bluuebunny/crossref_metadata_2025 huggingface dataset:
FAST SOLVER FOR INTERIOR POINT METHOD OF SVM TRAINING BY PARALLEL GMRES AND HSS
Authors: Zhao Di
Published: February 2021

Support Vector Machine (SVM) is one of the latest statistical models for machine learning. The key problem of SVM training is an optimization problem (mainly Quadratic Programming). Interior Point Method (IPM) is one of mainstream methods to solve Quadratic Programming problem. However, when large-scale dataset is used in IPM based SVM training, computational difficulty happens because of computationally expensive matrix operations. Preconditioner, such as Cholesky factorization (CF), incomplete Cholesky factorization and Kronecker factorization, is an effective approach to decrease time complexity of IPM based SVM training. In this paper, we reformulate SVM training into the saddle point problem. As the research question that motivates this paper, based on parallel GMRES and recently developed preconditioner Hermitian/Skew-Hermitian Separation (HSS), we develop a fast solver HSS-pGMRES-IPM for the saddle point problem from SVM training. Computational results show that, the fast solver HSS-pGMRES-IPM significantly increases the solution speed for the saddle point problem from SVM training than the conventional solver CF.
DETERMINING THE OPERATING DISTANCE OF AIR ULTRASOUND RANGE FINDERS: CALCULATIONS AND EXPERIMENTS
Authors: Sonbul Omar , Kalashnikov Alexander N.
Published: February 2021

Estimating the operating distance of air ultrasound range finders by using the suitably modified radar equation and experimental verification of the developed computational procedure is discussed. It is shown that, despite notable differences between operating conditions of radars and air ultrasonic range finders, the radar equation is applicable to the considered case, and calculations of the relevant terms for this case are presented. The experimental assessment was carried out by evaluating the probability of detection at various distances from the custom built device. The calculated and experimental results seem to agree well despite using a number of values with high degree of uncertainty. The described procedure can be used at the design stage of air ultrasound range finders in order to reduce the number of prototypes before finalizing the design to a single prototype.
THE PROJECT MANAGEMENT OF WATER SUPPLY COMPLEXES RECONSTRUCTION ON THE BASIS OF DECISION SUPPORT SYSTEMS
Authors: Gaida Anatoliy J. , Grigorian Tigran G. , Kharitonov Yuriy N. , Koshkin Volodymyr K.
Published: February 2021

The problems of managing projects of water supply system reconstruction are considered. The characteristics of such systems and especially the processes of planning and implementation of water supply systems reconstruction projects were analyzed. The necessity of sharing the methods of projects value assessing according to their social significance and economic benefit expectations are identified. On the basis of the fulfilled research a structural model of automated decision support system in project management of water supply systems reconstruction is proposed. The model is based on the use of the weighted sum method and assessment the current states of projects by the means of a neural network classifier.
FRACTAL APPROACH TO ANALYSE THE INSTRUMENTAL TIME SERIES OF TEMPERATURE OBSERVATIONS
Authors: Kolokolov Yury , Monovskaya Anna
Published: February 2021

The fractal approach allows to extend the knowledge about the nonlinear dynamics evolution caused by the growth of capacity and adequacy of information about regularities, uncertainties and abnormalities. The paper applies the fractal approach to analyze the instrumental time series observing the air temperature of the land surface during 1902-2012 years. An annual warming-cooling cycle is considered as a unit, so the peculiarities of the viewpoint on the temperature dynamics are connected with the analysis of the time series profiles. The corresponding fractal model – described the annual warming-cooling cycle dynamics - is adapted to the considered subject. Then it becomes possible to study comparatively the averaged, extremely and amplitude-frequency characteristics of the annual warming-cooling cycles. The observations – completed in the forty meteorological stations – are used in the six quasi-homogeneous climate regions. The individual and comparative study of the local climate systems exhibits both over-regional dominant tendencies and local uncertainties that enable to learn the local climate dynamics in more details.
PROBABILISTIC APPROACH TO DOMAIN SELECTION FOR INTEGRATING THE NORMAL’S FIELD IN 3D RECONSTRUCTION
Authors: Rusyn Bogdan , Lutsyk Oleksiy , Varetskyy Yarema
Published: February 2021

