Structure of weakly invertible semi-input-memory finite automata with delay 2

Semi-input-memory finite automata,a kind of finite automata introduced by the author of this paper for studying error propagation,are a generalization of input-memory finite automata by appending an autonomous finite automaton component.This paper gives a characterization on the structure of weakly invertible semi-input-memory finite automata with delay 2 in which input alphabets and output alphabets have two elements and autonomous finite automata are cyclic.For the structure of feedforward inverse finite automata with delay 2,Zhu first gave a characterization;from a result on mutual invertibility of finite automata,the result mentioned above also leads to a different characterization on the structure of feedforward result mentioned above also leads to a different characterization on the structure of feedforward inverse finite automata with delay 2.     

The power of communication: P systems with symport/antiport

In the attempt to have a framework where the computation is done by communication only, we consider the biological phenomenon of trans-membrane transport of couples of chemicals (one say symport when two chemicals pass together through a membrane, in the same direction, and antiport when two chemicals pass simultaneously through a membrane, in opposite directions). Surprisingly enough, membrane systems without changing (evolving) the used objects and with the communication based on rules of this type are computationally complete, and this result is achieved even for pairs of communicated objects (as encountered in biology). Five membranes are used; the number of membranes is reduced to two if more than two chemicals may collaborate when passing through membranes. Andrei Paun: He graduated the Faculty of Mathematics of Bucharest University in 1998, received his M.Sc. degree from The University of Western Ontario in 1999, and since then he is a PhD student in the Computer Science Department of University of Western Ontario, London, Canada (under the guidance of prof. Sheng Yu). The topic of his thesis is Molecular Computing (especially, DNA and Membrane Computing), but his research interests also include neural networks, implementing automata, combinatorics on words. Gheorghe Paun: (the proud father of two sons, including the first author of this paper) He is a member of the Romanian Academy, working as a senior researcher in the Institute of Mathematics of the Romanian Academy, Bucharest, and as a Ramon y Cajal researcher in Rovira i Virgili University of Tarragona, Spain. He is one of the most active authors in (the theory of) DNA Computing, (co)author of many papers in this area, (co)author and (co)editor of several books. In 1998 he has initiated the area of Membrane Computing. Other research interests: regulated rewriting, grammar systems, contextual grammars, combinatorics on words, computational linguistics.     

Family of models for describing one class of metallographic images

Models for images syntax are developed, tried, and tested in describing the syntax of microstructural metallographic images of wrought aluminum alloys. Gennadii Mikhailovich Tsibul’skii was born in 1947 and graduated from Krasnoyarsk Polytechnic Institute in 1973. Since 1975, he has been involved in the analysis of digital images. In 1978, he completed his postgraduate course at the Lenin Leningrad Electronic Technical Institute. He received his candidate’s degree in 1987 and a doctoral degree in engineering in 2006. He was appointed a professor in 2007. In 1996, he founded the Artificial Intelligence Systems Department and has worked there as a chairman since then. His scientific interests include the multiagent approach to images analysis, and he is the author of more than 70 publications (including one book published by the Siberian Branch of the Russian Academy of Sciences). At present, Gennadii Tsibul’skii is the director of the Space and Information Technologies Institute at Krasnoyarsk Siberian Federal University. Yurii Anatol’evich Maglinest was born 1965 and graduated from Krasnoyarsk Polytechnic Institute in 1973; he then pursued postgraduate studies there. He received his candidate’s degree in engineering in 1996 in the analysis of metallographic images. He is an associate professor at the State Commission for Academic Degrees and Titles of the Russian Federation. At present, he is a chair of the Scientific University Laboratory of Flexible Software Systems at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University. His scientific interests include aerospace information storage, processing and analysis, and flexible software systems. Dmitrii Al’bertovich Perfil’ev was born in 1968 and graduated from Krasnoyarsk Polytechnic Institute in 1992. Since 2000, he has been specializing in problems in digital images analysis and, in particular, in describing microstructural pictures of aluminum alloys. He received his candidate’s degree in engineering in 2007 and is the author of 8 publications related to the problem in question. At present, he is a researcher and a lecturer at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University.     

