Choice of the objective function for pseudogradient measurement of image parameters

Approaches to the choice of objective functions and the form of the pseudogradient are considered for pseudogradient evaluation of interframe geometrical image deformations. The possibilities of reducing the computational cost of calculation of the pseudogradient are analyzed. Minkina Galina Leonidovna. Born in 1983. Fifth-year student at Ulyanovsk State Technical University, specialty applied mathematics. Scientific interests: optimization of parameters of algorithms for estimating interframe geometrical image deformations. Samoilov Mikhail Yurievich. Born in 1981. Graduated from Moscow State University in 2003. PhD student at Ulyanovsk State Technical University. Scientific interests: minimization of the computational cost in estimation of interframe geometrical image deformations. Tashlinskii Alexander Grigorievich. Born in 1954. Graduated from the Ulyanovsk Polytechnical Institute in 1977. Received doctoral degree in 2000. Professor in the Department of Systems of Computer Aided Design. Scientific interests: statistical image processing, in particular, estimation of spatiotemporal deformations of sequences of dynamical images. Author of 200 publications, including more than 60 papers and a monograph on estimation of parameters of spatial deformations of sequences of images. Full member of the International Academy of Authors of Scientific Discoveries and Investigations and of the Russian Academy of Natural Sciences. Awarded medals from these academies.     

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.     

Identification of stable description elements using an active sensor

A two-phase method for the pattern-driven recognition of objects in images is presented, implemented, and tested numerically. The method is based on the use of an active sensor. Possibilities for development are envisaged. This approach was shown to have advantages in solving the object-background separation problem and a high recognition rate was achieved with slow learning. Dmitrii I. Mednikov. Born in 1987. Fifth-year student at the Faculty of Mathematics and Mechanics of Moscow State University. Scientific interests include image recognition and image processing. Author of one paper. Alexei Milovidov. Born in 1986. Graduated from the Faculty of Mathematics and Mechanics of Moscow State University in 2008. Scientific interests include image recognition. Author of 3 papers. Semen Yu. Sergunin. Born in 1980. Graduated from the Faculty of Mathematics and Mechanics of Moscow State University in 2002. Postgraduate student at the same faculty. Scientific interests include image recognition. Author of five papers. Mikhail I. Kumskov. Born in 1956. Graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in 1978. Received his candidate’s degree in Physics and Mathematics in 1981 and doctoral degree in 1997. Professor at the Department of Computational Mathematics of the Faculty of Mathematics and Mechanics of Moscow State University. Scientific interests include the prediction of properties of chemical compounds, optimization of structural objects representation for classification problems, and image processing. Author of more than 50 papers.     

基于SOA的企业应用系统集成研究与实现

文中分析了企业应用系统集成的必要性和点对点集成及EAI方法的不足.探讨了面向服务的架构模型及其集成思想.提出了基于SOA的企业应用系统集成的解决方案,并给出了一个具体应用集成的开发示例.充分论证了基于SOA的应用系统集成开发的优越性和必然性.Web服务技术是实现SOA的最佳实践.     