The method of integrating normal’s field is improved by using the proposed algorithm for breaking local areas based on Schwartz equations. It was proposed local criteria of splitting normal’s onto local areas, which is crucial for the next step of building the sequence chain. The process of classification in case of reducing the training set to select regions of integration is investigated. The reason for the effect of retraining is conditioned with a minimal number of errors on the training sample. It is shown that stratification of algorithms by mistakes and increasing their similarities reduce the likelihood of the retraining. Proposed approaches are implemented as a software and are suitable for the broad class of real surfaces.
EVALUATION OF COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR DAILY PRODUCT SALES FORECASTING
Authors: Gediminas Žylius Gediminas , Simutis Rimvydas , Vaitkus Vygandas
Published: February 2021

Product sales forecasting is crucial task in inventory control and whole supply chain management. Accuracy of sales forecasting determines product logistics performance. In this paper we present study that aims to answer three questions: what input set is most informative for daily sales time series forecasting; do weather input features improve forecasting performance; what computational intelligence model is most appropriate for daily sales forecasting. In order to answer those questions we selected three computational intelligence models that are used for regression task together with various input sets for daily time series forecasting. Data collected consist of 89 real life product sales time series from various stores with historical period of 15 months. Results show that most useful input set is extracted from time series itself. Secondly, research results show that weather features do not improve forecasting performance. And finally, best forecasting results are achieved using support vector regression model.
Computational Intelligence for Biometric Applications: a Survey
Authors: Donida Labati Ruggero , Genovese Angelo , Muñoz Enrique , Piuri Vincenzo , Scotti Fabio , Sforza Gianluca
Published: February 2021

Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.
PARALLELISM AND COMPLEXITY OF A SMALL-WORLD NETWORK MODEL
Authors: Hiromoto Robert E.
Published: February 2021

The small-world phenomena exhibits highly localized clustering and short-cut paths between vertices in a graph that reflect observed properties in social networks, epidemiological models and other real-world networks. The small-world models rely on the application of constraint-based randomness or the derivation of constraints on randomness to simulate the desired network complexities and their associated network connection properties. In this paper, rather than exploring the random properties of small-world networks, we employ deterministic strategies in the design of a computationally efficient distributed neuronal-axon network simulator that results in a small world network. These strategies are derived by addressing the parallel complexities of the proposed neuronal-axon network simulator, and also from physical constraints imposed by resource limitations of the distributed simulation architecture. The outcome of this study is the realization of a neuronal-axon network simulator that exhibits small-world characteristics of clustering with a logarithmic degree of separation between nodes without the need for long-range communication edges. The importance of this result is the deterministic application of reasoned optimization rules from which the small-world network emerges.
UB SWARM: HARDWARE IMPLEMENTATION OF HETEROGENEOUS SWARM ROBOT WITH FAULT DETECTION AND POWER MANAGEMENT
Authors: Patil Madhav , Abukhalil Tamer , Patel Sarosh , Sobh Tarek
Published: February 2021

In this work we present the hardware architecture of a mobile heterogeneous robot swarm, designed and implemented at the Interdisciplinary Robotics, Intelligent Sensing and Control (RISC) Laboratory, University of Bridgeport. Most of the recent advances in swarm robotics have mainly focused on homogeneous robot swarms and their applications. Developing and coordinating a multi-agent robot system with heterogeneity and a larger behavioral repertoire is a great challenge. To give swarm hardware heterogeneity we have equipped each swarm robot with different set of sensors, actuators, control and communication units, power supply, and an interconnection mechanism. This paper discusses the hardware heterogeneity of the robotic swarm and its challenges. Another issue addressed in paper is the active power management of the robotic agents. The power consumption of each robot in the UB robot swarm is calculated and the power management technique is also explained in this paper. We applied this heterogeneous robot swarm to perform three sample tasks – Mapping task, human rescue task and wall painting task.
A SIMULATION MODEL FOR ROBOT’S SLIP DISPLACEMENT SENSORS
Authors: Kondratenko Yuriy , Gerasin Oleksandr , Topalov Andriy
Published: February 2021

