improve eyesight.B．They may not reduce eyestrain.C．They can promote eye health.D．They can help to cure eye diseases.30．What can we infer from paragraph 2?A．A great many professors were involved in the study.B．Blue-blocking glasses on the market are harmful to eyes.C．The finding of the study comes as a surprise to the public.D．Data from previous trials help the study a lot.31．What does the underlined word “reinforces” mean in paragraph 3?A．Denies.B．Opposes.C．Strengthens.D．Evaluates.32．What should we do if we suffer from eyestrain according to Downie?A．Wear blue-light-filtering glasses.B．Have an examination in the hospital.C．Stop staring at the screen for ever.D．Focus on the frequency of phone usage.（2024·江苏连云港·一模）Not all birds sing, but several thousand species do. They sing to defend their territory and croon (柔声唱) to impress potential mates. “Why birds sing is relatively well-answered,” says Iris Adam, a behavioral neuroscientist. However, the big question for her was why birds sing so much.“As soon as you sing, you reveal yourself,” Adam says. “Like, where you are and where your territory is.” In a new study published in the journal Nature Communications, Adam and her co-workers offer a new explanation for why birds take that risk. They may have to sing a lot every day to give their vocal (发声的) muscles the regular exercise they need to produce top-quality songs. To figure out whether the muscles that produce birdsongsrequire daily exercise, Adam designed an experiment on zebra finches-the little Australian songbirds.She prevented them from singing for a week by keeping them in the dark cage almost around the clock. Light is what galvanizes the birds to sing, so she had to work to keep them from warbling (鸣叫). “The first two or three days, it’s quite easy,” she says. “But the longer the experiment goes, the more they are like, ‘I need to sing.’” At that point, she’d tap the cage and tell them to stop singing.After a week, the birds’ singing muscles lost half their strength. But Adam wondered whether that impacted the quality of songs. When she played a male’s song before and after the seven days of darkness, she couldn’t hear a difference. But when Adam played it to a group of female birds, six out of nine preferred the song that came from a male who’d been using his singing muscles daily.Adam’s conclusion shows that “songbirds need to exercise their vocal muscles to produce top-performance songs. If they don’t sing, they lose performance, and their songs get less attractive to females.” This may help explain songbirds’ continuous singing.It’s a good rule to live by, whether you’re a bird or a human-practice makes perfect, at least when it comes to singing one’s heart out.33．According to Iris Adam, birds sing so much to ______.A．warn other birds of risks B．produce more songsC．perform perfectly in singing D．defend their territory34．What does the underlined word “galvanizes” in Paragraph 3 mean?A．Prepares.B．Stimulates.C．Forbids.D．Frightens.35．What do we know about the caged birds in the experiment?A．They lost the ability to sing.B．They strengthened their muscles.C．Their songs showed no difference.D．Their songs became less appealing.36．What may Iris Adam agree with?A．The songbirds live on music.B．The songbirds are born singers.C．Daily exercise keeps birds healthy.D．Practice makes birds perfect singers.（23-24高三上·江苏扬州·期末）Sometimes called “Earth’s twin,” Venus is similar to our world in size and composition. The two rocky planets are also roughly the same distance from the sun, and both have an atmosphere. While Venus’s cold and unpleasant landscape does make it seem far less like Earth, scientists recently detected another striking similarity between the two, the presence of active volcanoes.When NASA’s Magellan mission mapped much of the planet with radar in the 1990sit revealed an。
sensme
sensmeSensMe: An Innovative Sensory TechnologyIntroductionIn today's modern world, technology has rapidly advanced and transformed various aspects of our lives. From smartphones to smart homes, there seems to be no limit to the possibilities that technology offers. One groundbreaking technology that has gained significant attention and has the potential to revolutionize the way we experience the world is SensMe. Developed by a team of dedicated researchers and engineers, SensMe integrates sensory technology into various devices, allowing users to interact with their surroundings in ways they never thought possible. In this document, we delve deeper into the concept of SensMe, exploring its features, applications, and potential impacts on various industries.What is SensMe?SensMe is a cutting-edge technology that enhances our sensory perception, enabling us to perceive and respond to our environment in new and exciting ways. It utilizes acombination of sensors and algorithms to collect and interpret data from our surroundings, transforming it into a comprehensive sensory experience. These sensors can detect a wide range of stimuli such as temperature, humidity, light, sound, and even more sophisticated inputs like facial expressions and gestures.How Does SensMe Work?At the core of SensMe lies a highly sophisticated algorithm that processes the data collected from the sensors. This algorithm analyzes and interprets the sensory inputs, generating meaningful content and responses tailored to the user's preferences. For example, SensMe can detect the user's mood through facial expressions and suggest appropriate music playlists to enhance their emotional state. Similarly, it can adjust lighting and temperature settings in a room based on the detected occupancy and environmental conditions, providing a comfortable and personalized experience.Applications of SensMeSensMe has wide-ranging applications across various industries. In the healthcare sector, SensMe can assist medical professionals in monitoring patients' vital signs in real-time,ensuring timely intervention in case of emergencies. In the automotive industry, SensMe can enhance the driving experience by dynamically adjusting the vehicle's settings based on the driver's preferences and environmental conditions. Additionally, SensMe can revolutionize the gaming industry by enabling more immersive and interactive gameplay, where the user's movements and gestures directly influence the virtual world.Impacts on IndustriesWith the integration of SensMe technology, industries are poised for significant transformations. In the retail sector, SensMe can revolutionize the shopping experience by personalizing recommendations based on the user's preferences and physiological state. For instance, when a shopper is browsing through a clothing store, SensMe can detect their body temperature and suggest suitable clothing options for the weather conditions. This level of personalization not only enhances customer satisfaction and loyalty but also opens up new revenue streams for businesses.In the entertainment industry, SensMe can amplify the user's experience by creating multisensory experiences. For example, while watching a movie, SensMe can synchronize the lighting, sound, and vibration patterns in a room to match the scenes,immersing the viewer in the movie's atmosphere. This level of immersion enhances the emotional impact of the content, making the entertainment experience more memorable and engaging.ConclusionSensMe is a monumental breakthrough in sensory technology that has the potential to reshape the way we experience the world. By harnessing the power of sensors and algorithms, SensMe offers a vast range of applications across industries, from healthcare to entertainment. As this technology continues to evolve, it will undoubtedly unlock new possibilities, allowing us to further enrich our daily lives and interactions with the world around us. SensMe opens the door to a future where our senses seamlessly merge with technology, creating a more personalized, interactive, and immersive reality.。
遗传算法原理(英文)
Soft Computing Lab.
WASEDA UNIVERSITY , IPS
2
Evolutionary Algorithms and Optimization:
Theory and its Applications
Part 2: Network Design
Network Design Problems Minimum Spanning Tree Logistics Network Design Communication Network and LAN Design
Book Info
Provides a comprehensive survey of selection strategies, penalty techniques, and genetic operators used for constrained and combinatorial problems. Shows how to use genetic algorithms to make production schedules and enhance system reliability.
Soft Computing Lab.
WASEDA UNIVERSITY , IPS
4
Evolutionary Algorithms and Optimization:
Theory and its Applications
Part 4: Scheduling
Machine Scheduling and Multi-processor Scheduling Flow-shop Scheduling and Job-shop Scheduling Resource-constrained Project Scheduling Advanced Planning and Scheduling Multimedia Real-time Task Scheduling
关于华为突破美国芯片封锁英语作文
