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Íàóêîâî-äîñë³äíèé ³íñòèòóò ²íòåëåêòóàëüíèõ êîìï'þòåðíèõ ñèñòåì Òåðíîï³ëüñüêèé Íàö³îíàëüíèé Åêîíîì³÷íèé Óí³âåðñèòåò |
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2003, Òîì 2, Âèïóñê 2 |
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Çì³ñò ³ ðåçþìå
Introduction to the special issue on “Intelligent Data Acquisition and Advanced Computing Systems 2003” Guest Editors Robert E. Hiromoto & John Pollard The theme of the biannual Workshop acknowledges the importance of attracting scientist and engineers from around the world to participate and exchange ideas that can benefit global economics, effective manufacturing processes, delivery of advanced medical systems, environmental monitoring, sensitive and accurate instrumentation, and homeland and cyber security to name just a few areas of interest. The role of intelligent data acquisition and high-performance computing systems comprise the foundation upon which these current and future areas of application areas must ultimately rely on for successful development. The primary contribution for the IDAACS Workshop then is to provide a needed forum to gather collectively professionals from academia, industry, computer hardware manufacturers, and public and private research institutions to discuss and solve problems of interdisciplinary nature. The Workshop sessions were organized under the following topic areas: Advanced Instrumentation in Data Acquisition Systems; Advanced and High-Performance Computing Systems; Artificial Intelligence for Advanced Data Acquisition and Computing Systems; Advanced Mathematical Methods for Data Acquisition and High Performance Computing; Artificial Neural Networks for Advanced Data Acquisition and Computing Systems; Homeland & Cyber Security; Advanced Distributed and Virtual Instrumentation; Modelling and Data Analysis; Intelligent Instrumentation in Distributed and Virtual Systems; Industrial Signal and Image Processing; Advanced Mathematical Methods and Signal Processing; Information Computing Systems; Information Computing Systems for Education and Commercial Applications; Advance Computing Techniques in Economics and Education. The use of mathematical theory and techniques such as neural networks, genetic algorithms and interval logic were backed by in-depth analysis of practical Digital Signal Processing (DSP) and software. The following series of papers applied neural network techniques to solutions that ranged from dynamical systems to homeland security. V. Golovko: “Determining of Lyapunov Spectrum from One-Dimensional Observations Using Neural Network,” describes the evaluation of the Lyapnuvov spectrum using a one dimensional time series from an unknown dynamical system. The approach is based on the reconstruction of attractor dynamics and the application of multilayer perceptron for forecasting the next state of the dynamical system from the previous state. N. Kussul, A. Shelestov, A. Sidorenko, S. Skakun, and V. Pasechnik: “Intelligent Multi-Agent Security System,” proposes a multi-agent approach in developing an intelligent intrusion detection system. In order to detect anomalies in a user’s activities, an on-line multilayer feed-forward neural network is employed to learn the regularities of the user. This approach provides a basis to compare a user model to real activities that may be captured as intrusions. V. Koval, V. Turchenko, V. Kochan, A. Sachenko, and G. Markowsky: “The License Plate Recognition System Based on Image Processing Using Neural Network,” address an emerging area of interest in homeland security. The authors propose a smart license plate recognition system based on image fusion, neural networks, and threshold techniques. A prototype of the system is currently undergoing integration and testing as part of a sensor