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The book presents selected papers that have been accepted at the seventh Conference on Sound and Music Technology (CSMT) in December 2019, held in Harbin, Hei Long Jiang, China. CSMT is a domestic conference focusing on audio processing and understanding with bias on music and acoustic signals. The primary aim of the conference is to promote the collaboration between art society and technical society in China. The organisers of CSMT hope the conference can serve as a platform for interdisciplinary research. In this proceeding, the paper included covers a wide range topic from speech, signal processing and music understanding, which demonstrates the target of CSMT merging arts and science research together.
These proceedings consist of plenary rapporteur talks covering topics of major interest to the high energy physics community and parallel sessions papers which describe recent research results and future plans.
The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIII International Conference on Neuroinformatics, held on October 18-22, 2021, Moscow, Russia.
This book covers the introduction, theory, development and applications of type-2 fuzzy logic systems, which represent the current state-of-the-art in various domains such as control applications, power plants, health care, image processing, mathematical applications etc. The book is also rich in discussing different applications in order to give the researchers a flavor of how type-2 fuzzy logic is designed for different types of problems. Type-2 fuzzy logic systems are now used extensively in engineering applications for many purposes. In simple language, this book covers the practical use of type-2 fuzzy logic and its optimization through different training methods. Furthermore, this book maintains the relationship between mathematics and practical implementations in the real world. This book chapter also contains the proper comparisons with available literature work. It shows that the presented enhanced techniques have better results. This book would serve as a handy reference guide for a variety of readers, primarily targeting research scholars, undergraduate and postgraduate researchers and practicing engineers working in Type-2 fuzzy logic systems and their applications.
Routledge Encyclopedia of Translation Technology, second edition, provides a state-of-the-art survey of the field of computer-assisted translation. It is the first definitive reference to provide a comprehensive overview of the general, regional, and topical aspects of this increasingly significant area of study. The Encyclopedia is divided into three parts: Part 1 presents general issues in translation technology, such as its history and development, translator training, and various aspects of machine translation, including a valuable case study of its teaching at a major university; Part 2 discusses national and regional developments in translation technology, offering contributions covering the crucial territories of China, Canada, France, Hong Kong, Japan, South Africa, Taiwan, the Netherlands and Belgium, the United Kingdom, and the United States; Part 3 evaluates specific matters in translation technology, with entries focused on subjects such as alignment, concordancing, localization, online translation, and translation memory. The new edition has five additional chapters, with many chapters updated and revised, drawing on the expertise of over 50 contributors from around the world and an international panel of consultant editors to provide a selection of chapters on the most pertinent topics in the discipline. All the chapters are self-contained, extensively cross-referenced, and include useful and up-to-date references and information for further reading. It will be an invaluable reference work for anyone with a professional or academic interest in the subject.
In this book, the major ideas behind Organic Computing are delineated, together with a sparse sample of computational projects undertaken in this new field. Biological metaphors include evolution, neural networks, gene-regulatory networks, networks of brain modules, hormone system, insect swarms, and ant colonies. Applications are as diverse as system design, optimization, artificial growth, task allocation, clustering, routing, face recognition, and sign language understanding.
This book presents a compilation of selected papers from the Fourth International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, held in August 2019 in Guiyang, China. The purpose of the symposium was to discuss inspection, testing, certification and research concerning the software and hardware of instrument and control (I&C) systems used at nuclear power plants (NPP), such as sensors, actuators and control systems. The event provides a venue for exchange among experts, scholars and nuclear power practitioners, as well as a platform for the combination of teaching and research at universities and enterprises to promote the safe development of nuclear power plants. Readers will find a wealth of valuable insights into achieving safer and more efficient instrumentation and control systems.
Evolving technologies in mass production have led to the development of advanced techniques in the field of manufacturing. These technologies can quickly and effectively respond to various market changes, necessitating processes that focus on small batches of multiple products rather than large, single-product lines. Formal Methods in Manufacturing Systems: Recent Advances explores this shifting paradigm through an investigation of contemporary manufacturing techniques and formal methodologies that strive to solve a variety of issues arising from a market environment that increasingly favors flexible systems over traditional ones. This book will be of particular use to industrial engineers and students of the field who require a detailed understanding of current trends and developments in manufacturing tools. This book is part of the Advances in Civil and Industrial Engineering series collection.
The problem of controlling sensory perception for use in discrete event feedback control systems is addressed in this thesis. The sensory perception controller (SPC) is formulated as a sequential Markov decision problem. The SPC has two main objectives; 1) to collect perceptual information to identify discrete events with high levels of confidence and 2) to keep the sensing costs low. Several event recognition techniques are available where each of the event recognisers produces confidence levels of recognised events. For a discrete event control system running in normal operation, the confidence levels are typically large and only a few event recognisers are needed. Then, as the event recognition becomes harder, the confidence levels will decrease and additional event recognisers are utilised by the SPC. The final product is an intelligent architecture with the ability to actively control the use of sensory input and perception to achieve high performance discrete event recognition. The discrete event control framework is chosen for several reasons. First, the theory of discrete event systems is applicable to a wide range of systems. In particular, manufacturing, robotics, communication networks, transportation systems and logistic systems all fall within the class of discrete event systems. Second, the dynamics of the sensing signals used by the event recognisers are often strong and contain a large amount of information at the occurrence of discrete events. Third, because of the discrete nature of events, feedback information is not required continuously. Hence, valuable processing time is available between events. Fourth, the discrete events are a natural common representational format for the sensors. A common sensor format aids the decision process when dealing with different sensor types. Fifth, the sensing aspect of discrete event systems has often been neglected in the literature. In this thesis we present a unique approach to on-line discrete event identification. The thesis contains both theoretical results and demonstrated real-world applications. The main theoretical contributions of the thesis are 1) the development of a sensory perception controller for the dynamic real-time selection of event recognisers. The proposed solution solves the Markov decision process using stochastic dynamic programming (SDP). SDP guarantees cost-efficiency of the real-time SPC by solving a sequential constrained optimisation problem. 2) A sensitivity analysis method for the sensory perception controller has been developed by exploring the relationship between Markov decision theory and linear programming. The sensitivity analysis aids in the robust tuning of the SPC by finding low sensitivity areas for the controller parameters. Two real-world applications are presented. First, several event recognition techniques have been developed for a robotic assembly task. Robotic assembly fits particularly well in the discrete event framework, where discrete events correspond to changes in contact states between the workpiece and the environment. Force measurements in particular contain a significant amount of information when the contact state changes. Second, the sensory perception control theory and the sensitivity analysis have been demonstrated for a mobile navigation problem. The cost-efficient use of sensory perception reduces the need for mobile robots to carry heavy computational resources.