关于华为突破美国芯片封锁英语作文Huawei Beats the BulliesMy big sister Sarah is really smart and loves learning about science and technology. She's been telling me all about this huge company in China called Huawei that makes amazing smartphones and other cool gadgets. But lately, she's been really upset because some not-so-nice people have been trying to stop Huawei from getting very important computer chips that they need to keep making their products.You see, these chips are like the brains inside phones, computers and all kinds of electronics that make them work properly. Without the right chips, companies like Huawei can't build new devices. And the bullies trying to block Huawei from getting chips are actually the government of the United States!Now, I don't really understand all the grown-up reasons behind this chip ban. From what Sarah explains, it has something to do with the US being worried that Huawei's technology could somehow be a security risk. But in my opinion, that seems pretty unfair and mean.Huawei is a great company that employs lots ofhard-working people. They've invented super cool 5G networksthat make our internet blazing fast. Their phones have amazing cameras that can take pictures from far away like bionic eyesight. Huawei even made the first foldable phone that bends in half like a futuristic gadget from the movies! Why would we want to stop such an innovative company from keeping up their great work?Well, those big bullies in the US government don't seem to care about being fair or not hurting innocent people with their actions. By blocking chip suppliers from selling to Huawei,they're trying to cripple the company and make life really difficult. It's so mean and heartless!But you know what? The awesome engineers and scientists at Huawei haven't just given up. They've been working super hard, day and night, to figure out ways to make their own chips so they don't need to rely on anyone else. It's like when you're being picked on by a bully at school - eventually you need to learn to stand up for yourself.From what I've heard from Sarah, Huawei has made incredible strides in developing their own chip technology in a very short period of time. This is despite the bullies constantly trying to throw up new roadblocks and restrictions to slow them down. But the geniuses at Huawei won't be deterred!Just last year, Huawei launched their own Kirin 9000 chipset that is one of the most advanced mobile processors in the world. It's incredibly powerful and energy efficient, letting their new phones have amazing performance and battery life. And get this - Huawei's chips are already being used by other major tech brands like Tesla for their self-driving vehicle systems!More recently, Huawei went even bigger by revealing their brand new Kirin 9000S chipset. This "super" chip takes performance and capabilities to an entirely new level. It's going to be used in Huawei's future flagship phones and devices, letting them stay at the cutting edge of innovation despite the mean bully tactics.Sarah tells me the Kirin 9000S is the first chip to use crazy tiny 3-nanometer transistors, which are so small you'd need a powerful microscope just to see them! That lets Huawei cram way more transistors onto the chip for better computing power than anyone else. The 9000S is also the first mobile chip that can connect to amazingly fast WiFi 7 networks and it has a dedicated computer vision processor for ultra-smart AI cameras.To me, it sounds like a mini super-computer that you can hold in your hand! And Huawei made this incredible technology all by themselves after being so meanly targeted. Instead ofletting the bullies win, they worked harder than ever and came out on top. It just goes to show that brains will always beat brawn, and innovation will prevail over bullying tactics.I'm so proud of the scientists and engineers at Huawei for being brave and persistent in the face of adversity. Thanks to their genius, Huawei can continue thrilling us with cutting-edge technologies for years to come despite those meanies trying to hold them back.In my books, the employees of Huawei are the real champions here. They make me feel hopeful that no matter how mean the bullies get, if youput your mind to it and work really hard, you can overcome any obstacles. Huawei's own struggles prove that world-class innovation will always find a way to break through and keep pushing forward.The bullies might have tried to beat Huawei down, but they just made Huawei even stronger, smarter and more determined in the end. If I learn anything from this story, it's to never give up and never let bullies hold me back from my ambitions. Just study hard, work my brain, and solve problems with creativity and hard work like the heroes at Huawei!。
高中英语科技论文翻译单选题40题
高中英语科技论文翻译单选题40题1. The term "nanotechnology" is often translated as "_____".A. 纳米技术B. 微观技术C. 微观科学D. 纳米科学答案：A。
“nanotechnology”常见且准确的翻译就是“纳米技术”，B 选项“微观技术”通常用“microtechnology”，C 选项“微观科学”一般是“microscopic science”，D 选项“纳米科学”是“nanoscience”。
2. "Artificial intelligence" is best translated to "_____".A. 人工智慧B. 人造智能C. 人工智能D. 人工智力答案：C。
“Artificial intelligence”最准确和常用的翻译是“人工智能”，A 选项“人工智慧”不太符合常见表达，B 选项“人造智能”不够准确，D 选项“人工智力”不是常用的翻译。
3. The phrase "genetic engineering" can be translated as "_____".A. 基因工程B. 遗传工程C. 基因技术D. 遗传技术答案：B。
“genetic engineering”常见的翻译是“遗传工程”，A 选项“基因工程”不太准确，C 选项“基因技术”通常是“genetic technology”，D 选项“遗传技术”一般是“genetic technique”。
4. "Quantum mechanics" is usually translated to "_____".A. 量子力学B. 量子机械学C. 量子物理学D. 量子动力学答案：A。
“Quantum mechanics”准确的翻译是“量子力学”，B 选项“量子机械学”这种表述不常见，C 选项“量子物理学”是“Quantum Physics”，D 选项“量子动力学”是“Quantum Dynamics”。
通感的认知究
内容提要本文研究了通感隐喻这一现象。
文章首先简要的概括了对强通感与弱通感（即通感隐喻）的多学科研究，讨论了强弱两种通感之间的关系，得出：通感隐喻（弱通感）具有～定的神经和心理基础。
接着，文章运用莱考夫与约翰逊的体验哲学理论、格莱迪的基本隐喻理论和弗科尼亚与特纳的概念整合理论，探究通感隐喻的内在本质。
文章揭示出：（１）通感隐喻是基于体验的，包括神经的与基本经验的体验性；（２）它们属于基本隐喻范畴：（３）其意义建构过程是动态的概念整合过程。
关键词：通感；通感隐喻；认知语言学：体验哲学理论；基本隐喻理论；概念整合理论ＡｂｓｔｒａｃｔＴｈｅ廿］【ｅｓｉｓｓｔｕｄｉｅｓｔ１１ｅｐｈｅｎｏｍｅｎｏｎＯｆｓｙＩｍｓｔｈｅｔｉｃｍｃｔａｐｈｏｒｓｗｈｉｃｈｅｎｔａｉｌ洳ｍ３ｔｅＸ鑫ｍ．ｍｅｓＭ以ｙ也ｅｔｒａｎｓｆｅｒｓｂｅｔｗｅｅｎｐｅ托ｅ弘工ａｉｄｏｍｇｂ．，ｒｈｅｍｕｌｔｉｄｉｓｃｉｐｌｉｎａｒｙｓｔｕｄｙｏｎｓｔｒｏｎｇｓｙｎａｅｓｔｈｅｓｉａ（ｒｅｆｂｒｒｉｎｇｔｏ吐ｌｅｒｅａｌｃｏ－ｓｅｎｓａｔｉｏｎ）ａｎｄｗｃａｌ（ｓ”ａｅ蚰ｅｓｉａ（ｒｅｆ毫ｒｒｉｎｇｔｏｔｈｅｉｍｅｒｓｅｎｓｏｒｙａｓｓｏｃｉａｔｉｏｎｓ），ａｔｌｄｄｉｓｃｕｓｓｅｓ也ｅｉｒｒｅｌａｔｉｏｎｓｈｉｐｓ．Ｆｒｏｍ也ｅｄｉｓｃｕｓｓｉｏｎ，ｉｔｉｓｃｏｎｃｌｕｄｅｄｔｈａｔｓｙｎａｅｓ也ｅｔｉｃｍｅｔ８ｐｈｏｒｓ（ｗｈｉｃｈｉｓａｎｏｔｈｅｒｎ锄ｅｆｏｒｗｅａｋｓｙｎａｅｓｍｅｓｉａ）ｍａｙｈａｖｅｓｏｍｅｎｅｌｌｒａｌａｎｄｐｓｙｃｈｏｌｏｇｉｃａｌｕｎｄｅｒｐｉｒｕｌｉｎｇｓ．Ｔｈｅｎ，ｄｒａｗｉｎｇｏｎｔｈｒｅｅｍ∞ｒｉｅｓｉ１１ｃｏｇｌｌｉｔｉＶｅｌｉ力ｇＩｌｉｓｔｉｃｓ，ｎ锄ｅｌｙＬａｋｏｆｒａｎｄＪｏｈｎｓｏｎ’ｓＥｍｂｏｄｉｅｄ蹦ｌｏｓｏｐｈ孔‰ｄｙ’ｓＰｒｉｆ啮ｒｙＭｅｔａｐｈｏｒｎｅｏ吼觚ｄＦａｕｃｏｎｎｉｅｒａｎｄｎ哪ｅｒ’ｓＢｌｅｎｄｉｎｇＴｈｅｏ观ｔｈｃａｕｔｈｏｒｅｎｑｕｉ坤ｓｉｎｔｏ也ｅｎａｔｕｒｅｏｆｓｙｎａｅｓｍｅｌｊｃｍｅｔａｐｈｏｒｓ柚ｄｃｏｍｅｓｔｏｔｈＩ∞ｉｍｐｏｎａｎｔｃｏＩｔｃｌｕｓｉｏｎｓａｓｆｏｌｌｏｗｓ．（１）ｓｙｎａｅｓｔｈｅ＆ｍｅ乜ｐｈｏｒｓａｒｅｎ黜ａ｝姆斑ｐｈｅｎｏｍ嘲Ｉｏｇｉｃａｌｌｙ姗ｂｏｄｉｅｄ．（２）ＴｈｅｙａｒｅＰｒｉｍａｒｙＭ曲巾ｈｏｒｓ．（３）Ｔｈｅｉｆｐｒｏｃｅｓｓｅｓｏｆｍｅａｎｉｎｇｃｏｎｓｔｎ】ｃｔ至ｏｎａｒｅｄｙｎ啪ｉｃｐｒｏｃｅｓｓｅｓｏｆｃＯｎｃｅｐｔｕａｌｉｎｔｅ掣ａｔｉｏｎ．Ｋｅ）ｗｏｒｄｓ：Ｓｙｎａｅｓｍｅｓｉａ，ｓｙｔｌａｅｓｔｈｅｔｉｃｍ啦ｐｈｏｒｓ，ｃｏｇＩＩｉｔｉｗｌｉｎ刚ｓｔｉｃＳ，ｔｈｅＥｍｂｏｄｉｅｄＰｈｉＩｏｓｏｐｈｙ，龇ＭｍａｒｙＭｅ协ｐｈｏｒ１１１ｅ０Ⅸ恤ＢｌｅｎｄｉｎｇＴｈｅｏｒｙＡｃｋｎｏｗｌｅｄｇｅｍｅｎｔｓＩｎｍｅｃｏ、ｌｒｓｅｏｆｗｒｉｔｉｎｇ也ｉｓｔｈｅｓｉｓ，ＩｈａｖｅｒｅｃｅｉＶｅｄａｔＴｅｍｅｎｄｏｕｓａｍｏｕｎｔｏｆｈｅｌｐａｎｄｓｕｐｐｏｒｔ，ａｎｄ１ｗｏｕｌｄｌｉｋｅｔｏｔａｌ【ｅⅡｌｅｏｐｐｏｎｕｎｉｔｙｔｏｅｘｐｒｅｓｓｍｙｄｅｅｐｅＳｔ伊ａｔｉｔｕｄｅｔｏａ１１ｔｈｏｓｅｗｈｏｈａｖｅｈｅｌｐｅｄｍｅ．ＦｉｒｓｔｏｆａＩＩ，Ｉａｍｇｒｃ砒ＩｙｉｎｄｅｂｔｅｄｔｏＰｍｆｅｓｓｏｒＳｕｘｉａｏｊｕｌｌ，ｍｙｓｕｐｅｒｖｉｓｏＬｗｈｏｈａｓｇｉｖｅｎｍｅａ１０ｔｏｆｅｎｌｉｇｈｔｅｎｉｎｇｉｎｓｔｍｃｔｉｏｎｓｉｎｍｙ血ｒｅｅ—ｙｅａｒｓｔｕｄｙａｎｄｈａｓｈｅｌｐｅｄ衄ｏｕ曲Ⅱ１ｅｖａｒｉｏｕｓｓｔａｇｅｓｏｆｔｈｅｄｅｖｅｌｏｐｍｅｎｔａｒＩｄｒｅＶｉｓｉｏｎｏｆｍｅｔｈｅｓｉｓ．Ｗｉｔｌｌｏｕｔｈｉｓｃｎｃｏｕｒａｇｅｍｅｎｔａｎｄｓｕｐｐ。
基于周期采样的分布式动态事件触发优化算法
第38卷第3期2024年5月山东理工大学学报(自然科学版)Journal of Shandong University of Technology(Natural Science Edition)Vol.38No.3May 2024收稿日期:20230323基金项目:江苏省自然科学基金项目(BK20200824)第一作者:夏伦超,男,20211249098@;通信作者:赵中原,男,zhaozhongyuan@文章编号:1672-6197(2024)03-0058-07基于周期采样的分布式动态事件触发优化算法夏伦超1,韦梦立2,季秋桐2,赵中原1(1.南京信息工程大学自动化学院,江苏南京210044;2.东南大学网络空间安全学院,江苏南京211189)摘要:针对无向图下多智能体系统的优化问题,提出一种基于周期采样机制的分布式零梯度和优化算法,并设计一种新的动态事件触发策略㊂该策略中加入与历史时刻智能体状态相关的动态变量,有效降低了系统通信量;所提出的算法允许采样周期任意大,并考虑了通信延时的影响,利用Lyapunov 稳定性理论推导出算法收敛的充分条件㊂数值仿真进一步验证了所提算法的有效性㊂关键词:分布式优化;多智能体系统;动态事件触发;通信时延中图分类号:TP273文献标志码:ADistributed dynamic event triggerring optimizationalgorithm based on periodic samplingXIA Lunchao 1,WEI Mengli 2,JI Qiutong 2,ZHAO Zhongyuan 1(1.College of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;2.School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China)Abstract :A distributed zero-gradient-sum optimization algorithm based on a periodic sampling mechanism is proposed to address the optimization problem of multi-agent systems under undirected graphs.A novel dynamic event-triggering strategy is designed,which incorporates dynamic variables as-sociated with the historical states of the agents to effectively reduce the system communication overhead.Moreover,the algorithm allows for arbitrary sampling periods and takes into consideration the influence oftime delay.Finally,sufficient conditions for the convergence of the algorithm are derived by utilizing Lya-punov stability theory.The effectiveness of the proposed algorithm is further demonstrated through numer-ical simulations.Keywords :distributed optimization;multi-agent systems;dynamic event-triggered;time delay ㊀㊀近些年,多智能体系统的分布式优化问题因其在多机器人系统的合作㊁智能交通系统的智能运输系统和微电网的分布式经济调度等诸多领域的应用得到了广泛的研究[1-3]㊂如今,已经提出各种分布式优化算法㊂文献[4]提出一种结合负反馈和梯度流的算法来解决平衡有向图下的无约束优化问题;文献[5]提出一种基于自适应机制的分布式优化算法来解决局部目标函数非凸的问题;文献[6]设计一种抗干扰的分布式优化算法,能够在具有未知外部扰动的情况下获得最优解㊂然而,上述工作要求智能体与其邻居不断地交流,这在现实中会造成很大的通信负担㊂文献[7]首先提出分布式事件触发控制器来解决多智能体系统一致性问题;事件触发机制的核心是设计一个基于误差的触发条件,只有满足触发条件时智能体间才进行通信㊂文献[8]提出一种基于通信网络边信息的事件触发次梯度优化㊀算法,并给出了算法的指数收敛速度㊂文献[9]提出一种基于事件触发机制的零梯度和算法,保证系统状态收敛到最优解㊂上述事件触发策略是静态事件触发策略,即其触发阈值仅与智能体的状态相关,当智能体的状态逐渐收敛时,很容易满足触发条件并将生成大量不必要的通信㊂因此,需要设计更合理的触发条件㊂文献[10]针对非线性系统的增益调度控制问题,提出一种动态事件触发机制的增益调度控制器;文献[11]提出一种基于动态事件触发条件的零梯度和算法,用于有向网络的优化㊂由于信息传输的复杂性,时间延迟在实际系统中无处不在㊂关于考虑时滞的事件触发优化问题的文献很多㊂文献[12]研究了二阶系统的凸优化问题,提出时间触发算法和事件触发算法两种分布式优化算法,使得所有智能体协同收敛到优化问题的最优解,并有效消除不必要的通信;文献[13]针对具有传输延迟的多智能体系统,提出一种具有采样数据和时滞的事件触发分布式优化算法,并得到系统指数稳定的充分条件㊂受文献[9,14]的启发,本文提出一种基于动态事件触发机制的分布式零梯度和算法,与使用静态事件触发机制的文献[15]相比,本文采用动态事件触发机制可以避免智能体状态接近最优值时频繁触发造成的资源浪费㊂此外,考虑到进行动态事件触发判断需要一定的时间,使用当前状态值是不现实的,因此,本文使用前一时刻状态值来构造动态事件触发条件,更符合逻辑㊂由于本文采用周期采样机制,这进一步降低了智能体间的通信频率,但采样周期过长会影响算法收敛㊂基于文献[14]的启发,本文设计的算法允许采样周期任意大,并且对于有时延的系统,只需要其受采样周期的限制,就可得到保证多智能体系统达到一致性和最优性的充分条件㊂最后,通过对一个通用示例进行仿真,验证所提算法的有效性㊂1㊀预备知识及问题描述1.1㊀图论令R表示实数集,R