Математика, механика, информатика. Рубрика в журнале - Сибирский аэрокосмический журнал
Context-dependent grammar design for describing complex scenes with multi-level object motion
Статья научная
The problems of context-dependent grammars formation which describes structural information about patterns and pattern interaction in complex scenes are discussed in this article. The application of three-level grammar based on the task of an image sequences syntactic analysis (with extended contents of main and auxiliary dictionaries) and the task of scene syntactic analysis with multi-level object motion is suggested.
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Cинтез оптимальных по быстродействию систем высокого порядка
Статья научная
Исследованы возможности синтеза систем автоматического регулирования, описываемых системами дифференциальных уравнений третьего и четвертого порядка, оптимальных по быстродействию. Выполнен анализ существующих разработок для создания систем высокого порядка, оптимальных по быстродействию. Представлена простая методика синтеза, основанная на методе фазовых траекторий. Предлагаемая методика включает в себя все этапы создания оптимальной по быстродействию системы от исходного описания в виде дифференциального уравнения или передаточной функции до формирования корректирующего звена. Сложность создания системы высокого порядка, оптимальной по быстродействию, заключается в необходи- мости иметь для управления информацию о n – 1 производных, где n – порядок системы дифференциальных уравнений. Однако технически получить такую информацию практически невозможно. Предлагается способ создания устройства для получения необходимой информации при синтезе систем высокого порядка, опти- мальных по быстродействию
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Determining the source of transverse oscillations of an elastic rod
Статья научная
Solvability of an inverse problem for the equations of transverse oscillations of an elastic rod (determining the source of oscillations on the basis of the rod deflection at the final time) is proved.
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Development and investigation of the effectiveness of the particle swarm optimization algorithm
Статья научная
This article deals with investigation of the effectiveness of the Particle Swarm Optimization (PSO) [1] algorithm for solving constrained and unconstrained one- and multi-criteria optimization problems. Besides the investigations were conducted both the standard and the binary PSO. Also parallelized modifications of these algorithms were developed for multi-processor operations and two real-world problems were solved.
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Dynamic modeling of a bucket-wheel excavator propelling motor
Статья научная
The article reviews algorithmization of a dynamic model of a bucket-wheel excavator propelling motor during its motion. There are essential schemes and formulas used in dynamic modeling algorithmization.
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Статья научная
The problem of vector-based semantic classification over the words and notions of the natural language is discussed. A set of generative grammar rules is offered for generating the semantic classification vector. Examples of the classification application and a theorem of optional formal classification incompleteness are presented. The principles of assigning the meaningful phrases functions over the classification word groups are analyzed.
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Empirical relationship for queue length estimation in a system with fractal shot input
Статья научная
Traffic in modern data networks and information systems are most adequately described by different classes of fractal models. This kind of models takes into account the following key characteristics of traffic as high variability events grouping and explicit correlation structure on different time scales. Fractal shot process FSNDP, referring to the fractal point process is sufficiently accurate approximation of the network load at individual workstations or small workgroups, is defined with five numerical parameters, with known estimating algorithms on available samples (based on actual traffic dumps). Studies based on queueing system simulation with input FSNDP stream managed to establish a stable relationship between the change in each of the input parameters and the average queue length in the system. Confirmed direct correlation queue length of the parameter characterizing the amplitude of the individual load bursts, found an inverse relationship of the index related to the Hurst parameter and master degree of fractal properties. Based on the identified dependencies, obtained empirical relations between parameters of FSNDP process and the average queue length in single-channel queueing system with unlimited queue and deterministic service discipline FSNDP/D/1. These relationships allow to estimate the average volume of buffer used and the average delay introduced by the network equipment in the load conditions expressed fractal properties from measurements of real traffic. The presence of the formulae increases the importance of traffic models based on FSNPD, since it makes possible to perform a full cycle analysis of queueing systems and queueing networks without involving the simulation methods.
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Статья научная
In this article the integrated approach for automatic formation ensembles of neural networks is proposed. The applying multi-criteria “Self-configurable” genetic programming is described. To each new generated network the mostefficient (“best”) network is added, which by two criteria were estimated on the first stage of the algorithm. Thusa population of neuralnetwork ensembles is created. The criterion of effectiveness of new networks is the third criterion –the effectiveness of ensemble decision, which includes in this network ensemble. Thefinal ensemble with selected net-works by third criteria is created. Also in this article the approach forformation of ensemble decision using the decisions of an added neural networks – Scheme ED1 is applied. Proposed method ondifferent tasks with different amountof inputs and outputs signals (neurons) in ANN was tested. In the resultthis method shows high efficiency.
