Static extensivity analysis for λ-definable functions over lattices

We employ a static analysis to examine the extensivity (∀x:x≤f(x)) of functions defined over lattices in a λ-calculus augmented with lattice operations. The need for such a verification procedure has arisen in our work on a generator system (called Zoo) of static program-analyzers. The input to Zoo is a static analysis specification that consists of lattice definitions and function definitions over the lattices. Once the extensivity of the functions is ascertained, the generated analyzer is guaranteed to terminate when the lattices have finite-heights. The extensivity analysis consists of a sound syntax-driven deductive rules whose satisfiability check is done by a constraint solving procedure. Hyunjun Eo: He is a Ph.D. candidate of Computer Science Dept. at KAIST (Korea Advanced Institute of Science and Technology). He received his B.S. and M.S. in Computer Science from KAIST in 1996 and 1998, respectively. For 1998–2003, he was a research assistant of the National Creative Research Initiative Center for Research On Program Analysis System. His research interest has been on static program analysis, program logics, and higher-order and typed languages. He is currently working on developing a tool for automatic generation of program analyzers. Kwangkeun Yi, Ph.D.: His research interest has been on semantic-based program analysis and systems application of language technologies. After his Ph.D. from University of Illinois at Urbana-Champaign he joined the Software Principles Research Department at Bell Laboratories, where he worked on various static analysis approaches for higher-order and typed programming languages. For 1995–2003, he was a faculty member in the Department of Computer Science, Korea Advanced Institute of Science and Technology. Since Fall 2003, he has been a faculty member in the School of Computer Science and Engineering, Seoul National University. Kwang-Moo Choe, Ph.D.: He is a professor of Computer Science at Korea Advanced Institute of Science and Technology. He received his B.S. from Seoul National University in 1976, and his M.S. and Ph.D. from Korea Advanced Institute of Science and Technology in 1978 and 1984, respectively. For 1985–1986, he was a technical staff of AT&T Bell Labs at Murray Hill. His research interest is formal language theory, parallel evaluation of logic programs, and optimizing compilers.     

A unified architecture for agent behaviors with selection of evolved neural network modules

To model complex systems for agent behaviors, genetic algorithms have been used to evolve neural networks which are based on cellular automata. These neural networks are popular tools in the artificial life community. This hybrid architecture aims at achieving synergy between the cellular automata and the powerful generalization capabilities of the neural networks. Evolutionary algorithms provide useful ways to learn about the structure of these neural networks, but the use of direct evolution in more difficult and complicated problems often fails to achieve satisfactory solutions. A more promising solution is to employ incremental evolution that reuses the solutions of easy tasks and applies these solutions to more difficult ones. Moreover, because the human brain can be divided into many behaviors with specific functionalities and because human beings can integrate these behaviors for high-level tasks, a biologically-inspired behavior selection mechanism is useful when combining these incrementally evolving basic behaviors. In this paper, an architecture based on cellular automata, neural networks, evolutionary algorithms, incremental evolution and a behavior selection mechanism is proposed to generate high-level behaviors for mobile robots. Experimental results with several simulations show the possibilities of the proposed architecture. Kyung-Joong Kim (Student Member, IEEE) received the B.S. and M.S. degree in computer science from Yonsei University, Seoul, Korea, in 2000 and 2002, respectively. Since 2002, he has been a Ph.D. student in the Department of Computer Science, Yonsei University. His research interests include evolutionary neural network, robot control, and agent architecture. Sung-Bae Cho (Member, IEEE) received the B.S. degree in computer science from Yonsei University, Seoul, Korea, in 1988 and the M.S. and Ph.D. degrees in computer science from Korea Advanced Institute of Science and Technology (KAIST), Taejeon, Korea, in 1990 and 1993, respectively. From 1991 to 1993, he worked as a Member of the Research Staff at the Center for Artificial Intelligence Research at KAIST. From 1993 to 1995, he was an Invited Researcher of Human Information Processing Research Laboratories at ATR (Advanced Telecommunications Research) Institute, Kyoto, Japan. In 1998, he was a Visiting Scholar at University of New South Wales, Canberra, Australia. Since 1995, he has been a Professor in the Department of Computer Science, Yonsei University. His research interests include neural networks, pattern recognition, intelligent man-machine interfaces, evolutionary computation, and artificial life. Dr. Cho is a Member of the Korea Information Science Society, INNS, the IEEE Computer Society, and the IEEE Systems, Man and Cybernetics Society. He was awarded outstanding paper prizes from the IEEE Korea Section in 1989 and 1992, and another one from the Korea Information Science Society in 1990. In 1993, he also received the Richard E. Merwin prize from the IEEE Computer Society. In 1994, he was listed in Who’s Who in Pattern Recognition from the International Association for Pattern Recognition and received the best paper awards at International Conference on Soft Computing in 1996 and 1998. In 1998, he received the best paper award at World Automation Congress. He was listed in Marquis Who’s Who in Science and Engineering in 2000 and in Marquis Who’s Who in the World in 2001.     

