bluuebunny/crossref_metadata_2025 huggingface dataset:
THERMOELECTRICAL PROCESSES SIMULATION IN RESISTIVE SUPERCONDUCTING FAULT CURRENT LIMITER
Authors: Fedasyuk Dmytro , Serdyuk Pavlo
Published: February 2021

Simulations of superconducting fault current limiter (SFCL) include modelling of thermal and electrical behaviour of the limiter. In this paper the mathematical model for the analysis of thermoelectrical processes in resistive superconducting fault current limiter for microelectronics devices protection is presented. An effective and fast halfanalytical computer simulation of the model is presented. Simulation results are presented.
ERROR ANALYSIS OF RICHARDSON’S EXTRAPOLATIONS
Authors: Petrov Nikolay
Published: February 2021

We propose estimators of a round off error contained in an approximation for Richardson’s extrapolation scheme under finite digit arithmetic. We also propose a stopping criterion, based on consideration of the round off error, for Richardson’s extrapolation scheme with respect to risk technical systems (automobile and railway transport, aircrafts, marine and river transport, chemical installations, munitions, information society suffering by terrorism). Usually the error of an approximation is evaluated by a truncation error. However, we can accurately estimate the behavior of this error utilizing both truncation and round off errors under finite digit arithmetic.
HEURISTIC TECHNIQUES FOR HANDWRITTEN SIGNATURE CLASSIFICATION
Authors: Adamski Marcin , Saeed Khalid
Published: February 2021

New theoretical and experimental techniques for offline classification of handwritten signatures are introduced in this paper. The proposed algorithms are mainly based on boundary tracing technique for extracting characteristic features. Outer and inner boundaries of the signature image are treated separately. The upper and lower parts of the boundaries are extracted to form two sequences of points. Three algorithms for calculating feature vectors are applied based on y coordinate, distances between consecutive points and from polar coordinates system. Experiments on classification of the resulted vectors were carried out by means of Dynamic Time Warping algorithm using window and slope constraints. A brief comparison between the authors' work and other known signature techniques is also discussed in the paper.
SOFT COMPUTING AS A SOLUTION TO TIME/COST DISTRIBUTOR
Authors: Hewahi Nabil M.
Published: February 2021

In this paper we present a theoretical model based on soft computing to distribute the time/cost among the industry/machine sensors or effectors based on the type of the application. One of the most unstudied significant work is to recognize which sensor in an industry for example has higher priority than others. This is important to know which sensor to be checked first and within time limits of the system response. The problem of such systems is their variant environmental situations. Based on these varied situations, the priority of the importance of each sensor might change from time to another. Due to this uncertainty and lack of some information, soft computing is considered to be one of the plausible solutions. The presented idea is based on initially training of the system and continuously exploiting the system experience of the degree of importance of the sensors. The proposed system has three main stages, the first stage is concerned with training the system to obtain the necessary system time to respond, the necessary time allocated to recognize which sensors to check (or which has higher priority), and the initial importance value for each sensor, which indicates the initial judgment about the sensor importance. The second stage is to use the system experience about the importance of the sensor using fuzzy logic to decide the final values of each sensor 's importance. Based on the output of the second stage and the output of the first stage, the system distributes the time/cost among the sensors (some sensors with lower priority might be neglected). The main idea of the proposed work is based on neurofuzzy.
SIMULATION MODELING OF NEURAL CONTROL SYSTEM FOR SECTION OF MINE VENTILATION NETWORK
Authors: Turchenko Iryna , Kochan Volodymyr , Sachenko Anatoly
Published: February 2021

Static and dynamic simulation models of a section of a mine ventilation network in order to research a sequential neural control scheme of mine airflow are developed in this paper. The techniques of neural network training set creation for both simulation models, a structure of neural network and its training algorithm are described. The simulation modeling results using static and dynamic models have showed good potential capabilities of neural control approach.
COMPUTER AIDED DIAGNOSIS USING SOFT-COMPUTING TECHNIQUES AND IMAGE’S ISSUED REPRESENTATION: APPLICATION TO BIOMEDICAL AND INDUSTRIAL FIELDS
Authors: Madani Kurosh , Voiry Matthieu , Amarger Veronique , Kanaoui Nadia , Chohra Amine , Houbre Francois
Published: February 2021

It is interesting to notice that from “problem’s formulation” point of view “Industrial Computer Aided Diagnosis” and “Biomedical Computer Aided Diagnosis” could be formulated as a same diagnosis riddle: “How point out a correct diagnosis from a set of symptoms?”. The only difference between the two above-mentioned groups of problems is the nature of the monitored (diagnosed) system: in the first group the monitored system is an artificial machinery (plant, industrial process, etc…), while in the second, the monitored system is a living body (animal or human).One of the most appealing classes of approaches allowing handling the Computer Aided Diagnosis Systems’ design in the frame of the aforementioned dual point of view is Soft-Computing based techniques, especially those dealing with neural networks and fuzzy logic. In this article, we present two soft-computing based approaches dealing with CADS design. One aims designing a biomedical oriented CADS and the other sets sights on conceiving a CADS to overcome a real-world industrial quality control dilemma. The goal of the first system is to diagnose the human’s auditory pathway’s health. The target of the second is to detect and diagnose the high tech optical devices’ defects.
USER AUTHENTICATION BASED ON BEHAVIORAL PATTERNS
Authors: Kapczynski Adrian , Kasprowski Pawel , Kuzniacki Piotr
Published: February 2021