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Evolutionary algorithm for automatic generation of neural network based noise suppression systems
Статья научная
We propose using neural network technology to noise suppress in information signals. Neural networks are automatically generated and adjusted with an evolutionary algorithm. It is shown that the evolutionary algorithm provides a reliable noise suppress system.
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Evolutionary design of neural networks for forecasting of financial time series
Статья научная
The problem offorecasting in various technical, economic, and other systems is an important problem of nowadays. The methods of artificial intelligence and machine learning analyze very effectively various data including financial ones. The main problem of such techniques is the choice of model structure and the configuration of its parameters. In this paper we propose an evolutionary method for the neural network designing that does not require any expert knowledge in the area of neural networks and optimization theory from the user. This algorithm has been applied to the FOREX forecasting task of 13 different currency pairs based on the historical data for 12,5 years. The performance of the proposed algorithm has been compared to the forecasting results of other 6 algorithms. The proposed algorithm has shown the best performance on more than half of the tasks. On remaining tasks the algorithm yields slightly to the multi-layer perceptron trained by the particle swarm optimization algorithm. However, the predominance of the proposed algorithm is more significant.
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Fault tolerant CMOS realization of a minority function for aerospace computer complexes
Статья научная
In recent years, increased attention is paid to the reliability of the critical applications of digital equipment. Reliability means radiation resistance of digital equipment. For aerospace computer systems it is extremely urgent to develop radiation-resistant components. It is one way to ensure that the radiation resistance is the creation of a special archi- tecture - RHBD (Radiation Hardened by Design). This approach includes triple redundancy (Triple Modular Redun- dancy, TMR). In implementing the triple redundancy to increase radiation resistance in the Xilinx FPGA Virtex used majoritarian elements based on a tristate buffer. One of the issuance of majority vote circuit for the loading sign to the pins of the FPGA is using a minority voting function. This feature ensures channel disconnection different from the other two. Only in this case, there is no conflict of signals at the outputs of buffers. Then it was realized majority func- tion (voting by a majority). The FPGA logic elements LUT (Look Up Table) werer used for it. However, in this case FPGA logic resources were spent. CMOS implementation element vote on the minority was described. The paper proposes a fault tolerant CMOS implementation of minority voting function as separate elements in order to improve the performance of redundant circuits and do not use FPGA logic resources. Simulation of CMOS voting member in the minority is made in the circuit simulation of National Instruments Electronics Workbench Group system. Simulation confirms efficiency of the proposed element, and evaluation of the probability of failure-free operation shows its high efficiency. Winning there is a considerable range of probabilities as opposed to triple scheme that gets worse unre- served already at the probability of the order of 0.88.
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Features selection for text classification based on constraints for term weights
Статья научная
Text classification is an important data analysis problem which can be applied in different domains including airspace industry. In this paper different text classification problems such as opinion mining and topic categorization are considered. Different text preprocessing techniques (TF-IDF, ConfWeight, and the Novel TW) and machine learning algorithms for classification (Bayes classifier, k-NN, SVM, and artificial neural network) are applied. The main goal of the presented investigations is to decrease text classification problem dimensionality by using features selection based on constraints for term weights. Such features selection provides significant reduction of dimensionality and less computational time for calculations. Besides, the use of constraints for term weights could increase classification effectiveness. We have observed such increase for three out of five problems. In the remaining two problems, no significant change and a decrease of classification effectiveness was observed.
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GERT-анализ формирования технологических циклов управления космическими аппаратами
Статья научная
Предложено использовать аппарат сетевого анализа, включая GERT-анализ, для решения задач оптимизации формирования технологических циклов управления (ТЦУ).
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Generation of the state tree based on generative grammar over trees of strings
Статья научная
In the article the principle of state trees generation is considered based on the generative grammars over trees of strings in such objects as the sentences of natural languages, as well as two and tree dimensional images. The image of the object as a forest is considered; including the trees of object different layouts for the purpose of complex system modeling.
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Graphic information processing using intelligent algorithms
Статья научная
Finding an appropriate set of features is an essential problem in the design of a shape recognition system. This paper attempts to show that for recognition of objects with high shape variability such as handwritten characters and human faces it is preferable to use the modified artificial neural network to feed the system with processed images by novel scale-and rotation-invariant interest point detectors and descriptors and to rely on learning to extract the right set of features. Experiments have confirmed the usefulness of the modified artificial neural network in a real-world application.