Connolly Surface on an Atomic Structure via Voronoi Diagram of Atoms

One of the most important geometric structures of a protein is the Connolly surface of protein since a Connolly surface plays an important role in protein folding, docking, interactions between proteins, amongst other things. This paper presents an algorithm for precisely and efficiently computing the Connolly surface of a protein using a proposed geometric construct called β-shape based on the Voronoi diagram of atoms in the protein. Given the Voronoi diagram of atoms based on the Euclidean distance from the atom surfaces, the proposed algorithm first computes a β-shape with an appropriate probe. Then, the Connolly surface is computed by employing the blending operation on the atomic complex of the protein by the given probe.     

Design and analysis of the Dual-Torus Network

In this paper, we propose a new topology called theDual Torus Network (DTN) which is constructed by adding interleaved edges to a torus. The DTN has many advantages over meshes and tori such as better extendibility, smaller diameter, higher bisection width, and robust link connectivity. The most important property of the DTN is that it can be partitioned into sub-tori of different sizes. This is not possible for mesh and torus-based systems. The DTN is investigated with respect to allocation, embedding, and fault-tolerant embedding. It is shown that the sub-torus allocation problem in the DTN reduces to the sub-mesh allocation problem in the torus. With respect to embedding, it is shown that a topology that can be embedded into a mesh with dilation δ can also be embedded into the DTN with less dilation. In fault-tolerant embedding, a fault-tolerant embedding method based on rotation, column insertion, and column skip is proposed. This method can embed any rectangular grid into its optimal square DTN when the number of faulty nodes is fewer than the number of unused nodes. In conclusion, the DTN is a scalable topology well-suited for massively parallel computation. Sang-Ho Chae, M.S.: He received the B.S. in the Computer Science and Engineering from the Pohang University of Science and Technology (POSTECH) in 1994, and the M.E. in 1996. Since 1996, he works as an Associate Research Engineer in the Central R&D Center of the SK Telecom Co. Ltd. He took part in developing SK Telecom Short Message Server whose subscribers are now over 3.5 million and Advanced Paging System in which he designed and implemented high availability concepts. His research interests are the Fault Tolerance, Parallel Processing, and Parallel Topolgies. Jong Kim, Ph.D.: He received the B.S. degree in Electronic Engineering from Hanyang University, Seoul, Korea, in 1981, the M.S. degree in Computer Science from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1983, and the Ph.D. degree in Computer Engineering from Pennsylvania State University, U.S.A., in 1991. He is currently an Associate Professor in the Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea. Prior to this appointment, he was a research fellow in the Real-Time Computing Laboratory of the Department of Electrical Engineering and Computer Science at the University of Michigan from 1991 to 1992. From 1983 to 1986, he was a System Engineer in the Korea Securities Computer Corporation, Seoul, Korea. His major areas of interest are Fault-Tolerant Computing, Performance Evaluation, and Parallel and Distributed Computing. Sung Je Hong, Ph.D.: He received the B.S. degree in Electronics Engineering from Seoul National University, Korea, in 1973, the M.S. degree in Computer Science from Iowa State University, Ames, U.S.A., in 1979, and the Ph.D. degree in Computer Science from the University of Illinois, Urbana, U.S.A., in 1983. He is currently a Professor in the Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea. From 1983 to 1989, he was a staff member of Corporate Research and Development, General Electric Company, Schenectady, NY, U.S.A. From 1975 to 1976, he was with Oriental Computer Engineering, Korea, as a Logic Design Engineer. His current research interest includes VLSI Design, CAD Algorithms, Testing, and Parallel Processing. Sunggu Lee, Ph.D.: He received the B.S.E.E. degree with highest distinction from the University of Kansas, Lawrence, in 1985 and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1987 and 1990, respectively. He is currently an Associate Professor in the Department of Electronic and Electrical Engineering at the Pohang University of Science and Technology (POSTECH), Pohang, Korea. Prior to this appointment, he was an Associate Professor in the Department of Electrical Engineering at the University of Delaware in Newark, Delaware, U.S.A. From June 1997 to July 1998, he spent one year as a Visiting Scientist at the IBM T. J. Watson Research Center. His research interests are in Parallel, Distributed, and Fault-Tolerant Computing. Currently, his main research focus is on the high-level and low-level aspects of Inter-Processor Communications for Parallel Computers.     