network. O. Adamiv, V. Koval, I. Turchenko: “Predetermined Movement of Mobile Robot Using Neural Networks,” looks at the problem of navigating a predetermined path in a partially or unknown environment features. The proposed system uses a video camera for viewing the predetermined direction of the robot motion and three-layer neural network with feed-forward links to adjust adaptively to the imperfections encountered during its motion. Laszlo Sragner, Gabor Horvath: “Improved Model Order Estimation for Nonlinear Dynamic Systems,” propose methods to estimate the order of a neural network when used in the modeling nonlinear dynamic systems. The approach taken is the application of the Lipschitz quotient. The associated drawback to this quotient approach is addressed by combining the original Lipschitz method with eh Errors in Variable approach. S. Vazquez-Rodriguez, R.J. Duro: “A Genetic Based Technique for the Determination of Power System Topological Observability,” applies genetic algorithms in the determination of the state of a large electric power system is proposed. A genotype-phenotype transformation scheme is developed to build a spanning tree of topological observables that represent the state of the power system. A. Lamas and R.J. Duro: “A Tool for the Automatic Design of Electronic Control Systems and Circuits for Manufacturing Plants,” describes a tool that applies evolutionary techniques to design automatically distributed digital circuits for the control of all elements in a manufacturing plant. The objective of this tool is to obtain the best set of controllers for the global operation of the plant; rather than, isolating the criteria of particular parameters of the electronic circuits or individual controllers. The application of Interval methods are presented by two papers involved with sensitivity analysis. A. Voschinin, N. Skibitski in “Interval Calibration Model of Multisensor System,” proposes an aggregate interval calibration model to overcome the drawbacks associated with the statistical approach in calibrating a single variable by m sensors of different types. In particular, the interval calibration method avoids the statistical inability to deal with non-statistical type errors and the use of available prior expert information. G. Shilo and N. Gaponenko: “Interval Methods of Assigning the Nominal Tolerances and Choosing Elements,” the use of interval mathematics is applied to the problem of assigning tolerances for the parameters of electronic devices. The authors develop methods of assigning the nominal tolerances by combining simplified interval models and the effects of changes of parameters of elements under stress. Homeland and cyber security is represented by four papers one of which I have discussed above in the neural network section. The papers below represent varying aspect of intrusion detection. S. Sengupta, B. Andriamanalimanana, S.W. Card, P. Kadam, S. Ranwadkar, K. Kas,andS.Parikh: ”Towards Data Mining Temporal Patterns for Anomaly Intrusion Detection Systems,” outlines a low-CPU-usage, low-bufferbased network-, host-, or router-centric intrusion detection system that is essentially an anomaly detector. The basic architecture is iterative where an encounter of a questionable event on it event list triggers an alert signal to the system administrator and post the event to a syslog file for further processing. V. Shyrochin, V. Mukhin, and Hu Zheng Bing: “Users Behavior Model in Tasks of Computer Systems Security Analysis,” describes the use of the state machine for modeling a user’s behavior as a detection tool to protect against unauthorized access. T. Wheeler, K. Vel: “A Scalable Inferencing System for Civilian Terrorism Intelligence,” describes information fusion within the context of its organization as graphs, the directionality of inferences, patterns matching and level of (un)certainties, etc. These characteristic features are then identified within the context of architectural realizations. The application of DSP are applied to new filtering methods and Line CCD sensors. The third paper describe a medical application. J.M. Gorriz, Carlos G. Puntonet, Moises Salmeron, and Julio Ortega: “New Method for Filtered ICA Signals Applied To Volatile Time Series,” proposes a new method for volatile time series forecasting using Independent Component Analysis (ICA) algorithms and Savitzky-Golay filtering as a preprocessing tool. L. Kejzlar, J. Fischer: “Inherent Signal Preprocessing in the Line CCD Sensor,” describes a new image sensor control method where they use a FIR filter mode of operation. I. Kalchev: “DSP-algorithms for cardiac symptoms investigations,” describes an application of digital signal processing (DSP) algorithms in advanced medical treatments has obvious implications. In this regards a DSP algorithm for ECG signal processing is presented and described. The underlying implementation is demonstrated using the MATLAB program. Mathematical theory and application techniques are represented in the following papers: S. Saukh: “Incomplete Cholesky Factorization in Fixed Memory with Flexible Drop-Tolerance Strategy,” proposes a new incomplete Cholesky factorization that is based on a two-parameter drop-tolerance strategy for the insignificant elements in the incomplete factor matrix. This two-parameter strategy has the advantage of forming the factor matrix in fixed memory. C. Renotte, A. Vande Wouwer: “Stochastic Approximation Techniques Applied to Parameter Estimation in a Biological Model,” studies the optimization benefits of a simultaneous perturbation stochastic approximation (SPSA) technique that allows computing an approximation to the gradient of the objective function by performing simultaneous random perturbations in all the parameters. Two variations of the SPSA algorithms are applied to the dynamic modeling of batch animal cell cultures from sets of experimental data. The teaching of mathematics to undergraduates with a range of prior experience is described by M. Iwase, S. Hatakeyama in his paper: “Development of a Teaching Material System for the Fundamental Mathematics Education for Information, Computers and Systems Engineering,” In this paper, the authors present an integrated computer network system for the delivery of fundamental mathematical education. The educational approach relies on both a graphical approach and relevant project style lectures where students are expected to participate in demonstrating the understanding of the mathematical concepts. The need for a simple classification environment to support e-commerce and multinational product processing is discussed by Peter J.A. Reusch and Pascal Reusch. In “Classification of Products and Services to Support Business Process Engineering and e-Commerce,” the author discusses and proposals a new XML-based language to support a unified approach in product and service classification that has significant implications to multinational corporations and e-Commerce in general. The following two papers applies artificial intelligence techniques to solve problems in object recognition and picture area matching. A. Vechur, A. Chayka, Y. Chemodakov: “The Component Method of Scene Analysis and Object Recognition,” proposes a method for scene analysis and object recognition in real time. The goal of their approach is to minimize the demands on the computer resources required to achieve acceptable recognition quality. The practical importance of this work occurs in real-time, environmental decision-making. A.A. Doudkin, R.Kh. Sadykhov, and M.E. Vatkin: “The Algorithms of Quasioptimal Picture Areas Matching,” this work is motivated by the image matching problems that arise in wafer production and printed circuit board