A programmable approach to maintenance of a finite knowledge base

In this paper,we present a programmable method of revising a finite clause set.We first present a procedure whose formal parameters are a consistent clause set Γand a clause A and whose output is a set of minimal subsets of Γwhich are inconsistent with A.The maximal consistent subsets can be generated from all minimal inconsistent subsets.We develop a prototype system based on the above procedure,and discuss the implementation of knowledge base maintenance.At last,we compare the approach presented in this paper with other related approaches,The main characteristic of the approach is that it can be implemented by a computer program.     

Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples

An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples. Maria Borisovna Chaley was born in 1963 and graduated from the Moscow Institute of Physics and Technology in 1988 (M.Sc.). She received her PhD in biophysics in 1993 and became a docent in bioinformatics in 2003. At the present time, she is a senior research fellow at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics, genetic text analysis, and molecular evolution. She is the author of over 40 research publications, including 14 journal articles. Nafisa Nailovna Nazipova was born in 1960 and graduated from the Department of Computational Mathematics and Cybernetics of Lenin Kazan State University in 1982 (MSc.). She received her PhD in physics and mathematics (mathematic modeling, numerical methods, and program complexes) in 2002. At the present time, she is the head of the bioinformatics laboratory at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics and the structural and functional organization of genetic sequences. She is the author of over 35 research publications, including 9 papers in refereed journals and 2 book chapters. Vladimir Andreyevich Kutyrkin was born in 1952 and graduated from the Department of Mechanics and Mathematics of Lomonosov Moscow State University in 1974 (MSc.). He received his PhD in physics and mathematics in 1995. At the present time, he is a docent of Bauman Moscow State Technical University. His research interests include applied mathematical statistics, computational and discrete mathematics, and bioinformatics. He is the author of over 20 research publications, including 11 journal papers.     

The Application of the Commparable Corpora in Chinese—English Cross—lingual Information Retrieval

This paper proposes a novel Chinese-English Cross-Lingual Information Retrieval(CECLIR)model PME,in which bilingual dictionary and comparable corpora are used to translate the query terms.The proximity and mutual information of the term-paris in the CHinese and English comparable corpora are employed not only to resolve the translation ambiguities but also to perform the query expansion so as to deal with the out-of -vocabulary issues in the CECLIR.The evaluation results show that the query precision of PME algorithm is about 84.4% of the monolingual information retrieval.     

Handling non-canonical software requirements based on Annotated Predicate Calculus

Eliciting requirements for a proposed system inevitably involves the problem of handling undesirable information about customer's needs, including inconsistency, vagueness, redundancy, or incompleteness. We term the requirements statements involved in the undesirable information non-canonical software requirements. In this paper, we propose an approach to handling non-canonical software requirements based on Annotated Predicate Calculus (APC). Informally, by defining a special belief lattice appropriate for representing the stakeholder's belief in requirements statements, we construct a new form of APC to formalize requirements specifications. We then show how the APC can be employed to characterize non-canonical requirements. Finally, we show how the approach can be used to handle non-canonical requirements through a case study. Kedian Mu received B.Sc. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 1997, M.Sc. degree in probability and mathematical statistics from Beijing Institute of Technology, Beijing, China, in 2000, and Ph.D. in applied mathematics from Peking University, Beijing, China, in 2003. From 2003 to 2005, he was a postdoctoral researcher at Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently an assistant professor at School of Mathematical Sciences, Peking University, Beijing, China. His research interests include uncertain reasoning in artificial intelligence, knowledge engineering and science, and requirements engineering. Zhi Jin was awarded B.Sc. in computer science from Zhejiang University, Hangzhou, China, in 1984, and studied for her M.Sc. in computer science (expert system) and her Ph.D. in computer science (artificial intelligence) at National Defence University of Technology, Changsha, China. She was awarded Ph.D. in 1992. She is a senior member of China Computer Federation. She is currently a professor at Academy of Mathematics and System Sciences, Chinese Academy of Science. Her research interests include knowledge-based systems, artificial intelligence, requirements engineering, ontology engineering, etc. Her current research focuses on ontology-based requirements elicitation and analysis. She has got about 60 papers published, including co-authoring one book. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He designed the “Tian Ma” software systems that have been widely applied in more than 20 fields, including the national defense and the economy. He has won two first class awards from Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Lookeng Prize for Mathematics. Yan Peng received B.Sc. degree in software from Jilin University, Changchun, China, in 1992. From June 2002 to December 2005, he studied for his M.E. in software engineering at College of Software Engineering, Graduate School of Chinese Academy of Sciences, Beijing, China. He was awarded M.E degree in 2006. He is currently responsible for CRM (customer relationship management) and BI (business intelligence) project in the BONG. His research interests include customer relationship management, business intelligence, data ming, software engineering and requirements engineering.     