A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids

This paper presents a hybrid classification method that utilizes genetic algorithms (GAs) and adaptive operations of ellipsoidal regions for multidimensional pattern classification problems with continuous features. The classification method fits a finite number of the ellipsoidal regions to data pattern by using hybrid GAs, the combination of local improvement procedures and GAs. The local improvement method adaptively expands, rotates, shrinks, and/or moves the ellipsoids while each ellipsoid is separately handled with a fitness value assigned during the GA operations. A set of significant features for the ellipsoids are automatically determined in the hybrid GA procedure by introducing “don’t care” bits to encode the chromosomes. The performance of the method is evaluated on well-known data sets and a real field classification problem originated from a deflection yoke production line. The evaluation results show that the proposed method can exert superior performance to other classification methods such as k nearest neighbor, decision trees, or neural networks. Ki K. Lee received the B.S. degree from Han Yang University, Seoul, Korea in 1994, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1996 and 2005, respectively. From 2001 to 2004, he was a senior research engineer in telecommunication systems laboratory of LG Electronics Inc. Since 2005, he has been an assistant professor in the School of Management at Inje University, Kimhae, Korea. His research interests include intelligent decision support systems, soft computing, and pattern recognition. Wan C. Yoon received the B.S. degree from Seoul National University, Korea in 1977, the M.S. degree from KAIST, Korea in 1979, and the Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology in 1987. He is professor of the Department of Industrial Engineering at KAIST, Korea. His research interests include application of artificial intelligence, human decision-making and aiding, information systems, and joint intelligent systems. Dong H. Baek received the B.S. degree from Han Yang University, Seoul, Korea in 1992, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1994 and 1999, respectively. He is an assistant professor in management information systems at department of business administration, Hanyang University, Korea. His research interests include management information systems, system engineering, and machine learning.     

Evolutionary fusion of a multi-classifier system for efficient face recognition

In this paper an evolutionary classifier fusion method inspired by biological evolution is presented to optimize the performance of a face recognition system. Initially, different illumination environments are modeled as multiple contexts using unsupervised learning and then the optimized classifier ensemble is searched for each context using a Genetic Algorithm (GA). For each context, multiple optimized classifiers are searched; each of which are referred to as a context based classifier. An evolutionary framework comprised of a combination of these classifiers is then applied to optimize face recognition as a whole. Evolutionary classifier fusion is compared with the simple adaptive system. Experiments are carried out using the Inha database and FERET database. Experimental results show that the proposed evolutionary classifier fusion method gives superior performance over other methods without using evolutionary fusion. Recommended by Guest Editor Daniel Howard. This work was supported by INHA UNIVERSITY Research Grant. Zhan Yu received the B.E. degree in Software Engineering from Xiamen University, China, in 2008. He is currently a master student in Intelligent Technology Lab, Computer and Information Department, Inha University, Korea. He has research interests in image processing, pattern recognition, computer vision, machine learning and statistical inference and computating. Mi Young Nam received the B.Sc. and M.Sc. degrees in Computer Science from the University of Silla Busan, Korea in 1995 and 2001 respectively and the Ph.D. degree in Computer Science & Engineering from the University of Inha, Korea in 2006. Currently, She is Post-Doctor course in Intelligent Technology Laboratory, Inha University, Korea. She’s research interest includes biometrics, pattern recognition, computer vision, image processing. Suman Sedai received the M.S. degree in Software Engineering from Inha University, China, in 2008. He is currently a Doctoral course in Western Australia University, Australia. He has research interests in image processing, pattern recognition, computer vision, machine learning. Phill Kyu Rhee received the B.S. degree in Electrical Engineering from the Seoul University, Seoul, Korea, the M.S. degree in Computer Science from the East Texas State University, Commerce, TX, and the Ph.D. degree in Computer Science from the University of Louisiana, Lafayette, LA, in 1982, 1986, and 1990 respectively. During 1982–1985 he was working in the System Engineering Research Institute, Seoul, Korea as a research scientist. In 1991 he joined the Electronic and Telecommunication Research Institute, Seoul, Korea, as a Senior Research Staff. Since 1992, he has been an Associate Professor in the Department of Computer Science and Engineering of the Inha University, Incheon, Korea and since 2001, he is a Professor in the same department and university. His current research interests are pattern recognition, machine intelligence, and parallel computer architecture. dr. rhee is a Member of the IEEE Computer Society and KISS (Korea Information Science Society).     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

Central pattern generator parameter search for a biped walking robot using nonparametric estimation based Particle Swarm Optimization

