Dr. Robert J. Howlett
Brighton Univeristy, United Kingdom

Title of talk

Smart Sustainability: The link between intelligent systems and sustainability

Abstract

 The need to reduce the rate at which the earth's resources are being consumed as a result of human activities, and alleviate the quantity of greenhouse gases that are being added to the atmosphere, constitutes an urgent and well-recognised problem for humankind.  There is a need to satisfy current needs without jeopardising the existence of future inhabitants of the planet. The research areas of information technology and intelligent systems are not often regarded as obvious contributors to the solution to this problem.  However, computer-based systems can provide accurate measurement, control, modelling and optimisation functions which can be applied as easily to sustainability-orientated problems as to other applications.  Such systems can make a significant contribution to the global sustainability agenda.

 "Smart Sustainability" is proposed as a new theme for intelligent systems research, focusing on the considerable benefits that can be gained from the application of artificially-intelligent systems techniques to global and local sustainability and alternative energy problems.  There are a number of examples where this approach can prove beneficial.  Renewable energy systems demand accurate and convenient sensing methods.  The ability of neural networks to provide indirect, virtual, sensing techniques can make a valuable contribution where physical energy sensors are not economically viable or practicably available.  Neural networks can also be used to conveniently construct black-box models in situations where complexity makes mathematical, or other types of model, difficult to derive, for example complex energy systems.  Energy systems formed using efficiently-utilised fossil fuels in combination with renewables demand control and scheduling algorithms that may not be amenable to conventional control techniques.  Fuzzy methodologies can offer advantages in this situation. Such systems benefit from modelling and simulation to determine optimum configurations and designs, and neural, fuzzy and genetic algorithms can be of use here. The optimum use of the earth's resources needs careful monitoring and full cradle to grave lifecycle assessment of materials and products is necessary to achieve this.  Rule-based and fuzzy techniques have been found to be advantageous here.

Short bio

Dr. Robert J. Howlett PhD, MPhil, BSc(Hons) is a member of both the UK Institution of Engineering and Technology and British Computer Society, a Chartered Engineer and a Chartered Information Technology Practitioner. He is Head of the Smart Systems Laboratory at the University of Brighton, UK.  He has a number of years experience of applying neural-networks, expert systems, fuzzy paradigms and other intelligent techniques to industrial problem domains e.g. sustainability; control, modelling and simulation of renewable energy systems; monitoring and control of internal combustion engines, particularly small engines for off road and power generation applications; and a range of condition monitoring and fault diagnosis problems.  He leads a research team, funded by grants and industrial contracts.  He has published widely on the subject, and has presented invited talks, keynote addresses etc. 

Dr Howlett is the Executive Chair of the KES (Knowledge-Based and Intelligent Engineering Systems) Organisation, dedicated to supporting and facilitating research, originally in intelligent systems, but more recently also in sustainability and alternative energy.  He is Editor-in-Chief of the International Journal of Knowledge-Based Intelligent Engineering Systems and Honorary Editor of Intelligent Decision Technologies: and International Journal.  Dr Howlett is a nationally known figure in knowledge transfer and the UK Government Knowledge Transfer Partnerships (KTP) programme.  He has supervised over 10 projects transferring university expertise to companies, mainly small to medium enterprises (SMEs). 

 He supports a number of journals through regional editorships and membership of advisory and review boards.  He is a past and current member of the scientific committees of a number of conferences.  He is on the editorial board of various book series.  He has authored over 50 publications in refereed journals and conferences, and edited over 20 books.  He has reviewed research project applications for the EPSRC, and internationally as an Expert Evaluator under Framework and for other EU programmes.

Prof. Adam Janiak
Wroclaw University of Technology, Poland

Title of talk

On Scheduling Problems with an Intelligent Use of the Learning Effect (Authors: Janiak A., Janiak W., Rudek R.)

Abstract

The talk is devoted to scheduling problems with an intelligent use of the learning effect, which is understood as a process of an acquiring experience that increases the efficiency of a processor. A measurable result of this effect is decreasing of job processing times. The existence of this phenomenon in many intelligent systems is undoubted, thus it is perceived as a worthwhile to be taken into consideration.

First, a short survey of the results concerning scheduling problems with the learning effect is provided. In particular, the existing models of the experience are presented along with a discussion on different shapes of the learning curve. We analyze scheduling problems in a single processor environment with the following minimization objectives: the makespan with job release dates, the maximum lateness and the number of late jobs. We prove that these problems become strongly NP-hard with the position dependent learning effect and stepwise learning curves. For this group of problems, we provide fast heuristic algorithms with their worst case analysis. Next, we show that an optimal solution of a two-machine flowshop problem with the makespan minimization does not have to be the `permutation' schedule if the position dependent learning effect is taken into consideration. We prove that the permutation versions of this problem with stepwise or piecewise-linear learning curves are strongly NP-hard. It is shown that permutation and non-permutation problems are NP-hard even if the learning effect, in a form of a step learning curve, characterizes only one machine.

Finally, we focus on a single machine makespan minimization problem with the experience dependent learning models. We prove that this problem is NP-hard even if the experience provided by each job is equal to its normal processing time and the learning curve is S-shaped, piecewise-linear or stepwise. To solve this problem, we prove some eliminating properties that are used to construct an efficient branch and bound algorithm. Polynomially solvable cases of the considered problems are also provided.

Short bio

Adam Janiak received the M.Eng. and Ph.D. degrees from the Wroclaw University of Technology, Wroclaw, Poland, in 1972 and 1977, respectively, and the Dr.Sc. degree from the Warsaw University of Technology, Warsaw, Poland, in 1992.