In this paper biometric techniques based on eye movement and keystroke dynamics were examined. In the first part theoretical aspects concerning biometrics were presented. In second part two prototype systems were characterized: first based on eye movement dynamics and the second based on keystroke dynamics. In the third part chosen system was taken into testing which quantitative effects were presented in categories of FRR, FAR and HTER indicators.
POWER PLANT IN ABSTRACTION MODEL USAGE FOR MANAGEMENT
Authors: Grabara Janusz Krzysztof
Published: February 2021

Power plant structure can be introduced in different ways. One of such ways is the tables used is structural analysis, which take into consideration organisational and technological divisions or determine directions of technical development in enterprise with use of simulation methods. All methods that optimise management decisions can be introduced as right structural models. It is necessary to think whether for Management System there is possibility to create universal structure, which allows building management structure model. The model has to fulfill certain requirement at the same time as simple as possible to implement to management computer system as a module of integrated system. The paper it is a trial of theoretical introducing of such model, which take into consideration streams of management system elements and decisions and their interactions. This model consists of matrix mutual connections of the system elements and decisions.
VIRTUAL LABORATORY AS REALISTIC TOOL FOR THE E-LEARNING IN THE ELECTRIC AND ELECTRONIC MEASUREMENT AND INSTRUMENTATION
Authors: Grimaldi Domenico , Rapuano Sergio , Riccio Mariella , Zoino Francesco
Published: February 2021

The paper deals with the research project adopting the e-learning methodologies for teaching in electrical and electronic measurement and instrumentation. The objective is to offer to the users the tool to achieve accurate and practical experience by working in real conditions and on real instruments. After the description of both the hardware and the software architecture of the Remote Didactic Laboratory, Laboratorio Didattico Remoto – LA.DI.RE. “G. Savastano”, the innovative solutions addressing the requirements of reality are examined and investigated. The core of the system is the Learning Management System (LMS) that provides didactic contents, enabling the account management, security protection, collaborative learning, student activity tracking, and feedback collection. LA.DI.RE. adds to the commercial LMS the innovative services dedicated to laboratory activities concerning the remote control of real instrumentation and the real-time visualization of instrument front panels during the execution of the experiment chosen by the user.
VIRTUAL LABORATORY FOR COMPUTER-AIDED DESIGN OF BIOSENSORS
Authors: Palagin O. , Romanov V. , Galelyuka I. , Kachanovska M.
Published: February 2021

Fast development of information technologies puts strict requirements to terms and costs of designing of computer devices and systems. These requirements may be fulfilled with help of virtual methods of designing, which are realized by means of virtual laboratories of computer-aided design.
A ROBUST SPEECH RECOGNITION SYSTEM USING A GENERAL REGRESSION NEURAL NETWORK
Authors: Amrouche Abderrahmane , Taleb-Ahmed Abdelmalik , Rouvaen Jean Michel , Yagoub Mustapha C. E.
Published: February 2021

General Regression Neural Networks (GRNN) have been applied to phoneme identification and isolated word recognition in clean speech. In this paper, the authors extended this approach to Arabic spoken word recognition in adverse conditions. In fact, noise robustness is one of the most challenging problems in Automatic Speech Recognition (ASR) and most of the existing recognition methods, which have shown to be highly efficient under noise free conditions, drastically fail drastically in noisy environments. The proposed system has been tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multitalker babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 data base. The proposed scheme was successfully compared to similar recognizers based on the Multilayer Perceptron (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results show that the use of nonparametric regression with an appropriate smoothing factor (spread) improved the generalization power of the neural network and the global performance of the speech recognizer in noisy environments.
MULTI DIMENSIONAL DYNAMIC SCENE ANALYSIS. MULTIDIMENSIONAL IMAGE OBJECT SEGMENTATION AND TARGET TRACKING
Authors: Iliev Panayot , Tzvetkov Plamen , Petrov George
Published: February 2021

This paper presents an unconventional approach for object racking using image statistical criteria and 3D image entropy sequence analysis. The experimental results prove that the relationship between statistical characteristics of the 3D image entropy sequences and process of motion estimation is a guarantee for creating reliable and high precision target detection and tracking system. Using 2D and 3D multistage entropy functions analysis provide us a better way to reduce sequence channels for tracking moving and non moving objects. Vector based approach is used for object searching and detection inside image sequences. This way we provide necessary information for object based image sequences compression format that is much more efficient than standard MGEG video stream. Object based compression format is much more acceptable for high demanding security and military systems.
FAILURES DETECTION METHODOLOGY IN NON RECOVERY COMPUTER SYSTEMS BASED ON DIVERSITY MODELING
Authors: Popov George
Published: February 2021