A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment. Recommended by Editorial Board member Euntai Kim under the direction of Editor Jae-Bok Song. Jeong-Jung Kim received the B.S. degree in Electronics and Information Engineering from Chonbuk National University in 2006 and the M.S. degree in Robotics from Korea Advanced Institute of Science and Technology in 2008. He is currently working toward a Ph.D. at the Korea Advanced Institute of Science and Technology. His research interests include biologically inspired robotics and machine learning. Jun-Woo Lee received the B.S. degree in Electronics, Electrical and Communication Engineering from Pusan National University in 2007. He is currently working toward an M.S. in the Korea Advanced Institute of Science and Technology. His research interests include swarm intelligence and machine learning. Ju-Jang Lee was born in Seoul, Korea, in 1948. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in 1973 and 1977, respectively, and the Ph.D. degree in Electrical Engineering from the University of Wisconsin, in 1984. From 1977 to 1978, he was a Research Engineer at the Korean Electric Research and Testing Institute, Seoul. From 1978 to 1979, he was a Design and Processing Engineer at G. T. E. Automatic Electric Company, Waukesha, WI. For a brief period in 1983, he was the Project Engineer for the Research and Development Department of the Wisconsin Electric Power Company, Milwaukee. He joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, in 1984, where he is currently a Professor. In 1987, he was a Visiting Professor at the Robotics Laboratory of the Imperial College Science and Technology, London, U.K. From 1991 to 1992, he was a Visiting Scientist at the Robotics Department of Carnegie Mellon University, Pittsburgh, PA. His research interests are in the areas of intelligent control of mobile robots, service robotics for the disabled, space robotics, evolutionary computation, variable structure control, chaotic control systems, electronic control units for automobiles, and power system stabilizers. Dr. Lee is a member of the IEEE Robotics and Automation Society, the IEEE Evolutionary Computation Society, the IEEE Industrial Electronics Society, IEEK, KITE, and KISS. He is also a former President of ICROS in Korea and a Counselor of SICE in Japan. He is a Fellow of SICE and ICROS. He is an Associate Editor of IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics.     

Temporal disjunctive logic programming

In this paper we introduce the logic programming languageDisjunctive Chronolog which combines the programming paradigms of temporal and disjunctive logic programming. Disjunctive Chronolog is capable of expressing dynamic behaviour as well as uncertainty, two notions that are very common in a variety of real systems. We present the minimal temporal model semantics and the fixpoint semantics for the new programming language and demonstrate their equivalence. We also show how proof procedures developed for disjunctive logic programs can be easily extended to apply to Disjunctive Chronolog programs. Manolis Gergatsoulis, Ph.D.: He received his B.Sc. in Physics in 1983, the M.Sc. and the Ph.D. degrees in Computer Science in 1986 and 1995 respectively all from the University of Athens, Greece. Since 1996 he is a Research Associate in the Institute of Informatics and Telecommunications, NCSR ‘Demokritos’, Athens. His research interests include logic and temporal programming, program transformations and synthesis, as well as theory of programming languages. Panagiotis Rondogiannis, Ph.D.: He received his B.Sc. from the Department of Computer Engineering and Informatics, University of Patras, Greece, in 1989, and his M.Sc. and Ph.D. from the Department of Computer Science, University of Victoria, Canada, in 1991 and 1994 respectively. From 1995 to 1996 he served in the Greek army. From 1996 to 1997 he was a visiting professor in the Department of Computer Science, University of Ioannina, Greece, and since 1997 he is a Lecturer in the same Department. In January 2000 he was elected Assistant Professor in the Department of Informatics at the University of Athens. His research interests include functional, logic and temporal programming, as well as theory of programming languages. Themis Panayiotopoulos, Ph.D.: He received his Diploma on Electrical Engineering from the Department of Electrical Engineering, National Technical Univesity of Athens, in 1984, and his Ph.D. on Artificial Intelligence from the above mentioned department in 1989. From 1991 to 1994 he was a visiting professor at the Department of Mathematics, University of the Aegean, Samos, Greece and a Research Associate at the Institute of Informatics and Telecommunications of “Democritos” National Research Center. Since 1995 he is an Assistant Prof. at the Department of Computer Science, University of Piraeus. His research interests include temporal programming, logic programming, expert systems and intelligent agent architectures.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. Chunming Hu is a research staff in the Institute of Advanced Computing Technology at the School of Computer Science and Engineering, Beihang University, Beijing, China. He received his B.E. and M.E. in Department of Computer Science and Engineering in Beihang University. He received the Ph.D. degree in School of Computer Science and Engineering of Beihang University, Beijing, China, 2005. His research interests include peer-to-peer and grid computing; distributed systems and software architectures. Yanmin Zhu is a Ph.D. candidate in the Department of Computer Science, Hong Kong University of Science and Technology. He received his B.S. degree in computer science from Xi’an Jiaotong University, Xi’an, China, in 2002. His research interests include grid computing, peer-to-peer networking, pervasive computing and sensor networks. He is a member of the IEEE and the IEEE Computer Society. Jinpeng Huai is a Professor and Vice President of Beihang University. He serves on the Steering Committee for Advanced Computing Technology Subject, the National High-Tech Program (863) as Chief Scientist. He is a member of the Consulting Committee of the Central Government’s Information Office, and Chairman of the Expert Committee in both the National e-Government Engineering Taskforce and the National e-Government Standard office. Dr. Huai and his colleagues are leading the key projects in e-Science of the National Science Foundation of China (NSFC) and Sino-UK. He has authored over 100 papers. His research interests include middleware, peer-to-peer (P2P), grid computing, trustworthiness and security. Yunhao Liu received his B.S. degree in Automation Department from Tsinghua University, China, in 1995, and an M.A. degree in Beijing Foreign Studies University, China, in 1997, and an M.S. and a Ph.D. degree in computer science and engineering at Michigan State University in 2003 and 2004, respectively. He is now an assistant professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include peer-to-peer computing, pervasive computing, distributed systems, network security, grid computing, and high-speed networking. He is a senior member of the IEEE Computer Society. Lionel M. Ni is chair professor and head of the Computer Science and Engineering Department at Hong Kong University of Science and Technology. Lionel M. Ni received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, Indiana, in 1980. He was a professor of computer science and engineering at Michigan State University from 1981 to 2003, where he received the Distinguished Faculty Award in 1994. His research interests include parallel architectures, distributed systems, high-speed networks, and pervasive computing. A fellow of the IEEE and the IEEE Computer Society, he has chaired many professional conferences and has received a number of awards for authoring outstanding papers.     

