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.