Hierarchical Geometric Constraint Model for Parametric Feature Based Modeling

A new geometric constraint model is described,which is hierarchical and suitable for parametric feature based modeling.In this model,different levels of geometric information are repesented to support various stages of a design process.An efficient approach to parametric feature based modeling is also presented,adopting the high level geometric constraint model.The low level geometric model such as B-reps can be derived automatically from the hig level geometric constraint model,enabling designers to perform their task of detailed design.     

基于集成化建模的个性化和自动化的学习资源选择平台(英文)

In order to automatically select learning resources in an Internet environment according to the background knowledge and the learning objectives of the learners,a platform based on integrated modeling is developed.The platform constitutes three levels and four views.The three levels are database level,knowledge analysis level and modeling level.The four views are user modeling,learning resource modeling,knowledge points modeling and learning process modeling.The process model is used to connect the learners and the learning resources.The workflow optimization method is used to optimize the learning resource selection according to the objectives and background knowledge level of the learner,at the same time,a learning plan is given.Finally,a web-based prototype system is developed by java.     

基于主动轮廓模型的肺纹理自动提取新方法

Computed tomography (CT) is the primary imaging modality for investigation of lung function and lung diseases. High resolution CT slice images of chest contain lots of texture information, which provides powerful datasets to research computer aid-diagnosis (CAD) system. But the extraction of lung tissue textures is a challenge task. In this paper, we introduce a novel method based on level set to extract lung tissue texture tree, which is automatic and effectual. Firstly, we propose an improved implicit active contour model driven by local binary fitting energy, and the parameters are dynamic and modulated by image gradient information. Secondly, a new technique of painting background based on intensity nonlinear mapping is brought forward to remove the influence of background during the evolution of single level set function. At last, a number of contrast experiments are performed, and the results of 3D surface reconstruction show our method is efficient and powerful for the segmentation of fine lung tree texture structures.     

Three-Dimensional Volume Datafield Reconstruction from Physical Model

This paper focuses on entirety interpretation,representation and reconstruction of three-dimensional volume data sets based on the physical model of the data.The data model is represented by three-dimensional geometric model The surfaces inside the datafield are extracted and matched to the model surfaces in order to reconstruct the new datafield based on the model.A conclusion is drawn that physical modeling provides a good basis and approach to interpret and represent the data sets.This paper also presents a subdivision algorithm to fast trace B-spline curve and the contrary algorithms is adopted to extract the geometry feature of the curve.     

Lazy Slicing for State-Space Exploration

CEGAR (Counterexample-guided abstraction refinement)-based slicing is one of the most important techniques in reducing the state space in model checking.However,CEGAR-based slicing repeatedly explores the state space handled previously in case a spurious counterexample is found.Inspired by lazy abstraction,we introduce the concept of lazy slicing which eliminates this repeated computation.Lazy slicing is done on-the-fly,and only up to the precision necessary to rule out spurious counterexamples.It identifies a spurious counterexample by concretizing a path fragment other than the full path,which reduces the cost of spurious counterexample decision significantly.Besides,we present an improved over-approximate slicing method to build a more precise slice model.We also provide the proof of the correctness and the termination of lazy slicing,and implement a prototype model checker to verify safety property.Experimental results show that lazy slicing scales to larger systems than CEGAR-based slicing methods.     

Eliciting dependability requirements: a control cases based approach

At present,great demands are posed on software dependability.But how to elicit the dependability requirements is still a challenging task.This paper proposes a novel approach to address this issue.The essential idea is to model a dependable software system as a feedforward-feedback control system,and presents the use cases+control cases model to express the requirements of the dependable software systems.In this model,while the use cases are adopted to model the functional requirements,two kinds of control cases(namely the feedforward control cases and the feedback control cases)are designed to model the dependability requirements.The use cases+control cases model provides a unified framework to integrate the modeling of the functional requirements and the dependability requirements at a high abstract level.To guide the elicitation of the dependability requirements,a HAZOP based process is also designed.A case study is conducted to illustrate the feasibility of the proposed approach.     

MRF model and FRAME model-based unsupervised image segmentation

This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses "Filters, Random and Maximum Entropy (Abb. FRAME)" model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an exp     

A Distributed Model of WebGIS Based on Java Servlett

According to the application of Java in Web Geographical Information System （WebGIS）, a model of WebGIS based on Java Servlet is defined and a new distributed model is proposed. The dislributed model relies on the implementation of distribt, tcd Servlet. And in the model a locator is adopted to manage client access to the Servlet and balance the loads of cluster Web servers. The programming rule of distributcd Servlet and the operating principle of locator are studied in detail. The model has been applied in a practical system, and the actual operating effect shows that it remarkably improves access performance of the WebGIS application and possesses characteristics of load balance, fail-over and so on.     