Diversity is a known approach for increasing reliability of computer systems. The goal of this work is to present quantitative criteria for measure of diversity in non recovery computer systems. For this purpose, the model of diversity-based system with two failure types: detectable and undetectable is presented and a formula to calculate it is proposed.
ACOUSTIC INVARIANT APPROACH TO SPEECH SOUND ANALYSIS FOR BRAND NEW SPEECH RECOGNITION SYSTEMS (UKRAINIAN AND ENGLISH)
Authors: Vakulenko Maksym O.
Published: February 2021

On the basis of acoustic invariant speech analysis (AISA), the permanent spectral characteristics of the Ukrainian vowels are obtained for various ways of pronunciation including ordinary speech, whisper and changing tone. It is shown that the lowest phonemic frequencies due to vocal fold oscillations or to Helmholtz resonance are not associated with persistent sound features. It is conjectured that the only phonemic invariant is the ratio between formant frequencies, not their absolute values. This analysis is complemented by the computer sound synthesis. We show also that the acoustic invariants of the Ukrainian sound [i] are close to that of English [I]. The results obtained may be useful for specialists in the field of experimental phonetics and speech modelling.
DISTRIBUTED TRUST IN ePAYMENTS SYSTEM
Authors: Vidya K. P.
Published: February 2021

In this paper, a secret sharing scheme that is based on the Parallel Pollard rho Attack of the Elliptic Curve Discrete Logarithm Problem (ECDLP) is proposed for hierarchical access structures that can be activated dynamically. The shares of the scheme are distributed across two levels of participants but the reconstruction of the secret takes place at level zero which is the central processor or the trusted party of the scheme. The scheme finds its application in the Payments System of Banks and facilitates the replacement of paper cheques with eCheques. It also provides an efficient method of processing the payments at the Clearing House of Banks.
THE CHOICE AND SUBSTANTIATION OF THE MATHEMATICAL MODEL OF ELECTRORETINOGRAM IN THE FORM OF LINEAR STOCHASTIC PROCESS
Authors: Dzubin S. V. , Matsiuk A. V. , Martsenko S. V. , Pryimak M. V.
Published: February 2021

The mathematical model of electroretinogram as a linear random process which takes into account the mechanism of eye retina biopotentials forming and is suitable for solving of the measuring and diagnosing problems is developed in the work. The approach is proposed, the method is developed and the statistics are given to estimate the kernel of the linear random process as electroretinogram model. The obtained results may be used in the applied research of eye diagnosis.
AN ESTIMATION OF QUANTITY OF THE IDENTIFIED OBJECTS OF THE CONTROL SYSTEM IS A TONE-FREQUENCY SIGNAL
Authors: Dombrovskiy Zbishek
Published: February 2021

Application of tone signals from limited range low frequencies (LF) band is an effective mean for energy consumption systems (СS) objects identification under their control above radio channels networks. With purpose for determination of requirements to characteristics of digital tone signals processing a method of evaluation of maximum quantity of confidence-identified of СS objects in the network with a radio channel is offered. On the foundation of had been determined requirements for the construction of digital devices of generation and filtration of tone signals is compatible with authentication of СS objects the appropriate structure is offered and its confidence is explored. Graphics of dependences of probability identification of the objects vs. signal to noise ratio at the different fault probability are presented and results of their comparing analyses are given.
ESTIMATION OF TRANSMISSION RATE DEPENDENCE IN WI-FI NETWORK FROM INFORMATION CHARACTER
Authors: Gumenyuk Bogdan , Vaskiv Grygoriy , Yankevych Volodymyr
Published: February 2021

This paper shows the results of research in to data transmission rate in Wi-Fi networks from influence the topology of the network, information character, services of enciphering and authentification.
HUMAN FACE DETECTION METHODS USING COMBINED CASCADE OF CLASSIFIERS
Authors: Paliy Ihor
Published: February 2021

The paper presents the improved human face detection method using the combined cascade of classifiers with the improved face candidates’ verification approach, as well as methods and algorithms for the verification level (convolutional neural network) structure generation and training. The combined cascade shows a high detection rate with a very small number of false positives and the proposed candidates’ verification approach is in almost 3 times faster in comparison with the classic verification scheme. The network’s structure generation method allows creating the sparse asymmetric structure of the convolutional neural network automatically. The improved training method uses the adaptive training examples ratio to obtain a trained network with a very low classification error for the positive examples.
THE USE OF NEURAL NETWORKS IN RARE VEGETATION COMMUNITIES CLASSIFICATION
Authors: Gambarova E. M.
Published: February 2021