The use of pattern recognition methods in tasks of biomedical diagnostics and forecasting

Questions of use of pattern recognition methods in medical tasks are discussed. The Recognition program system is presented, which contains a variety of pattern recognition and cluster analysis methods. Some methods based on voting by a system of regularities are discussed in more detail. An example of solved tasks of hysteromyoma relapse forecasting is given. The text was submitted by the authors in English. Yuri Ivanovich Zhuravlev. Born in Voronezh on January 14, 1935. In 1957, graduated from Moscow State University. Doctor of physics and mathematics since 1965, professor since 1967, and an academician of the RAS since 1992. Deputy director of the Dorodnicyn Computing Center of the RAS, chair of the Mathematics Department of the RAS, head of the Chair of Moscow State University, and editor in chief of Pattern Recognition and Image Analysis. Foreign member of the Spanish Royal Academy of Sciences, the National Academy of Sciences of Ukraine, and the European Academy of Sciences. Winner of the Lenin and Lomonosov prizes. Main fields of scientific interest: mathematical logic; control systems theory; the mathematical theory of pattern recognition, image analysis, and forecasting; operation research; and artificial intelligence. Developed such new lines of research as the theory of local optimization algorithms, algorithms of estimate calculation, and the algebraic theory of recognition algorithms. Oleg Valentinovich Senko. Born in Salavat, Bashkortostan, in 1957. In 1981, graduated from the Moscow Institute of Physical Technology; in 1985–1989, took graduate courses, receiving a PhD in 1990. Senior researcher, Dorodnicyn Computing Center, RAS. Doctor of physics and mathematics since 2007. Scientific interests: data mining, mathematical models of pattern recognition, classification and forecasting, and practical applications in medicine and other fields. Vladimir Vasilievich Ryazanov. Graduated from the Moscow Institute of Physical Technology in 1973 and completed graduate courses at the Computing Center of the USSR Academy of Sciences in 1976. Received a PhD in 1977 in computer science and the degree of professor in 1994 (“Applications of Mathematical Methods, Mathematical Modeling, and Computers in Scientific Research”). At the Dorodnicyn Computing Center of the RAS since 1976. Head of the Situations Recognition Sector in the Department of Recognition Problems and Combinatorial Analysis. Author of approximately 150 papers. Scientific interests: data mining; mathematical models of pattern recognition; classification and forecasting; optimization of recognition and classification models; learning; synthesis of optimal collective solutions in the problem of classification; creation of intelligent program systems for data analysis and recognition; and practical applications in technology, medicine, and industry. Anna Victorovna Kuznetsova. Born in Samara (Kuibyshev) in 1961. In 1986 graduated from the Second Moscow Medical Institute. In 1991–1994, took graduate courses, receiving a PhD in biology in 1995. Senior researcher, Emanuel Institute of Biochemical Physics. Scientific interests: pattern recognition, intellectual methods of data analysis, and development of diagnostic and forecasting algorithms in medicine and biological and medical applications in medicine and other fields. Mikhail Afanasievich Botwin. Born in the village of Rashevka of Poltava oblast in Ukraine in 1941. Doctor of medical sciences and Professor of the Sechenov Moscow Medical Academy. Scientific interests: hysteromyoma: pathogenesis, diagnostics, influence on pregnancy, aperiodicities of birth activity, methods of treatment.     