This paper deals with a simulation model of slip displacement sensors for the object slip signals’ registration in the adaptive robot’s gripper. The study presents the analysis of different methods for slip displacement signals detection, as well as authors’ solutions. Special attention is paid to the investigations of the developed sensor with the resistive registration element in rod type structure of sensitive elements, which is able to operate in harsh and corrosive environments. A sensing system for the object slip signals’ registration in the adaptive robot’s gripper with a clamping force correction is developed for proposed slip displacement sensor with multi-component resistive registration elements. The hardware implementation of the sensing system for slip signals’ registration and obtained results are considered in details. The simulation model of the proposed slip displacement sensor based on polytypic conductive rubber is modeled by Proteus software. The intelligent approaches with the use of a field programmable gate array (FPGA) and VHDL-model to the sensing system designing allow to define the slippage direction in slip displacement sensor based on resistive registration elements. Thus, this expands the functionality of the developed sensor.
THE METHOD OF FINDING THE SPAM IMAGES BASED ON THE HASH OF THE KEY POINTS OF THE IMAGE
Authors: Korsunov Nikolay I. , Toropchin Dmitri A.
Published: February 2021

The article deals with the problems of the existing image recognition algorithms and also presents a method allowing to minimize the drawbacks of existing algorithms, and even to surpass them in certain moments. The proposed method is based on the description of the key points of the image by using perceptual hash. As a demonstration of application of the developed algorithm it is proposed to make the spam filter for images. The purpose of the spam filter is to separate, to divide the images from the collection into certain classes. The core of the spam filter is the developed method. In its conclusion the article contains information that shows the accuracy and performance of the proposed method in comparison with existing methods.
SIMULATION MODELING OF FUZZY LOGIC CONTROLLER FOR AIRCRAFT ENGINES
Authors: Pasieka Marcin , Grzesik Norbert , Kuźma Konrad
Published: February 2021

This article described project of fuzzy logic controller witch control the scope of opening cowl flaps in Piper Seneca V engines. This allows to maintain optimum engine temperature by varying the amount of air flow through the engine compartment. The main task of controller is to reduce pilot workload during the entire flight, as well as to maintain optimum engine operating parameters in order to increase its service life. The designed fuzzy controller generates a cowl flaps control signal according to the three most important input parameters: Cylinder Head Temperature (CHT), Vertical Speed Indicator (VSI), Manifold Pressure (MP). The project uses Matlab and Fuzzy Logic Toolbox software. The authors presented the performance of the system based on twenty samples for research, which simulate position flaps. This articles presented also control surfaces, due to which it is possible to analyze controller performance. At the end showed the simulation process in the Simulink software package with the preset input values.
USING MULTIPLE SEMANTIC MEASURES FOR COREFERENCE RESOLUTION IN ONTOLOGY POPULATION
Authors: Garanina Natalia , Sidorova Elena , Kononenko Irina , Gorlatch Sergei
Published: February 2021

The problem of populating an ontology consists in adding to it some new, domain-specific content from an input expressed, in particular, in a natural language. We focus on an important aspect in the ontology population process – finding and resolving coreferences, i.e., similar mentions of entities in the input text. Our contribution is a novel formal framework that extends the state-of-the-art approaches to coreference resolution by using multiple semantic similarity properties in the resolution process, i.e., we extend the list of the ontological properties used for coreference resolution with additional properties such as inverse, symmetry, intersection, union, etc. We use the proposed framework to improve our previously proposed algorithm for coreference resolution used in our general approach to text analysis and information extraction for populating subject domain ontologies. We describe a multi-agent implementation of our information extraction system and we show that using additional semantic similarity measures for evaluating coreferential candidates improves the quality of the coreference resolution process, especially for complex objects whose coreferencing has not been yet studied in detail.
MODELS OF ROBOT’S WHEEL-MOVER BEHAVIOR ON FERROMAGNETIC SURFACES
Authors: Taranov Mykyta O. , Kondratenko Yuriy P.
Published: February 2021

