The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.
This Brief identifies various aspects of energy challenges faced by the Chinese central/local governments, and also provides an opportunity to study how best to achieve green growth and a low-carbon transition in a developing country like China. The progress of China’s carbon mitigation policies also has significant impacts on the on-going international climate change negotiations. Therefore, both policy- makers and decision-makers in China and other countries can benefit from studying the challenges and opportunities in China’s energy development.
This book constitutes the proceedings of the 21st International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2022, held in Barranquilla, Colombia, in July 2022. The 28 papers presented together with 3 keynotes were carefully reviewed and selected from 68 submissions. The main topics covered by the chapters in this book are biometrics, security systems, multimedia, classification and clustering, and industrial management as well as interesting papers on computer information systems as applied to wireless networks, computer graphics, and intelligent systems.
In recent years there has been a tremendous upsurge of interest in manufac turing systems design and analysis. Large industrial companies have realized that their manufacturing facilities can be a source of tremendous opportunity if managed well or a huge corporate liability if managed poorly. In particular industrial managers have realized the potential of well designed and installed production planning and control systems. Manufacturing, in an environment of short product life cycles and increasing product diversity, looks to tech niques such as manufacturing resource planning, Just In Time (lIT) and total quality control among others to meet the challenge. Customers are demanding high quality products and very fast turn around on orders. Manufacturing personnel are aware of the lead time from receipt of order to delivery of completed orders at the customer's premises. It is clear that this production lead time is, for the majority of manufacturing firms, greatly in excess of the actual processing or manufacturing time. There are many reasons for this, among them poor coordination between the sales and manufacturing function. Some are within the control of the manufacturing function. Others are not.
The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.
This is a handbook for policy makers and environmental managers in water authorities and engineering companies engaged in water quality programmes, especially in developing countries. It is also suitable for use as a textbook or as training material for water quality management courses. It is a companion volume to Water Quality Assessment and Water
System Test and Diagnosis is the first book on test and diagnosis at the system level, defined as any aggregation of related elements that together form an entity of sufficient complexity for which it is impractical to treat all of the elements at the lowest level of detail. The ideas presented emphasize that it is possible to diagnose complex systems efficiently. Since the notion of system is hierarchical, these ideas are applicable to all levels. The philosophy is presented in the context of a model-based approach, using the information flow model, that focuses on the information provided by the tests rather than the functions embedded in the system. Detailed algorithms are offered for evaluating system testability, performing efficient diagnosis, verifying and validating the models, and constructing an architecture for system maintenance. Several advanced algorithms, not commonly available in existing diagnosis tools, are discussed, including reasoning with inexact or uncertain test data, breaking large problems into manageable smaller problems, diagnosing systems with time sensitive information and time dependent tests and learning from experience. The book is divided into three parts. The first part provides motivation for careful development of the subject and the second part provides the tools necessary for analyzing system testability and computing diagnostic strategies. The third part presents advanced topics in diagnosis. Several case studies are provided, including a single detailed case study. Smaller case studies describe experiences from actual applications of the methods discussed. The detailed case study walks the reader through a complete analysis of a system to illustrate the concepts and describe the analyses that are possible. All case studies are based upon real systems that have been modeled for the purposes of diagnosis. System Test and Diagnosis is the culmination of nearly twelve years of research into diagnosis modeling and its applications. It is designed as a primary reference for engineers and practitioners interested in system test and diagnosis.