n表示向量集,R nˑn表示n ˑn实矩阵的集合㊂将包含n个智能体的多智能体系统的通信网络用图G=(V,E)建模,每个智能体都视为一个节点㊂该图由顶点集V={1,2, ,n}和边集E⊆VˑV组成㊂定义A=[a ij]ɪR nˑn为G 的加权邻接矩阵,当a ij>0时,表明节点i和节点j 间存在路径,即(i,j)ɪE;当a ij=0时,表明节点i 和节点j间不存在路径,即(i,j)∉E㊂D=diag{d1, ,d n}表示度矩阵,拉普拉斯矩阵L等于度矩阵减去邻接矩阵,即L=D-A㊂当图G是无向图时,其拉普拉斯矩阵是对称矩阵㊂1.2㊀凸函数设h i:R nңR是在凸集ΩɪR n上的局部凸函数,存在正常数φi使得下列条件成立[16]:h i(b)-h i(a)- h i(a)T(b-a)ȡ㊀㊀㊀㊀φi2 b-a 2,∀a,bɪΩ,(1)h i(b)- h i(a)()T(b-a)ȡ㊀㊀㊀㊀φi b-a 2,∀a,bɪΩ,(2) 2h i(a)ȡφi I n,∀aɪΩ,(3)式中: h i为h i的一阶梯度, 2h i为h i的二阶梯度(也称黑塞矩阵)㊂1.3㊀问题描述考虑包含n个智能体的多智能体系统,假设每个智能体i的成本函数为f i(x),本文的目标是最小化以下的优化问题:x∗=arg minxɪΩðni=1f i(x),(4)式中:x为决策变量,x∗为全局最优值㊂1.4㊀主要引理引理1㊀假设通信拓扑图G是无向且连通的,对于任意XɪR n,有以下关系成立[17]:X T LXȡαβX T L T LX,(5)式中:α是L+L T2最小的正特征值,β是L T L最大的特征值㊂引理2(中值定理)㊀假设局部成本函数是连续可微的,则对于任意实数y和y0,存在y~=y0+ω~(y -y0),使得以下不等式成立:f i(y)=f i(y0)+∂f i∂y(y~)(y-y0),(6)式中ω~是正常数且满足ω~ɪ(0,1)㊂2㊀基于动态事件触发机制的分布式优化算法及主要结果2.1㊀考虑时延的分布式动态事件触发优化算法本文研究具有时延的多智能体系统的优化问题㊂为了降低智能体间的通信频率,提出一种采样周期可任意设计的分布式动态事件触发优化算法,95第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法其具体实现通信优化的流程图如图1所示㊂首先,将邻居和自身前一触发时刻状态送往控制器(本文提出的算法),得到智能体的状态x i (t )㊂然后,预设一个固定采样周期h ,使得所有智能体在同一时刻进行采样㊂同时,在每个智能体上都配置了事件检测器,只在采样时刻检查是否满足触发条件㊂接着,将前一采样时刻的智能体状态发送至构造的触发器中进行判断,当满足设定的触发条件时,得到触发时刻的智能体状态x^i (t )㊂最后,将得到的本地状态x^i (t )用于更新自身及其邻居的控制操作㊂由于在实际传输中存在时延,因此需要考虑满足0<τ<h 的时延㊂图1㊀算法实现流程图考虑由n 个智能体构成的多智能体系统,其中每个智能体都能独立进行计算和相互通信,每个智能体i 具有如下动态方程:x ㊃i (t )=-1h2f i (x i )()-1u i (t ),(7)式中u i (t )为设计的控制算法,具体为u i (t )=ðnj =1a ij x^j (t -τ)-x ^i (t -τ)()㊂(8)㊀㊀给出设计的动态事件触发条件:θi d i e 2i (lh )-γq i (lh -h )()ɤξi (lh ),(9)q i (t )=ðnj =1a ij x^i (t -τ)-x ^j (t -τ)()2,(10)㊀㊀㊀ξ㊃i (t )=1h[-μi ξi (lh )+㊀㊀㊀㊀㊀δi γq i (lh -h )-d i e 2i (lh )()],(11)式中:d i 是智能体i 的入度;γ是正常数;θi ,μi ,δi 是设计的参数㊂令x i (lh )表示采样时刻智能体的状态,偏差变量e i (lh )=x i (lh )-x^i (lh )㊂注释1㊀在进行动态事件触发条件设计时,可以根据不同的需求为每个智能体设定不同的参数θi ,μi ,δi ,以确保其能够在特定的情境下做出最准确的反应㊂本文为了方便分析,选择为每个智能体设置相同的θi ,μi ,δi ,以便更加清晰地研究其行为表现和响应能力㊂2.2㊀主要结果和分析由于智能体仅在采样时刻进行事件触发条件判断,并在达到触发条件后才通信,因此有x ^i (t -τ)=x^i (lh )㊂定理1㊀假设无向图G 是连通的,对于任意i ɪV 和t >0,当满足条件(12)时,在算法(7)和动态事件触发条件(9)的作用下,系统状态趋于优化解x ∗,即lim t ңx i (t )=x ∗㊂12-β2φm α-τβ2φm αh -γ>0,μi+δi θi <1,μi-1-δi θi >0,ìîíïïïïïïïï(12)式中φm =min{φ1,φ2}㊂证明㊀对于t ɪ[lh +τ,(l +1)h +τ),定义Lyapunov 函数V (t )=V 1(t )+V 2(t ),其中:V 1(t )=ðni =1f i (x ∗)-f i (x i )-f ᶄi (x i )(x ∗-x i )(),V 2(t )=ðni =1ξi (t )㊂令E (t )=e 1(t ), ,e n (t )[]T ,X (t )=x 1(t ), ,x n (t )[]T ,X^(t )=x ^1(t ), ,x ^n (t )[]T ㊂对V 1(t )求导得V ㊃1(t )=1h ðni =1u i (t )x ∗-x i (t )(),(13)由于ðni =1ðnj =1a ij x ^j (t -τ)-x ^i (t -τ)()㊃x ∗=0成立,有V ㊃1(t )=-1hX T (t )LX ^(lh )㊂(14)6山东理工大学学报(自然科学版)2024年㊀由于㊀㊀X (t )=X (lh +τ)-(t -lh -τ)X ㊃(t )=㊀㊀㊀㊀X (lh )+τX ㊃(lh )+t -lh -τhΓ1LX^(lh )=㊀㊀㊀㊀X (lh )-τh Γ2LX^(lh -h )+㊀㊀㊀㊀(t -lh -τ)hΓ1LX^(lh ),(15)式中:Γ1=diag (f i ᶄᶄ(x ~11))-1, ,(f i ᶄᶄ(x ~1n ))-1{},Γ2=diag (f i ᶄᶄ(x ~21))-1, ,(f i ᶄᶄ(x ~2n))-1{},x ~1iɪ(x i (lh +τ),x i (t )),x ~2i ɪ(x i (lh ),x i (lh+τ))㊂将式(15)代入式(14)得㊀V ㊃1(t )=-1h E T (lh )LX ^(lh )-1hX ^T (lh )LX ^(lh )+㊀㊀㊀τh2Γ2X ^T (lh -h )L T LX ^(lh )+㊀㊀㊀(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )㊂(16)根据式(3)得(f i ᶄᶄ(x ~i 1))-1ɤ1φi,i =1, ,n ㊂即Γ1ɤ1φm I n ,Γ2ɤ1φmI n ,φm =min{φ1,φ2}㊂首先对(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )项进行分析,对于t ɪ[lh +τ,(l +1)h +τ),基于引理1和式(3)有(t -lh -τ)h2Γ1X ^T (lh )L T LX ^(lh )ɤβhφm αX ^T (lh )LX ^(lh )ɤβ2hφm αðni =1q i(lh ),(17)式中最后一项根据X^T (t )LX ^(t )=12ðni =1q i(t )求得㊂接着分析τh2Γ2X ^(lh -h )L T LX ^(lh ),根据引理1和杨式不等式有:τh2Γ2X ^T (lh -h )L T LX ^(lh )ɤ㊀㊀㊀㊀τβ2h 2φm αX ^T (lh -h )LX ^(lh -h )+㊀㊀㊀㊀τβ2h 2φm αX ^T (lh )LX ^(lh )ɤ㊀㊀㊀㊀τβ4h 2φm αðni =1q i (lh -h )+ðni =1q i (lh )[]㊂(18)将式(17)和式(18)代入式(16)得㊀V ㊃1(t )ɤβ2φm α+τβ4φm αh -12()1h ðni =1q i(lh )+㊀㊀㊀τβ4φm αh ðni =1q i (lh -h )+1h ðni =1d i e 2i(lh )㊂(19)根据式(11)得V ㊃2(t )=-ðni =1μih ξi(lh )+㊀㊀㊀㊀ðni =1δihγq i (lh -h )-d i e 2i (lh )()㊂(20)结合式(19)和式(20)得V ㊃(t )ɤ-12-β2φm α-τβ4φm αh ()1h ðni =1q i (lh )+㊀㊀㊀㊀τβ4φm αh 2ðn i =1q i (lh -h )+γh ðni =1q i (lh -h )-㊀㊀㊀㊀1h ðni =1(μi -1-δi θi)ξi (lh ),(21)因此根据李雅普诺夫函数的正定性以及Squeeze 定理得㊀V (l +1)h +τ()-V (lh +τ)ɤ㊀㊀㊀-12-β2φm α-τβ4φm αh()ðni =1q i(lh )+㊀㊀㊀τβ4φm αh ðni =1q i (lh -h )+γðni =1q i (lh -h )-㊀㊀㊀ðni =1(μi -1-δiθi)ξi (lh )㊂(22)对式(22)迭代得V (l +1)h +τ()-V (h +τ)ɤ㊀㊀-12-β2φm α-τβ2φm αh-γ()ðl -1k =1ðni =1q i(kh )+㊀㊀τβ4φm αh ðni =1q i (0h )-㊀㊀12-β2φm α-τβ4φm αh()ðni =1q i(lh )-㊀㊀ðlk =1ðni =1μi -1-δiθi()ξi (kh ),(23)进一步可得㊀lim l ңV (l +1)h -V (h )()ɤ㊀㊀㊀τβ4φm αh ðni =1q i(0h )-16第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法㊀㊀㊀ðni =1(μi -1-δi θi )ðl =1ξi (lh )-㊀㊀㊀12-β2φm α-τβ2φm αh-γ()ð l =1ðni =1q i(lh )㊂(24)由于q i (lh )ȡ0和V (t )ȡ0,由式(24)得lim l ң ðni =1ξi (lh )=0㊂(25)基于ξi 的定义和拉普拉斯矩阵的性质,可以得到每个智能体的最终状态等于相同的常数,即lim t ңx 1(t )= =lim t ңx n (t )=c ㊂(26)㊀㊀由于目标函数的二阶导数具有以下性质:ðni =1d f ᶄi (x i (t ))()d t =㊀㊀㊀㊀-ðn i =1ðnj =1a ij x ^j (t )-x ^i (t )()=㊀㊀㊀㊀-1T LX^(t )=0,(27)式中1=[1, ,1]n ,所以可以得到ðni =1f i ᶄ(x i (t ))=ðni =1f i ᶄ(x ∗i )=0㊂(28)联立式(26)和式(28)得lim t ңx 1(t )= =lim t ңx n (t )=c =x ∗㊂(29)㊀㊀定理1证明完成㊂当不考虑通信时延τ时,可由定理1得到推论1㊂推论1㊀假设通信图G 是无向且连通的,当不考虑时延τ时,对于任意i ɪV 和t >0,若条件(30)成立,智能体状态在算法(7)和触发条件(9)的作用下趋于最优解㊂14-n -1φm -γ>0,μi+δi θi <1,μi-1-δi θi >0㊂ìîíïïïïïïïï(30)㊀㊀证明㊀该推论的证明过程类似定理1,由定理1结果可得14-β2φm α-γ>0㊂(31)令λn =βα,由于λn 是多智能体系统的全局信息,因此每个智能体很难获得,但其上界可以根据以下关系来估计:λn ɤ2d max ɤ2(n -1),(32)式中d max =max{d i },i =1, ,n ㊂因此得到算法在没有时延情况下的充分条件:14-n -1φm -γ>0㊂(33)㊀㊀推论1得证㊂注释2㊀通过定理1得到的稳定性条件,可以得知当采样周期h 取较小值时,由于0<τ<h ,因此二者可以抵消,从而稳定性不受影响;而当采样周期h 取较大值时,τβ2φm αh项可以忽略不计,因此从理论分析可以得出允许采样周期任意大的结论㊂从仿真实验方面来看,当采样周期h 越大,需要的收剑时间越长,但最终结果仍趋于优化解㊂然而,在文献[18]中,采样周期过大会导致稳定性条件难以满足,即算法最终难以收敛,无法达到最优解㊂因此,本文提出的算法允许采样周期任意大,这一创新点具有重要意义㊂3㊀仿真本文对一个具有4个智能体的多智能体网络进行数值模拟,智能体间的通信拓扑如图2所示㊂采用4个智能体的仿真网络仅是为了初步验证所提算法的有效性㊂值得注意的是,当多智能体的数量增加时,算法的时间复杂度和空间复杂度会增加,但并不会影响其有效性㊂因此,该算法在更大规模的多智能体网络中同样适用㊂成本函数通常选择凸函数㊂例如,在分布式传感器网络中,成本函数为z i -x 2+εi x 2,其中x 表示要估计的未知参数,εi 表示观测噪声,z i 表示在(0,1)中均匀分布的随机数;在微电网中,成本函数为a i x 2+b i x +c i ,其中a i ,b i ,c i 是发电机成本参数㊂这两种情境下的成本函数形式不同,但本质上都是凸函数㊂本文采用论文[19]中的通用成本函数(式(34)),用于证明本文算法在凸函数上的可行性㊂此外,通信拓扑图结构并不会影响成本函数的设计,因此,本文的成本函数在分布式网络凸优化问题中具有通用性㊂g i (x )=(x -i )4+4i (x -i )2,i =1,2,3,4㊂(34)很明显,当x i 分别等于i 时,得到最小局部成本函数,但是这不是全局最优解x ∗㊂因此,需要使用所提算法来找到x ∗㊂首先设置重要参数,令φm =16,γ=0.1,θi =1,ξi (0)=5,μi =0.2,δi =0.2,26山东理工大学学报(自然科学版)2024年㊀图2㊀通信拓扑图x i (0)=i ,i =1,2,3,4㊂图3为本文算法(7)解决优化问题(4)时各智能体的状态,其中设置采样周期h =3,时延τ=0.02㊂智能体在图3中渐进地达成一致,一致值为全局最优点x ∗=2.935㊂当不考虑采样周期影响时,即在采样周期h =3,时延τ=0.02的条件下,采用文献[18]中的算法(10)时,各智能体的状态如图4所示㊂显然,在避免采样周期的影响后,本文算法具有更快的收敛速度㊂与文献[18]相比,由于只有当智能体i 及其邻居的事件触发判断完成,才能得到q i (lh )的值,因此本文采用前一时刻的状态值构造动态事件触发条件更符合逻辑㊂图3㊀h =3,τ=0.02时算法(7)的智能体状态图4㊀h =3,τ=0.02时算法(10)的智能体状态为了进一步分析采样周期的影响,在时延τ不变的情况下,选择不同的采样周期h ,其结果显示在图5中㊂对比图3可以看出,选择较大的采样周期则收敛速度减慢㊂事实上,这在算法(7)中是很正常的,因为较大的h 会削弱反馈增益并减少固定有限时间间隔中的控制更新次数,具体显示在图6和图7中㊂显然,当选择较大的采样周期时,智能体的通信频率显著下降,同时也会导致收敛速度减慢㊂因此,虽然采样周期允许任意大,但在收敛速度和通信频率之间需要做出权衡,以选择最优的采样周期㊂图5㊀h =1,τ=0.02时智能体的状态图6㊀h =3,τ=0.02时的事件触发时刻图7㊀h =1,τ=0.02时的事件触发时刻最后,固定采样周期h 的值,比较τ=0.02和τ=2时智能体的状态,结果如图8所示㊂显然,时延会使智能体找到全局最优点所需的时间更长,但由于其受采样周期的限制,最终仍可以对于任意有限延迟达成一致㊂图8㊀h =3,τ=2时智能体的状态36第3期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀夏伦超,等:基于周期采样的分布式动态事件触发优化算法4 结束语本文研究了无向图下的多智能体系统的优化问题,提出了一种基于动态事件触发机制的零梯度和算法㊂该机制中加入了与前一时刻智能体状态相关的动态变量,避免智能体状态接近最优值时频繁触发产生的通信负担㊂同时,在算法和触发条件设计中考虑了采样周期的影响,在所设计的算法下,允许采样周期任意大㊂对于有时延的系统,在最大允许传输延迟小于采样周期的情况下,给出了保证多智能体系统达到一致性和最优性的充分条件㊂今后拟将本算法向有向图和切换拓扑图方向推广㊂参考文献:[1]杨洪军,王振友.基于分布式算法和查找表的FIR滤波器的优化设计[J].山东理工大学学报(自然科学版),2009,23(5):104-106,110.[2]CHEN W,LIU L,LIU G P.Privacy-preserving distributed economic dispatch of microgrids:A dynamic quantization-based consensus scheme with homomorphic encryption[J].IEEE Transactions on Smart Grid,2022,14(1):701-713.[3]张丽馨,刘伟.基于改进PSO算法的含分布式电源的配电网优化[J].山东理工大学学报(自然科学版),2017,31(6):53-57.[4]KIA S S,CORTES J,MARTINEZ S.Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication[J].Automatica,2015,55:254-264.[5]LI Z H,DING Z T,SUN J Y,et al.Distributed adaptive convex optimization on directed graphs via continuous-time algorithms[J]. IEEE Transactions on Automatic Control,2018,63(5):1434 -1441.[6]段书晴,陈森,赵志良.一阶多智能体受扰系统的自抗扰分布式优化算法[J].控制与决策,2022,37(6):1559-1566. [7]DIMAROGONAS D V,FRAZZOLI E,JOHANSSON K H.Distributed event-triggered control for multi-agent systems[J].IEEE Transactions on Automatic Control,2012,57(5):1291-1297.[8]KAJIYAMA Y C,HAYASHI N K,TAKAI S.Distributed subgradi-ent method with edge-based event-triggered communication[J]. IEEE Transactions on Automatic Control,2018,63(7):2248 -2255.[9]LIU J Y,CHEN W S,DAI H.Event-triggered zero-gradient-sum distributed convex optimisation over networks with time-varying topol-ogies[J].International Journal of Control,2019,92(12):2829 -2841.[10]COUTINHO P H S,PALHARES R M.Codesign of dynamic event-triggered gain-scheduling control for a class of nonlinear systems [J].IEEE Transactions on Automatic Control,2021,67(8): 4186-4193.[11]CHEN W S,REN W.Event-triggered zero-gradient-sum distributed consensus optimization over directed networks[J].Automatica, 2016,65:90-97.[12]TRAN N T,WANG Y W,LIU X K,et al.Distributed optimization problem for second-order multi-agent systems with event-triggered and time-triggered communication[J].Journal of the Franklin Insti-tute,2019,356(17):10196-10215.[13]YU G,SHEN Y.Event-triggered distributed optimisation for multi-agent systems with transmission delay[J].IET Control Theory& Applications,2019,13(14):2188-2196.[14]LIU K E,JI Z J,ZHANG X F.Periodic event-triggered consensus of multi-agent systems under directed topology[J].Neurocomputing, 2020,385:33-41.[15]崔丹丹,刘开恩,纪志坚,等.周期事件触发的多智能体分布式凸优化[J].控制工程,2022,29(11):2027-2033. [16]LU J,TANG C Y.Zero-gradient-sum algorithms for distributed con-vex optimization:The continuous-time case[J].IEEE Transactions on Automatic Control,2012,57(9):2348-2354. [17]LIU K E,JI Z J.Consensus of multi-agent systems with time delay based on periodic sample and event hybrid control[J].Neurocom-puting,2016,270:11-17.[18]ZHAO Z Y.Sample-baseddynamic event-triggered algorithm for op-timization problem of multi-agent systems[J].International Journal of Control,Automation and Systems,2022,20(8):2492-2502.[19]LIU J Y,CHEN W S.Distributed convex optimisation with event-triggered communication in networked systems[J].International Journal of Systems Science,2016,47(16):3876-3887.(编辑:杜清玲)46山东理工大学学报(自然科学版)2024年㊀。