inspection. The problem of finding an optimum matching of partially overlapped picture areas is considered. The authors offer two schemes where the first restricts the areas to rectangular regions, and the second is restricted to areas with identical scale. The design of information computing systems are explored in two papers. V. Hahanov, G. Krivoulya, and I. Hahanova, V. Obrizan in “High Performance Fault Simulation for Digital Systems,” develops a fast fault simulation method to address the testing of large-scale digital devices that contains millions of gates. The proposed Backtracked Deductive-Parallel(BDP) fault simulation method is based on the application of Group Theory. Y. Nykolaychuk, N. Krutskevych, O. Zastavniy, T. Grinchyshyn in “Perspective Architecture and Components of Computer Networks,” analyze and design a network with added connectivity for optimal communication and computing balance. Finally, C. Papageorgiou, Th. Laopoulos in “Self-Calibration of Ultrasonic Transducers in an Intelligent Data Acquisition System,” presents their design of an advanced on-line automated testing and calibration technique to improve the performance and lifetime of ultrasonic transducers.> Robert E. Hiromoto Dr. John K. Pollard, Chris Papageorgiou, Theodore Laopoulos Electronics Lab. Physics Dept.,
гñò âèêîðèñòàííÿ ïîòóæíèõ îäíîêðèñòàëüíèõ ì³êðîêîìï’þòåð³â äëÿ êîíòðîëþ ³ çáîðó äàíèõ äຠçìîãó ðîçðîáëÿòè íà ¿õ îñíîâ³ ñó÷àñí³ âèì³ðþâàëüí³ ñèñòåìè. Ó ïðåäñòàâëåí³é ðîáîò³ çàïðîïîíîâàíî íîâèé ï³äõ³ä äî êîíô³ãóðàö³¿ ñèñòåì êîíòðîëþ â ðåæèì³ ðåàëüíîãî ÷àñó äëÿ âäîñêîíàëåííÿ ðîáîòè ³ çðîñòàííÿ òðèâàëîñò³ ñëóæáè óëüòðàçâóêîâèõ ïåðåòâîðþâà÷³â çàâäÿêè çàñòîñóâàííþ àâòîìàòè÷íèõ ìåòîä³â òåñòóâàííÿ ³ êàë³áðóâàííÿ. Àëãîðèòì ðîáîòè ³íñòðóìåíòàëüíî¿ ñèñòåìè áàçóºòüñÿ íà øâèäêîìó âèì³ðþâàíí³ ÷àñòîòè ³ àìïë³òóäè çà äîïîìîãîþ çàïðîïîíîâàíî¿ êîíô³ãóðàö³¿. Êîìá³íàö³ÿ îòðèìàíî¿ ³íôîðìàö³¿ ç ÷àñîì ïðîõîäæåííÿ êîæíî¿ ïîñë³äîâíîñò³ ³ìïóëüñ³â âèêîðèñòîâóºòüñÿ äëÿ âèçíà÷åííÿ ïðàêòè÷íî âñ³õ õàðàêòåðèñòèê óëüòðàçâóêîâèõ ïåðåòâîðþâà÷³â. Çàâäÿêè íèçüêèì âàðò³ñíèì òà ãàáàðèòíèì õàðàêòåðèñòèêàì, ñèñòåìà ìîæå âèêîðèñòîâóâàòèñÿ ÿê äëÿ îïèñó, òàê ³ êëàñèô³êàö³¿ ïåðåòâîðþâà÷³â, à òàêîæ äëÿ òåñòóâàííÿ ³ àâòîìàòè÷íîãî êàë³áðóâàííÿ âáóäîâàíîãî áëîêó ó áóäü-ÿê³é âèñîêîïðîäóêòèâí³é óëüòàçâóêîâ³é ñèñòåì³. S. Vazquez-Rodriguez, R. J. Duro Grupo de Sistemas Autonomos,
 äàí³é ñòàòò³ ìè àäðåñóâàëè ïðîáëåìó ñïîñòåðåæóâàíîñò³ ïîòóæíîñò³ ñèñòåì ç òî÷êè çîðó òîïîëîã³÷íî¿ ñïîñòåðåæóâàíîñò³ ³ âèêîðèñòàííÿ ãåíåòè÷íèõ àëãîðèòì³â äëÿ ¿õ âèçíà÷åííÿ. Ìåòà ðîáîòè – çíàéòè øëÿõ äëÿ âèçíà÷åííÿ ïîòóæíîñò³ ñèñòåìè ç âèêîðèñòàííÿì òîïîëîã³÷íèõ äàíèõ ³ îäåðæàòè òîïîëîã³÷íå äåðåâî ñèñòåìè âèì³ðþâàííÿ, ÿêà ïåðåâ³ðÿº êîíêðåòí³ çíà÷åííÿ ³ç âèêîðèñòàííÿì äîñòóïíèõ âèì³ðþâàíü. Äëÿ öüîãî ðîçðîáëåíî ñõåìó ãåíîòèïíî–ôåíîòèïíîãî ïåðåòâîðåííÿ äëÿ ãåíåòè÷íèõ àëãîðèòì³â, ÿêà äຠçìîãó âèêîðèñòîâóâàòè äóæå ïðîñò³ ãåíåòè÷í³ îïåðàòîðè íàä áàçîâèìè õðîìîñîìàìè ö³ëîãî ÷èñëà äëÿ ïîáóäîâè ñêëàäíèõ òîïîëîã³÷íèõ äåðåâ. Ïðîöåäóðà áóëà óñï³øíî çàñòîñîâàíà äî ñòàíäàðòíèõ ñèñòåì êîíòðîëþ ³ â ðîáîò³ ïðåäñòàâëåíî äåÿê³ ðåçóëüòàòè êîíòðîëüíîãî òåñòó. Sergey Saukh G.Y. Pukhov's Institute of Modelling Problems in Power
Engineering,
NAS of Ukraine
Çàïðîïîíîâàíî íåïîâíå ðîçêëàäàííÿ íà ìíîæíèêè Õîëºñüêîãî äëÿ ðîçâ’ÿçêó ïîçèòèâíî-âèçíà÷åíèõ ñèñòåì ð³âíÿíü âåëèêî¿ ðîçì³ðíîñò³ ³ çíàõîäæåííÿ âåëèêî¿ äîâ³ð÷î¿ îáëàñò³ ï³äðîçâ’ÿçê³â. Ðîçêëàäàííÿ íà ìíîæíèêè áàçóºòüñÿ íà äâîïàðàìåòðè÷í³ (m,p) – ñòðàòå㳿 çíèæåííÿ òîëåðàíòíîñò³ äëÿ íåçíà÷íèõ åëåìåíò³â â íåïîâí³é ôàêòîðí³ ìàòðèö³. Çàïðîïîíîâàíèé ðîçêëàä íà ìíîæíèêè ïî ñóò³ çìåíøóº íåãàòèâí³ ïðîöåñè íåðåãóëÿðíîãî ðîçïîä³ëó ³ íàãðîìàäæåííÿ ïîõèáîê â ìàòðèö³ ôàêòîð³â ³ çàáåçïå÷óº îïòèìàëüíó ïðîïîðö³þ çàïîâíåííÿ ïàì’ÿò³ ³ñòîòíèìè íåíóëüîâèìè åëåìåíòàìè. Íà ïðîòèâàãó â³äîìîìó p– çáåðåæåííÿ ³ t-ñòðàòå㳿 çíèæåííÿ òîëåðàíòíîñò³, (m,p) – ñòðàòåã³ÿ äຠçìîãó ñôîðìóâàòè ìàòðèöþ ôàêòîð³â ó ïîñò³éí³é ïàì’ÿò³. Vladimir Golovko Brest State Technical University,
