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.
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.
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
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.
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.
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.
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.
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.
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.
%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.