(2024年高考真题含解析)2024年普通高等学校招生全国统一考试英语试卷 新课标Ⅰ卷(含解析)
2024年普通高等学校招生全国统一考试新课标Ⅰ卷英语试卷姓名________________ 准考证号________________全卷共12页，满分150分，考试时间120分钟。
养成良好的答题习惯，是决定成败的决定性因素之一。
做题前，要认真阅读题目要求、题干和选项，并对答案内容作出合理预测;答题时，切忌跟着感觉走，最好按照题目序号来做，不会的或存在疑问的，要做好标记，要善于发现，找到题目的题眼所在，规范答题，书写工整;答题完毕时，要认真检查，查漏补缺，纠正错误。
考生注意：1. 答题前，请务必将自己的姓名、准考证号用黑色字迹的签字笔或钢笔分别填写在试题卷和答题纸规定的位置上。
2. 答题时，请按照答题纸上“注意事项”的要求，在答题纸相应的位置上规范作答，在本试题卷上的作答一律无效。
第一部分听力（共两节，满分30分）做题时，先将答案标在试卷上。
录音内容结束后，你将有两分钟的时间将试卷上的答案转涂到答题纸上。
第一节（共5小题；每小题1.5分，满分7.5分）听下面5段对话。
每段对话后有一个小题，从题中所给的A、B、C三个选项中选出最佳选项。
听完每段对话后，你都有10秒钟的时间来回答有关小题和阅读下一小题。
每段对话仅读一遍。
例：How much is the shirt?A. £19.15.B. £9.18.C. £9.15.答案是C。
1．What is Kate doing?A．Boarding a flight. B．Arranging a trip. C．Seeing a friend off.2．What are the speakers talking about?A．A pop star. B．An old song. C．A radio program.3．What will the speakers do today?A．Go to an art show. B．Meet the man's aunt. C．Eat out with Mark.4．What does the man want to do?A．Cancel an order. B．Ask for a receipt. C．Reschedule a delivery.5．When will the next train to Bedford leave?A．At 9:45. B．At 10:15. C．At 11:00.第二节（共15小题；每小题1.5分，满分22.5分）听下面5段对话或独白。
高中英语科技论文阅读单选题40题
高中英语科技论文阅读单选题40题1. The term "nanotechnology" refers to the manipulation of matter on an extremely small _____.A. scaleB. levelC. degreeD. range答案：A。
本题考查名词词义辨析。
“scale”有“规模、范围、程度”的意思，“on a small scale”表示“小规模地”，在“nanotechnology”（纳米技术）中，“scale”强调物质操作的规模大小；“level”侧重于水平、级别；“degree”指程度、度数；“range”表示范围、幅度。
此处“nanotechnology”涉及的是物质操作的极小规模，所以选A。
2. In the field of artificial intelligence, the concept of "machine learning" involves the ability of computers to ______ patterns in data.A. detectB. discoverC. exposeD. reveal答案：A。
“detect”有“察觉、探测、发现”的意思，强调通过观察或检测来发现；“discover”侧重于首次发现原本存在但未被知晓的事物；“expose”指暴露、揭露；“reveal”意为揭示、展现。
在“machine learning”（（机器学习）中，计算机是通过分析数据来“察觉”模式，A 选项更符合语境。
3. The "quantum physics" phenomenon is characterized by the behavior of particles at the ______ level.A. atomicB. molecularC. subatomicD. microscopic答案：C。
Genetic Algorithms for Machine Learning
Abstraቤተ መጻሕፍቲ ባይዱt
One approach to the design of learning systems is to extract heuristics from existing adaptive systems. Genetic algorithms are heuristic learning models based on principles drawn from natural evolution and selective breeding. Some features that distinguish genetic algorithms from other search methods are: learning systems that use genetic algorithms to learn strategies for sequential decision problems [5]. In our Samuel system [7], the \chromosome" of the genetic algorithm represents a set of condition-action rules for controlling an autonomous vehicle or a robot. The tness of a rule set is measured by evaluating the performance of the resulting control strategy on a simulator. This system has successfully learned highly e ective strategies for several tasks, including evading a predator, tracking a prey, seeking a goal while avoiding obstacles, and defending a goal from threatening agents. As these examples show, we have a high level of interest in learning in multi-agent environments in which the behaviors of the external agents are not easily characterized by the learner. We have found that genetic algorithms provide an ecient way to learn strategies that take advantage of subtle regularities in the behavior of opposing agents. We are now beginning to investigate the more general case in which the behavior of the external agents changes over time. In particular, we are interested in learning competitive strategies against an opponent that is itself a learning agent. This is, of course, the usual situation in natural environments in which multiple species compete for survival. Our initial studies lead us to expect that genetic learning systems can successfully adapt to changing environmental conditions. While the range of applications of genetic algorithms continues to grow more rapidly each year, the study of the theoretical foundations is still in an early stage. Holland's early work [9] showed that a simple form of genetic algorithm implicitly estimates the utility of a vast number of distinct subspaces, and allocates future trials accordingly. Speci cally, let H be a hyperplane in the representation space. For example, if the structures are represented by six binary features, then the hyperplane denoted by H =0#1### consists of all structures in which the rst feature is absent and the third feature is present. Holland showed that the expected number of samples (o spring) allocated to a hyperplane H at time t + 1 is given by: ( + 1) M (H; t) 3
英语作文通过数字永生技术延长生命
英语作文通过数字永生技术延长生命全文共3篇示例，供读者参考篇1Advancements in technology have revolutionized many aspects of our lives, including the field of medicine. In recent years, scientists have made significant progress in the development of a groundbreaking technology known as digital immortality, which aims to extend human life span indefinitely through the preservation of consciousness and memories in a digital format.Digital immortality involves the use of advanced computer algorithms and artificial intelligence to create a virtual replica of a person's mind and consciousness. Through the storage of personal data, memories, and brain scans, individuals can theoretically live on indefinitely in a digital form even after their physical body has ceased to exist.One of the key benefits of digital immortality is the potential to significantly extend human life span and potentially achieve immortality. By transferring one's consciousness and memories into a digital format, individuals can continue to exist in asimulated environment long after their physical body has passed away. This has the potential to revolutionize the concept of death and open up new possibilities for expanding the human lifespan.Furthermore, digital immortality offers the opportunity for individuals to preserve their personal legacies and memories for future generations. By storing a wealth of data and information about one's life experiences, relationships, and accomplishments, individuals can ensure that their memories and stories are passed down to future generations in a format that is accessible and interactive.Additionally, digital immortality has the potential to revolutionize the fields of healthcare and medicine by offering new ways to treat and prevent age-related diseases and disorders. By analyzing vast amounts of data collected from individuals' digital consciousnesses, scientists can gain valuable insights into the aging process and develop innovative therapies and interventions to extend human life span and improve quality of life.Despite the many potential benefits of digital immortality, there are also ethical and philosophical considerations that must be taken into account. Questions about the nature ofconsciousness, the preservation of personal identity, and the implications of living indefinitely in a digital format are some of the key issues that need to be addressed as this technology continues to develop.In conclusion, digital immortality represents a transformative and revolutionary technology that has the potential to redefine the concept of death and revolutionize the way we think about life and consciousness. While there are still many challenges and uncertainties to overcome, the development of digital immortality holds immense promise for extending human life span and preserving personal legacies for future generations.