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 as Battery Electric Vehicles (BEVs). The second category of PEVs, which is more commercialized than the EVs, is the Plug in
标签： Electric Vehicles Grids Smart Plug In in
上传时间： 2020-06-07
上传用户：shancjb
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 as Battery Electric Vehicles (BEVs). The second category of PEVs, which is more commercialized than the EVs, is Plug in Hybrid Electric Vehicles (PHEVs) where the role of the energy storage is to supplement the power produced by the IC engine.
上传时间： 2020-06-07
上传用户：shancjb
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.
标签： Electric Vehicles Smart Grids in
上传时间： 2020-06-07
上传用户：shancjb
Introduction to Radio Frequency Identification (RFID): RFID is a wireless modulation and demodulation technique for automatic identification of objects, tracking goods, smart logistics, and access con- trol. RFID is a contactless, usually short‐distance transmission and reception technique for unique ID data transfer from a tagged object to an interrogator (reader). The generic configuration of an RFID system comprises (i) an ID data‐carrying tag, (ii) a reader, (iii) a middleware, and (iv) an enterprise application.
标签： Chipless_Radio_Frequency_Identifi cation
上传时间： 2020-06-08
上传用户：shancjb
Identification is pervasive nowadays in daily life due to many complicated activities such as bank and library card reading, asset tracking, toll collecting, restricted access to sensitive data and procedures and target identification. This kind of task can be realized by passwords, bio- metric data such as fingerprints, barcode, optical character recognition, smart cards and radar. Radiofrequencyidentification(RFID)isatechniquetoidentifyobjectsbyusingradiosystems. It is a contactless, usually short distance, wireless data transmission and reception technique for identification of objects. An RFID system consists of two components: the tag (also called transponder) and the reader (also called interrogator).
标签： Processing Digital Signal RFID for
上传时间： 2020-06-08
上传用户：shancjb
There exist two essentially different approaches to the study of dynamical systems, based on the following distinction: time-continuous nonlinear differential equations ⇋ time-discrete maps One approach starts from time-continuous differential equations and leads to time-discrete maps, which are obtained from them by a suitable discretization of time. This path is pursued, e.g., in the book by Strogatz [Str94]. 1 The other approach starts from the study of time-discrete maps and then gradually builds up to time-continuous differential equations, see, e.g., [Ott93, All97, Dev89, Has03, Rob95]. After a short motivation in terms of nonlinear differential equations, for the rest of this course we shall follow the latter route to dynamical systems theory. This allows a generally more simple way of introducing the important concepts, which can usually be carried over to a more complex and physically realistic context.
标签： Systems_Rainer Introduction Dynamical Klages to
上传时间： 2020-06-10
上传用户：shancjb
This introductory chapter is devoted to reviewing the fundamental ideas of control from a multivariable point of view. In some cases, the mathematics and operations on systems (modelling, pole placement, etc.), as previously treated in introductory courses and textbooks, convey to the readers an un- realistic image of systems engineering. The simplifying assumptions, simple examples and “perfect” model set-up usually used in these scenarios present the control problem as a pure mathematical problem, sometimes losing the physical meaning of the involved concepts and operations. We try to empha- sise the engineering implication of some of these concepts and, before entering into a detailed treatment of the different topics, a general qualitative overview is provided in this chapter.
标签： MultivariableControlSystems
上传时间： 2020-06-10
上传用户：shancjb
In this book for the optimisation of assembly conveyor lines we are dealing with series part production featured by a medium complexity degree and a medium number of individual components and assembly technique alternatives. Modern production techniques for medium to large series products or mass production usually involve assembly conveyor lines. They still use hand labour more or less automated. The aim is to have monotonous and similar in type operations or such causing fatigue, stress and production traumas, gradually replaced by automated assembly cycles, means and techniques. This usually widely involves industrial robots and handlers. Higher productivity, lower cost and higher quality of assembled products are usually required.
上传时间： 2020-06-10
上传用户：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
标签： interpretable-machine-learning
上传时间： 2020-06-10
上传用户：shancjb