This paper describes training of Multilayer Perceptron Neural classifier to extract rare vegetation objects from high spatial resolution IKONOS satellite imagery. There have been considered three options of training of the Multilayer Perceptron Neural according to three different classification schemes. At first 12 type of rare vegetation community types were defined, a main classification scheme (“Initial classification scheme”) was designed on that base. After prelim statistical tests on training samples two modification algorithms of the classification scheme were defined: the first one led to creating of scheme consisting of 7 classes (“Modified classification scheme”) and second one led us to creating of 5-classes scheme (“Optimized classification scheme“). The learning procedures of these classifiers are described as well as analysis and post processing of extraction results of objects of interest using Geoinformation Technologies in details.
THE TERNOPIL EDUCATIONAL COMMUNICATION CENTER: A NATO PROJECT TO INTEGRATE REGIONAL INFORMATION TECHNOLOGY RESOURCES
Authors: Markowsky George , Sachenko Anatoly , Voznyak Serhiy , Spilchuk Viktor , Romanyak Roman , Turchenko Volodymyr , Romanets Ihor
Published: February 2021

The Ternopil Educational Communication Center was developed with NATO funding to improve computing for the universities of Ternopil, Ukraine. It provides high speed access for all of Ternopil’s universities to the Internet and the World Wide Web. It enables high speed communication within Ternopil, and between institutions in Ternopil using the UarNet and URAN networks. It provides an integration of on-line library services, and it supports such activities as video conferencing, distance education, and class to class participation. The universities of Ternopil and the University of Maine are developing programs and projects to involve students and faculty both in Ukraine and the US.
MOBILE ROBOT LOCALIZATION USING WLAN SIGNAL STRENGTHS
Authors: Röhrig Christof , Künemund Frank
Published: February 2021

Many buildings are already equipped with a WLAN infrastructure, as an inexpensive communication technology. In this paper two methods that estimate the position and the heading (pose) of a mobile robot using WLAN technology are described. The proposed techniques for localizing a mobile robot are based on the use of received signal strength values of WLAN access points in range. Both use a radio map based method. For interpolation of the radio map weigthed Euclidean distance and Euclidean distance in combination with Delaunay triangulation is proposed. Measured signal strength values of an omnidirectional antenna and a beam antenna are compared with the values of a radio map, in order to estimate the pose of a mobile robot, whereby the directionality of the beam antenna is used to estimate the heading of the robot. The paper presents the experimental results of measurements in an office building.
COMPARISON OF SEVERAL METHODS FOR PARETO SET GENERATION IN MULTI-CRITERIA PORTFOLIO OPTIMIZATION
Authors: Radziukyniene Ingrida , Žilinskas Antanas
Published: February 2021

Pareto set generation methods are considered with respect to their application for multi criteria portfolio selection. Several such methods were compared experimentally including some recently proposed evolutionary methods and the method of adjustable weights. Test problems were based on standard portfolio quality criteria and data on stocks of 10 Lithuanian companies. The experimental data on the performance of the considered algorithms in different metrics are presented and discussed.
UNIVERSAL EMBEDDED RECONFIGURABLE HARDWARE PLATFORM FOR MULTIMEDIA APPLICATIONS IN REAL-TIME
Authors: Livshitz Michael , Petrovsky Alexey , Stankevich Andrey , Kachinsky Mikhail , Petrovsky Alexander
Published: February 2021

This paper deals with reconfigurable hardware platform for different purposes real-time speech and audio signal processing. A design conception and turnkey solution are described. Much attention is paid to reconfigurable peripheral processor meant for external interface realization, pre- and post- data processing as well as digital signal processing algorithms implementation with the object of the DSP unloads. Moreover, three applications implemented on the considered platform are demonstrated.
MULTIPLE VIEWS IN PEER DATA MANAGEMENT FOR E-COMMERCE APPLICATIONS
Authors: Al-Mourad M. B. , Mohamed Rozalina , Khalifa Yaser M. A.
Published: February 2021

This document presents the required layout of papers to be submitted for publication in the “Computing” International journal. The abstract may not be longer than 150 words. Peer-to-peer (P2P) systems are revival paradigm for information sharing among distributed nodes in the network. A P2P network is a network that relies primarily on the computing power and bandwidth of the participants in the network rather than concentrating it in a relatively low number of servers. P2P software systems like Kazaa and Napster rank amongst the most popular software applications ever. Numerous web businesses and sites have promoted "peer to peer" technology as the future of Internet networking for E-commerce. Multiple views for data are created for mediating between data sources on the Semantic Web. Our goal is to support users’ different needs. This is due to the fact that different users have different needs for joining the P2P community and their requirements may change over time as new information become available. Hence the same information may participate in many different ways in multiple data sources’ mapping efforts. The idea of creating multiple data views has been adopted in federated database systems. However, it is not yet implemented in peer-to-peer systems. This is due to the lack of shared knowledge among peers. Moreover, it is not trivial task to create multiple data views in an unpredictable environment such as P2P. This work investigates the possibility to construct multiple data views in P2P environment. This will be achieved through the development of a tourism application framework. The described framework would benefit small businesses by participating in the P2P network without being forced to use compatible standard indexable-web data-bases provided by large corporations.
CHC ALGORITHM FOR ANTENNA ARRAY WITH FAILED ELEMENTS OPTIMIZATION
Authors: Artyushenko Bogdan , Shilo Galina , Krischuk Volodymyr
Published: February 2021