篇2Advancements in technology have always been aimed at improving our quality of life, and one of the most groundbreaking developments in recent years is the concept of digital immortality. Scientists and researchers are exploring the possibility of extending human life by uploading our consciousness onto a digital platform, allowing us to live forever in the virtual realm.The idea of achieving eternal life through technology may seem like something out of a science fiction novel, but it isactually becoming a very real possibility. By digitizing our memories, thoughts, and personalities, we could potentially live on long after our physical bodies have ceased to exist. This concept raises a host of ethical and philosophical questions, but there is no denying the incredible potential that this technology holds.One of the key benefits of digital immortality is the ability to preserve our knowledge and experiences for future generations. Imagine being able to pass down not just physical possessions, but your entire self to your children and grandchildren. Your wisdom, your stories, your essence - all kept alive in the digital world for eternity.Furthermore, digital immortality could revolutionize the way we think about death and grief. Instead of mourning the loss of a loved one, we could take solace in the fact that their consciousness lives on in a digital form. We could interact with them, talk to them, and continue to learn from them long after they have passed away.Of course, there are also potential downsides to this technology. The idea of living forever raises questions about overpopulation, resource allocation, and the potential for digital identities to be hacked or manipulated. And then there is thequestion of whether digital immortality would truly capture the essence of what it means to be human, or whether it would simply be a shallow imitation of life.Despite these concerns, the concept of digital immortality is an incredibly exciting prospect. It has the potential to transform our understanding of life and death, and to fundamentally alter the way we relate to the world around us. While the technology is still in its early stages, the possibilities it presents are endless.In conclusion, the idea of achieving eternal life through digital means is a fascinating concept that holds incredible promise for the future of humanity. As scientists continue to explore the possibilities of digital immortality, we can only begin to imagine the ways in which it will reshape our world. Whether or not we will one day be able to upload our consciousness and live forever remains to be seen, but one thing is for certain - the future of technology is full of endless possibilities.篇3Title: Extending Life through Digital Immortality TechnologyIn recent years, the development of technology has brought numerous breakthroughs in the field of medicine and healthcare. One of the most revolutionary advancements is the concept ofdigital immortality, which aims to use advanced computer algorithms and artificial intelligence to create virtual versions of individuals that can interact and communicate with loved ones even after their physical death.Digital immortality technology works by collecting vast amounts of data about an individual, including their memories, personality traits, and mannerisms, and then using this information to create a digital avatar that accurately replicates the person's thoughts and behavior. These avatars can be programmed to respond to questions and engage in conversations, allowing friends and family members to continue interacting with their loved ones long after they have passed away.The potential benefits of digital immortality are immense. For one, it offers a way for individuals to preserve their memories and experiences for future generations, ensuring that their legacy lives on even after they are gone. It also provides a sense of comfort and closure to those left behind, allowing them to continue their relationships with their deceased loved ones in a meaningful way.Additionally, digital immortality technology has the potential to significantly impact the field of healthcare byextending the human lifespan. By creating digital copies of individuals, doctors and researchers can study the effects of different treatments and interventions on these avatars, allowing them to develop personalized healthcare plans that are tailored to each person's unique genetic makeup and medical history. This could lead to more effective treatments for a wide range of illnesses and diseases, ultimately helping people live longer, healthier lives.In conclusion, digital immortality technology holds great promise for extending the human lifespan and preserving the memories of loved ones. While there are still many ethical and technical challenges to overcome, the potential benefits of this technology are significant and cannot be ignored. As we continue to push the boundaries of science and technology, digital immortality may soon become a reality, forever changing the way we approach life and death.。
Test Case Generation of Component Software Based o
Journal of Communication and Computer 8 (2011) 503-507Test Case Generation of Component Software Based on UML Activity Diagram and Genetic AlgorithmYu Song and Xinhong WangSchool of Control and Computer Engineering, North China Electric Power University, Baoding 071003, ChinaReceived: January 27, 2011 / Accepted: February 26, 2010 / Published: June 30, 2011.Abstract: With people pay more and more attention to component software testing, the generation of test case is as one of the important works of software testing, it becomes a hotspot inevitably. For component software testing, making combination of the testing method based on model and testing method based on Genetic Algorithm, this paper proposes a generating method of test case which based on UML (Unified Modeling Language) activity diagram and Genetic Algorithm, and gives the overall design and implementation steps of the method. Then this paper applies the proposed method to model the adding table business in catering management system, and design test case. The example verifies the feasibility and validity of the method.Key words: Component software, test case, activity diagram, genetic algorithm.1. IntroductionWith the development of component technology and component-based software development technology, the research of component software testing has become a hot topic. Test case is the core of software testing [1], the effective generation of test case can not only to improve the efficiency of testing and developing, but also can improve the quality of software. The efficient generation of test case is a necessary means to simplify testing process and improve efficiency of testing in component software testing.With the widely used of software development based on UML model and Rational Unified Process development process, software testing based on UML model become the main research direction of software testing based on model gradually [2]. The typical testing models of software testing are used include: Markov Chain model [3], Finite-state Machine model [4] and UML model [5]. In the research field of test case generation of component software, there are manyXinhong Wang, graduate student, research fields: software component, architecture technology.Corresponding author: Yu Song, professor, research field: softwareengineering.E-mail:***************.studies using the method based on UML model, for example, combining with UML extension which are state chart and sequence diagram, the method founds generating test case on four elements that can model the characteristics of the interactions between components [6], the generation technology of test case that making combination of the metadata thoughts and UML method [7].Genetic Algorithm [8] is a calculation model which simulates the biological evolution process of Darwin Genetic selection and natural selection. Genetic Algorithm to optimize the first generation of test cases, it can improve the efficiency of testing. Genetic Algorithm usually combination with other algorithms to generate test cases in actual applications, for example, improved immune genetic algorithm supporting test case generation for component-based software [9], the testing model of component software based on Genetic Algorithm and probe model [10].For the characteristics of component software, making combination of the testing based on model and the testing based on Genetic Algorithm; this paperproposes a new method of test case generation which isll Rights Reserved.Test Case Generation of Component Software Based on UML Activity Diagram and Genetic Algorithm 504based on UML activity diagram and GeneticAlgorithm.2. The Method of Test Case GenerationBased on UML Activity Diagram andGenetic Algorithm2.1 The Overall Design of the MethodActivity diagram is a tool to describe the dynamicbehavior of system in UML, UML activity diagram is aflow chart in essence. Combining the test casegeneration based on UML activity diagram and the testcase generation based on Genetic Algorithm, themethod can make full use of the advantage of both, andgenerate the test case effectively.The general steps of the method are as follows:(1) Establishing testing model based on UMLactivity diagramBy analyzing the specifications of component, withUML statute information, we use UML activitydiagram to establish model for the dynamic behaviorbetween components, and do further analysis to get thebasic flow and optional flow, then get test scenarios bycombining the generated flows.(2) Executing the operations of Genetic AlgorithmBy using the Genetic Algorithm to do selecting,crossing and mutating, it can optimize the testscenarios, and then get the optimal test scenario sets. (3) Generating the test caseFinally, for the optimal test scenario sets, it determines the input variables, and assigns values to variables, and then generates test cases.The process of test case generation is as shown in Fig. 1.2.2 Test Scenario Generation Based on UML Activity DiagramThe process of based on UML activity diagram has two steps: establishing the UML activity diagram model and constitute test scenario.