Early Estimation of Users’ Perception of Software Quality

This paper presents a methodology for estimating users’ opinion of the quality of a software product. Users’ opinion changes with time as they progressively become more acquainted with the software product. In this paper, we study the dynamics of users’ opinion and offer a method for assessing users’ final perception, based on measurements in the early stages of product release. The paper also presents methods for collecting users’ opinion and from the derived data, shows how their initial belief state for the quality of the product is formed. It adapts aspects of Belief Revision theory in order to present a way of estimating users’ opinion, subsequently formed after their opinion revisions. This estimation is achieved by using the initial measurements and without having to conduct surveys frequently. It reports the correlation that users tend to infer among quality characteristics and represents this correlation through a determination of a set of constraints between the scores of each quality characteristic. Finally, this paper presents a fast and automated way of forming users’ new belief state for the quality of a product after examining their opinion revisions. Dimitris Stavrinoudis received his degree in Computer Engineering from Patras University and is a Ph.D. student of Computer Engineering and Informatics Department. He worked as a senior computer engineer and researcher at the R.A. Computer Technology Institute. He has participated in research and development projects in the areas of software engineering, databases and educational technologies. Currently, he works at the Hellenic Open University. His research interests include software quality, software metrics and measurements. Michalis Xenos received his degree and Ph.D. in Computer Engineering from Patras University. He is a Lecturer in the Informatics Department of the School of Sciences and Technology of the Hellenic Open University. He also works as a researcher in the Computer Technology Institute of Patras and has participated in over 15 research and development projects in the areas of software engineering and IT development management. His research interests include, inter alia, Software Engineering and Educational Technologies. He is the author of 6 books in Greek and over 30 papers in international journals and conferences. Pavlos Peppas received his B.Eng. in Computer Engineering from Patras University (1988), and his Ph.D. in Computer Science from Sydney University (1994). He joined Macquarie University, Sydney, as a lecturer in September 1993, and was promoted to a senior lecturer in October 1998. In January 2000, he took up an appointment at Intrasoft, Athens, where he worked as a senior specialist in the Data Warehousing department. He joint Athens Information Technology in February 2003 as a senior researcher, and since November 2003 he is an associate professor at the Dept of Business Administration at the University of Patras. He also holds an adjunct associate professorship at the School of Computer Science and Engineering at the University of New South Wales. His research interests lie primarily within the area of Artificial Intelligence, and more specifically in logic-based approaches to Knowledge Representation and Reasoning with application in robotics, software engineering, organizational knowledge management, and the semantic web. Dimitris Christodoulakis received his degree in Mathematics from the University of Athens and his Ph.D. in Informatics from the University of Bonn. He was a researcher at the National Informatics Centre of Germany. He is a Professor and Vice President of Computer Engineering and Informatics Department of Patras University. Scientific Coordinator in many research and development projects in the followings sections: Knowledge and Data Base Systems, Very large volume information storage, Hypertext, Natural Language Technology for Modern Greek. Author and co-author in many articles published in international conferences. Editor in proceedings of conventions. Responsible for proofing tools development for Microsoft Corp. He is Vice Director in the Research Academic Computer Technology Institute (RACTI).     

