Robert J. Howlett

Knowledge-based and intelligent information and engineering systems

Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between topdown expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: “[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the models defining design principles. In listing the advantages of its THOT technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, “weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11],

Intelligent Interactive Multimedia Systems and Services

KES International (KES) is a worldwide organisation that provides a professional community and association for researchers, originally in the discipline of Kno- edge Based and Intelligent Engineering Systems, but now extending into other related areas. Through this, KES provides its members with opportunities for publication and beneficial interaction. The focus of KES is research and technology transfer in the area of Intelligent Systems, i.e. computer-based software systems that operate in a manner analogous to the human brain, in order to perform advanced tasks. Recently KES has started to extend its area of interest to encompass the contribution that intelligent systems can make to sustainability and renewable energy, and also the knowledge transfer, innovation and enterprise agenda. Involving several thousand researchers, managers and engineers drawn from universities and companies world-wide, KES is in an excellent position to faci- tate international research co-operation and generate synergy in the area of arti- cial intelligence applied to real-world Smart systems and the underlying related theory. The KES annual conference covers a broad spectrum of intelligent systems t- ics and attracts several hundred delegates from a range of countries round the world. KES also organises symposia on specific technical topics, for example, Agent and Multi Agent Systems, Intelligent Decision Technologies, Intelligent Interactive Multimedia Systems and Services, Sustainability in Energy and Bui- ings and Innovations through Knowledge Transfer. KES is responsible for two peer-reviewed journals, the International Journal of Knowledge based and Intel- gent Engineering Systems, and Intelligent Decision Technologies: an International Journal.

Sustainability in Energy and Buildings

Sustainability in energy and buildings , Sustainability in energy and buildings , کتابخانه دیجیتال جندی شاپور اهواز

Innovation through knowledge transfer

The book contains 20 chapters describing some of the latest research and best practice in the innovation and knowledge transfer area. It brings insight and understanding to the latest developments in the field.

Emerging Paradigms in Machine Learning

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.

Innovation through Knowledge Transfer 2012

Across the world there is growing awareness of the importance of innovation and knowledge transfer. Innovation in the sense of generating new knowledge and making better use of existing knowledge, coupled with knowledge transfer and sharing paradigms, have never been more relevant to the universities, industry, commerce and the third sector. This volume represents the proceedings of the Innovation through Knowledge Transfer 2012 Conference which formed an excellent opportunity to disseminate, share and discuss the impact of innovation, knowledge sharing, enterprise and entrepreneurship. The volume contains papers presented at a Workshop on The Meta Transfer of Knowledge: Challenges in the Transfer of Knowledge in Industry, others from thematic sessions on Next-Practice in University Based Open Innovation, Social Innovation and Related Paradigms, Engagement with Industry and Commerce and Knowledge Exchange. All papers were thoroughly reviewed by referees knowledgeable in practical and theoretical aspects of the subject.

Knowledge Transfer between UK Universities and Business

In this paper, knowledge transfer between universities and business in the UK is examined at a number of different levels. The term ’knowledge transfer’ has different meanings in different contexts and so the meaning of the term from a UK perspective is discussed. As UK knowledge transfer is usually part of the innovation agenda, the meaning of ’innovation’ is also considered. A number of different activities, considered to be part of the third mission agenda, are often thought of as being capable of achieving knowledge transfer. The most common of these are described and the potential of each for actually achieving knowledge transfer is discussed. The UK government flagship knowledge transfer scheme, Knowledge Transfer Partnerships, is widely acknowledged to a very effective knowledge transfer paradigm. The Knowledge Transfer Partnerships methodology is described, and two case studies of projects that have been successfully carried out using this paradigm are presented. These case studies illustrate the point that while knowledge transfer was effectively achieved during the partnerships, innovation was also facilitated as a vital part in the process. The factors encouraging and supporting innovation during a knowledge transfer partnership are discussed. The conclusion is drawn that the knowledge transfer partnerships methodology forms a framework exhibiting a number of features that makes it more likely that innovation will arise, and that it is this combination of knowledge transfer and innovation that makes the scheme so effective and successful.

Advances in Smart, Multimedia and Computer Gaming Technologies

The chapter summarizes the contents of this book highlighting recent advances in smart systems, multimedia, and serious gaming technologies through a fusion of these approaches. Such fusion is a nascent area that potentially can hybridize the features and advantages of the relevant areas, and, as a result, provide users with advanced and enhanced functionality and features, which currently does not exist.

Editorial: Design and application of neural networks and intelligent learning systems

Advances in computing technologies have opened up the way for designing and developing intelligent learning systems that are able to solve complex real-world problems. In this aspect, computational intelligence-based techniques, which include neural computing, evolutionary computing, fuzzy computing, and other data-based computing methods, are useful in undertaking different tasks that are difficult to tackle using conventional approaches. In this special issue, a total of ten papers, based on extended papers from the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2008) as well as from other submissions, are presented. The papers address how neural network-based systems as well as other intelligent learning systems can be applied to solve practical problems in a variety of domains. A summary of each paper is as follows. Neural-network-based controllers have been used in many power electronics circuits. In the first paper, a B-spline network is proposed to function as a controller for power electric systems. The B-spline network is suitable for real-time implementation owing to its linear nature and local weight-updating procedure. The B-spline network controller is designed and analyzed using a frequency domain stability model. The design process of the controller is simple and straightforward, which is an important consideration in industrial applications. Applicability of the controller to power converter in a UPS is demonstrated, and the results show that the proposed B-spline network controller is able to achieve low steady-state error with fast error convergence. Induction motors are commonly used in modern electric drives. They require non-linear control systems as owing to variability of the motor parameters under different conditions. In the second paper, a study of the indirect rotor field oriented control system of an induction motor including deviations in the stator resistance is described. An approach based on an adaptive model reference system is used to identify the rotor time constant, and a neural network model is used to produce the estimated rotor speed. The difference between the actual and the estimated rotor speed is utilized for manual tuning and automatic stator resistance tuning based on the fuzzy logic principles. The results obtained show the effectiveness of the proposed approach. Feature selection is a key success factor for neural network applications. In the third paper, the circle segments method is used to provide visualization of the relationship between the input features and target outputs, and to select important features. Based on the selected features, the multi-layer perceptron network is used for function approximation and pattern classification. The efficacy of the proposed approach is evaluated empirically. A performance comparison with the response surface

Special Issue: Knowledge-based intelligent systems and their applications

Intelligent techniques derived from knowledge-based engineering and related computing paradigms have provided useful concepts and tools to undertake a variety of real-world problems. These systems mimic the analytical and learning capabilities of the human brain. They harness the benefits of knowledge and intelligence to form an integrated framework for problem solving. In this special issue, a total of thirteen articles comprising extended papers from the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2008) as well as from other submissions that highlight a small number of innovative knowledge-based intelligent systems and their applications to solving problems in different domains are presented. A summary of each article is as follows. With the development of advanced travelers information systems, it is important to have a prompt and accurate travel time prediction system for road networks. In the first article, two travel time prediction algorithms using naive Bayesian classification and rulebased classification are proposed. Based on a historical traffic database, the algorithms are able to yield high accuracy in travel time prediction. The algorithms are also useful for road networks with arbitrary travel routes. The results also reveal that naive Bayesian classification produces better mean absolute relative error than that of rule-based classification. For large-scale complex process plants that involve safety critical systems, real-time diagnosis is an important aspect. In the second article, an ontology for