2006 Graduate Catalog and 2005 Annual R & D Report - Sirindhorn ...

2006 Graduate Catalog and 2005 Annual R & D Report
Sirindhorn International Institute of Technology (SIIT)
Dr. Stanislav S. Makhanov
Associate Professor
M. Appl. Math., Moscow State University, Faculty of Computational Mathematics and Cybernetics, Moscow
Diploma in English Language, Moscow Institute of Foreign Languages, Moscow
Ph.D. in Applied Mathematics, Computer Center of the Russian Academy of Science, Moscow
Areas of Specialization: Image processing, Robotics, Grid generation, Computational fluid dynamics.
Research Interests:
Software for Optimization of the Tool-Path of
Industrial Milling Robots
Innovations in the field of mechanical engineering
have enhanced the involvement of milling robots in
various manufacturing processes. Nowadays,
computer guided milling machines are employed to
produce free-shape surfaces in mass manufacturing
industries such as automobile, airplane, ship-building,
etc. However, several physical phenomena, such as
machine kinematics, thermal effects, static and
dynamic loading, and common-cause failures often
affect the quality of the desired surface. Although
recent research papers have displayed a number of
advanced methods to improve the characteristics of
machining, a robust algorithm to generate the optimal
tool-path for geometrically complex workpieces is still
an open problem.
Image/Signal Reconstruction
Image processing and restoration has revolutionized
the fields of medicine, space exploration, geology,
and oceanography. A fundamental issue of image
restoration is identification of the distortion in the
presence of observation noise. However, it is well
known that small variations of the initial data could
lead to solutions far from a correct one. Moreover, the
performance of the identification procedures critically
depends on the assumptions regarding the size and
the shape of the distortion. Therefore, an efficient
procedure should be smart enough to perform an
appropriate regularization and to recognize the size
and the pattern of the distortion. These features are
particularly important in the case of multi band
wavelet based schemes since the procedure can not
be decomposed with regard to filtered components of
the image. The up-to-date Literature on Image
Processing clearly indicates the need for further
research.
Grid Generation Technologies
Grid generation techniques emerged as a subdiscipline
of Computational Fluid Dynamics in the
early seventies. Nowadays grid generators are
among the major components employed by versatile
codes in Geometrical Modeling, Computer Graphics,
CAD/CAM, Structural Analysis, Aerodynamics and
Computational Fluid Dynamics. However, in spite of
considerable efforts and a long time spent on
curvilinear and moving grid generation, the theoretical
principles have not been yet established. Grid
generation today is still much more of an art than a
science. Since many different approaches exist and
are being used, creative craftsmen are needed to
operate the various packages. Therefore, from an
industrial point of view, issues surrounding efficient
implementation, interactive, graphical user interface,
visualization and software engineering in grid
generation are of paramount importance.
Dr. Thanaruk Theeramunkong
Associate Professor
B.Eng. in Electrical and Electronics Engineering, Tokyo Institute of Technology, Japan.
M.Eng. in Computer Science, Tokyo Institute of Technology, Japan.
D.Eng. in Computer Science, Tokyo Institute of Technology, Japan.
Areas of Specialization: Artificial Intelligence (AI), Natural Language Processing (NLP), Information Retrieval
(IR), Knowledge Data Discovery, Data Mining, Machine Learning (ML), and Intelligent Information Systems.
Research Interests:
Natural Language Processing
(1) Robust NLP and Linguistic Knowledge
Acquisition
While NLP systems are gradually becoming accepted
by a wider range of people both in academic and
business area, many difficult problems are still
unsolved. One of the important problems is how to
improve robustness and adaptiveness in NLP system,
especially how to analyze and interpret various
phrases and sentences which are ungrammatical
(also called ill-formed inputs). A user-friendly system
should be robust and flexible in that it can analyze
any well-formed and ill-formed input efficiently. The
system should also be adaptive to deal with
phrases/sentences including unseen construction and
vocabulary, for instance learning some new grammar
rules. Currently, we are focusing on both rule-based
and corpus-based approaches to cope with ill-formed
inputs and, when needed, to acquire novel linguistic
knowledge. On the increase of very large electronic
corpora, statistics obtained from such corpora are a
useful clue for this problem.
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