Automatic model-based 3D object recognition by combining feature matching with tracking

We propose a vision-based robust automatic 3D object recognition, which provides object identification and 3D pose information by combining feature matching with tracking. For object identification, we propose a robust visual feature and a probabilistic voting scheme. An initial object pose is estimated using correlations between the model image and the 3D CAD model, which are predefined, and the homography, byproduct of the identification. In tracking, a Lie group formalism is used for robust and fast motion computation. Experimental results show that object recognition by the proposed method improves the recognition range considerably. Sungho Kim received the B.S. degree in Electrical Engineering from Korea University, Korea in 2000 and the M.S. degree in Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology, Korea in 2002. He is currently pursuing his Ph.D. at the latter institution, concentrating on 3D object recognition and tracking. In So Kweon received the Ph.D. degree in robotics from Carnegie Mellon University, Pittsburgh, PA, in 1990. Since 1992, he has been a Professor of Electrical Engineering at KAIST. His current research interests include human visual perception, object recognition, real-time tracking, vision-based mobile robot localization, volumetric 3D reconstruction, and camera calibration. He is a member of the IEEE, and Korea Robotics Society (KRS).     

Dynamic Voltage Scaling for Digital Control System Implementation

For real-time computer-controlled systems, control performances of tasks as well as energy consumption of overall system must be optimized. A control task does not have a fixed period but a range of periods in which the control performance varies. Hence, when more than one control tasks are scheduled on a single processor, an optimization problem appears. Furthermore, when an energy saving technique such as dynamic voltage scaling is used, its properties affect the control performance.Using a performance index that involves control performance and energy consumption, a static solution is proposed to obtain the optimal processor speed and a set of periods for given control tasks in O(k). Also a dynamic solution is proposed to utilize system services of real-time operating systems to overcome unavoidable deficiencies of the static solution and to further reduce the energy consumption of the overall system. The performances of proposed solutions are revealed via simulation studies.Hyung Sun Lee received his B.S. and M.S. degrees in electronics engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2000 and 2002, respectively. He is currently a Ph.D. student in the Department of Electrical Engineering and Computer Science (EECS) at KAIST. His research interests include real-time control and power-aware real-time embedded systems.Byung Kook Kim received his B.S. degree in Electronics Engineering from Seoul National University in 1975, and his M.S. and Ph.D. degrees from KAIST in 1977 and 1981, respectively. Dr. Kim was a manager and founder of the Calibration Laboratory, Woojin Instrument Co. Ltd, in 1981. He performed his postdoctoral research at the University of Michigan, Ann Arbor, Michigan, from 1982 to 1983. He returned to Woojin Instruments as a chief researcher of the R&D Department from 1984 to 1986. He joined the faculty of the Department of Electrical Engineering at KAIST in 1986, where he is currently a professor. His research interests include real-time systems, parallel and distributed systems, fault-tolerant computing, mobile robot sensing and navigation, and manipulator control.     

A Power-Aware Branch Predictor by Accessing the BTB Selectively

Microarchitects should consider power consumption, together with accuracy, when designing a branch predictor, especially in embedded processors. This paper proposes a power-aware branch predictor, which is based on the gshare predictor, by accessing the BTB （Branch Target Buffer） selectively. To enable the selective access to the BTB, the PHT （Pattern History Table） in the proposed branch predictor is accessed one cycle earlier than the traditional PHT if the program is executed sequentially without branch instructions. As a side effect, two predictions from the PHT are obtained through one access to the PHT, resulting in more power savings. In the proposed branch predictor, if the previous instruction was not a branch and the prediction from the PHT is untaken, the BTB is not accessed to reduce power consumption. If the previous instruction was a branch, the BTB is always accessed, regardless of the prediction from the PHT, to prevent the additional delay/accuracy decrease. The proposed branch predictor reduces the power consumption with little hardware overhead, not incurring additional delay and never harming prediction accuracy. The simulation results show that the proposed branch predictor reduces the power consumption by 29-47%.     

Development of the multi-functional indoor service robot PSR systems

This paper discusses the development of the multi-functional indoor service robot PSR (Public Service Robots) systems. We have built three versions of PSR systems, which are the mobile manipulator PSR-1 and PSR-2, and the guide robot Jinny. The PSR robots successfully accomplished four target service tasks including a delivery, a patrol, a guide, and a floor cleaning task. These applications were defined from our investigation on service requirements of various indoor public environments. This paper shows how mobile-manipulator typed service robots were developed towards intelligent agents in a real environment. We identified system integration, multi-functionality, and autonomy considering environmental uncertainties as key research issues. Our research focused on solving these issues, and the solutions can be considered as the distinct features of our systems. Several key technologies were developed to satisfy technological consistency through the proposed integration scheme. Woojin Chung was born in Seoul, Korea, in 1970. He received the B.S. at the department of mechanical design and production engineering, Seoul National University in 1993. He received the M.S. degree in 1995 and Ph.D degree in 1998 at the department of Mechano-Informatics, the University of Tokyo. He was a senior research scientist at the Korea Institute of Science and Technology from 1998 to 2005. He joined the department of mechanical engineering, Korea University in 2005 as an assistant professor. He received an excellent paper award from the Robotics Society of Japan in 1996 and a best transactions paper award from the IEEE robotics and automation society in 2002. His research interests include the design and control of nonholonomic underactuated mechanical systems, trailer system design and control, mobile robot navigation, a dexterous robot hand and a system integration of intelligent robots. He is a member of the IEEE, the robotics society of Japan, the institute of control, automation and systems engineers and the Korea robotics society. Gunhee Kim received the B.S. and M.S. degrees at the department of mechanical engineering, Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1999 and 2001, respectively. He was a research scientist in Intelligent Robotics Research Center, at Korea Institute of Science and Technology (KIST), Korea, from 2001 to 2006. Currently, he is a graduate student in the Robotics Institute, Carnegie Mellon University. His research interests include computer vision, artificial intelligence, mobile robot navigation, and discrete event systems. He is a member of the IEEE. Munsang Kim received the B.S. and M.S degree in Mechanical Engineering from the Seoul National University in 1980 and 1982 respectively and the Dr.-Ing. degree in Robotics from the Technical University of Berlin, Germany in 1987. Since 1987 he has been working as a research scientist at Korea Institute of Science and Technology. He has led the Intelligent Robotics Research Center since 2000 and became the director of the “Intelligent Robot—The Frontier 21 Program” since Oct. 2003. His current research interests are design and control of novel mobile manipulation systems, haptic device design and control, and sensor application to intelligent robots.     