(1) By analyzing the specifications of component and UML statute information, it builds the activity Fig. 1 The process of test case generation.diagram by using the Visio tools.During the process of the establishing activity diagram, we need to pay attention to the three basic testing criteria of functional testing based on activity diagram; that are: activity coverage criteria, transfer edge coverage criteria and basic path coverage criteria.(2) Constitute test scenario.After getting the activity diagrams of all the executive process of system, we can identify and determine the basic flow and the optional flow easily by using the activity diagram, and then generate test scenario. The specific steps that test scenario generation by activity diagram are as follows:ll Rights Reserved.Test Case Generation of Component Software Based on UML Activity Diagram and Genetic Algorithm 505(1) Get the basic flow and the optional flow from the activity diagram. In activity diagram, the basic flow is the line-down activity from top to bottom, and the optional flow is the activity that the return of cycle of the skip of basic flow;(2) The basic flow and the optional flow is corresponding to a scene respectively, they are the most fundamental and most simple test scene;(3) Make the combination of optional flows to establish test scenario.2.3 The Design and Operation of Genetic Algorithm2.3.1 The Coding and Decoding of Design of Genetic AlgorithmIn this paper, we use multilevel parameters coding, the coding method of each parameter use dynamic variable-length binary code. The decoding process is an opposite process of coding. The decoding process divides the individual X=X1X2…X n into n equal parts, each part is the code length that its length is m, and then decode respectively, translate the binary number of each part into decimal number.2.3.2 The Parameter Design of Genetic Algorithm The basic parameters of Genetic Algorithm include the size of group, iterations, the probability of hybridization and the probability of mutation, and so on. This paper defines the size of group is thirty. When the design of iteration meets one of the following two conditions, the algorithm will end. First: the numbers of evolution reach fifty generation; and second, the highest fitness values of consecutive three generation population are not changed. This paper calculates the probability of selection for every selected individual, and then rotates the roulette to select individuals. The crossover operation in this paper uses single point crossover operator, the crossover probability is set to 0.7. In this paper, the mutation rate set to 0.005.2.3.3 The Fitness Function Design of Genetic AlgorithmOur optimizational goal of this algorithm is that making the test scenarios which combined by basic flow and optional flow can meet the requirements of testing in greatest degree, so we can construct the fitness function by using the coverage of test scenarios. Assuming that any basic flow and optional flow is a path, the individual’s fitness can defined the ration of the number of path covering by a particular test scene and all paths, s expresses the sum of the basic flow and the optional flow derived by activity diagram, a denotes the number of path covering by a particular test scene, then the fitness of this test scene is as shown in Eq. (1):saf=(1) The efficiency of the intelligent algorithm of test case generation is affected by the fitness function design directly. With the rate of coverage increases, the efficiency of component software testing is higher, and the value of fitness function is higher too, so the linear fitness function design in this paper is:()baffF+=(2) We define Eq. (2) as F(f)denotes fitness function, f denotes the fitness of individual, a and b are modulus that the value is between 0 and 1.2.4 The Generation of Test CaseAfter Genetic operations are over, we get optimized test scenario, then we can generate test case by referring decision table. The specific steps are as follows: finding out variables from each step of the scene, giving different test options for each variable, Combing the different test options to generate test cases, using the real data values to replace each variable options of test cases respectively, then generating the final test cases.3. ExampleThis section applies the proposed method of test case generation, tests the adding table business in catering management system which is developed by using method of component based software development. The development of catering management system isll Rights Reserved.Test Case Generation of Component Software Based on UML Activity Diagram and Genetic Algorithm 506based on Visual Studio2008 and the componentcombination of Management Information Systemplatform. The entry of the whole catering managementsystem is login module, when the users get the interfaceof system, verify firstly, only users who pass thesystem verification are able to use the system,according to the different operating permissions, userscan operate the system differently. This system has twooperating permissions: the common user andadministrator.Applying the method to establish the model ofadding table business firstly, and from the activitydiagram of adding table business we can get the basicflow and the optional flow of adding the tableTable 1 The basic flow and the optional flow of addingthe table informationthe basic flow the description of thebasic flowtheoptionalflowthe description of theoptional flowM1 the validation of user B1 this user doesn’t existM2 the validation ofusers’ authoritiesB2have no the authorityof addingM3 input the name ofadded tableB3the inputted name ofadded table isn’treasonableM4input the name ofadded table for shortB4the inputted name ofadded table for shortisn’t reasonableM5 input the position ofadded tableB5the inputted positionof added table isn’treasonableM6 input the type ofadded tableB6the inputted type ofadded table isn’tlegalM7input the cost ofprivate room that isaddedB7the inputted cost ofprivate room isn’treasonableM8 input the remark of added tableM9 save the informationof added tableinformation by system administrator. Makingcombination of the basic flow and the optional flowwhich are shown in Table1, we get the ten scenes. Theten scenes are also the ten individuals of the Geneticoperation. Executing the operations of GeneticAlgorithm, we get the optimized test scenarios that arescene 6, 7 and scene 10. The next step is generatingtest case from these scenes.4. ConclusionsThis paper proposes a test case generation methodbased on UML activity diagram and Genetic Algorithm,this method not only makes fully use of the informationand ability in test case generation based on UML model,saves the cost of testing, makes the process of test casegeneration is more normative, and more concise, butalso makes fully use of the advantages of GeneticAlgorithm in optimizing, the proposed method makescombination of the both, so it can guarantee the partsthat is operated frequently in the system can get morefully testing, and ensure the resources of testing candistribute reasonably between each path, and realize theutilization of resources efficiently and reasonably. Theexample shows the validity and rationality of thismethod.References[1]L.L. Yu, C.Y. Fu, M. Fangbo, Software testing methodsand choosing cases, Journal of Jiamusi University(NaturalScience Edition) 26 (2008) 240-241.[2]J. Yan, L. Wang, H.W. Chen, Survey of model-basedsoftware testing, Computer Science 31 (2004)184-187.[3]H.Y. Ma, S.G. Zhang, Research and implementation ofsoftware testing method based on Markov Chain model,Journal of Automation and Instrumentation 2 (2009)78-80.[4]Y.K. Zhang, Y.M. Hou, D.W. Gui, Integrate testmethod for component-based software based on theoryof finite automation, Computer Engineering 32 (2006)75-78.[5]X. Xin, Research and implement on generating test casefrom UML model, Computer Knowledge and Technology5 (2009) 6439-6441.[6]X.Q. Shang, Y.K. Zhang, Research of UML-basedll Rights Reserved.Test Case Generation of Component Software Based on UML Activity Diagram and Genetic Algorithm 507generating test case for component integration testing,Computer Engineering 32 (2006) 96-98.[7]L.L. Ma, Y.S. Lu, M.R.Liu, Research of component testcase generating techniques based on metadata and UML,Computer Engineering and Design 27 (2006) 4444-4578.[8]H.M. Li, Overview of genetic algorithms, Software Guide8 (2009) 67-68.[9]Z. Ma, Y.K. Zhang, J.Y. Li, Improved immune geneticalgorithm supporting test case generation for component-based software, Computer Engineering andApplication 35 (2006) 101-106.[10]L.P. Zhao, Research on optimizing for component testcase, Ship Electronic Engineering 28 (2008) 188-191.ll Rights Reserved.。
如何防止基因泄密英语作文
如何防止基因泄密英语作文Title: Preventing Genetic Data Leakage。