Automatic updating of raster images of electronic charts

The technology being developed to ensure remote updating of raster images of electronic charts (EC) via global data transmission networks is considered. Implemented methods are presented that can automatically synchronize EC with minimal requirements for the channel capacity. The constructed version of the control system and similar general-purpose software are compared in terms of their efficiency in detecting the differences in graphic data for BIG images. Yurii G. Vasin was born in 1940 and graduated from Gorki State University in 1962. He received his doctoral (doctor of science) degree in 1988, is a member of the Russian Academy of Natural Sciences, and a recipient of the Prize of the Council of Ministers of the USSR in 1990. At present, he is the director of the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science, pattern recognition and image processing, and information technologies. He is the author of more than 100 publications. Sergei V. Zherzdev was born in 1976 and graduated from the State University of Nizhni Novgorod in 1999. At present, he is a postgraduate student at the same university and a software engineer at the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science and data compression techniques. He is the author of 7 publications. Evgenii S. Sorokin was born in 1985 and graduated from the Lobachevskii State University of Nizhni Novgorod from the Computational Mathematics and Cybernetics Department in 2007 and the Economics Department in 2008. At present, he is a postgraduate student at the Intellectual Information Systems and Geoinformatics Department. His scientific interests include applied computer science, geoinformation systems, and efficient data processing techniques. He is the author of 6 publications.     

Construction and Application of Hybrid Wavelet and Other Parametric Orthogonal Transforms

We introduce new methods for construction and implementation of various parametric and hybrid orthogonal transforms, including generalized Haar-like, Daubechies, and Coiflet wavelet transforms. The corresponding fast algorithms of computations are briefly discussed and the variance properties of these transforms in analyzing 1-st order Markov processes are investigated. The designed hybrid transforms can be useful in various specific signal processing applications where combining properties of Hadamard and wavelet transforms may be of particular benefit. We also present some numerical results pertaining to image zonal and threshold coding using these hybrid transforms and compare their efficacy with those of traditional orthogonal transforms.Hakob Sarukhanyan received his M.S. degree in Applied Mathematics from Yerevan State University in 1973, and his Ph.D. and D.Sc. degrees in Technical Sciences from the National Armenian Academy of Sciences (NAAS) in 1982 and 1999 accordingly. He has worked as a faculty in the Department of Applied Mathematics at Yerevan State University in 1968–73, and as a junior and senior researcher in the Laboratory of Image Processing Systems at the Institute for Informatics and Automation Problems (IIAP) of the NAAS in 1973–93. He has been the Head of the above Laboratory since 1993 and has been elected a member of the Doctoral Council at IIAP in 2000. He has been a visiting professor at the Tampere Institute of Technology, Finland, in 1999–2001. He is a recipient of research grants from various European funding agencies as well as from the US Civilian and Research Foundation (sponsored by the NSF and the US Department of State). His main research interests are in construction of Hadamard matrices and their applications in wireless communications, combinatorics theory, and fast orthogonal transforms for image processing. He is the author of more than 70 scientific publications in major scientific media.Arthur Petrosian received his M.S. Summa Cum Laude degree in Mathematics from Moscow State University in 1983, and a Ph.D. in Applied Mathematics from the Institute for Problems of Informatics & Automation of the National Armenian Academy of Sciences in 1989. He was a visiting scientist at the Institute of General and Physical Chemistry at Belgrade University, Yugoslavia (1991), an NIH supported postdoctoral fellow at the University of Michigan, Ann Arbor (1992–93), and a research instructor at the Medical College of Ohio, Toledo (1993–94). He joined Texas Tech University Health Sciences Center as an Assistant Professor in 1994, and was appointed as an Adjunct Professor of Mathematics and Electrical and Computer Engineering at Texas Tech University in 1995. He was promoted to the Associate Professor level at Texas Tech University Health Sciences Center in 2000. While at Texas Tech, he received a number of research grant awards to conduct research in EEG signal processing and in biomedical signal/image compression, including from the Federal Administration on Aging, Alzheimers Association, and the US Civilian and Research Foundation (sponsored by the NSF and the US Department of State, to promote cooperative research between the wavelet theory groups in United States and ex-USSR). He is a Senior Member of IEEE and a past member of the New York Academy of Sciences. He is currently a Scientific Review Administrator in the Surgery, Biomedical Imaging, and Bioengineering integrated review group at the National Institutes of Health, US Department of Health and Human Services.     