A new CHC based method for optimization of antenna array, with failed elements, is developed. Performances of CHC and canonical genetic algorithm are compared. Numerical results show that CHC gives better results with the same working time. To improve computational efficiency parallelization possibilities are studied.
DATA DIMENSIONALITY REDUCTION FOR NEURAL BASED CLASSIFICATION OF OPTICAL SURFACES DEFECTS
Authors: Voiry Matthieu , Madani Kurosh , Véronique Amarger Véronique , Bernier Joël
Published: February 2021

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterization in products. This challenging operation is very important since it is directly linked with the produced optical component’s quality. A classification phase is mandatory to complete optical devices diagnosis since a number of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on the behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can decrease the problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evaluate their impact on classification tasks performances.
FUZZY CLUSTERING METHODS IN MULTISPECTRAL SATELLITE IMAGE SEGMENTATION
Authors: Sadykhov Rauf Kh. , Ganchenko Valentin V. , Podenok Leonid P.
Published: February 2021

Segmentation method for subject processing the multi-spectral satellite images based on fuzzy clustering and preliminary non-linear filtering is represented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson- Kessel, and Gath-Geva have been utilized. The experimental results obtained using these algorithms with and without preliminary nonlinear filtering to segment multi-spectral Landsat images have approved that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy Cmeans, Gustafson-Kessel, and Gath-Geva algorithms have got linear computational complexity depending on initial cluster amount and image size for single iteration step. They assume internal parallel implementation. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence more clear segment outlining…
A NEW ALGORITHM FOR TIME SERIES DATA MINING BY USING ROUGH SET
Authors: Hao Fei , Yeung Ling Hei
Published: February 2021

This paper is to apply Rough Set to data mining of time series. Firstly, we process the time series data by attribute selection and similarity sequence search. Secondly, the time series is partitioned into some sets of pattern by Mobile Window Method (MWM) and each pattern is a trend of time series. Thirdly, an information table is made by predicting attributes and targeting attribute in trending variation ratio structure sequence (TVRSS). Then, the original information table is made suitably for rough set to discover knowledge. Finally, the extracting rules can predict the time series behavior in the future. The total process is four steps. In the end, we show some examples to demonstrate our method on the time series data of stock market.
AN ITERATIVE METHOD FOR THE EVALUATION OF THE REGULARIZATION PARAMETER IN REGULARIZED IMAGE RESTORATION
Authors: El-Khamy Said E. , Hadhoud Mohiy M. , Dessouky Moawad I. , Salam Bassiouny M. , Abd El-Samie Fathi E.
Published: February 2021

Regularized restoration is one of the powerful image restoration techniques because it preserves image details with a high degree of fidelity in the restored image. The main problem encountered in regularized image restoration is the evaluation of the regularization parameter. There are several methods for the evaluation of this parameter which require knowledge of the noise variance in the degraded image. After evaluating this parameter, regularized restoration is implemented by applying a regularization filter on the degraded image. In this paper, we propose a new iterative method for the evaluation of this parameter. This method depends on the maximization of the power in the restored image by the coincidence of the passband of the regularization filter with the frequency band in which, most of the image power exists. The suggested method doesn’t require a priori knowledge of the noise variance. Results show that the estimated value of the regularization parameter leads to a minimum mean square restoration error.
A MODIFIED A* ALGORITHM FOR ALLOCATING TASK IN HETEROGENEOUS DISTIRBUTED COMPUTING SYSTEMS
Authors: Bahnasawy Nirmeen A. , Attiya Gamal M. , Mosa Mervat , Koutb Magdy A.
Published: February 2021

Distributed computing can be used to solve large scale scientific and engineering problems. A parallel application could be divided into a number of tasks and executed concurrently on different computers in the system. This paper provides an optimal task assignment algorithm under memory constraints to minimize required time of finishing a parallel application. The proposed algorithm is based on the optimal assignment sequential search (OASS) of the A* algorithm with additional modifications. This modified algorithm yields optimal solution, lower time complexity, reduces the turnaround time of the application and considerably faster compared with the sequential search algorithm.
A CHOICE OF THE SHORTEST OF THE ALTERNATIVE COMPRESSED BLOCKS OF THE DYNAMIC HUFFMAN’S CODES IN THE FORMAT OF PNG
Authors: Shport’ko Alexander
Published: February 2021

In the article the algorithm of generation of the alternative compressed blocks of dynamic Huffman’s codes for every block of data, choice of the shortest compressed block of alternative and iterative diminishing of its size for the improvement of the compression of images in the format of PNG is offered. The methods of the estimation of the size of block of Huffman’s codes according to the absolute frequencies of its elements are examined in details. The fragment of programs outlaying the language of C, which allows exactly to determine the size of the compressed block by principle of generation of dynamic Huffman’s codes and separate working of small frequencies of elements, using for this purpose on the average not more time, than for the close estimation of size with the use of entroping. As experiments show, realization of the offered algorithm, allows to improve the indexes of the compression of the majority of images in the format of PNG in 2 – 6 %.
DETERMINING THE ELEMENTS OF THE TEXT ON IMAGE USING OF FUZZY COGNITIVE MODELS
Authors: Pyatikop Elena
Published: February 2021