The mobile robots which can move on complicated working surfaces play a significant role in the automation of various technological processes, in particular, ship repair, fire fighting, inspection of welding quality, rescue operations, etc. This work is a continuation of the authors’ investigation of the mobile robot’s moving on inclined and vertical ferromagnetic surfaces based on a magnetically operated wheel-mover. Special attention is paid to constructing magnetically operated wheel-mover with twelve legs and modeling of the robot’s wheel-mover behavior in different working modes including investigations of the wheel-mover center trajectory, behavior of control signals, etc. Geometrical dependences between a number of wheel-mover legs and deviation of the wheel center path from horizontal line are described. In the present article the modeling results for movement of the wheel-mover on both plain and non-plain surfaces are discussed. For this purpose, the mathematical model of the wheel-mover was created and analyzed and the results were verified using a simulation approach.
ASSOCIATION RULES MINING IN BIG DATA
Authors: Shakhovska Nataliya , Kaminskyy Roman , Zasoba Eugen , Tsiutsiura Mykola
Published: February 2021

The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.
MODELING OF CLAMPING MAGNETS INTERACTION WITH FERROMAGNETIC SURFACE FOR WHEEL MOBILE ROBOTS
Authors: Kondratenko Yuriy , Zaporozhets Yuriy , Rudolph Joachim , Gerasin Oleksandr , Topalov Andriy , Kozlov Oleksiy
Published: February 2021

The mathematical models of clamping permanent and electro- magnets for clamping force determination by means of field theory (method of secondary sources and features of integrated forces calculation) are offered for magnetically operated movers of mobile robots (MR) for moving on inclined or vertical ferromagnetic surfaces. The results of experimental investigations of magnetic induction and clamping force measurements in different spatial positions of clamping magnet relative to the ferromagnetic surface are given. The developed simulation models based on field theory and finite element method allow to validate the obtained functional dependencies with respect to gaps between magnet and working surface, caused by excrescences of shells, and ferromagnetic surface thickness changes due to its technological features, as well as help at the stage of MR’s design. Authors propose control and measuring system of clamping force that can be successfully used for new and existing MRs’ constructions for their reliable operating on complex surfaces taking into account the nature of their uncertainties (ferromagnetic or non-ferromagnetic).
A HYBRID OPTIMIZATION APPROACH FOR COMPLEX NONLINEAR OBJECTIVE FUNCTIONS
Authors: Obadan Samuel O. , Wang Zenghui
Published: February 2021

With respect to the ‘no free launch’ theorem, no single algorithm has a better performance when tested against a completely stochastic algorithm on all objective functions. Consequently, choosing the best algorithm for a particular problem is often more of an art than science. The complexity of an objective function can be determined by certain features such as the modality, the basins, the valleys, the separability, and the dimensionality of the objective function. While the separability and modality contribute to the complexity of the function, the dimensionality and domain range increases the function’s search space exponentially. In this paper, the authors analyze the algorithmic constructs of Simulated Annealing (SA), Cuckoo-search (CK), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) along with two hybrid paradigms. In addition, an extensive comparative study was conducted using 30 standard bench mark functions to demonstrate how an ingenious hybrid algorithm could significantly shorten the amount of function calls (generations) needed to attain the optimal or rather near optimal solution for almost any complex objective function. Results from empirical analysis unveil the precision, robustness and success of the hybrid algorithm (without compromising run-time complexity) over its counterparts.
PROVIDING ADITTIONAL COTENT TO PRINT MEDIA USING AUGMENTED REALITY
Authors: Letellier Julien , Reinhardt Jens , Scholl Peter , Sieck Jürgen , Thiele-Maas Michael
Published: February 2021

While not a new concept, Augmented Reality (AR) has gained increasing momentum in the last few years through the widespread propagation of high-performance mobile devices. These powerful handheld devices allow users to see the real world enhanced with computer-generated content anywhere at anytime. This has led to a massive rise of AR technology; large companies and start-ups are adding affordable AR accessories to their product portfolios. In this paper we describe a joint project between our research group and the Konzerthaus Berlin to use AR to enrich the Konzerthaus’ printed media. With the aid of AR, the readers of the seasonal brochure of the Konzerthaus Berlin are given the opportunity to access additional content complementing the brochure. These contents include images, illustrations, audio, video, simple 2D and 3D animations, interactive media as well as 3D objects. The application was implemented using Vuforia and Unity. This is an extended version of the paper presented at the Conference on Culture and Computer Science 2017 [17].
NOVEL PRE-PROCESSING FRAMEWORK TO IMPROVE CLASSIFICATION ACCURACY IN OPINION MINING
Authors: V. L. Helen Josephine , S. Duraisamy
Published: February 2021