 äàíí³é ñòàòò³ îáãîâîðþºòüñÿ íåéðîìåðåæåâèé ï³äõ³ä äëÿ îá÷èñëåííÿ ñïåêòðà Ëÿïóíîâà ç âèêîðèñòàííÿì îäíîì³ðíîãî ÷àñîâîãî ðÿäó îòðèìàíîãî â³ä íåâ³äîìî¿ äèíàì³÷íî¿ ñèñòåìè. Òàêèé ï³äõ³ä áàçóºòüñÿ íà ðåêîíñòðóêö³¿ äèíàì³êè àòðàêòîðà ³ çàñòîñóâàííÿ áàãàòîøàðîâèõ ïåðöåïòðîí³â äëÿ ïðîãíîçóâàííÿ íàñòóïíîãî ñòàíó äèíàì³÷íî¿ ñèñòåìè íà îñíîâ³ ïîïåðåäíüîãî. Öå äຠçìîãó òî÷íî ³ åôåêòèâíî îö³íþâàòè ñïåêòð Ëÿïóíîâà â³ä íåâ³äîìî¿ äèíàì³÷íî¿ ñèñòåìè âèêîðèñòàííÿ çà äîïîìîãîþ ò³ëüêè îäíîãî ñïîñòåðåæåííÿ. Íàâåäåíî ðåçóëüòàòè åêñïåðèìåíò³â. N. Kussul, A. Shelestov, A. Sidorenko, S. Skakun, V. Pasechnyk Space Research Institute NASU-NSAU,
Çàïðîïîíîâàíî ï³äõ³ä ç âèêîðèñòàííÿì àãåíò³â äëÿ ñòâîðåííÿ ³íòåëåêòóàëüíî¿ ñèñòåìè âèÿâëåííÿ âòîðãíåííÿ. Ñèñòåìà äຠçìîãó âèÿâëÿòè â³äîì³ òèïè àòàê ³ àíîìà볿 â àêòèâíîñò³ êîðèñòóâà÷à ³ ïîâåä³íö³ îá÷èñëþâàëüíî¿ ñèñòåìè. Ñèñòåìà âêëþ÷ຠð³çí³ òèïè ³íòåëåêòóàëüíèõ àãåíò³â, ïðè÷îìó íàéá³ëüø âàæëèâèì º àãåíò êîðèñòóâà÷à, ÿêèé áàçóºòüñÿ íà íåéðîìåðåæåâ³é ìîäåë³ éîãî ïîâåä³íêè. Çàïðîïîíîâàíèé ï³äõ³ä åêñïåðèìåíòàëüíî ïåðåâ³ðåíî â ä³þ÷³é ³íòðàìàðåæ³ ²íñòèòóòó Ô³çèêè ³ Òåõíîëîã³é Íàö³îíàëüíîãî Òåõí³÷íîãî Óí³âåðñèòåòó Óêðà¿íè “Êè¿âñüêèé Ïîë³òåõí³÷íèé ²íñòèòóò ”. ²ÍÒÅËÅÊÒÓÀËÜÍÀ ÑÈÑÒÅÌÀ ÐÎÇϲÇÍÀÂÀÍÍß ÍÎÌÅÐÍÈÕ ÇÍÀʲ ÍÀ ÎÑÍβ ÎÁÐÎÁÊÈ ÇÎÁÐÀÆÅÍÜ Ç ÂÈÊÎÐÈÑÒÀÍÍßÌ ÍÅÉÐÎÍÍί ÌÅÐÅÆ² V. Koval*, V. Turchenko*, V. Kochan*, A. Sachenko*, G. Markowsky** * Ternopil Academy of National Economy, Institute of Computer
Information
Technologies,
 äàí³é ñòàòò³ îïèñóºòüñÿ ³íòåëåêòóàëüíà ñèñòåìà ñòåæåííÿ çà àâòîìîá³ëÿìè, ÿêà ìîæå áóòè ³íñòàëüîâàíà íà ïðîïóñêíîìó ïóíêò³ äëÿ àâòîìàòèçîâàíîãî ðîçï³çíàâàííÿ íîìåðíèõ çíàê³â àâòîìîá³ë³â ç âèêîðèñòàííÿì ôîòîãðàô³¿ àâòîìîá³ëÿ. Àâòîìàòèçîâàíà ñèñòåìà ìîæå áóòè âïðîâàäæåíà äëÿ êîíòðîëþ ïëàòåæ³â çà ïàðêîâêó, îïëàòè çà êîðèñòóâàííÿ àâòîáàíàìè, ìîñòàìè ÷è òóíåëÿìè òîùî. Ðîçãëÿäàºòüñÿ ï³äõ³ä äî ³äåíòèô³êóâàííÿ àâòîìîá³ëÿ øëÿõîì ðîçï³çíàâàííÿ íîìåðíîãî çíàêó ç âèêîðèñòàííÿì òåõíîëî㳿 çëèòòÿ çîáðàæåíü, íåéðîííèõ ìåðåæ, ïîðîãîâî¿ òåõíîëî㳿, à òàêîæ ðåçóëüòàòè åêñïåðèìåíòàëüíèõ äîñë³äæåíü ç ðîçï³çíàâàííÿ íîìåðíèõ çíàê³â. Aleksander Vechur, Aleksey Chayka, Yuriy Chemodakov Kharkov National University of Radioelectronics,
Îñíîâíà ìåòà ö³º¿ ðîáîòè – çàïðîïîíóâàòè ìåòîä, ÿêèé äîçâîëÿº äåÿê³é ìàøèí³ ³äåíòèô³êóâàòè íàâêîëèøí³ îá’ºêòè. Âàæëèâî, ùî àëãîðèòì ïîâèíåí âèêîðèñòîâóâàòè îáìåæåí³ êîìï’þòåðí³ ðåñóðñè òàê, ùîá ìàøèíà ìîãëà á ïðàöþâàòè â ðåàëüíîìó ÷àñ³. Äëÿ âäîñêîíàëåííÿ ñåãìåíòàö³¿ íà ïðèðîäíèõ çîáðàæåííÿõ íåîáõ³äíî åôåêòèâíî îá’ºäíàòè ð³çíîð³äí³ îçíàêè. Òåîðåòè÷íèé àíàë³ç ï³äòâåðäæåíî åêñïåðèìåíòàëüíèìè ðåçóëüòàòàìè. Sam Sengupta*, Bruno Andriamanalimanana*, Stuart W. Card**, Pradnya Kadam*, Saket Ranwadkar*, Kaustav Das*, Sagar Parikh* * State University of New York Institute of Technology, Utica
NY
13504-3050,
Îáãðóíòîâàíî ìîäåëü àðõ³òåêòóðè íåñêëàäíîãî ³ ìåðåæåâîãî öåíòðó IDS, ùî áàçóºòüñÿ íà àíîìà볿 êåðîâàíîãî ïîíÿòòÿ ñòàíó “àíîìàë³ÿ ”. Ïðè öüîìó â³äïîâ³äíà äèñòðèáóòèâíà ôóíêö³ÿ íå çàëèøàºòüñÿ ïîñò³éíîþ ³ ìîæå ì³ãðóâàòè â³ä ñòàíó äî ñòàíó áåç áóäü-ÿêîãî àïð³îðíîãî ïîïåðåäæåííÿ ïîêè ÷àñ ¿¿ ³ñíóâàííÿ â íàñòóïíîìó ñò³éêîìó ñòàí³ º äîñòàòíüî òðèâàëèì, ùîá çðîáèòè òàì ä³éñí³ ñïîñòåðåæåííÿ. Ðîçãëÿíóòî ò³ëüêè ò³ ïî䳿 âòîðãíåííÿ (ïî ñóò³ DOS ³ ð³çíîìàí³òí³ñòü DDOS), ÿê³ çäàòí³ äî ïåðåìèêàííÿ íåïðàâèëüíèõ ïîòîê³â, à òàêîæ/àáî ö³ëüîâ³ êîíòðîëüí³ òî÷êè íà ïåðøîìó ð³âí³ âèÿâëåííÿ. ϳäêðåñëåíî, ùî íà íàñòóïíîìó ð³âí³ âèÿâëåííÿ, ô³ëüòðîâàí³ ñòàíè ìîãëè á áóòè âïîðÿäêîâàí³ â ïàêåòíîìó ðåæèì³ äî äîáóâàííÿ íåïðèéíÿòíèõ ðÿäê³â êîìàíä àáî â³äîìèõ ï³äïèñ³â àòàê. A.A. Doudkin*, R.Kh. Sadykhov**, M.E. Vatkin* * United Institute of Informatics Problems of NASB, 6,
Surganova
str.,
BY-220012, Minsk, Belarus,