A Hierarchical Reconfiguration Strategy for Bus-Based Multiprocessors

A method of providing redundancy to a class of bus-based multiprocessor arrays is discusse.The reconfiguration is hierarchical,providing global spare replacement at the array level and local reconfiguration within the spare block,Results of yield analysis performed on a 32 processor array are presented.     

A metamodel for modeling system features and their refinement, constraint and interaction relationships

This paper presents a metamodel for modeling system features and relationships between features. The underlying idea of this metamodel is to employ features as first-class entities in the problem space of software and to improve the customization of software by explicitly specifying both static and dynamic dependencies between system features. In this metamodel, features are organized as hierarchy structures by the refinement relationships, static dependencies between features are specified by the constraint relationships, and dynamic dependencies between features are captured by the interaction relationships. A first-order logic based method is proposed to formalize constraints and to verify constraints and customization. This paper also presents a framework for interaction classification, and an informal mapping between interactions and constraints through constraint semantics. Hong Mei received the BSc and MSc degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1984 and 1987, respectively, and the PhD degree in computer science from the Shanghai Jiao Tong University in 1992. He is currently a professor of Computer Science at the Peking University, China. His current research interests include Software Engineering and Software Engineering Environment, Software Reuse and Software Component Technology, Distributed Object Technology, and Programming Language. He has published more than 100 technical papers. Wei Zhang received the BSc in Engineering Thermophysics and the MSc in Computer Science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999 and 2002, respectively. He is currently a PhD student at the School of Electronics Engineering and Computer Science of the Peking University, China. His research interests include feature-oriented requirements modeling, feature-driven software architecture design and feature-oriented software reuse. Haiyan Zhao received both the BSc and the MSc degree in Computer Science from the Peking Univeristy, China, and the Ph.D degree in Information Engineering from the University of Tokyo, Japan. She is currently an associate professor of Computer Science at the Peking University, China. Her research interests include Software Reuse, Domain Engineering, Domain Specific Languange and Program Transformation.     

Information access in multimedia databases based on feature models

With the increasing popularity of the WWW, the main challenge in computer science has become content-based retrieval of multimedia objects. Access to multimedia objects in databases has long been limited to the information provided in manually assigned keywords. Now, with the integration of feature-detection algorithms in database systems software, content-based retrieval can be fully integrated with query processing. We describe our experimentation platform under development, making database technology available to multimedia. Our approach is based on the new notion of feature databases. Its architecture fully integrates traditional query processing and content-based retrieval techniques. Arjen P. de Vries, Ph.D.: He received his Ph.D. in Computer Science from the University of Twente in 1999, on the integration of content management in database systems. He is especially interested in the new requirements on the design of database systems to support content-based retrieval in multimedia digital libraries. He has continued to work on multimedia database systems as a postdoc at the CWI in Amsterdam as well as University of Twente. Menzo Windhouwer: He received his MSc in Computer Science and Management from the University of Amsterdam in 1997. Currently he is working in the CWI Database Research Group on his Ph.D., which is concerned with multimedia indexing and retrieval using feature grammars. Peter M.G. Apers, Ph.D.: He is a full professor in the area of databases at the University of Twente, the Netherlands. He obtained his MSc and Ph.D. at the Free University, Amsterdam, and has been a visiting researcher at the University of California, Santa Cruz and Stanford University. His research interests are query optimization in parallel and distributed database systems to support new application domains, such as multimedia applications and WWW. He has served on the program committees of major database conferences: VLDB, SIGMOD, ICDE, EDBT. In 1996 he was the chairman of the EDBT PC. In 2001 he will, for the second time, be the chairman of the European PC of the VLDB. Currently he is coordinating Editor-in-Chief of the VLDB Journal, editor of Data & Knowledge Engineering, and editor of Distributed and Parallel Databases. Martin Kersten, Ph.D.: He received his PhD in Computer Science from the Vrije Universiteit in 1985 on research in database security, whereafter he moved to CWI to establish the Database Research Group. Since 1994 he is professor at the University of Amsterdam. Currently he is heading a department involving 60 researchers in areas covering BDMS architectures, datamining, multimedia information systems, and quantum computing. In 1995 he co-founded Data Distilleries, specialized in data mining technology, and became a non-executive board member of the software company Consultdata Nederland. He has published ca. 130 scientific papers and is member of the editorial board of VLDB journal and Parallel and Distributed Systems. He acts as a reviewer for ESPRIT projects and is a trustee of the VLDB Endowment board.     