The growth of information technology led to the Internet development that in turn helped people in many ways. The major one is to express their views about the products and services through reviews, blogs, feedback, and comments on the website and in social media. The buyers are forced to go through investigation on these reviews/blogs, before choosing any product or service. Out of all online services, Mobile learning app places a vital role to increase the thirst for knowledge. But to identify the suitable mobile learning app, the opinions of the existing customers need to be mined. This research paper analyzes the mobile learning reviews which are available in the corpus. A novel preprocessing framework is proposed in this paper to improve classification accuracy in the dataset - mobile learning app review dataset. The corpus dimension is reduced using SVD through which, the data is prepared for mining. The classification accuracy is evaluated by applying Multinomial Naïve Bayes, Random Forest data mining algorithms and Learning Vector Quantization (LVQ), Elman Neural Network (ENN), Feed Forward Neural Network (FFNN) algorithms with the dataset obtained by the proposed processing method.
IMPLEMENTATION OF SMART MONITORING SYSTEM WITH FALL DECTECTOR FOR ELDERLY USING IOT TECHNOLOGY
Authors: Harum Norharyati , Abidin Zaheera Zainal , Shah Wahidah Md , Hassan Aslinda
Published: February 2021

Current global statistics shows that increasing number of elderly people live alone. Considering this unavoidable situation, a smart IoT system that can ease young family members to monitor their elderly family member from anywhere has been proposed. In this paper, the system uses a low-cost single board computer, named Raspberry Pi, with embedded webcam to perform 24 hours monitoring is demonstrated. A fall incident can be detected by a captured video that will be processed using an image processing technique. This fall detection is done by several basic activities; separating moving objects from the background, calculating the parameters for these areas and finally, fall detection itself. The fall detector is essential for elderly person monitoring since most of them suffer from chronic diseases and thus need more attention from their young family members. The system can also send notification to the user using social media application when detecting fall incidents in the monitoring area. Video captured by the system will be stored in cloud server, so that it can be used for any incident investigation in the future. By using the system, incidents such as death of elderly family members can be avoided by notifying fall incidents to family members that might be away from home.
A DEEP CONVOLUTIONAL AUTO-ENCODER WITH POOLING – UNPOOLING LAYERS IN CAFFE
Authors: Turchenko Volodymyr , Chalmers Eric , Luczak Artur
Published: February 2021

This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of MNIST dataset. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder’s encoder and decoder parts. Our results show that the developed models provide very good results in dimensionality reduction and unsupervised clustering tasks, and small classification errors when we used the learned internal code as an input of a supervised linear classifier and multi-layer perceptron. The best results were provided by a model where the encoder part contains convolutional and pooling layers, followed by an analogous decoder part with deconvolution and unpooling layers without the use of switch variables in the decoder part. The paper also discusses practical details of the creation of a deep convolutional auto-encoder in the very popular Caffe deep learning framework. We believe that our approach and results presented in this paper could help other researchers to build efficient deep neural network architectures in the future.
DEVELOPING A FASTER PATTERN MATCHING ALGORITHMS FOR INTRUSION DETECTION SYSTEM
Authors: Obeidat Ibrahim , AlZubi Mazen
Published: February 2021

Fast pattern matching algorithms mostly used by IDS, which are considered one of the important systems used to monitor and analyze host and network traffic. Their main function is to detect various types of malicious and malware files by examining incoming and outgoing data through the network. As the network speed growing, the malicious behavior and malware files are increasing; the pattern matching algorithms must be faster. In this research paper we are presenting a new method of pattern matching, which could be a platform for enhancement in the future. In this field, researchers spared no efforts to introduce fast algorithms for pattern matching. The Most popular algorithms are Boyer-Moore, Aho–Corasick, Naïve String search, Rabin Karp String Search and Knuth–Morris–Pratt. Based on studying these techniques we are developing algorithms that process the text data, using different algorithm technique and then we’ll test the performance and compare the processing time with the fastest proven pattern matching algorithms available. Document the result and draw the overall conclusion.
TEST SCENARIO SPECIFICATION LANGUAGE FOR MODEL-BASED TESTING
Authors: Halling Evelin , Vain Jüri , Boyarchuk Artem , Illiashenko Oleg
Published: February 2021