Using adaptive and intelligent methods in constructing of a model of seismic noise

In the paper, an attempt is made to develop an approach to analysis of seismic signals with the use of Data Mining techniques. A system for registering of signals of seismic noise is described. Algorithms for signal segmentation are proposed. Geppener Vladimir Vladimirovich. Born 1940. Graduated from the Leningrad Electrotechnical Institute in 1964. Received candidate’s degree (in Engineering) in 1969 and Doctoral degree (in Engineering) in 2000. Professor at the Chair of Mathematical Software and Computer Applications of the St. Petersburg Electrotechnical University. Scientific interests: systems of signal processing, methods of artificial intelligence, and pattern recognition theory. Author and coauthor of more than 150 scientific publications. Member of the Russian Association for Pattern Recognition and Image Analysis. Tristanov Aleksandr Borisovich. Born 1981. Graduated with honors from the Kamchatka State Technical University in 2003. Post-graduate student at the Kamchatka State Pedagogical University. Works as an Assistant Professor at the Kamchatka State Pedagogical University. Scientific interests: systems of digital signal processing, methods of artificial intelligence, and frequency-time analysis of signals. Author of 10 scientific publications. Firstov Pavel Pavlovich. Born 1941. Graduated with honors from Polzunov Altai State Polytechnical Institute in 1963. Candidate of Sciences in Physics and Mathematics. Since 1965 works at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences; Head of a laboratory. Scientific interest: volcanic acoustics, nature of earthquake predecessors. Author and coauthor of more than 100 papers and one monograph. Rulenko Oleg Petrovich. Born 1946. Graduated with honors from the Department of Physics and Mathematics of the Kamchatka State Pedagogical Institute in 1968. Received candidate’s degree (in Physics and Mathematics) in 1994. Senior Researcher at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences. Scientific interests: atmospheric electricity, interaction of lithosphere and atmosphere, physics of earthquake predecessors. Author of 29 scientific publications.     

Ontology-Based Semantic Cache in AOKB

When querying on a large-scale knowledge base,a major technique of improving performance is to preload knowledge to minimize the number of roundtrips to the knowledge base.In this paper,an ontology-based semantic cache is proposed for an agent and ontology-oriented knowledge base (AOKB).In AOKB,an ontology is the collection of relationships between a group of knowledge units (agents and/or other sub-ontologies).When loading some agent A,its relationships with other knowledge units are examined,and those who have a tight semantic tie with A will be preloaded at the same time,including agents and sub-ontologies in the same ontology where A is.The proloaded agents and ontologies are saved at a semantic cache located in the memory.Test results show that up to 50% reduction in running time is achieved.     

Adaptive correlation filters for pattern recognition

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984 and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. He is now a titular researcher at the Centro de Investigatión Cientifica y de Educatión Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing and pattern recognition. Mikhail Mozerov received his MS degree in Physics from Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He is with the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, and digital holography. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. He received his candidate’s degree in 1953 and doctoral degree in Information Theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of the IEEE and Popov Radio Society.     