Object-spatial layout-route based hybrid map and global localization for mobile robots

This paper presents new object-spatial layout-route based hybrid map representation and global localization approaches using a stereo camera. By representing objects as high-level features in a map, a robot can deal more effectively with different contexts such as dynamic environments, human-robot interaction, and semantic information. However, the use of objects alone for map representation has inherent problems. For example, it is difficult to represent empty spaces for robot navigation, and objects are limited to readily recognizable things. One way to overcome these problems is to develop a hybrid map that includes objects and the spatial layout of a local space. The map developed in this research has a hybrid structure that combines a global topological map and a local hybrid map. The topological map represents the spatial relationships between local spaces. The local hybrid map combines the spatial layout of the local space with the objects found in that space. Based on the proposed map, we suggest a novel coarse-to-fine global localization method that uses object recognition, point cloud fitting and probabilistic scan matching. This approach can accurately estimate robot pose with respect to the correct local space. Recommended by Editor Jae-Bok Song. This research was performed for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Soonyong Park received the B.S. and M.S. degrees from the Department of Mechanical Engineering, Kyunghee University, Seoul, Korea, in 2001 and 2003, respectively. He is currently working toward the Ph.D. degree in the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. Since 2001, he has been a student researcher in the Center for Cognitive Robotics Research, Korea Institute of Science and Technology (KIST), Seoul, Korea. His research interests include mobile robot navigation and computer vision. Mignon Park received the B.S. and M.S. degrees in Electronics from Yonsei University, Seoul, Korea, in 1973 and 1977, respectively. He received the Ph.D. degree in University of Tokyo, Japan, 1982. He was a researcher with the Institute of Biomedical Engineering, University of Tokyo, Japan, from 1972 to 1982, as well as at the Massachusetts Institute of Technology, Cambridge, and the University of California Berkeley, in 1982. He was a visiting researcher in Robotics Division, Mechanical Engineering Laboratory, Ministry of International Trade and Industry, Tsukuba, Japan, from 1986 to 1987. He has been a Professor in the Department of Electrical and Electronic Engineering in Yonsei University, since 1982. His research interests include fuzzy control and application, robotics, and fuzzy biomedical system. Sung-Kee Park is a principal research scientist for Korea Institute of Science and Technology (KIST). He received the B.S. and M.S. degrees in Mechanical Design and Production Engineering from Seoul National University, Seoul, Korea, in 1987 and 1989, respectively. He received the Ph.D. degree (2000) from Korea Advanced Institue of Science and Technology (KAIST), Korea, in the area of computer vision. Since then, he has been working for the center for cognitive robotics research at KIST. During his period at KIST, he held a visiting position at the Robotics Institute of Carnegie Mellon University in 2005, where he did research on object recognition. His recent work has been on cognitive visual processing, object recognition, visual navigation, and human-robot interaction.     