In mission critical systems a single failure might cause catastrophic consequences. This sets high expectations to timely detection of design faults and runtime failures. By traditional software testing methods the detection of deeply nested faults that occur sporadically is almost impossible. The discovery of such bugs can be facilitated by generating well-targeted test cases where the test scenario is explicitly specified. On the other hand, the excess of implementation details in manually crafted test scripts makes it hard to understand and to interpret the test results. This paper defines high-level test scenario specification language TDLTP for specifying complex test scenarios that are relevant for model-based testing of mission critical systems. The syntax and semantics of TDLTP operators are defined and the transformation rules that map its declarative expressions to executable Uppaal Timed Automata test models are specified. The scalability of the method is demonstrated on the TUT100 satellite software integration testing case study.
FORMALIZED REPRESENTATION FOR THE DATA MODEL WITH THE UNIVERSAL BASIS OF RELATIONS
Authors: Yesin Vitalii I. , Karpinski Mikolaj , Yesina Maryna V. , Vilihura Vladyslav V.
Published: February 2021

The goal of the article is to develop a universal (standard) data model that allows you to get rid of the need for a costly policy of doing extra work when developing new ones or transforming existing relational databases (RDBs) caused by dynamic changes in the subject domain (SD). The requirements for the developed data model were formulated. In accordance with the formulated requirements, the data model was synthesized. To simplify the process of creating relational database schemas an algorithm for transforming the description of the subject domain into the relations of the universal basis of the developed model was proposed. The scientific novelty of the obtained results is: a data model that, unlike known ones, allows us to simplify the creation of RDB schemas at the stage of logical design of relational databases, under the conditions of dynamic changes in subject domains, due to the introduced universal basis of relations, as a means of describing structures and the presentation of data for various SDs has been developed.
SIMPLE EFFECTIVE FAST INVERSE SQUARE ROOT ALGORITHM WITH TWO MAGIC CONSTANTS
Authors: Horyachyy Oleh , Moroz Leonid , Otenko Viktor
Published: February 2021

The purpose of this paper is to introduce a modification of Fast Inverse Square Root (FISR) approximation algorithm with reduced relative errors. The original algorithm uses a magic constant trick with input floating-point number to obtain a clever initial approximation and then utilizes the classical iterative Newton-Raphson formula. It was first used in the computer game Quake III Arena, causing widespread discussion among scientists and programmers, and now it can be frequently found in many scientific applications, although it has some drawbacks. The proposed algorithm has such parameters of the modified inverse square root algorithm that minimize the relative error and includes two magic constants in order to avoid one floating-point multiplication. In addition, we use the fused multiply-add function and iterative methods of higher order in the second iteration to improve the accuracy. Such algorithms do not require storage of large tables for initial approximation and can be effectively used on field-programmable gate arrays (FPGAs) and other platforms without hardware support for this function.
МЕТОДИ ТА ВИСОКОПРОДУКТИВНІ ПАРАЛЕЛЬНІ СИСТЕМИ ДЛЯ ОБРОБКИ ТА РОЗПІЗНАВАННЯ ЗОБРАЖЕНЬ У РЕАЛЬНОМУ ЧАСІ
Authors: Грицик В. В. , Березький О. М.
Published: February 2021

В статті розглянуті основні проблеми, які виникають під час обробки і розпізнавання зображень у реальному часі та запропоновані методи і високопродуктивні паралельні засоби засоби для їх вирішення.
A COMPUTATIONAL APPROACH FOR CONSTRUCTING THE REACHABLE WORKSPACES FOR REDUNDANT MANIPULATORS
Authors: Alameldin Tarek K. , Badler Norman , Sobh Tarek , Mihali Raul
Published: February 2021