In the age of advanced technology and burgeoning genetic research, the protection of genetic data is paramount. Genetic data leakage poses significant risks to individual privacy, healthcare systems, and even national security. Hence, robust measures must be implemented to safeguard genetic information. This essay explores various strategies to prevent genetic data leakage.Firstly, encryption plays a pivotal role in securing genetic data. Employing state-of-the-art encryption algorithms ensures that genetic information remains unreadable to unauthorized parties. Encryption should be applied at multiple levels, including during data transmission, storage, and processing. By encrypting genetic data, even if unauthorized access occurs, the information remains incomprehensible and thus maintains confidentiality.Secondly, strict access controls are imperative to prevent unauthorized individuals from gaining entry to genetic databases. Access should be granted only to authorized personnel with a legitimate need for the data, such as researchers and healthcare professionals. Implementing multi-factor authentication, role-based access controls, and regular access audits enhances the security of genetic databases, reducing the risk of unauthorized data breaches.Thirdly, anonymization and de-identification techniques can be utilized to protect the privacy of individuals represented in genetic datasets. By removing personally identifiable information from genetic records, such as names and social security numbers, individuals' identities are safeguarded. However, it's crucial to recognize that complete anonymization is challenging due to the unique nature of genetic data. Therefore, implementing robust de-identification methods coupled with strict access controls is essential for preserving privacy while enabling legitimate research and healthcare initiatives.Furthermore, stringent regulatory frameworks are indispensable in governing the collection, storage, and use of genetic data. Governments must enact comprehensive legislation that mandates data protection standards, establishes penalties for data breaches, and ensures transparency regarding the handling of genetic information. Regulatory bodies should conduct regular audits to assess compliance with these standards and enforce penalties for non-compliance. Additionally, international collaboration is vital to harmonize regulations across jurisdictions and address challenges posed by cross-border data flows.Moreover, fostering a culture of awareness and education regarding genetic privacy is crucial. Individuals must understand the implications of genetic data sharing and the importance of safeguarding their privacy. Educational campaigns, workshops, and informational materials can empower individuals to make informed decisions about consenting to genetic testing and sharing their data. Furthermore, raising awareness among healthcare providers and researchers regarding ethical guidelines andbest practices in genetic data handling promotes responsible data stewardship.In addition to technological and regulatory measures, ethical considerations should guide the responsible use of genetic data. Researchers and healthcare professionals must prioritize the privacy and autonomy of individuals whose data they handle. Ethical review boards should oversee research projects involving genetic data to ensure adherence to ethical principles and protection of participants' rights. Transparency and informed consent should be upheld throughout the data lifecycle, from collection to dissemination, to uphold ethical standards and foster trust in genetic research and healthcare practices.In conclusion, preventing genetic data leakage requires a multifaceted approach encompassing technological, regulatory, educational, and ethical measures. By employing robust encryption, access controls, anonymization techniques, and regulatory frameworks, coupled with fostering awareness and upholding ethical principles, wecan mitigate the risks associated with genetic data leakage and ensure the privacy and security of individuals' genetic information. Through concerted efforts and collaboration, we can harness the transformative potential of genetic research while safeguarding individual rights and privacy.。
在数字时代培养有效的信息辨别技能英语作文
在数字时代培养有效的信息辨别技能英语作文In the digital age, we are constantly bombarded with vast amounts of information from various sources. With the rise of social media, fake news, and unreliable sources, it has become crucial for individuals to develop effective information discernment skills. In this article, we will explore the importance of cultivating these skills and provide tips on how to navigate the information overload in the digital world.One of the key reasons why it is essential to develop effective information discernment skills is the prevalence of fake news and misinformation online. With the ease of sharing information on social media platforms, it has become increasingly difficult to differentiate between credible sources and unreliable sources. This can lead to confusion, misinformation, and even harm if individuals act on false information.Furthermore, in today's fast-paced society, we are constantly inundated with information from multiple sources such as news websites, social media, and online forums. It is easy to feel overwhelmed and struggle to filter out the noise to focus on what is relevant and accurate. Developing information discernment skills can help individuals sift through theabundance of information and identify what is trustworthy and valuable.So, how can we cultivate effective information discernment skills in the digital age? Here are some tips to help you navigate the information overload and separate fact from fiction:1. Verify the source: Before believing or sharing information, always check the source to ensure it is credible and reliable. Look for reputable news outlets, official websites, or experts in the field.2. Cross-check information: Don't just rely on one source for your information. Cross-check facts and details from multiple sources to ensure accuracy and legitimacy.3. Question everything: Be critical of the information you come across and ask questions such as: Who is the author? What is their motive? Is the information biased or objective?4. Fact-check: Use fact-checking websites like Snopes or PolitiFact to verify claims and debunk myths. Don't spread information without verifying its accuracy.5. Stay informed: Keep yourself updated on current events, trends, and developments in your areas of interest. The moreinformed you are, the better equipped you will be to discern information.6. Develop critical thinking skills: Practice analyzing information, evaluating arguments, and making informed decisions based on evidence and logic.By honing these skills and adopting a critical mindset, you can navigate the digital landscape with confidence and discernment. Remember, in the era of fake news and information overload, it is more important than ever to cultivate effective information discernment skills.。
高三科学伦理英语阅读理解20题
高三科学伦理英语阅读理解20题1<背景文章>Gene editing technology has emerged as a revolutionary tool in the field of biotechnology. It allows scientists to make precise changes to the DNA of living organisms. The most well-known gene editing technique is CRISPR-Cas9. This powerful tool has the potential to cure genetic diseases, improve agricultural crops, and even create new forms of life.However, the use of gene editing technology also raises serious ethical concerns. One of the main issues is the possibility of creating "designer babies." This refers to the use of gene editing to enhance certain traits in unborn children, such as intelligence, physical appearance, or athletic ability. Critics argue that this could lead to a new form of eugenics and create a society divided between genetically enhanced and unenhanced individuals.Another ethical concern is the potential for unintended consequences. Gene editing is a complex process, and there is always a risk that unintended changes may occur in the genome. These changes could have harmful effects on the health of the organism or the environment.Despite these concerns, many scientists believe that gene editing technology has great potential for good. For example, it could be used todevelop new treatments for diseases that are currently incurable. It could also be used to improve food security by creating crops that are more resistant to pests and diseases.In the future, gene editing technology is likely to become even more advanced. As our understanding of genetics continues to grow, we may be able to use gene editing to address a wide range of problems. However, it is important that we approach this technology with caution and ensure that it is used in an ethical and responsible manner.1. What is the most well-known gene editing technique?A. PCRB. CRISPR-Cas9C. Gel electrophoresisD. DNA sequencing答案：B。
【名师整理】2020年高考英语精选考点专项突破18 阅读理解(科普类)