Wiener estimation method in estimating of spectral reflectance from RGB images

Color is one of the most important features in digital images. The representation of color in digital form with a three-component image (RGB) is not very accurate, hence the use of a multiple-component spectral image is justified. At the moment, acquiring a spectral image is not as easy and as fast as acquiring a conventional three-component image. One answer to this problem is to use a regular digital RGB camera and estimate its RGB image into a spectral image by the Wiener estimation method, which is based on the use of a priori knowledge. In this paper, the Wiener estimation method is used to estimate the spectra of icons. The experimental results of the spectral estimation are presented. The text was submitted by the authors in English. Pekka Tapani Stigell. Year of birth 1976. Year of graduation and name of institution: Last year undergraduate student in the Department of Computer Science in the University of Joensuu, Finland. Affiliation: InFotoics Center, Department of Computer Science, University of Joensuu. Position: Trainee. Area of research: Color research. Number of publications: 1. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition). Prizes for achievements in research or applications: The best young scientist award in PRIA-7-2004 (shared with two other scientists). Kimiyoshi Miyata. Year of birth: 1966. Year of graduation and name of institution: 2000. Graduate School of Science and Technology, Chiba University, Japan. Year of graduation: 1990, BE degree (Chiba University), 1992, ME degree (Chiba University), 2000, Ph.D degree (Chiba University). Affiliation: Museum Science Division, Research Department, National Museum of Japanese History. Position: Assistant Professor. Area of research: Improvement of image quality, color management, application of imaging science and technology to museum activities. Number of publications: 11. Membership to scientific societies: Society of Photographic Science and Technology of Japan, Optical Society of Japan, Institute of Image Electronics Engineers of Japan, Society for Imaging Science and Technology. Prizes for achievements in research or applications: Progressing Award from Society of Photographic Science and Technology of Japan in 2000, Itek Award from Society for Imaging Science and Technology in 2000. Markku Hauta-Kasari. Year of birth: 1970. Graduation and name of the institution: University of Technology, Lappeenranta, Finland. Year of graduation: 1999, Ph.D. degree (University of Technology, Lappeenranta). Affiliation: InFotonics Center, Department of Computer Science, University of Joensuu. Position: Director. Area of research: Color research, neural computation, pattern recognition, optical pattern recognition, computer vision, image processing. Number of publications: Articles in refereed international scientific journals: 5, Articles in refereed international scientific conferences: 9, Other Scientific Publications: 40. Membership to academies: Chairman of the Pattern Recognition Society of Finland May 2003. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition), Finnish Information Processing Association, Finnish Union of University Researchers and Teachers, Optical Society of Japan, Optical Society of America. Prizes for achievements in research or applications: The best Ph.D.-thesis award in the field of pattern recognition in 1998–1999 in Finland. Award was issued by the Pattern Recognition Society of Finland on April 25, 2000.     

Adaptive Control of Bifurcation Modes in Nonautonomous Nonlinear Systems

A new statement of the problem of adaptive control aimed at stabilizing the desired properties of bifurcation modes of nonlinear nonautonomous systems is put forward. An algorithm of control over measurements of the output is worked out for the class of Lurie systems, which affords the convergence of adjustable parameters to objective unknown values ensuring the desired properties of bifurcation modes of a system. The solution rests on the use of the theory of passive systems and adaptive observers. Examples of the analytical calculation and computational modeling are given, which illustrate the effectiveness and capacity of the obtained solution: a neuron integrator, excitation of a resonance mode for a pendulum, and an oscillatory system of the third order.__________Translated from Avtomatika i Telemekhanika, No. 5, 2005, pp. 97–108.Original Russian Text Copyright © 2005 by Efimov.This work was supported by the Fund of assistance of the native science and Program no. 19 of the Presidium of the Russian Academy of Sciences, project no. 1.4.     

Two approaches to determining similarity of two digraphs

An approach to solving the problem of determining similarity with application of a maximal common fragment of two graphs is considered. Its two main disadvantages are specified. Two new approaches to solving the problem of determining similarity of digraphs are proposed: a generalized substructural-metric approach and an approach using a stratified system of matrix models of the digraph complexity. New features for investigating similarity of digraphs are formulated. The original problem of calculating similarity of layout of fragments in the digraph is formalized with account of quantitative and qualitative features of fragments of the digraph. A methodology, involving two systems of methods for solving the problem, is developed. The first system of methods takes into account the precise layout of fragments in the digraph, while the second one deals with the approximate layout of fragments. A new class of problems is distinguished, which consists in calculating similarity of digraphs with account of similarity of the layout of fragments of the specified type. An example of solving the problem of finding semantic networks that are most similar to a network-template is presented.     