Partial evaluation technique for distributed image processing

In this paper, a partial evaluation technique to reduce communication costs of distributed image processing is presented. It combines application of incomplete structures and partial evaluation together with classical program optimization such as constant-propagation, loop unrolling and dead-code elimination. Through a detailed performance analysis, we establish conditions under which the technique is beneficial. Andrei Tchernykh received his Ph.D. degree in computer science from the Institute of Precise Mechanics and Computer Technology of the Russian Academy of Sciences (RAS), Russia in 1986. From 1975 to 1995 he was with the Institute of Precise Mechanics and Computer Technology of the RAS, Scientific Computer Center of the RAS, and at Institute for High Performance Computer Systems of the RAS, Moscow, Russia. Since 1995 he has been working at Computer Science Department at the CICESE Research Center, Ensenada, Baja California, Mexico. His main interests include cluster and Grid computing, incomplete information processing, and on-line scheduling. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctoral degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigation Cientifica y de Educatión Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Alfredo Cristóbal-Salas received his Ph.D. degree in computer science from the Computer Science Department at the CICESE Research Center, Ensenada, Baja California, México. Now he is a researcher at School of Chemistry Sciences and Engineering, University of Baja California, Tijuana, B.C. Mexico His main interests include cluster and Grid computing, incomplete information processing, and online scheduling. Iosif A. Ovseevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Reliable detection of targets in motion-degraded images

A correlation filter for reliable recognition of a target embedded into a cluttered background blurred by relative motion between a camera and an input scene is derived. With a help of computer simulations the performance of the proposed filter in terms of discrimination capability and location errors is analyzed. The article is published in the original. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, his PhD degree in 1992, and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at CICESE, México. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received Candidate’s degree in 1953 and Doctor’s degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Definitions of invariants for n-dimensional traced vector fields of dynamic systems

In this paper, the method of definitions of invariants of n-dimensional vector fields of dynamic systems based on orthogonal decomposition of vector fields to tangential and gradient components has been studied. This method is meant for defining invariant characteristics at visualization of the vector fields of a dynamic system. Sergei Nikolaevich Chukanov was born in 1951. He graduated from Omsk State Pedagogical Institute in 1974. He received his candidate’s degree (05.07.09) in 1990 and his doctoral degree (05.07.09) in 2000. Currently, he is with the Omsk Department of the Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences as head of a laboratory. His scientific interests included the following: geometrical methods of investigation of control systems of complex dynamic systems and visualization of processes in complex dynamic systems. He is the author of 90 papers.     

Formal Ontology: Foundation of Domain Knowledge Sharing and Reusing

Domain analysis is the activity of identifying and representing the relevant information in a domain,so that the information can be shared and reused in similar systems.But until now,no efficient approaches are available for capturing and representing the results of domain analysis and then for sharing and reusing the domain knowledge.This paper proposes an ontology-oriented approach for formalizing the domain models.The architecture for the multiple-layer structure of the domain knowledge base is also discussed.And finally,some genetic algorithm-based methods have been given for supporting the knowledge sharing and reusing.     

New algorithms for the Perspective-Three-Point Problem

In this paper,two approaches are used to solve the Perspective-Three-Point Problem(P3)):the symbolic computation approach and the geometric approach.In the symbolic computation approach,we use Wu-Ritt‘s zero decomposition algorithm to give a complete triangular decomposition for the P3P equation system.This decomposition provides the firest complete analytical solution to the P3P problem.In the geometric approach,we give some pure geometric criteria for the number of real physical solutions.The complete solution classification for two special cases with three and four paramters is also given.     

Interleaving Radiosity

A system of linear equations is in general solved to approximate discretely the illumination function in radiosity computation.To improve the radiosity solution,a method that performs shooting and gathering in an interleaving manner is proposed in the paper.Besides,a criterion has been set up and tested for choosing object elements used in the gathering operation,and a criterion is established to quantify th solution errors by taking into account more reasonably of the human perception of the radiosity solution.Experimental results show that the method proposed has nice performance in improving the radiosity solution.