This book presents the up-to-date research developments and novel methodologies on multi-sensor filtering fusion (MSFF) for a class of complex systems subject to censored data under a constrained network environment. The contents of this book are divided into two parts covering centralized and distributed MSFF design methodologies. The work provides a framework of optimal centralized/distributed filter design and stability and performance analysis for the considered systems along with designed filters. Simulations presented in this book are implemented using MATLAB. Features: Includes concepts, backgrounds and models on censored data, filtering fusion and communication constraints. Reviews case studies to provide clear engineering insights into the developed fusion theories and techniques. Provides theoretic values and engineering insights of the censored data and constrained network. Discusses performance evaluation of the presented multi-sensor fusion algorithms. Explores promising research directions on future multi-sensor fusion. This book is aimed at graduate students and researchers in networked control, sensor networks, and data fusion.
The Zheng family of merchants and militarists emerged from the tumultuous seventeenth century amid a severe economic depression, a harrowing dynastic transition from the ethnic Chinese Ming to the Manchu Qing, and the first wave of European expansion into East Asia. Under four generations of leaders over six decades, the Zheng had come to dominate trade across the China Seas. Their average annual earnings matched, and at times exceeded, those of their fiercest rivals: the Dutch East India Company. Although nominally loyal to the Ming in its doomed struggle against the Manchus, the Zheng eventually forged an autonomous territorial state based on Taiwan with the potential to encompass the family's entire economic sphere of influence. Through the story of the Zheng, Xing Hang provides a fresh perspective on the economic divergence of early modern China from western Europe, its twenty-first-century resurgence, and the meaning of a Chinese identity outside China.
The earliest book-length treatise in Chinese literary criticism, the Wenxin diaolong is of central importance in the Chinese tradition. The work was compiled in the sixth century, one of the most fertile and original periods in Chinese critical thinking. Its author, Liu Xie, was a Buddhist monk as well as a Confucian scholar, and so represented the main persuasions of China. The Wenxin diaolong first came to be noted in the seventeenth century, when it was studied by scholars and edited by Mei Qingsheng. When the study of literary criticism became an independent discipline early in the twentieth century, it developed into a cynosure that was widely discussed and provided with learned annotations. This volume presents a fresh translation of the Wenxin diaolong that is at once authoritative and elegant. It may well be regarded as a standard reference by students of sinology and comparative literature.
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Drawing on the sun, moon, dragon, phoenix, Nuwa, Yandi, Huangdi and other widely circulated cultural elements as examples, this book addresses the development and evolution of the most representative Chinese creation myths regarding nature, totems, ancestors and saints. The book not only interprets key creation myths, but also elaborates on the connection between the myths and some of the core values and concepts in Chinese civilization. For example, the long and jade culture is rooted in the Yellow Emperor’s revered jade weapon. Further, the book reveals the kernels of truth in the myths by presenting new research findings and research methods.
This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
This book shows the various porous structures and supramolecular architectures that result from the cucurbituril-based coordination, hydrogen bonding, ion-dipole interactions, π∙∙∙π stacking and C–H∙∙∙π processes. It includes two chapters presenting essential examples of these cucurbituril-based structures, depending on the types of non covalent interactions and inducer species. It also includes one chapter dealing with the utilization of cucurbiturils as a molecular container in supramolecular chemistry and demonstrating a wide range of potential applications of supramolecular assemblies with cucurbiturils in catalysis, separation, absorption and polymer materials. The book offers an interesting and valuable guide for readers working in the areas of supramolecular chemistry and materials.
Focusing on the hybrid maritime world of Hong Kong, Pearl River Delta and West River in the last two decades of the late Qing period, this work tells a vivid trading and competition story of previously unknown private Chinese traders and junk masters. This challenges the prevailing view of the domination of China’s maritime trade by modern foreign steamships. Making use of unpublished Kowloon Maritime Customs and British diplomatic records in the late 19th and early 20th century, Henry Sze Hang Choi convincingly shows how these private Chinese traders flexibly adopted to the foreign-dominated maritime customs agencies and treaty port system in defending their Chinese homeland stronghold against the invasion of foreign economic power.
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
The editorials collected in this book date from 1975 to 1984 when the signing of the Joint Declaration between Britain and China, and Hong Kong lead to intense debates about this incongruous scenario. Dr. Lam's editorials and conjectures provided a focal point for discussing Hong Kong's future. His views on housing, assimilating immigrants, the collusion of politics and business still inform.
Additive Friction Stir Deposition is a comprehensive summary of the state-of-the-art understanding on this emerging solid-state additive manufacturing technology. Sections cover additive friction stir deposition, encompassing advances in processing science, metallurgical science and innovative applications. The book presents a clear description of underlying physical phenomena, shows how the process determines the printing quality, covers resultant microstructure and properties in the as-printed state, highlights its key capabilities and limitations, and explores niche applications in repair, cladding and multi-material 3D printing. Serving as an educational and research guide, this book aims to provide a holistic picture of additive friction stir deposition-based solid-state additive manufacturing as well as a thorough comparison to conventional beam-based metal additive manufacturing, such as powder bed fusion and directed energy deposition. - Provides a clear process description of additive friction stir deposition and highlights key capabilities - Summarizes the current research and application of additive friction stir deposition, including material flow, microstructure evolution, repair and dissimilar material cladding - Discusses future applications and areas of research for this technology
This book presents the up-to-date research developments and novel methodologies on multi-sensor filtering fusion (MSFF) for a class of complex systems subject to censored data under a constrained network environment. The contents of this book are divided into two parts covering centralized and distributed MSFF design methodologies. The work provides a framework of optimal centralized/distributed filter design and stability and performance analysis for the considered systems along with designed filters. Simulations presented in this book are implemented using MATLAB. Features: Includes concepts, backgrounds and models on censored data, filtering fusion and communication constraints. Reviews case studies to provide clear engineering insights into the developed fusion theories and techniques. Provides theoretic values and engineering insights of the censored data and constrained network. Discusses performance evaluation of the presented multi-sensor fusion algorithms. Explores promising research directions on future multi-sensor fusion. This book is aimed at graduate students and researchers in networked control, sensor networks, and data fusion.
The Zheng family of merchants and militarists emerged from the tumultuous seventeenth century amid a severe economic depression, a harrowing dynastic transition from the ethnic Chinese Ming to the Manchu Qing, and the first wave of European expansion into East Asia. Under four generations of leaders over six decades, the Zheng had come to dominate trade across the China Seas. Their average annual earnings matched, and at times exceeded, those of their fiercest rivals: the Dutch East India Company. Although nominally loyal to the Ming in its doomed struggle against the Manchus, the Zheng eventually forged an autonomous territorial state based on Taiwan with the potential to encompass the family's entire economic sphere of influence. Through the story of the Zheng, Xing Hang provides a fresh perspective on the economic divergence of early modern China from western Europe, its twenty-first-century resurgence, and the meaning of a Chinese identity outside China.
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