The chief objective of Electric Machinery continues to be to build a strong foundation in the basic principles of electromechanics and electric machinery. Through all of its editions, the emphasis of Electric Machinery has been on both physical insight and analytical techniques. Mastery of the material covered will provide both the basis for understanding many real-world electric-machinery applications as well as the foundation for proceeding on to more advanced courses in electric machinery design and control.
上传时间： 2020-06-10
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This book describes a unifying framework to networked teleoperation systems cutting across multiple research fields including networked control system for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation, cooperative teleoperation, and some teleoperation application examples. Networked control has been deeply studied at the intersection of systems & control and robotics for a long time, and many scholarly books on the topic have been already published. Nevertheless, the approach remains active even in several new research fields, such as bilateral teleoperation, single master and multiple slaves, trilateral teleoperation, and multilateral teleoperation
标签： Teleoperation Intelligent Networked Control
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Despite the development of a now vast body of knowledge known as modern control theory, and despite some spectacular applications of this theory to practical situations, it is quite clear that much of the theory has yet to find application, and many practical control problems have yet to find a theory which will successfully deal with them. No book of course can remedy the situation at this time. But the aim of this book is to construct one of many bridges that are still required for the student and practicing control engineer between the familiar classical control results and those of modern control theory.
上传时间： 2020-06-10
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Despite the development of a now vast body of knowledge known as modern control theory, and despite some spectacular applications of this theory to practical situations, it is quite clear that some of the theory has yet to find application, and many practical control problems have yet to find a theory that will successfully deal with them. No one book, of course, can remedy the situation. The aim of this book is to construct bridges that are still required for the student and practicing control engineer between the familiar classical control results and those of modern control theory.
标签： Quadratic Optimal Control Methods Linear
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Recent years have seen a rapid development of neural network control tech- niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings.
标签： Stable_adaptive_neural_network_co ntrol
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n recent years, there have been many books published on power system optimization. Most of these books do not cover applications of artifi cial intelligence based methods. Moreover, with the recent increase of artifi cial intelligence applications in various fi elds, it is becoming a new trend in solving optimization problems in engineering in general due to its advantages of being simple and effi cient in tackling complex problems. For this reason, the application of artifi cial intelligence in power systems has attracted the interest of many researchers around the world during the last two decades. This book is a result of our effort to provide information on the latest applications of artifi cial intelligence to optimization problems in power systems before and after deregulation.
标签： Intelligence Artificial System Power in
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The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
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Although state of the art in many typical machine learning tasks, deep learning algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained wearables have only been possible through wireless connections with a resourceful cloud. This setup has several drawbacks. First, there are privacy concerns. Cloud computing requires users to share their raw data—images, video, locations, speech—with a remote system. Most users are not willing to do this. Second, the cloud-setup requires users to be connected all the time, which is unfeasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, wireless connections are very inefficient—requiringtoo much energyper transferredbit for real-time data transfer on energy-constrained platforms.
标签： Embedded_Deep_Learning Algorithms
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Computer science as an academic discipline began in the 1960’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970’s, the study of algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on a wealth of applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect, and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks as central aspects of daily life presents both opportunities and challenges for theory.
标签： Foundations Science Data of
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Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上传时间： 2020-06-10
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