This publication describes the modeling results of knowledge of cognitive psychology about the primary processing in the visual system to analyze the images in purpose to determine the elements of the text. Processing images at the level of primary visual cortex formalized using fuzzy sets.
SOFTWARE FOR DETERMINATION OF BEHAVIOR PATTERNS OF WEBFORUM MEMBERS
Authors: Syerov Yuriy , Kravets Ruslan
Published: February 2021

Article considers actual problem of Web-forums’ members’ behavior and classifying information system development. In the article existing Web-forum CMS’ were analyzed, process of Web-community members’ behavior analyzing was researched and overviewed, the process of information system developing was described.
A METHOD FOR STRUCTURAL SYNTHESIS OF NETWORK CAPABLE APPLICATION PROCESSORS
Authors: Maykiv I. , Stepanenko A. , Wobschall D.
Published: February 2021

A method for structural synthesis of Network Capable Application Processors (NCAPs) is proposed. It is based on a method of morphological analysis and synthesis and includes phases of functional analysis, structural synthesis, and search for a set of optimal solutions. The proposed method combines lexicographical criterion of preference (L- criterion) at a stage of functional analysis and unconditional criterion of preference (Pareto optimality) during the search phase, which are considered in literature as alternative methods of search for optimal solutions. Combining of criteria, makes it possible to reduce the number of synthesized alternative variants at a stage of structural synthesis and all obtained solutions are allowable.
ANALYSIS OF THE 2-SUM PROBLEM AND THE SPECTRAL ALGORITHM
Authors: Kolomiychuk Alexander
Published: February 2021

This paper presents the analysis of the 2-sum problem and the spectral algorithm. The spectral algorithm was proposed by Barnard, Pothen and Simon in [1]; its heuristic properties have been advocated by George and Pothen in [4] by formulation of the 2-sum problem as a Quadratic Assignment Problem. In contrast to that analysis another approach is proposed: permutations are considered as vectors of Euclidian space. This approach enables one to prove the bound results originally obtained in [4] in an easier way. The geometry of permutations is considered in order to explain what are ‘good’ and ‘pathological’ situations for the spectral algorithm. Upper bounds for approximate solutions generated by the spectral algorithm are proved. The results of numerical computations on (graphs of) large sparse matrices from real-world applications are presented to support the obtained results and illustrate considerations related to the ‘pathological’ cases.
MAXIMAL USING OF SPECIFICS OF SOME BOUNDARY PROBLEMS IN POTENTIAL THEORY AFTER THEIR NUMERICAL ANALYSIS
Authors: Mochurad L. I. , Harasym Y. S. , Ostudin B. A.
Published: February 2021

Some typical problems in the numerical analysis of certain types of boundary value problems of the potential theory in substantially spatial formulation are considered. On the basis of the integral equation method (IE) an approximate scheme of solving one model example is built and investigated. It is also considered that the doubly connected open surface where boundary conditions are set obtains the Abelian group of symmetry of the eighth order. This article shows how using the apparatus of the group theory it is possible to solve an initial problem by the help of the sequence of the eight independent IEs, where the integration is realized only on one of the congruent constituents of the surface. It creates the conditions for two parallel processes of problem solution in general. The collocation method for obtaining approximate values of needed “density of charge distribution” in the particular two-dimensional integral equations is used. To take into account the singular way of solving the problem in the circuit of the open surface the a posteriori method of error evaluation is created and the procedure of integrating clarification of solving the task in the mesh node is implemented. To prove the reliability and estimation of the technique efficiency the number of numerical experiments is carried out including the use of so called “plane” approximation of the examined spatial problem.
TEXTURE SEGMENTATION OF IMAGES OF WEAR ZONES OF CUTTING TOOLS BY AMPLITUDE RECTIFYING METHOD
Authors: Polyakova Marina , Krylov Victor , Volkova Natalia
Published: February 2021

The amplitude rectifying method of the texture segmentation of the image of wear zone of cutting tools with tracks of the fragile destruction is developed. The mathematical model of the image of wear zone of cutting tools with tracks of the fragile destruction as a spectral-statistical model of the texture image with the amplitude modulation of intensity is proposed.
MOBILE ROBOT LOCALIZATION USING WLAN, ODOMETRY AND GYROSCOPE DATA
Authors: Lategahn Julian , Kuenemund Frank , Roehrig Christof
Published: February 2021

In this paper a method for estimation of position and motion of a mobile robot in an indoor environment is introduced. The proposed method uses WLAN signal strength to estimate the global position of a mobile robot in an office building. Thus signal strengths of the received access points are stored in the radio map in calibration phase. In localization phase the stored values are compared with actually measured one’s. Therefore a fingerprinting algorithm, that was introduced before, is used. The improvement of the presented work is the multi sensor fusion using Kalman filter, which enhances the accuracy of fingerprinting algorithms and tracking of the robot. For this reason odometric and gyroscopic sensors of the robot are fused with the estimated position of the fingerprinting algorithm. The paper presents the experimental results of measurements made in an office building.
IDENTITY AND ACCESS MANAGEMENT ACCORDING TO THE IMPLEMENTATION OF THE SIMOIT PROJECT AND TNC@FHH
Authors: Eren Evren , Detken Kai-Oliver
Published: February 2021