A method of defuzzification based on the approach of areas' ratio

The new method of defuzzification of output parameters from the base of fuzzy rules for a Mamdani fuzzy controller is given in the paper. The peculiarity of the method is the usage of the universal equation for the area computation of the geometric shapes. During the realization of fuzzy inference linguistic terms, the structure changes from the triangular into a trapezoidal shape. That is why the universal equation is used. The method is limited and can be used only for the triangular and trapezoidal membership functions. Gaussian functions can also be used while modifying the proposed method. Traditional defuzzification models such as Middle of Maxima − MoM, First of Maxima − FoM, Last of Maxima − LoM, First of Suppport − FoS, Last of Support − LoS, Middle of Support − MoS, Center of Sums − CoS, Model of Height − MoH have a number of systematic errors: curse of dimensionality, partition of unity condition and absence of additivity. The above-mentioned methods can be seen as Center of Gravity − CoG, which has the same errors. These errors lead to the fact that accuracy of fuzzy systems decreases, because during the training root mean square error increases. One of the reasons that provokes the errors is that some of the activated fuzzy rules are excluded from the fuzzy inference. It is also possible to increase the accuracy of the fuzzy system through properties of continuity. The proposed method guarantees fulfilling of the property of continuity, as the intersection point of the adjustment linguistic terms equals 0.5 when a parametrized membership function is used. The causes of errors and a way to delete them are reviewed in the paper. The proposed method excludes errors which are inherent to the traditional and non- traditional models of defuzzification. Comparative analysis of the proposed method of defuzzification with traditional and non-traditional models shows its effectiveness.     

Evaluation of local dependencies of images wavelet decomposition

An approach to identifying local area structure that is used in the spatial interaction models adapted to the image characteristic properties using mutual information criterion is reviewed in this article. Experimental results that demonstrate the value of using the represented method are shown. The text was submitted by authors in English. Arcady Lvovich Zhiznyakov. Candidate of engineering science. Docent of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of more than 150 science publications. Vasilii Evgenevich Gai. A postgraduate student of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of about 40 science publications. Sultan Sadykovich Sadykov. Doctor of engineering science, professor. Professor of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of more than 200 science publications.     

Classes of cost functions for string edit distance

Finding a sequence of edit operations that transforms one string of symbols into another with the minimum cost is a well-known problem. The minimum cost, or edit distance, is a widely used measure of the similarity of two strings. An important parameter of this problem is the cost function, which specifies the cost of each insertion, deletion, and substitution. We show that cost functions having the same ratio of the sum of the insertion and deletion costs divided by the substitution cost yield the same minimum cost sequences of edit operations. This leads to a partitioning of the universe of cost functions into equivalence classes. Also, we show the relationship between a particular set of cost functions and the longest common subsequence of the input strings. This work was supported in part by the U.S. Department of Defense and the U.S. Department of Energy.     

Studies in the theory of algebraic models of programs with procedures

Algebraic models of programs with procedures extend algebraic models of programs that are free of procedures (simple models of programs). A specific feature of both types of models is that they are built for some formalization of software programs. Models of programs are intended for studying functional equivalence of formalized programs and constructing wide sets of equivalent transformations of programs. Two basic problems in the theory of algebraic models of programs are the equivalence problem and the problem of building complete systems of equivalent transformations. An increasing interest in models of programs with procedures is due to the abundance of results obtained for simple models of programs. The most suitable model of programs with procedures is a gateway model. A remarkable feature of these models is that every such model is induced by some simple model of programs. This paper gives a survey of the latest results obtained for gateway models of programs.     