An efficient computation of 3D workspaces for redundant manipulators is based on a “hybrid” algorithm between direct kinematics and screw theory. Direct kinematics enjoys low computational cost, but needs edge detection algorithms when workspace boundaries are needed. Screw theory has exponential computational cost per workspace point, but does not need edge detection. Screw theory allows computing workspace points in prespecified directions, while direct kinematics does not. Applications of the algorithm are discussed.
DEVELOPMENT OF DISTANCE EDUCATION SYSTEM IN ICIT TANE
Authors: Bilousov Igor , Novosad Oleksander , Adamiv Oleh
Published: February 2021

In the presented material particularises of functioning of the distance education centre as well as consultant centres are considered. Also we consider development of such education system in Institute of Computer Information Technologies (ICIT) of Ternopil Academy of National Economy (TANE).
МЕТОД ЗМЕНШЕННЯ НАДЛИШКОВОСТІ ГАРМОНІЧНИХ СИГНАЛІВ НА ОСНОВІ КОДІВ ПОЛЯ ГАЛУА
Authors: Яцків Наталія
Published: February 2021

в статті запропонований та досліджений метод зменшення надлишковості гармонічних сигналів на основі адаптивного кодування в базисі Галуа, який базується на реєстрації 1/8 періоду та аналізі параметрів сигналу. Даний метод дозволяє зменшити об’єми даних, які реєструються на об’єктах електроенергетики.
PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS
Authors: Adamiv Oleh , Koval Vasyl , Turchenko Iryna
Published: February 2021

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.
IMPROVED MODEL ORDER ESTIMATION FOR NONLINEAR DYNAMIC SYSTEMS
Authors: Sragner Laszlo , Horvath Gabor
Published: February 2021

In system modeling the choice of proper model structure is an essential task. Model structure is defined if both the model class and the size of the model within this class are determined. In dynamic system modeling model size is mainly determined by model order. The paper deals with the question of model order estimation when neural networks are used for modeling nonlinear dynamic systems. One of the possible ways of estimating the order of a neural model is the application of Lipschitz quotient. Although it is easy to use this method, its main drawback is the high sensitivity to noisy data. The paper proposes a new way to reduce the effect of noise. The idea of the proposed method is to combine the original Lipschitz method and the Errors In Variables (EIV) approach. The paper presents the details of the proposed combined method and gives the results of an extensive experimental study.
CONCEPTUAL DECISION MODEL
Authors: Sirola Miki
Published: February 2021

Decision making is mostly based on decision concepts and decision models built in decision support systems. Type of decision problem determines application. This paper presents a conceptual decision model that utilises rule- based methodologies, numerical algorithms and procedures, statistical methodologies including distributions, and visual support. Selection of used decision concepts is based on case-based needs. Fine tuning of the model is done during construction of the computer application and analysis of the case examples. A kind of decision table is built including pre-filtered decision options and carefully chosen decision attributes. Each attribute is weighted, decision table values are given, and finally total score is calculated. This is done with a many-step procedure including various elements. The computer application is built on G2 platform. The case example choice of career is analysed in detail. The developed prototype should be considered mostly as an advisory tool in decision making. More important than the numerical result of the analysis is to learn about the decision problem. Evaluation expertise is needed in the development process. The model constructed is a kind of completed multi-criteria decision analysis concept. This paper is also an example of using a theoretical methodology in solving a practical problem.
VERIFYING OF THE HYPOTHESES AS FOR EXPENSES CAUSED BY ENTERPRISES ACTIVITY INFLUENCE ON ENVIRONMENT MODELING
Authors: Bartkova Lyudmyla , Satkowiak Frederick
Published: February 2021

How does industry effect on the environment cause health problems among the population? What levels of pollution affect the health of the population? Can the effects be measured, predicted, or controlled? Can the costs of health care be predicted based on ecological factors? Is there any correlation between the health of a population and environmental factors? The hypotheses as for the correlation between integrated ecological and social-economical factors and expenses caused by industry on environment are represented in the paper. The verifying of the hypotheses is illustrated by means of the concrete examples.
ONTOGENETIC TEACHING OF MOBILE AUTONOMOUS ROBOTS WITH DYNAMIC NEUROCONTROLLERS
Authors: Mayer Helmut A.
Published: February 2021