This paper considers some of the identity and access management mechanisms, and adds to this the new requirements posed by identity management in mobile ubiquitous environments. The authors present the state-of-the-art in identity management standards and initiatives in the context of the SIMOIT project and the TNC@FHH implementation. Furthermore, the paper discusses the results achieved by these projects with respect to the generic requirements in different scenarios and compares the achievements.
SPINE-BASED APPLICATION DEVELOPMENT ON HETEROGENEOUS WIRELESS BODY SENSOR NETWORKS
Authors: Fortino Giancarlo , Galzarano Stefano , Giannantonio Roberta , Gravina Raffaele , Guerrieri Antonio
Published: February 2021

Wireless sensor networks (WSNs) are a novel technology enabling new classes of applications and systems for ubiquitous and pervasive computing. In particular, WSNs for the human body, also known as Wireless Body Sensor Networks (WBSNs), will enable not only continuous, multi-purpose monitoring of people but also will support social interaction among people coming into physical contact. In these contexts, applications demand a wide range of functionalities, in terms of sensor types, processing performance, communication capabilities. Moreover the development of such applications has to deal with the issue of handling heterogeneous WBSNs since different kinds of sensor node architectures could be necessary to fulfill all the application requirements. This paper proposes an approach based on the SPINE frameworks (SPINE1.x and SPINE2) for the programming of signal processing applications on heterogeneous wireless sensor platforms. In particular, two integrable approaches based on the proposed frameworks are described that allow the development of applications for WBSNs constituted by heterogeneous sensor nodes. The approaches are exemplified through a human activity recognition system based on a WBSN composed of two types of sensor nodes, heterogeneous with respect to base software and hardware.
FREQUENCY NOISE INFLUENCE ON FUNCTIONING OF FREDKIN QUANTUM GATE
Authors: Gorskyi Gennadiy P. , Deibuk Vitaliy G.
Published: February 2021

The influence of detuning of radio frequency magnetic field (RFMF) on the functioning of nuclear magnetic resonance (NMR) quantum Fredkin gate is considered in this paper. It is shown that detuning of frequency decreases a probability of correct answer. If the spectral broadband of RFMF signal is increasing, then the main value of correct answer probability is decreasing too and standard deviation of this probability is increasing.
SIMULATION OF MECHANICAL SYSTEM STABILIZATION WITH ACTIVE RESONANCE CONTROLLER
Authors: Slavko Olena
Published: February 2021

A simulation model of a mechanical system with a control algorithm based on the active resonance control approach is created. Methodology and virtual laboratory for researches of the simulation models of mechanical systems stabilization based on the active resonance control algorithm are developed. This virtual laboratory allows to research quality and stability of the active resonance algorithm for different types of mechanical systems and external disturbances for various initial conditions and environment parameters. The active resonance control algorithm for the first time is adapted and improved by using of numerical values of velocity and acceleration of a controlled object on each step of a simulation for a system described by a second order differential equation.
METHODS OF CREATING RICH USER INTERFACES BASED ON FLEX FRAMEWORK
Authors: Sakowicz Bartosz , Mazur Piotr , Kamiński Marek , Wojtera Michał , Dębiński Adam
Published: February 2021

The article, by describing features of sample application for project management, presents methods of creating graphical interfaces of modern applications that run in web browser environment. Sample program is written with the use of FLEX Framework and some FLEX supporting tools. The FLEX technology is described as well as other RIA techniques.
DIGITAL SIGNAL CHAINS IN PARTIAL DISCHARGE MEASUREMENT
Authors: Vedral Josef , Kriz Martin , Zalis Karel
Published: February 2021

The paper presents digital techniques in partial discharge measurement. The discharge current pulses are measured on high voltage machines such as power plant generators and transformers. The presented modern digital techniques are suitable for all measurement where impulse energy will be calculated.
Emprego de Rede Neural Artificial para Predição de Temperatura de Topo de Óleo em Transformador de Potência
Authors: M. Kaminski Antônio , H. Medeiros Leonardo , M. Bueno Daniela , C. Bender Vitor , M. Oliveira Micael , B. Marchesan Tiago , B. F. Neto José , M. Wilhelm Helena
Published: February 2021

A temperatura de topo de óleo é de grande importância para avaliação das condições de operação de transformadores de potência, onde sua predição correta possibilita melhor aproveitamento da capacidade nominal e prolongamento da vida útil do transformador. O presente trabalho desenvolve um modelo dinâmico para predição de temperatura de topo de óleo de um transformador através de uma Rede Neural Artificial (RNA) utilizando dados reais de monitoramento e propõe a utilização de uma arquitetura de rede Non-linear AutoRegressive with Exogenous inputs (NARX), ainda não utilizada para este propósito. Após seu desenvolvimento, foi realizado um teste da capacidade de previsão da rede com um conjunto de dados que não participou do processo de treinamento. Os resultados obtidos demonstram que a rede neural desenvolvida replica de forma satisfatória o comportamento térmico dinâmico do transformador real com níveis aceitáveis de erro.
Análise Comparativa da Modelagem de Sistema HVDC no ATP Para o Estudo de Transitórios Eletromagnéticos
Authors: T. C. Souza Maria , M. Silva Sidelmo , De Conti Alberto
Published: February 2021