Software environment for simulating algorithms for image analysis and processing

We consider the purpose, functionality, configuration, and structure of a software environment designed for simulation and investigation of methods, algorithms, and information technology for digital images analysis and processing. Mikhail V. Gashnikov. Born 1975. Graduated from the Samara State Airspace University (SSAU) in 1998. Received candidate’s degree in Technology in 2004. He is now an assistant professor at the chair of earth information of the SSAU. Scientific interests: image processing, compression, statistical coding. Author of more than 30 publications, including 12 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis. Evgenii V. Myasnikov. Born 1981. Graduated from the Samara State Airspace University in 2004. He is now a post-graduate student at the Chair of Earth Information of the Samara State Airspace University. Scientific interests: development of software systems, image processing, image retrieval in databases. Author of 6 publications, including one paper. Member of the Russian Association for Pattern Recognition and Image Analysis. Andrei V. Chernov. Born 1975. Graduated from the Samara State Airspace University (SSAU) in 1998. Received candidate’s degree in Technology in 2004. He is now an assistant professor at the Chair of Earth Information of the SSAU and a research fellow at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing, pattern recognition, geoinformation systems. Author of more than 50 publications, including 11 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis. Nikolai I. Glumov. Born 1962. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1985. Received candidate’s degree in Technology in 1994. He is now a senior researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing and pattern recognition, compression of images, simulation of systems of digital image formation. Author of more than 50 publications, including 21 papers and one monographs (in coauthorsip). Member of the Russian Association for Pattern Recognition and Image Analysis. Vladislav V. Sergeev. Born 1951. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1974. Received doctoral degree in Technology in 1993. Head of the Laboratory of Mathematical Methods for Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: digital signal processing, image analysis, pattern recognition, earth information. Author of more than 150 publications, including approximately 40 papers and two monographs (in coauthorship). President of the Povolzh’e Branch of the Russian Association for Pattern Recognition and Image Analysis. Corresponding member of the Russian Ecological Academy and of the Russian Academy of Engineering Sciences. Member of the International Society for Optical Engineering. A laureate of the Samara Provincial Government prize in science and engineering. Marina A. Chicheva. Born 1964. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1987. Received candidate’s degree in Technology in 1998. She is now a senior researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image recognition, compression, fast algorithms for discrete transformations. Author of more than 40 publications, including 15 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis.     

Segmentation of isolated words based on dynamics of parameters of short correlation functions

In the paper, results of theoretical and experimental studies of dynamics of parameters of short correlation functions of speech signals are exposed. We present the results of the theoretical investigation of the dependence of the maxima of correlation functions of quasi-periodic signals in a general form on the characteristics defining the degree of their quasi-periodicity. Based on these dependences, estimates for the parameters of the quasi-periodicity degree, of the length of quasi-periodic intervals, of the main period, etc. are constructed. The obtained estimates are interpreted in the case of speech signals, and it is shown that many important parameters used in speech technologies can be calculated from them. Experimental results on segmentation of isolated words into separate phonemes are given as an example of efficiency of the approach based on the analysis of correlation functions. It is shown that the segmentation based on monitoring of singularities of dynamics of parameters of short correlation turns out to be stable and adequate to perception. Vyacheslav E. Antsiperov. Born in 1959. Graduated from the Moscow Institute of Physics and Technology in 1982. Received candidate’s degree in 1986. Senior Researcher at the Institute of Radio Engineering and Electronics, Russian Academy of Sciences. Scientific interests: information theory, recognition and identification theory, and computer modeling of biological aspects of human activities, including speech and image recognition and modeling of other functions of central nervous system. Author of more than 30 papers. Vladimir A. Morozov. Born in 1932. Graduated from the Moscow Institute of Energetics in 1956. Received candidate’s degree in 1964. Chief of the Statistical Radiophysics Department of the Institute of Radio Engineering and Electronics, Russian Academy of Sciences. Scientific interests: information theory, weak signal detection, signal processing, stochastic recognition, and speech recognition. Author of more than 80 papers. Sergei A. Nikitov. Born in 1955. Graduated from the Moscow Institute of Physics and Technology in 1979. Received candidate’s degree in 1982 and Doctoral degree in 1991. Professor, Principal Researcher, the Head of Laboratory at the Institute of Radio Engineering and Electronics, Russian Academy of Sciences. Since 2004 corresponding member of the Russian Academy of Sciences. Scientific interests: informatics, including speech processing, magnetoelectronics, and nonlinear dynamics. Author of more than 100 papers.     

