Comprehensive resource on design of power electronics converters for three-phase AC applications Design of Three-phase AC Power Electronics Converters contains a systematic discussion of the three-phase AC converter design considering various converter electrical, thermal, and mechanical subsystems and functions. . Focusing on establishing converter components and subsystems models needed for the design, the text demonstrates example designs for these subsystems and for whole three-phase AC converters considering interactions among subsystems. The design methods apply to different applications and topologies. The text consists of four parts. Part I is an introduction, which presents the basics of the three-phase AC converter, its design, and the goal and organization of the book. Part II focuses on characteristics and models important to the converter design for components commonly used in three-phase AC converters. Part III is on the design of subsystems, including passive rectifiers, inverters and active rectifiers, electromagnetic interference (EMI) filters, thermal management system, control and auxiliaries, mechanical system, and application considerations. Part IV is on design optimization, which presents methodology to achieve optimal design results for three-phase AC converters. Specific sample topics covered in Design of Three-phase AC Power Electronics Converters include: Models and characteristics for devices most commonly used in three-phase converters, including conventional Si devices , and emerging SiC and GaN devices. Models and selection of various capacitors; characteristics and design of magnetics using different types of magnetic cores, with a focus on inductors Optimal three-phase AC converter design including design and selection of devices, AC line inductors, DC bus capacitors, EMI filters, heatsinks, and control. The design considers both steady state and transient conditions Load and source impact converter design, such as motors and grid condition impacts. For researchers and graduate students in power electronics, along with practicing engineers working in the area of three-phase AC converters, Design of Three-phase AC Power Electronics Converters serves as an essential resource for the subject and may be used as a textbook or industry reference.

This book discusses efforts to control the low-frequency vibration transmission of typical power equipment and pipeline systems of ships, exploring the use of active and passive hybrid vibration isolation and adjustable dynamic vibration absorption technologies. It also proposes an adaptive feed-forward control strategy and studies a distributed feed-forward control hardware system. In addition, the book presents a three-way dynamic vibration absorption theory used to design a pipeline-system adjustable dynamic vibration absorber, which offers a number of advantages, such as compact structure, easy assembly and disassembly, low power consumption, excellent vibration control effect and wide frequency band adjustable ability, etc. This book is a valuable resource for researchers and engineers in the fields of noise and vibration control, active control systems, active vibration isolation and adaptive dynamic vibration absorption.

This book constitutes the refereed proceedings of the Second CCF Internet Conference of China, ICoC 2013, held in Zhangjiajie, China, in July 2013. The 24 revised full papers presented were carefully reviewed and selected from 63 submissions. The papers address issues such as future Internet architecture, Internet routing, network security, network management, data center networks, green networks, wireless networks, P2P networks, mobile Internet and the Internet of Things.

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient's imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician's prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.

We are pleased to present this set of peer-reviewed papers from the ?rst MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support. The MICCAI conference has been the ?agship conference for the m- ical imaging community re?ecting the state of the art in techniques of segm- tation, registration, and robotic surgery. Yet, the transfer of these techniques to clinical practice is rarely discussed in the MICCAI conference. To address this gap, we proposed to hold this workshop with MICCAI in London in September 2009. The goal of the workshop was to show the application of content-based retrieval in clinical decision support. With advances in electronic patient record systems, a large number of pre-diagnosed patient data sets are now bec- ing available. These data sets are often multimodal consisting of images (x-ray, CT, MRI), videos and other time series, and textual data (free text reports and structuredclinicaldata). Analyzing thesemultimodalsourcesfordisease-speci?c information across patients can reveal important similarities between patients and hence their underlying diseases and potential treatments. Researchers are now beginning to use techniques of content-based retrieval to search for disea- speci?c information in modalities to ?nd supporting evidence for a disease or to automatically learn associations of symptoms and diseases. Benchmarking frameworks such as ImageCLEF (Image retrieval track in the Cross-Language Evaluation Forum) have expanded over the past ?ve years to include large m- ical image collections for testing various algorithms for medical image retrieval and classi?cation.

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

At the heart of modern power electronics converters are power semiconductor switching devices. The emergence of wide bandgap (WBG) semiconductor devices, including silicon carbide and gallium nitride, promises power electronics converters with higher efficiency, smaller size, lighter weight, and lower cost than converters using the established silicon-based devices. However, WBG devices pose new challenges for converter design and require more careful characterization, in particular due to their fast switching speed and more stringent need for protection. Characterization of Wide Bandgap Power Semiconductor Devices presents comprehensive methods with examples for the characterization of this important class of power devices. After an introduction, the book covers pulsed static characterization; junction capacitance characterization; fundamentals of dynamic characterization; gate drive for dynamic characterization; layout design and parasitic management; protection design for double pulse test; measurement and data processing for dynamic characterization; cross-talk consideration; impact of three-phase system; and topology considerations.