Este trabalho compara duas diferentes estratégias de modelagem do elo HVDC Xingu-Estreito com o objetivo de avaliar sua resposta transitória frente à aplicação de manobra de religamento tripolar em uma linha de transmissão vizinha. As estratégias avaliadas são a modelagem detalhada do elo HVDC ou sua representação simplificada na forma de fontes de corrente equivalentes. É demonstrado que o modelo simplificado do elo HVDC é capaz de levar a resultados semelhantes àqueles obtidos com o modelo detalhado, com a vantagem possuir menor complexidade computacional e de levar a menores tempos totais de simulação.
Análise Econômica da Utilização do Transformador de Potência Isolado a Óleo Vegetal em Relação ao Transformador Isolado a Óleo Mineral em Redes de Distribuição de Energia
Authors: S. Almeida Larissa , R. Muniz Pablo
Published: February 2021

A utilização dos transformadores de potência é imprescindível para a continuidade de operação do sistema elétrico. Um dos componentes utilizados no transformador é o óleo isolante, responsável pelo seu isolamento e refrigeração. Com a crescente preocupação ambiental, viu-se a necessidade de buscar novas alternativas para o sistema de isolamento dos transformadores, uma vez que o óleo mineral isolante é proveniente do petróleo, considerado tóxico, nocivo ao ambiente e de baixa biodegradabilidade. Pesquisas atuais mostraram que o óleo vegetal isolante é uma alternativa a ser considerada por empresas do setor elétrico, por ser um fluido biodegradável, com ponto de fulgor e combustão elevados, sendo caracterizado como um fluido com maior segurança. O presente trabalho analisa as vantagens e desvantagens econômicas da utilização do óleo vegetal isolante comparado com o óleo mineral isolante em transformadores de redes de distribuição de energia. Conclui-se que o óleo vegetal isolante apresenta grandes vantagens econômicas, se mostrando atrativo em relação ao tradicional óleo mineral isolante.
Estimativa do Tempo para Falha de Transformadores de Potência Utilizando Dados do Centro de Operação e Redes Neurais Artificiais
Authors: R. Sodré Bruno , G. Sotelo Guilherme , Ferreira Vitor Hugo
Published: February 2021

Transformadores de potência são cada vez mais exigidos, visto a necessidade cada vez maior de se manter o fornecimento contínuo de energia elétrica por parte das distribuidoras de energia. Interrupções de energia, por conta da falha desses equipamentos podem gerar compensações financeiras da ordem de centenas de milhares de reais para as distribuidoras, desta forma se tornando cada vez mais importante o monitoramento do tempo de falha desses equipamentos. Grande parte dos trabalhos relacionados ao diagnóstico de falhas em transformadores de potência, utilizam como dado de entrada a análise de gases dissolvidos em tempo real, porém para transformadores de pequeno porte, o custo para implementação dessa informação pode ter o custo do equipamento. Assim, este trabalho propõe a utilização de dados convencionais monitorados em qualquer centro de controle do mundo, como: ciclo de carregamento e eventos de curto-circuito (CC), com o objetivo de realizar a previsão de falhas, através da modelagem de uma rede neural artificial (RNA), que foi treinada, validada e testada, a partir de dados reais de operação de um transformador que operou durante 945 dias.
Um Método Inteligente para a Correção da Forma de Onda Secundária de Transformadores de Corrente de Proteção
Authors: Dos Santos Saraiva Silva Bruno , Caneloi do Santos Ricardo , Belati Edmarcio Antônio
Published: February 2021

Um dos equipamentos mais importantes presentes no sistema de proteção é o Transformador de Corrente (TC). No entanto, tal equipamento possui características físicas que o torna susceptível à saturação, resultando na distorção da sua forma de onda secundária. Diante desta distorção o relé de proteção pode falhar, deixando de gerar o sinal de trip diante de situações de falta no sistema elétrico. Neste sentido, este trabalho propõe o uso de Redes Neurais Artificiais (RNAs) para corrigir a forma de onda secundária distorcida proveniente de um TC saturado. Os resultados mostram claramente que uma forma de onda altamente distorcida pode ser totalmente reconstruída pela RNA, fornecendo aos relés de proteção um sinal confiável para a correta tomada de decisão.
Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems
Authors: Maruch Barreto Nathan Elias , Monteiro Baer Ciro , Jaensen Daros Mateus , Fritzen Marlon Alexsandro , Schneider de Oliveira Guilherme , Rodrigues Rafael , Rasi Aoki Alexandre
Published: February 2021

This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.