Detection of stable synoptical features of sea surface from a series of infrared satellite images

Maps of the dominant orientation of thermal contrasts (statistically significant tangents to isotherms) on infrared images obtained from NOAA satellites are capable of representing the sea circulation structure in difficult cloudy conditions and, thus, can be used for quantitative analysis of synoptical-scale processes on the sea surface. For this purpose, one should plot compositional maps and estimate the lifetime of the dominant orientations. An approach to plotting such maps is described, the lifetime of the dominants is estimated, and the applicability conditions of the proposed method are investigated. The dominants stable for a week are obtained and their correlation with synoptical-scale objects is revealed. It is demonstrated that such objects can be detected using maps of the dominant orientations of thermal contrasts. Aleksanin Anatolii Ivanovich. Born 1956. Graduated from the Moscow Institute of Physics and Technology in 1979. Received candidate’s degree (in Engineering) in 1987. Works at the Institute of Automatics and Control Processes, Far-East Division, Russian Academy of Sciences, as the Head of the Laboratory of Satellite Monitoring. Scientific interests: dynamics of the atmosphere of the earth and sea, investigation of the Earth from the space, data processing. Author of 37 scientific publications. Aleksanina Marina Georgievna. Born 1960. Graduated from the Moscow Institute of Physics and Technology in 1983. Received candidate’s degree (in Engineering) in 1998. Works at the Institute of Automatics and Control Processes, Far-East Division, Russian Academy of Sciences, as Senior Researcher. Scientific interests: investigation of the Earth from the space, processing and analysis of images of sea surface and atmosphere. Author of 24 scientific publications.     

Parallel structures and self-organization of heterogeneous polynomial neural networks for pattern recognition and diagnostics of states

We consider different parallel architectures and methods for self-organization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of pattern recognition and in diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows. Adil’ Vasil℉evich Timofeev. Born in 1944. Graduated from the Moscow State Technical University in 1967. Received candidate’s degree in 1970 and doctoral degree in 1988. Works at the St. Petersburg Institute for Informatics and Automation of RAS as Head of the Laboratory of Neuroinformatics and Intelligent Control, at the Research Institute of Applied Mathematics and Automation of RAS as Head of Department of Intellectualization of Information-Control Systems, and at the St. Petersburg State University as Professor of the Chair of Informatics. Scientific interests: neuroinformatics, pattern recognition, adaptive and intelligent control, virtual reality, intelligent robotics, and multiagent systems. Author of 17 monographs and 219 papers. Member of International Academy of Navigation and Motion Control and International Academy of Technological Cybernetics; member of the Editorial Boards of the journals Information Theories and Applications, Differential Equations and Control Processes, and Mechatronics, Automation, Control. Honored Worker of Science.     

Wait-free linearization with a mechanical proof

Summary The correctness of a program for wait-free linearization ofan arbitrary shared data object in bounded memory is verified mechanically. The program uses atomic read-write registers, an array of consensus registers and one compare and swap register. In the program, a number of processes concurrently inspect and modify a pointer structure without waiting. Consequently, the proof of correctness is very delicate. The theorem prover NQTHM of Boyer and Moore has been used to mechanically certify the correctress. Wim H. Hesselink received his Ph.D. in mathematics from the University of Utrecht in 1975. After ten years of research in Lie algebras he turned to computer science. In 1986/1987 he was on leave with the Department of Computer Sciences of the University of Texas at Austin. Currently, he is chairman of the Department of Computing Science at the University of Groningen. His research interests include distributed programming, design and correctness of algorithms, mechanicaltheorem proving, and predicate transformation semantics of recursive procedures with nondeterminacy of various flavors.     