Ðîçãëÿíóòî âèêîðèñòàííÿ çàãàëüíîãî óçãîäæåííÿ â³äïîâ³äíèõ êðèòåð³¿â ÿêîñò³ äëÿ ïðîáëåìè îïòèìàëüíî¿ â³äïîâ³äíîñò³ ÷àñòêîâî ïåðåêðèòèõ îáëàñòåé çîáðàæåííÿ. Çàïðîïîíîâàíî äâ³ ñõåìè àëãîðèòì³â äëÿ êâàç³-îïòèìàëüíîãî âèð³øåííÿ ïðîáëåìè ç íàñòóïíèìè îáìåæåííÿìè: îáëàñò³ º ïðÿìîêóòíèìè ³ ìàþòü îäíàêîâó øêàëó. Ó ïåðø³é ñõåì³ âèêîðèñòàíî ëîêàëüí³ êðèòåð³¿, ùîá îö³íèòè êâàäðàòí³ ôðåéìè, ðîçòàøîâàí³ â êâàäðàòí³é ìàòðèö³, à ó äðóã³é ñõåì³ – ñïåö³àëüíó ôóíêö³þ â³äñòàí³, ÿêà áàçóºòüñÿ íà êðèòå𳿠â³äïîâ³äíîñò³ ñóñ³äíîãî çîáðàæåííÿ. Àëãîðèòìè ðåàë³çîâàíî â ïðîãðàìí³é ñèñòåì³ ïëàíóâàííÿ â³äíîâëåííÿ ìåòàë³çàö³¿ ³íòåãðàëüíèõ ñõåì. Oleh Adamiv*, Vasyl Koval**, Iryna Turchenko*** Ternopil Academy of National Economy, Institute of Computer
Information
Technologies,
Äàíà ñòàòòÿ îïèñóº åêñïåðèìåíòàëüí³ ðåçóëüòàòè âèêîðèñòàííÿ íåéðîííèõ ìåðåæ äëÿ óïðàâë³ííÿ ìîá³ëüíèì ðîáîòîì çà âèçíà÷åíîþ òðàåêòîð³ºþ ðóõó. Ðîçãëÿäàºòüñÿ ïðîöåñ ôîðìóâàííÿ íàâ÷àëüíî¿ íàá³ðêè äëÿ íåéðîííî¿ ìåðåæ³, ¿õ ñòðóêòóðè ³ îñîáëèâîñò³ ïðîöåñó ìîäåëþâàííÿ. Äîñë³äæåííÿ ïîêàçàëè íàä³éíèé ðóõ ìîá³ëüíîãî ðîáîòà íà ð³çíèõ ä³ëÿíêàõ äîðîãè. Juan M. Gorriz*, Carlos G. Puntonet**, Moises Salmeron**, E.W. Lang*** 1) Dpto. Ingenierya de Sistemas y Automatica, Tec.
Electronica y
Electronica,
 äàí³é ñòàòò³ çàïðîïîíîâàíî íîâèé ìåòîä äëÿ ïðîãíîçóâàííÿ êîðîòêîòðèâàëèõ ÷àñîâèõ ðÿä³â, ÿêèé ïåðåäáà÷ຠâèêîðèñòàííÿ íåçàëåæíèõ àëãîðèòì³â êîìïîíåíòíîãî àíàë³çó ³ ô³ëüòðà Savitzky-Golay â ÿêîñò³ ³íñòðóìåíòàëüíîãî çàñîáó ïîïåðåäíüî¿ îáðîáêè. Äàí³ ïîïåðåäíüî¿ îáðîáêè ïðåäñòàâëåíî íà îñíîâ³ áàçîâî¿ ðàä³àëüíîé ôóíêö³¿ øòó÷íî¿ íåéðîííî¿ ìåðåæ³, à ðåçóëüòàò ïðîãíîçó ïîð³âíþºòüñÿ ç òèì, ÿêèé îòðèìàíî áåç âèêîðèñòàííÿ ³íñòðóìåíòàëüíèõ çàñîá³â ïîïåðåäíüî¿ îáðîáêè àáî êëàñè÷íîãî çàñîáó êîìïîíåíòíîãî àíàë³çó. ²ÍÑÒÐÓÌÅÍÒÀËÜÍÈÉ ÇÀѲÁ ÄËß ÀÂÒÎÌÀÒÈ×ÍÎÃÎ ÏÐÎÅÊÒÓÂÀÍÍß ÅËÅÊÒÐÎÍÍÈÕ ÓÏÐÀÂËßÞ×ÈÕ ÑÈÑÒÅÌ ² Ê²Ë ÄËß ÏÐÎÌÈÑËÎÂÈÕ Ï²ÄÏÐȪÌÑÒ A. Lamas, R. J. Duro Grupo de Sistemas Autonomos,
²íñòðóìåíòàëüíèé çàñ³á âèïðîáóâàíî â ðåàëüíîìó çàñòîñóâàíí³ ìåáåëüíîãî âèðîáíè÷îãî ï³äïðèºìòâà íà äåìîíñòðàö³éí³é ë³í³¿ ëàêóâàííÿ. Alexander Voschinin*, Nikita Skibitski** * Prof, Doctor of Technical Sciences,
 ÷èñëåííèõ äîäàòêàõ âàæëèâå ì³ñöå çàéìຠïðîáëåìà êàë³áðóâàííÿ ìóëüòèñåíñîðíèõ ñèñòåì. ×àñò³øå âñüîãî ¿¿ âèð³øóþòü ç âèêîðèñòàííÿì ñòàòèñòè÷íèõ ìåòîä³â ç âèêîðèñòàííÿì äàíèõ êàë³áðóâàëüíîãî åêñïåðèìåíòó. Îäíàê, â áàãàòüîõ âèïàäêàõ íåâèçíà÷åí³ñòü ³ íåòî÷í³ñòü åêñïåðèìåíòàëüíèõ äàíèõ äîö³ëüíî îïèñóâàòè íå â òåðì³íàõ âèïàäêîâèõ ïîìèëîê, à ó âèãëÿä³ îáìåæåíîãî çíà÷åííÿ àáñîëþòíî¿ ïîõèáêè âèì³ðþâàííÿ. Äëÿ öüîãî âèïàäêó îïèñàíî ³íòåðâàëüíó êàë³áðóâàëüíó ìîäåëü, ÿêà îñíîâàíà íà çàïðîïîíîâàíîìó ïîíÿòò³ ³íòåðâàëüíèõ âèì³ðþâàíü.  ðàìêàõ ³íòåðâàëüíî¿ ïàðàäèãìè äîñèòü ïðîñòî âèð³øóþòüñÿ âñ³ îñíîâí³ çàäà÷³ êàë³áðóâàííÿ, âêëþ÷àþ÷è ïåðåâ³ðêó ÷óòëèâîñò³ ñåíñîðà, âèáîð ï³äìíîæèíè êðàùèõ ñåíñîð³â ³ çíàõîäæåííÿ àãðåãîâàíî¿ ³íòåðâàëüíî¿ îö³íêè âèì³ðþâàëüíî¿ âåëè÷èíè. Íàâåäåíî ÷èñëîâ³ ïðèêëàäè. ÇÀÑÒÎÑÎÂÀÍÍß ÌÅÒÎIJ ÑÒÎÕÀÑÒÈ×Íί ÀÏÐÎÊÑÈÌÀÖ²¯ ÄÎ ÎÖ²ÍÊÈ ÏÀÐÀÌÅÒв Á²ÎËÎò×Íί ÌÎÄÅ˲ C. Renotte, A. Vande Wouwer Service d’Automatique,
Ñèíõðîííà ïåðòóðáàö³éíà ñòîõàñòè÷íà àïðîêñèìàö³ÿ º êëàñîì îïòèì³çàö³éíèõ àëãîðèòì³â, ùî îá÷èñëþþòü íàáëèæåííÿ ãðà䳺íòà ³ /àáî Ãåñåíñüêî¿ îá’ºêòèâíî¿ ôóíêö³¿ çà äîïîìîãîþ îäíî÷àñíî¿ çì³íè âñ³õ åëåìåíò³â âåêòîðà ïàðàìåòðà ³ òàêèì ÷èíîì, âèìàãàþòü ò³ëüêè äåê³ëüêà îá’ºêòèâíèõ îö³íîê ôóíêö³¿, ùîá îäåðæàòè ³íôîðìàö³þ ïåðøîãî àáî äðóãîãî ð³âíÿ. Îòæå, ö³ àëãîðèòìè îñîáëèâî ï³äõîäÿòü äî ïðîáëåì ç âêëþ÷åííÿì âåëèêîãî ðÿäó ïàðàìåòð³â ïðîåêòó. Ó öüîìó âèïàäêó ¿õ ïîòåíö³éí³ ìîæëèâîñò³ îö³íþþòüñÿ â êîíòåêñò³ íåë³í³éíîãî ñèñòåìíîãî îòîòîæíåííÿ. Ó çâ’ÿçêó ³ç öèì ðîçãëÿíóòî âèêîðèñòàííÿ ñòèìóëþþ÷èõ ìîäåëåé, òîáòî äèíàì³÷íå ìîäåëþâàííÿ ïàêåòó êë³òèí òâàðèííèõ êóëüòóð ç íàáîð³â åêñïåðèìåíòàëüíèõ äàíèõ. Îáãîâîðåíî ðåàë³çàö³þ àëãîðèòì³â îïòèì³çàö³¿ â ïîêàçíèêàõ åôåêòèâíîñò³, òî÷íîñò³ ³ ëåãêîñò³ âèêîðèñòàííÿ. Laszlo Sragner*, Gabor Horvath** Department of Measurement and Information Systems,