He received the Professor title in 1999 from President of Poland. He was invited as a Visiting Professor to universities in Australia, Canada, Germany, Hong Kong, Israel, New Zealand, Thailand, China, Spain, USA, Greece, Great Britain, Holland and France. Currently he is a Full Professor in computer science and operations research of industrial engineering areas with the Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, where he is the Head of the Department of Artificial Intelligence and Algorithms Design. He has authored three books and more than 200 papers in edited books, international journals, and conference proceedings (including 57 publications in journals from ISI Master Journal List). His papers were cited over 300 times (according to data from ISI journals database). His research interests include sequencing and scheduling problems with classical and generalized models of operations in computer and manufacturing systems, resource allocation problems, complexity theory, and theory of algorithms (physical design automation of VLSI).

Prof. Janiak is a corresponding member of the Polish Academy of Sciences, a vice-president of the Computer Science Committee of the Polish Academy of Sciences and a head of the panel: “Computer Methods in Science” of the Polish Research Council. He is the expert of the State Accreditation Committee. He has served on the program committees for several international conferences on operations research and is a regular reviewer for a number of prestigious journals and conferences. He is an Associated Editor for International Journal of Applied Mathematics and Computer Science, Decision Making in Manufacturing and Services, Recent Patents on Computer Science and Book Series Computational Intelligence and its Applications.

Prof. Tzung-Pei Hong
National University of Kaohsiung

Title of talk

Some Techniques and Applications in Data Mining

Abstract:

     Data mining plays a central role in knowledge discovery. It involves applying specific algorithms to extract patterns or rules from data sets in a particular representation. Many researchers in database and machine-learning fields are interested in this new research topic since it offers opportunities to discover useful information and important relevant patterns in large databases, thus helping decision-makers analyze data easily and make good decisions regarding the domains in question. Years of effort in data mining have produced a variety of efficient techniques and applications. In this speech, I would like to present some currently popular and interesting techniques developed in our research group. They include the integration of data mining with soft computing, privacy and ontology. The integration of data mining with soft computing can easily handle quantitative transactions and infer linguistic knowledge. Data mining with privacy can hide some transaction data or rules in the mining process for safety. Using ontology can take domain knowledge into consideration and improve the effectiveness of mining results. Besides, I will also introduce some interesting applications such as knowledge warehouse, web mining and health care.

Short Bio

     Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992.

     He was in charge of the whole computerization and library planning for National University of Kaohsiung in Preparation from 1997 to 2000 and served as the first director of the library and computer center in National University of Kaohsiung from 2000 to 2001, as the Dean of Academic Affairs from 2003 to 2006 and as the Vice President from 2007 to 2008. He is currently a professor at the Department of Computer Science and Information Engineering and at the Department of Electrical Engineering.

     He has published more than 160 research papers in international/national journals and more than 300 in conferences. He has also planned more than fifty information systems. He is the board member of more than twenty journals and the program committee member of nearly a hundred conferences. His current research interests include artificial intelligence, data mining, soft computing, management information systems, and www applications.

Prof. Adam Grzech
Wroclaw University of Technology, Poland.

Title of talk

Intelligent distributed detection systems of computer communication systems

Abstract

    Continued growth of amount and complexity of services offered by providers and required by customers in contemporary distributed computer communication systems is resulting in an increasingly complex, interconnected infrastructure. In gain to assure efficiency, flexibility, quality and security of distributed communication systems, the infrastructure require intelligent management systems that are responsive, adaptive, proactive and less centralized than those deployed. Such required properties are offered by distributed approaches that give the potential to develop more advanced and effective network-based strategies replacing traditional node-based approaches.

    The talk is devoted to present various architectures of intelligent, distributed network-based intrusion detection systems and measures of distributed intrusion detection system quality. Moreover an impact of network and their intrusion detection system architectures parameters on the intrusion detection systems quality is discussed and illustrated.

Short Bio

    Adam Grzech received the M.S. degree in automatic control in 1977, Ph.D. degree in computer science in 1979 and D.Sc. degree in computer science from the Wroclaw University of Technology, Poland. Currently, he is a professor in the Institute of Computer Science, Wroclaw University of Technology. His research interests include computer communication networks, networks architecture and protocols, distributed communication systems, networks performance and quality of network services.

Prof. Kazumi Nakamatsu
University of Hyogo, Japan

Title of talk

Application of Paraconsistent Annotated Logic Program EVALPSN to Control/Safety Verification

Abstract

     I have already proposed a paraconsistent annotated logic program called Extended Vector Annotated Logic Program with Strong Negation (EVALPSN), which can deal with defensible deontic reasoning. EVALPSN has been applied to various intelligent control and safety verification systems such as pipeline valve control, railway interlocking safety verification, etc. Moreover, EVALPSN has been developed to deal with before-after relation between processes and it can be applied to process order control and its safety verification. The developed EVALPSN is called bf (before-after) EVALPSN. It will be introduced how to apply EVALPSN and bf-EVALPSN to intelligent control and safety verification with   examples and simulation results.

Short Bio

      Prof. Kazumi Nakamatsu has taken his Doctor of Science from Kyushu University 1999 and been a professor at School of Human Science and Environment, University of Hyogo since 2004. His research focuses on application of formal logics, especially paraconsistent annotated logic program, with applications to computer science area. He has developed a paraconsistent logic program called an EVALPSN, and applied it to intelligent control and safety verification for various systems such as railway interlocking safety verification, pipeline valve control, traffic signal control, etc. He has applied a PAT in terms of intelligent process order control based on bf-EVALPSN.