专题18 阅读理解（科普类）1.C【2019·全国I】As data and identity theft becomes more and more common, the market is growing for biometric(生物测量)technologies—like fingerprint scans—to keep others out of private e-spaces. At present, these technologies are still expensive, though.Researchers from Georgia Tech say that they have come up with a low-cost device(装置)that gets around this problem: a smart keyboard. This smart keyboard precisely measures the cadence(节奏)with which one types and the pressure fingers apply to each key. The keyboard could offer a strong layer of security by analyzing things like the force of a user's typing and the time between key presses. These patterns are unique to each person. Thus, the keyboard can determine people's identities, and by extension, whether they should be given access to the computer it's connected to—regardless of whether someone gets the password right.It also doesn't require a new type of technology that people aren't already familiar with. Everybody uses a keyboard and everybody types differently.In a study describing the technology, the researchers had 100 volunteers type the word “touch”four times using the smart keyboard. Data collected from the device could be used to recognize different participants based on how they typed, with very low error rates. The researchers say that the keyboard should be pretty straightforward to commercialize and is mostly made of inexpensive, plastic-like parts. The team hopes to make it to market in the near future.28. Why do the researchers develop the smart keyboard?A. To reduce pressure on keys.B. To improve accuracy in typingC. To replace the password system.D. To cut the cost of e-space protection.29. What makes the invention of the smart keyboard possible？A. Computers are much easier to operate.B. Fingerprint scanning techniques develop fast.C. Typing patterns vary from person to person.D. Data security measures are guaranteed.30. What do the researchers expect of the smart keyboard？all 1o soisgitieoco oll.A. It'll be environment-friendly.B. It'll reach consumers soon.C. It'll be made of plasticsD. It'll help speed up typing.31. Where is this text most likely from?A. A diary.B. A guidebookC. A novel.D. A magazine.【答案】28. D 29. C 30. B 31. D【解析】本文是一篇说明文。
5g技术原理与自动驾驶的关系英语作文
5g技术原理与自动驾驶的关系英语作文5G and Autonomous Vehicles: A Symphony of Speed and Intelligence The world is on the cusp of a transportation revolution, driven by the convergence of two transformative technologies: 5G and autonomous vehicles. This is not merely atale of faster internet speeds and self-driving cars; it's a narrative of howthese technologies intertwine, creating a symphony of speed and intelligence that promises to reshape our cities, redefine mobility, and leave an indelible mark on our daily lives. 5G, the fifth generation of cellular network technology, offersa quantum leap in data speed, capacity, and latency. Imagine downloading a high-definition movie in seconds, or experiencing virtual reality without a hint of lag. This blistering speed is not just about convenience; it's the foundation uponwhich autonomous vehicles can truly flourish. Self-driving cars are essentially data centers on wheels, constantly gathering information from their surroundings through a myriad of sensors. They then process this information in real-time, making split-second decisions to navigate safely and efficiently. 5G's low latency ensures that this data exchange happens instantaneously, enabling vehicles toreact to their environment with the precision and speed required for safe autonomous operation. Beyond speed, 5G's increased capacity is crucial for the future of autonomous vehicles. As more and more self-driving cars hit the road,the demand for data transmission will skyrocket. Imagine a city teeming with autonomous vehicles, each communicating with each other and the surrounding infrastructure, sharing information about traffic conditions, road hazards, and optimal routes. This constant exchange of data, facilitated by 5G's high capacity, will create a dynamic, interconnected transportation ecosystem, improving traffic flow, reducing congestion, and minimizing accidents. The impact of 5G on autonomous vehicles extends beyond mere technical capabilities. It has thepotential to redefine the very concept of mobility. Imagine a world where cars are no longer just a means of transportation, but mobile offices, entertainment hubs, or even relaxation pods. With 5G's high speeds and low latency, passengers in autonomous vehicles can work, stream movies, or even immerse themselves in virtual reality experiences while their car seamlessly navigates the roads. This transformation of the in-car experience will fundamentally change how we view andutilize our time spent on the road. However, the journey towards a 5G-powered autonomous future is not without its challenges. Concerns regarding data security, privacy, and ethical considerations need to be addressed. Building the necessary infrastructure for widespread 5G coverage requires significant investment and collaboration between governments, telecom companies, and automotive manufacturers. Furthermore, navigating the complex regulatory landscape surrounding autonomous vehicles is crucial for ensuring their safe and responsible integration into our society. Despite these challenges, the potential of 5G and autonomous vehiclesis undeniable. It's a future where technology seamlessly blends with our lives, creating a safer, more efficient, and more convenient transportation landscape.The symphony of speed and intelligence has begun, and its melody promises to revolutionize the way we move, connect, and experience the world around us.。
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3. CONTRIBUTION Mobile sensor networks usually face two limitations. First of all, the environment is dynamic. The position of other mobile identities and the geographical features of the environment are usually dynamic. This makes offline planning of deployment by searching through the static map of the environment very inefficient and inaccurate. Second, sensor networks are very sensitive to power consumptions because they are usually designed for applications that run for a long time, e.g. a surveillance system. The embedded systems of the mobile nodes also constrain the available power reservation. To solve these problems, the algorithm this paper presents contributes in the following ways: 1. Provision of an online GA-based deployment algorithm that is interactive with the dynamics of the environment. 2. Taking into account the power consumption known to a node itself as a consideration when deciding on the locomotion strategy to monitor the power consumption during deployment. 3. Randomization at different stages allows the deployment to converge to a static global optimum in coverage. 4. ALGORITHM In our algorithm, the network consists of two domains: a server, and clusters of nodes. The server assigns a base-station in each cluster. The base-stations help to monitor the deployment processes from a global point of view. In the following sections, we will describe five important components of the algorithm in detail. Fig 1 is the overview of the algorithm.
2. RELATED WORK The deployment problem that this paper addresses is the blanket coverage problem described by Gage [2]. In blanket coverage, the objective is to achieve a static arrangement of nodes that maximize the total detection area. Howard et al used potential field techniques and spread the nodes over the environment by driving them with a virtual “force” [2]. The GA approach in this paper achieves a similar repulsive behavior in spreading the nodes and obstacle avoidance. However, this algorithm avoids the local optima problem faced by many other algorithms [6], [7] and [8].
Abstract. This paper describes a genetic-algorithm (GA) based deployment algorithm of mobile sensor network. The algorithm is designed for real-time online deployment for maximum coverage of the environment. The paper presents the details on the algorithm and the implementation, including the major components in our design: recombination, mutation, and the fitness function. The algorithm considers power metrics of the nodes for real-time planning of the next movement. The algorithm was implemented with Java Genetics Algorithm Package [4] and simulated with Network Simulator 2 [5] for performance evaluation. The simulation showed that the algorithm helped the network to avoid local maxima in coverage.
A Genetic-Algorithm Based Mobile Sensor Network Deployment Algorithm EE382Ctems Final Report Yulai Suen Department of Electrical and Computer Engineering The University of Texas at Austin
1. INTRODUCTION A mobile sensor network is composed of a collection of nodes that has sensing, computation, communication, and locomotion capabilities [1]. This paper aims to describe an algorithm for mobile sensor network deployment in an unknown environment by a real-time genetic algorithm (GA). The algorithm allows the network to achieve maximum coverage with minimum energy consumption. It is applicable in both a dynamic environment and a dynamic network topology.
Genetic algorithm was first proposed by Goldberg et al in 1989 [3]. It has wide applications in model checking and satisfiability (SAT) problems. The advantage of GA is that the process is completely automatic and avoids local maxima. GA consists of three important components: recombination, mutation, and fitness evaluation. In particular, the fitness function in this algorithm considers the nodes’ power metrics, which is the fundamental limitation on both embedded systems and sensor networks. The algorithm is based on the assumption that each node is equipped with a sensor, such as a scanning laser range-finder and omni-camera, which provides the node with the relative distance and bearing of the nearby nodes and obstacles. The following sections of this paper describe these components in details and the adjustments to general GA for real-time applications. At the end, the paper presents the result of simulations using a discrete-event simulator.