Ó ñèñòåìíîìó ìîäåëþâàíí³ âèá³ð â³äïîâ³äíî¿ ñòðóêòóðè ìîäåë³ º ïåðøî÷åðãîâèì çàâäàííÿì. Ñòðóêòóðà ìîäåë³ º âèçíà÷åíîþ, ÿêùî ³ êëàñ ìîäåë³, ³ ðîçì³ð ìîäåë³ âñåðåäèí³ öüîãî êëàñó º âèçíà÷åíèìè. Ó äèíàì³÷í³é ñèñòåì³ ìîäåëþâàííÿ ðîçì³ð ìîäåë³ ïåðåâàæíî âèçíà÷àºòüñÿ çà äîïîìîãîþ ïîðÿäêó ìîäåë³.  ñòàòò³ ðîçãëÿäàºòüñÿ ìîäåëü îö³íêè ïîðÿäêó ñèñòåìè, êîëè íåéðîíí³ ìåðåæ³ âèêîðèñòîâóþòüñÿ äëÿ ìîäåëþâàííÿ íåë³í³éíèõ äèíàì³÷íèõ ñèñòåì. Îäèí ³ç ìîæëèâèõ øëÿõ³â îö³íþâàííÿ ïîðÿäêó íåéðîííî¿ ìîäåë³ º âèêîðèñòàííÿ êîåô³ö³ºíòó ˳ïøèöà. Õî÷à öåé ìåòîä ëåãêî âèêîðèñîâóâàòè, ãîëîâíîþ ïåðåøêîäîþ º âèñîêà ÷óòëèâ³ñòü äî çàøóìëåíèõ äàíèõ. Çàïðîïîíîâàíî íîâèé øëÿõ çìåíøåííÿ åôåêòó øóìó. ²äåÿ çàïðîïîíîâàíîãî ìåòîäó – îá’ºäíàòè ïåðâèííèé ìåòîä ˳ïøèöà ³ ï³äõ³ä ïîõèáîê ó çì³ííèõ. Ïðåäñòàâëåíî äåòàë³ çàïðîïîíîâàíîãî îá’ºäíàíîãî ìåòîäó ³ ïîäàíî ðåçóëüòàòè îáøèðíîãî åêñïåðèìåíòàëüíîãî âèâ÷åííÿ. Ludek Kejzlar, Jan Fischer Czech Technical University,
Äàíó ñòàòòþ ïðèñâÿ÷åíî îïèñó ³ ïðàêòè÷í³é ïåðåâ³ðö³ íîâîãî ìåòîäó ë³í³éíîãî ñåíñîðíîãî CCD êîíòðîëþ. Öåé ìåòîä âèêîðèñòàíî äëÿ âíóòð³øíüî¿ ïîïåðåäíüî¿ îáðîáêè ñèãíàëó àáî îáðîáêè â ë³í³éíîìó äàò÷èêó CCD. Çàïðîïîíàâàíèé ìåòîä º ïðèäàòíèì äëÿ êîìïåíñàö³¿ ðóõó, îäíî-ôðåéìîâîãî àáî äâî-ôðåéìîâîãî ô³ëüòðóâàííÿ. ×åðåç ïîä³áí³ñòü ì³æ íåðåêóðñèâíèì öèôðîâèì ô³ëüòðîì ç ê³íöåâîþ ³ìïóëüñíîþ õàðàêòåðèñòèêîþ (β ) ³ íîâèì ìåòîäîì éîãî íàçâàíî ìåòîäîì β.  äàíó ñòàòòþ òàêîæ âêëþ÷åíî âèñíîâêè ³ ïîðàäè, ïðèäàòí³ äëÿ âèêîðèñòàííÿ ë³í³éíîãî äàò÷èêà CCD â ðåæèì³ Î². Ivan Kalchev Faculty of Automatics,
Äîñÿãíåííÿ
ïåðåäîâî¿ ìåäèöèíè íåìîæëèâ³ áåç âèêîðèñòàííÿ òåõí³÷íèõ íàóê. Ñïðîáè
îòðèìàòè
äîñêîíàë³ ³íñòðóìåíòè ³ âèñîêó òî÷í³ñòü òåõí³÷íèõ ñèñòåì äëÿ àíàë³çó
ñåðöåâî¿
àêòèâíîñò³ º äîñ³ âèêëèêîì äî ³íæåíåð³â. Âèíàõ³ä ñèñòåì åôåêòèâí³øèõ ³
ç êðàùèìè ïîêàçíèêàìè ÿêîñò³ çàáåçïå÷èòü øâèäêó ä³àãíîñòèêó, ³ òàêèì
÷èíîì
êðàùå ìåäè÷íå îáñëóãîâóâàííÿ.
Galina Shilo*, Nikolay Gaponenko** * Ph.D, Zaporizhzhia National Technical University,
Çàïðîïîíîâàíî ïðîöåäóðó çàäàííÿ íîì³íàëüíî¿ òîëåðàíòíîñò³. Âèêîðèñòàíî ìîäåë³ ³íòåðâàëüíî¿ ñòðóêòóðè. Ïðèéìàºòüñÿ äî óâàãè âïëèâ ôàêòîðà âèÿâëåííÿ. Ãàðàíòîâàíî ìàêñèìàëüíèé â³äíîñíèé îáñÿã òîëåðàíòíîñò³. Ïðèéíÿòî äî óâàãè ìîæëèâ³ñòü âèáîðó åëåìåíò³â. Vladimir Hahanov, Gennadiy Krivoulya, Irina Hahanova, Olga Melnikova, Vladimir Obrizan Professor, Ukraine, 61166, Kharkov,
Çàïðîïîíîâàíî øâèäêèé ìåòîä äåäóêòèâíî-ïàðàëåëüíîãî ìîäåëþâàííÿ äëÿ â³äñë³äêîâóâàííÿ äåôåêò³â, ùî îð³ºíòóºòüñÿ íà îáðîáêó ñêëàäíèõ öèôðîâèõ ïðèëàä³â, êîòð³ ì³ñòÿòü ñîòí³ òèñÿ÷ ð³âíîö³ííèõ âåíòèë³â. Îïèñàíî ñòðóêòóðè äàíèõ ³ àëãîðèòìè äëÿ ðåàë³çàö³¿ ìåòîäó. Yaroslav Nykolaychuk*, Nazar Krutskevych*, Oleg Zastavniy*, Taras Grinchyshyn** * Ternopil Academy of National Economy,
Äàíó ñòàòòþ ïðèñâÿ÷åíî âèêîðèñòàííþ ïåðñïåêòèâíèõ àðõ³òåêòóð. Êîìïîíåíòè êîìï’þòåðíèõ ìåðåæ çá³ëüøàòü ïðîäóêòèâí³ñòü ³ íàä³éí³ñòü ñïåö³àë³çîâàíèõ êîìï’þòåðíèõ ìåðåæ íå çà ðàõóíîê ïîêðàùåííÿ åëåìåíò³â ñèñòåìè. Masami Iwase, Shoshiro Hatakeyama, Katsuhisa Furuta Department of Computers and Systems Engineering,
Íåùîäàâíî, îñîáëèâî â ïðèâàòíèõ êîëåäæàõ ßïîí³¿, â³äçíà÷åíî ðîçêèä îñâ³òí³õ äîñÿãíåííü ñòóäåíò³â, ³ ôóíäàìåíòàëüíà îñâ³òà äëÿ ïåðøîêóðñíèê³â ñòຠâàæëèâ³øîþ. Âèìàãàþòüñÿ îñâ³òí³ ìàòåð³àëè, ÿê³ ï³ä³éìàþòü ð³âåíü ñòóäåíò³â ç³ ñëàáêèìè äîñÿãíåííÿìè ³ îäíî÷àñíî çàö³êàâëÿòü ³íøèõ ñòóäåíò³â. Òîìó â ö³é ñòàòò³ ïðåäñòàâëåíî íàâ÷àëüíó ìàòåð³àëüíó ñèñòåìó ³ íàâ÷àííÿ â ïðîåêòíîìó ñòèë³ ç îñíîâ ìàòåìàòèêè äëÿ ïåðøîêóðñíèê³â, ÿêèé ïðîäåìîíñòðîâàíî íà íàø³é êàôåäð³. ϳäêðåñëåíî, ùî âèêîðèñòîâóþ÷è äàíó ñèñòåìó, êîæíîìó ñòóäåíòó ïðèçíà÷àþòüñÿ ïåâí³ äàí³, ÿê³ ñêëàäàþòü ïðîåêò, òàê, ùîá ìàòè óñï³õ íå ò³ëüêè ó âäîñêîíàëåíí³ îñâ³òíüîãî åôåêòó, àëå òàêîæ ó ñòâîðåíí³ äåöåíòðàë³çîâàíî¿ ñèñòåìè ñòâîðåííÿ/çáîðó ñòóäåíòñüêèõ äàíèõ. Peter J. A. Reusch*, Pascal Reusch** * University of Applied Sciences Dortmund,
Äåñÿòèð³÷÷ÿ
òîìó ³íäèâ³äóàëüí³ ï³äõîäè äî êëàñèô³êàö³¿ ïðîäóêò³â ³ ïîñëóã áóëè
ïðåäñòàâëåí³
âñåðåäèí³ êîìïàí³é äëÿ óí³ô³êàö³¿ ïðîäóêò³â ³ äëÿ çìåíøåííÿ çàïàñ³â ³
âèòðàò.
