Plug in Electric Vehicles (PEVs) use energy storages usually in the form of battery banks that are designed to be recharged using utility grid power. One category of PEVs are Electric Vehicles (EVs) without an Internal-Combustion (IC) engine where the energy stored in the battery bank is the only source of power to drive the vehicle. These are also referred to as Battery Electric Vehicles (BEVs). The second category of PEVs, which is more commercialized than the EVs, is the Plug in Hybrid Electric Vehicles (PHEVs) where the role of energy storage is to supplement the power produced by the IC engine.
標(biāo)簽： Electric Vehicles Smart Grids in
上傳時(shí)間： 2020-06-07
上傳用戶：shancjb
There have been many developments in the field of power electronics since the publication of the second edition, almost five years ago. Devices have become bigger and better - bigger silicon die, and current and voltage ratings. However, semiconductor devices have also become smaller and better, integrated circuit devices, that is. And the marriage of low power integrated circuit tecnology and high power semiconductors has resulted in benefit to both fields.
標(biāo)簽： Electronics Handbook Edition Power
上傳時(shí)間： 2020-06-07
上傳用戶：shancjb
In order to enhance the efficiency and reliability of the power grid, diversify energy resources, improve power security, and reduce greenhouse gas emission, many countries have been putting great efforts in designing and constructing their smart grid(SG) infrastructures.Smart gridcommunicationsnetwork(SGCN) is oneof the key enabling technologies of the SG. However, a successful implementation of an efficient and cost-effective SGCN is a challenging task
上傳時(shí)間： 2020-06-07
上傳用戶：shancjb
It was the publisher’s idea that I write RFID in the Supply Chain: A Guide to Selection and Implementation . Not only am I editor of Enterprise Inte- gration System , Second Edition Handbook and author of The Complete Book of Middleware , I also had some innovative business process and project management ideas on improving the effectiveness of integrating enterprise systems with information on product traceability, the scope of which has been widened by the RFID technology mandates.
標(biāo)簽： Selection Supply Chain Guide RFID the in to
上傳時(shí)間： 2020-06-08
上傳用戶：shancjb
The purpose of this book is to help anyone involved in small-scale geophys- ical surveys. It is not a textbook in the traditional sense, in that it is designed for use in the field and concerns itself with practical matters – with the- ory taking second place. Where theory determines field practice, it is stated, not developed or justified. For example, no attempt is made to explain why four-electrode resistivity works where two-electrode surveys do not.
標(biāo)簽： Field_Geophysics
上傳時(shí)間： 2020-06-09
上傳用戶：shancjb
This book is an outgrowth of a course developed at Stanford University over the past five years. It is suitable as a self-contained textbook for second-level undergraduates or for first-level graduate students in almost every field that employs quantitative methods. As prerequisites, it is assumed that the student may have had a first course in differential equations and a first course in linear algebra or matrix analysis. These two subjects, however, are reviewed in Chapters 2 and 3, insofar as they are required for later developments.
標(biāo)簽： Introduction_to_Dynamic_Systems
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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.
標(biāo)簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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.
標(biāo)簽： Embedded_Deep_Learning Algorithms
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Artificial Intelligence (AI) has undoubtedly been one of the most important buz- zwords over the past years. The goal in AI is to design algorithms that transform com- puters into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness shown by superhuman; it rather often involves very basic problems that humans solve very frequently in their day-to-day life. This can be as simple as recognizing faces in an image, driving a car, playing a board game, or reading (and understanding) an article in a newspaper. The intelligent behaviour ex- hibited by humans when “reading” is one of the main goals for a subfield of AI called Natural Language Processing (NLP). Natural language 1 is one of the most complex tools used by humans for a wide range of reasons, for instance to communicate with others, to express thoughts, feelings and ideas, to ask questions, or to give instruc- tions. Therefore, it is crucial for computers to possess the ability to use the same tool in order to effectively interact with humans.
標(biāo)簽： Embeddings Processing Language Natural in
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
%this is an example demonstrating the Radial Basis Function %if you select a RBF that supports it (Gausian, or 1st or 3rd order %polyharmonic spline), this also calculates a line integral between two %points.
標(biāo)簽： RBF 神經(jīng)網(wǎng)絡(luò)
上傳時(shí)間： 2021-07-02
上傳用戶：19800358905
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