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Greedy heuristic method for location problems
Статья научная
Authors consider multi-source location problems, k-means, k-median and k-medoid. Such problems are popular models in logistics (optimal location of factories, warehouses, transportation hubs etc.) and cluster analysis, approximation theory, estimation theory, image recognition. Various distance metrics and gauges allow using these models for clustering various kinds of data: continuous and discrete numeric data, Boolean vectors, documents. Wide area of application of such problems leads to growing interest of researchers in Russia and worldwide. In this paper, the authors propose a new heuristic method for solving such problems which can be used as a standalone local search method (local search multi-start) or as the main part of a new algorithm based on ideas of the probability changing method. For the parameters self-tuning of such algorithm, the authors propose new meta-heuristic which allows using new algorithm without learning specific features of each solved problem. Algorithms were tested on various data sets of size up to 160000 data vectors from the UCI repository and real data of semiconductor devices examination. For testing purposes, various distance metrics were used. Computational experiments showed the high efficiency of new algorithms in comparison with local search methods used traditionally for the considered problems. In addition, results were compared with the evolutionary methods and a deterministic algorithm based on the Information Bottleneck Clustering method. Such comparison illustrated the ability of new algorithms to reach higher preciseness of the results in reasonable time.
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H-модели для безынерционных систем с запаздыванием
Статья научная
Рассматривается проблема моделирования нового класса процессов, имеющих «трубчатую» структуру в пространстве «входных-выходных» переменных. Модели процессов этого класса существенно отличаются от общепринятых параметрических моделей, представляющих собой поверхности в том же пространстве. Специально анализируется вопрос о моделировании многомерных систем при наличии малых объемов обучающих выборок. Для построения обучающихся параметрических моделей «трубчатых» процессов вводится соответствующий непараметрический индикатор. Приводится новый класс обучающихся параметрических моделей и некоторые результаты их численного исследования.
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Human-human task-oriented conversations corpus for interaction quality modeling
Статья научная
Speech is the main modality for human communication. It can tell a lot about its owner: their emotions, intelligence, age, psychological portrait and others properties. Such information can be useful in different fields: in call centres for improvement in the quality of service, in designing Spoken Dialogue Systems for better adaptation of a system to users' behaviour, in the automatization of some processes for analysing people's psychological state in a situation with a high level of responsibility, for example, in a space programme. One such characteristic is the Interaction Quality. The Interaction Quality is a quality metric, which is used in the field of Spoken Dialogue Systems to evaluate the quality of human-computer interaction. As well as in Spoken Dialogue Systems, the Interaction Quality can be applied for estimating the quality of human-human conversations. As with any investigation in the field of speech analytics, for modelling the Interaction Quality for human-human conversations a specific corpus of task-oriented dialogues is required. Although there is a large number of speech corpora, for some tasks, as, for example, for Interaction Quality modelling, it is still difficult to find appropriate specific corpora. That is why we decided to generate our own corpus based on dialogues between the customers and agents of one company. In this paper we describe the current state of this corpus. It contains 53 dialogues, corresponding to 1165 exchanges. It includes audio features, paralinguistic information and experts' labels. We plan to extend this corpus both in the feature set and in the observations.
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Статья научная
A fuzzy classifier is one of the intelligent information technologies allowing the generation of a fuzzy rule base suitable for interpretation by human experts. For a fuzzy classifier automated design the hybrid self-configuring evolutionary algorithm is proposed. The self-configuring genetic programming algorithm is suggested for the choice of effective fuzzy rule bases. For the tuning of linguistic variables the self-configuring genetic algorithm is used. A hybridization of self-configuring genetic programming algorithms (SelfCGPs) with a local search in the space of trees is fulfilled to improve their performance for fuzzy rule bases automated design. The local search is implemented with two neighborhood systems (1-level and 2-level neighborhoods), three strategies of a tree scanning (“full”, “incomplete” and “truncated”) and two ways of a movement between adjacent trees (transition by the first improvement and the steepest descent). The Lamarckian local search is applied on each generation to ten percent of best individuals. The performance of all developed memetic algorithms is estimated on a representative set of test problems of the functions approximation as well as on real-world classification problems. It is shown that developed memetic algorithm requires comparable amount of computational efforts but outperforms the original SelfCGP for the fuzzy rule bases automated design. The best variant of the local search always uses the steepest descent and full scanning for fuzzy classifier design. Additional advantage of the approach proposed is a possibility of the automated features selection. The numerical experiment results show the competitiveness of the approach proposed.
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Implementation error of relative measurements
Статья научная
The RNE time scale parameters impact the NSC signal-tracking system operation and the forming of radio navigation signal parameter evaluations had been studied.
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