Modeling semantics in composite Web service requests by utility elicitation

When meeting the challenges in automatic and semi-automatic Web service composition, capturing the user’s service demand and preferences is as important as knowing what the services can do. This paper discusses the idea of semantic service requests for composite services, and presents a multi-attribute utility theory (MAUT) based model of composite service requests. Service requests are modeled as user preferences and constraints. Two preference structures, additive independence and generalized additive independence, are utilized in calculating the expected utilities of service composition outcomes. The model is also based on an iterative and incremental scheme meant to better capture requirements in accordance with service consumers’ needs. OWL-S markup vocabularies and associated inference mechanism are used as a means to bring semantics to service requests. Ontology conceptualizations and language constructs are added to OWL-S as uniform representations of possible aspects of the requests. This model of semantics in service requests enables unambiguous understanding of the service needs and more precise generation of the desired compositions. An application scenario is presented to illustrate how the proposed model can be applied in the real business world. Qianhui Althea Liang received her Ph.D from the Department of Electrical and Computer Engineering, University of Florida in 2004. While pursuing her Ph.D, she was a member of Database Systems Research and Development Center at the University of Florida. She received both her bachelor’s and master’s from the Department of Computer Science and Engineering, Zhejiang University, China. She joined the School of Information Systems at Singapore Management University, Singapore, as an assistant professor in 2005. Her major research interests are service composition, dynamic service discovery, multimedia Web services, and applied artificial intelligence. Jen-Yao Chung received the M.S. and Ph.D degrees in computer science from the University of Illinois at Urbana-Champaign. Currently, he is the senior manager for Engineering and Technology Services Innovation, where he was responsible for identifying and creating emergent solutions. He was Chief Technology Officer for IBM Global Electronics Industry. Before that, he was program director for IBM Institute for Advanced Commerce Technology office. He is the co-founder of IEEE technical committee on e-Commerce (TCEC). He has served as general chair and program chair for many international conferences, most recently he served as the steering committee chair for the IEEE International Conference on e-Commerce Technology (CEC06) and general chair for the IEEE International Conference on e-Business Engineering (ICEBE06). He has authored or coauthored over 150 technical papers in published journals or conference proceedings. He is a senior member of the IEEE and a member of ACM. Miller is founding Dean of the School of Information Systems (SIS) at Singapore Management University, and also serves as Practice Professor of Information Systems. Since 2003, he has led efforts to launch and establish the undergraduate, graduate and professional programs of the SIS. Immediately prior to joining SMU, Dr. Miller served as Chief Architect Executive for the Business Consulting Services unit of IBM Global Services in Asia Pacific. He held prior industry appointments with Fujitsu Network Systems, and with RWD Technologies. Dr. Miller started his professional career as an Assistant Professor at Carnegie Mellon University, conducting research and teaching related to Computer-Integrated Manufacturing and Robotics applications and impacts. He has a Bachelors of Engineering Degree in Systems Engineering (Magna Cum Laude) from the University of Pennsylvania and a Masters of Science in Statistics and a Ph.D in Engineering and Public Policy from Carnegie Mellon University.     

Vibration suppression of a flexible robot manipulator with a lightweight piezo-composite actuator

The increasing demand for high-speed performance and low energy consumption has necessitated the design of lightweight mechanical systems. The active vibration suppression of a flexible manipulator is important in many engineering applications, such as robot manipulators and high-speed flexible mechanisms, because the flexibility of lightweight manipulators induces a vibration problem. Frequently, the optimal parameters determined for a certain control algorithm might not cover a wide range of operating conditions. Hence, we have proposed and developed a lookup table control method for a flexible manipulator that can tune itself to optimal parameters on the basis of the initial maximum responses of the controlled system and a genetic algorithm. The genetic algorithm is used to search for optimal parameters with regard to positive position feedback and thereby minimizes the objective functions determined from the initial maximum responses. Our lookup table, which has the optimal parameters of the positive position feedback as a function of the initial maximum responses, can be used in a real-time control algorithm. Recommended by Editorial Board member Hyoukryeol Choi under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation under grant KRF 2006-005-J03302 and the Korea Science and Engineering Foundation under grant KOSEF R0A-2007-000-20012-0. Van Phuoc Phan received the BS (2006) from the Department of Aeronautical Engineering, HCM University of Technology, Vietnam. Currently, he is a Master student at the Department of Advanced Technology Fusion, Konkuk University in Seoul, Korea. His interests are structural dynamics of small systems, smart structure and material, and finite element analysis. Nam Seo Goo graduated from the Department of Aeronautics Engineering of Seoul National University with honors in 1990, and got master and Ph.D. degrees in Department of Aerospace Engineering at the same university in 1992 and 1996, respectively. His Ph.D. degree was on the structural dynamics of aerospace systems. As soon as he got a Ph.D. degree, he entered the agency for defense development as a Senior Researcher. After four years’ service, he joined Department of Aerospace Engineering in Konkuk University, Seoul, Korea in 2002, currently serving an Associate Professor of Department of Advanced Technology Fusion. His current research interests are structural dynamics of small systems, smart structure and material, and MEMS applications. Hoon Cheol Park received his BS (1985) and MS(1987) degrees from Seoul National University in Seoul, Korea and Ph.D.(1994) degree from the University of Maryland at College Park, MD, USA. He joined the Department of Aerospace Engineering, Konkuk University in Seoul, Korea in 1995, and he is currently a Professor in the Department of Advanced Technology Fusion. His professional experience includes Kia Motors (1986–1988) and Korea Aerospace Research Institute (1994–1995). His specialty is the finite element analysis and recent research topic is mainly biomimetics.