²íø³ ï³äõîäè áóëè ïðåäñòàâëåí³ äëÿ ï³äòðèìêè ì³æíàðîäíî¿ òîðã³âë³ ³
òàðèôíèõ
ñèñòåì. Ñüîãîäí³ íîâ³ ï³äõîäè ïðåäñòàâëÿþòüñÿ äëÿ ï³äòðèìêè åëåêòðîííî¿
òîðã³âë³ ³ âäîñêîíàëåííÿ á³çíåñ-ïðîöåñ³â. Âñ³ ö³ ï³äõîäè â³äð³çíÿþòüñÿ
òèì, ÿê êëàñèô³êóâàòè, ùî êëàñèô³êóâàòè, ³ ÿê³ îòðèìóþòüñÿ ðåçóëüòàòè –
³ ìîâó, ÿêó âîíè ïðè öüîìó âèêîðèñòîâóþòü. Ãàðìîí³çàö³ÿ âñ³õ öèõ
ï³äõîä³â
º äóæå âàæêîþ. Àëå êîìïàí³¿, ÿê³ áàæàþòü áðàòè ó÷àñòü â
á³çíåñ³-äî-á³çíåñó,
îñîáëèâî ïîòðåáóþòü ìîñò³â ì³æ ð³çíèìè ï³äõîäàìè.
Thomas J. Wheeler*, Karpagavalli Vel** * University of Maine,
Äàíà
ñòàòòÿ
îïèñóº ï³äõ³ä äî ðîçðîáêè ³íòåëåêòóàëüíî¿ ñèñòåìè äëÿ ³íôîðìóâàííÿ
öèâ³ëüíîãî
íàñåëåííÿ ïðî òåðîðèñò³â. ª î÷åâèäíà ïîòðåáà â òàê³é ñèñòåì³ â ñâ³òë³
íåâäà÷
ç ³íôîðìóâàííÿ ñóñï³ëüñòâà, ÿêå ïðèâåëî äî àòàê 11 âåðåñíÿ. Òóò
ðîçó쳺òüñÿ
äîäàòîê äî ëþäñüêîãî ³íôîðìàö³éíîãî àíàë³çó; â òîé ÷àñ, ÿê ³íôîðìàö³éí³
àíàë³òèêè º ôàõ³âöÿìè ñâ ñïðàâè, âàæêî ðîç³áðàòèñÿ, ÿêà ³íôîðìàö³ÿ º
âàæëèâîþ òà ³íòåãðóâàòè ¿¿. ²íôîðìàö³ÿ ³ âèâ³ä íå çàâæäè ñïðÿìîâóþòüñÿ
â ïîòð³áíîìó íàïðÿìêó. Àâòîìàòèçîâàíà ïîðàäà ìîæå äîïîìîãòè
³íôîðìàö³éíèì
àíàë³òèêàì ³ ìåíåäæåðàì â çàïîá³ãàíí³ ³íøèì òðàãåä³ÿì.
V.P. Shyrochin*, V.E. Mukhin**, Hu Zheng Bing*** * Ukraine, Professor
Áåçïåêà êîìï’þòåðíèõ ñèñòåì ð³çíîìàí³òíîãî ïðèçíà÷åííÿ òà â³äïîâ³äí³ ³íôîðìàö³éí³ òåõíîëî㳿 çíà÷íîþ ì³ðîþ çàëåæàòü â³ä çàñîá³â ³äåíòèô³êàö³¿ òà àóòåíòèô³êàö³¿ êîðèñòóâà÷à, à òàêîæ â³ä ³íñòðóìåíò³â àíàë³çó ¿õ ïîâåä³íêè ³ ïîâåä³íêè ¿õ ïðîãðàìíîãî çàáåçïå÷åííÿ ïðîòÿãîì äîñòóïó äî òèõ ÷è ³íøèõ ³íôîðìàö³éíèõ ðåñóðñ³â. Äàíà ñòàòòÿ ïðèñâÿ÷åíà äîâåäåííþ ìåòîäó âèêîðèñòàííÿ â³äîìîãî ôîðìàë³çìó – ê³íöåâèõ àâòîìàò³â äëÿ ìîäåëþâàííÿ ïîâåä³íêè êîðèñòóâà÷³â ³ äî òåñòóâàííÿ çàõèñíèõ ³íñòðóìåíòàëüíèõ çàñîá³â íà âèÿâëåíí³ ñïðîá íåñàíêö³îíîâàíîãî äîñòóïó äî ³íôîðìàö³éíèõ ðåñóðñ³â, âêëþ÷àþ÷è ðàíí³ ñòà䳿 ï³äãîòîâêè äî òàêèõ ä³é. |