New Meta-Heuristic for Combinatorial Optimization Problems: Intersection Based Scaling

Combinatorial optimization problems are found in many application fields such as computer science,engineering and economy. In this paper, a new efficient meta-heuristic, Intersection-Based Scaling (IBS for abbreviation), is proposed and it can be applied to the combinatorial optimization problems. The main idea of IBS is to scale the size of the instance based on the intersection of some local optima, and to simplify the search space by extracting the intersection from the instance, which makes the search more efficient. The combination of IBS with some local search heuristics of different combinatorial optimization problems such as Traveling Salesman Problem (TSP) and Graph Partitioning Problem (GPP) is studied, and comparisons are made with some of the best heuristic algorithms and meta-heuristic algorithms. It is found that it has significantly improved the performance of existing local search heuristics and significantly outperforms the known best algorithms.     

Efficient Computation of k-Medians over Data Streams Under Memory Constraints

In this paper, we study the problem of efficiently computing k-medians over high-dimensional and high speed data streams. The focus of this paper is on the issue of minimizing CPU time to handle high speed data streams on top of the requirements of high accuracy and small memory. Our work is motivated by the following observation： the existing algorithms have similar approximation behaviors in practice, even though they make noticeably different worst case theoretical guarantees. The underlying reason is that in order to achieve high approximation level with the smallest possible memory, they need rather complex techniques to maintain a sketch, along time dimension, by using some existing off-line clustering algorithms. Those clustering algorithms cannot guarantee the optimal clustering result over data segments in a data stream but accumulate errors over segments, which makes most algorithms behave the same in terms of approximation level, in practice. We propose a new grid-based approach which divides the entire data set into cells （not along time dimension）. We can achieve high approximation level based on a novel concept called （1 - ε）-dominant. We further extend the method to the data stream context, by leveraging a density-based heuristic and frequent item mining techniques over data streams. We only need to apply an existing clustering once to computing k-medians, on demand, which reduces CPU time significantly. We conducted extensive experimental studies, and show that our approaches outperform other well-known approaches.     

Recent Progress on Selected Topics in Database Research ——-A Report by Nine Young Chinese Researchers Working in the United States

The study on database technologies, or more generally, the technologies of data and information management, is an important and active research field. Recently, many exciting results have been reported. In this fast growing field, Chinese researchers play more and more active roles. Research papers from Chinese scholars, both in China and abroad,appear in prestigious academic forums.In this paper,we, nine young Chinese researchers working in the United States, present concise surveys and report our recent progress on the selected fields that we are working on.Although the paper covers only a small number of topics and the selection of the topics is far from balanced, we hope that such an effort would attract more and more researchers,especially those in China,to enter the frontiers of database research and promote collaborations. For the obvious reason, the authors are listed alphabetically, while the sections are arranged in the order of the author list.     

PAN: A portable,parallel Prolog: Its design,realisation and performance

PAN is a general purpose, portable environment for executing logic programs in parallel. It combines a flexible, distributed architecture which is resilient to software and platform evolution with facilities for automatically extracting and exploiting AND and OR parallelism in ordinary Prolog programs. PAN incorporates a range of compile-time and run-time techniques to deliver the performance benefits of parallel execution while rertaining sequential execution semantics. Several examples illustrate the efficiency of the controls that facilitate the execution of logic programs in a distributed manner and identify the class of applications that benefit from distributed platforms like PAN. George Xirogiannis, Ph.D.: He received his B.S. in Mathematics from the University of Ioannina, Greece in 1993, his M.S in Artificial Intelligence from the University of Bristol in 1994 and his Ph.D. in Computer Science from Heriot-Watt University, Edinburgh in 1998. His Ph.D. thesis concerns the automated execution of Prolog on distributed heterogeneous multi-processors. His research interests have progressed from knowledge-based systems to distributed logic programming and data mining. Currently, he is working as a senior IT consultant at Pricewaterhouse Coopers. He is also a Research Associate at the National Technical University of Athens, researching in knowledge and data mining. Hamish Taylor, Ph.D.: He is a lecturer in Computer Science in the Computing and Electrical Engineering Department of Heriot-Watt University in Edinburgh. He received M.A. and MLitt degrees in philosophy from Cambridge University and an M.S. and a Ph.D. degree in computer science from Heriot-Watt University, Scotland. Since 1985 he has worked on research projects concerned with implementing concurrent logic programming languages, developing formal models for automated reasoning, performance modelling parallel relational database systems, and visualisizing resources in shared web caches. His current research interests are in applications of collaborative virtual environments, parallel logic programming and networked computing technologies.     

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%.     

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.     

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.