Modeling user action planning: A comprehension based approach

We review our efforts to model user command production in an attempt to characterize the knowledge users of computers have at various stages of learning. We modeled computer users with a system called NETWORK (Mannes and Kintsch, 1988; 1991) and modeled novice, intermediate, and expert UNIX command production data collected by Doane et al. (1990b) with a system called UNICOM (Doane et al., 1989a; 1991). We use the construction-integration theory of comprehension proposed by Kintsch (1988) as a framework for our analyses. By focusing on how instructions activate the knowledge rele/ant to the performance of the specified task, we have successfully modeled major aspects of correct user performance by incorporating in the model knowledge about individual commands and knowledge that allows the correct combination of elementary commands into complex, novel commands. Thus, experts can be modeled in both NETWORK and in UNICOM. We further show that salient aspects of novice and intermediate performance can be described by removing critical elements of knowledge from the expert UNICOM model. Results suggest that our comprehension-based approach has promise for understanding user interactions and implications for system design are discussed.Dr. Stephanie Doane is Assistant Professor of Psychology and appointed at the Beckman Institute at the University of Illinois. Shereceived her BAin Experimental Psychology from the University of California, Santa Barbara, her MS in Experimental Psychology from Villanova University, and her PhD in Cognitive Psychology from the University of California, Santa Barbara. Dr. Doane's research has focused on skill acquisition and the development and validation of theoretically-based computational models of cognitive processes. Her current research addresses issues of learning to interact with complex systems and the role of learning context in skill acquisition.Dr. Suzanne Mannes is Assistant Professor of Psychology at the University of Delaware. She received her BA in Psychology from the State University of New York College at Plattsburgh and received her PhD in Cognitive Psychology from the University of Colorado at Boulder. Her experimental research focuses on the role of prior knowledge in text comprehension, particularly as it pertains to problem-solving abilities. She also investigates the use of hybrid computer systems to simulate results from such studies.Dr. Walter Kintsch is Professor of Psychology and Director of the Institute of Cognitive Science at the University of Colorado in Boulder. He received his MA and PhD degrees in Experimental Psychology from the University of Kansas. His main area of interest has been the psychology of language and memory. He is currently the editor of the Psychological review.Peter Poison is Professor of Psychology and member of the Institute of Cognitive Science at the University of Colorado. He received his BA degree in Psychology and BS degree in Industrial Engineering from Stanford University and his PhD degree in Psychology from Indiana University. Dr. Poison's research has focused on the development and empirical evaluation of mathematical and computer simulation models of cognitive processes including transfer of training, problem solving, and the acquisition of cognitive skills. His current research deals with quantitative models of human-computer interaction and the application of such models to the design of more easily learned computer systems.     

基于Android 平台的工程图学助教助学系统研究与实现

针对空间想象能力培养的教学需要,对基于Android 平台的工程图学助教助学系统 进行了研究。根据教与学的需要,确定了系统功能,设计了软件的框架结构。分析研究了移动设 备的系统平台,选择了系统的开发及运行平台。通过分析Android 框架中OpenGL ES 的设计接口, 实现了模型的轴测显示模式。通过分析OpenGL ES 中glDrawElements 方法的数据需求和VRML97 的数据格式,设计了虚拟模型加载器,实现了Android 系统中的VRML 模型3D 浏览器。开发了 软件的核心功能模块,设计实现了基于Android 平台的工程图学助教助学系统。该系统因其创新 性和实用性,在2014 年第十四届全国多媒体课件大赛中荣获一等奖。     

多管火箭射击精度仿真试验新方法

该文提出了多管火箭射击精度的仿真试验新思路,构造了射击精度仿真试验的基本框架。对多管火箭射击精度仿真试验的研究内容、研究方法和技术途径进行了论述,表明系统仿真模型的校核、验证和确认是关键。提出应用多体系统传递矩阵法,建立刚柔耦合多管火箭动力学模型,通过建立增广特征矢量,获得其正交性条件,实现对多管火箭振动特性和动力响应的精确分析的仿真算法。结合简易控制和弹道理论,建立多管火箭射击精度仿真系统。应用最大熵方法,形成了多管火箭射击精度仿真试验的新方法。     

Neural network algorithms for analyzing multidimensional time series for predicting events and their application to study of Sun-Earth relations

In the paper, an original neural network algorithm for analysis of time series is presented. This algorithm allows predicting the occurrence of a certain event and finding a time interval to which a phenomenon (a precursor or a cause of the event) belongs. The characteristics of the algorithm functioning are investigated applied to the study of the solar-terrestrial relationship. Yu. V. Orlov. Candidate in Physics and Mathematics. Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis. Yu. S. Shugai. Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction. I. G. Persiantsev. Professor, Doctor in Mathematics and Physics. Head of the Laboratory, Leading Researcher at the Institute of Nuclear Physics, Moscow State University Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction, inverse problems. Laureate of the USSR State Prize. S. A. Dolenko. Candidate in Physics and Mathematics. Senior Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction, inverse problems.