After a brief survey of work dealing with dynamic neurocontrollers changing their internal structure during the “lifetime” of a mobile autonomous robot, we present experiments employing a standard sensor–motor neurocontroller with self–adapting weights. The change of behavior of the robot is linked to inputs from the environment that cause the emission of artificial neuromodulators (ANMs) in the robot’s neurocontroller. In its simplest form an outside teacher (human or machine) constantly evaluates the robot’s actions by transmitting positive or negative feedback signals to the robot initiating the internal changes. The focus of investigations is put on the mechanisms of the interaction of teaching input and structural changes. A well–known concept for this interaction is Hebbian learning, which is regulated by ANMs in the presented approach. In extension to related work in evolutionary robotics (ER), we analyze important details of robotic (ontogenetic) learning by experiments measuring the ability of robots to learn simple tasks in a simulated environment without employing evolution. Specifically, we are interested in the comparison of Hebb learning variants, and the crucial question of the correct interpretation of reward or punishment signals by the robot.
AN ANTHROPOMORPHIC SPEECH PROCESSING BASED ON THE COCHLEAR MODEL AND ITS APPLICATION FOR CODING TASK
Authors: Petrovsky A. A. , Likhachov D. S. , Wan W.
Published: February 2021

According to antisymmetry of basilar membrane (BM) movements, a new mathematical model of cochlea is derived using viscous cochlear fluid theory, and then transformed into a digital cochlear model with bilinear transformation. The frequency responses are found to be quite consistent with the experimental data, especially the high frequency slope is much more improved. A new cochlear map and 3 dB bandwidth characteristics for cochlear filter banks are obtained and presented, which will make applications of cochlear model more quantitative and accurate. Due to simplicity of its structure and reality of its characteristics, it will be proved the model can be used easily in speech processing system.
DECISION CONCEPTS
Authors: Sirola Miki
Published: February 2021

Decision making is done in many application areas. Still most studies are done in such fields as economy and production planning. In methodologies used there exists more variation. This paper reviews the decision concepts discussed in the literature. Also some decision models by the author are commented. The field and practise in decision science is summarized. Although decision support systems are the final results of many projects, they are mostly based on the decision concepts behind the studies that deserve also more detailed examination. Decision analysis approach and knowledge-based technologies are examples of commonly used concepts.
PROBABILISTIC PROPERTIES AND EVALUATION OF DESCRIPTIONS OF INITIAL SIGNAL OF NONLINEAR SYSTEM AT ACTION OF PERIODIC WHITE NOISE
Authors: Lupenko Sergiy , Pryjmak Mykola
Published: February 2021

We prove the theorem of moment functions of higher frequency output process chasoinvariantnoyi nonlinear system, provided that the input is valid on its periodic white noise. Grounded statistical methods for evaluation of periodic functions torque output process nonlinear system.
VEHICLE ROUTING PROBLEM WITH TIME WINDOWS
Authors: Vacic Vladimir , Sobh Tarek M.
Published: February 2021

The topic of this paper is a Genetic Algorithm solution to the Vehicle Routing Problem with Time Windows, a variant of one of the most common problems in contemporary operations research. The paper will introduce the problem starting with more general Traveling Salesman and Vehicle Routing problems and present some of the prevailing strategies for solving them, focusing on Genetic Algorithms. At the end, it will summarize the Genetic Algorithm solution proposed by K.Q. Zhu which was used in the programming part of the project.
SIMULATION OF DC-DC CONVERTER SYMBOLIC DYNAMICS: DETECTION AND IDENTIFICATION OF BIFURCATION TYPES
Authors: Hamzaoui Abdelaziz , Ustinov Pavel , Sholonik Andrey , Kolokolov Yury
Published: February 2021

In this paper continuous-time model to discrete-time symbolic model transformation for an algorithmization of the DC-DC buck converter dynamics investigation process is derived. The proposed concept of symbolic index allows evaluate quantitatively the symbolic model change and makes possible the “nonsmooth” bifurcations revealing. The numerical simulation that had been carried out verifies utilizing efficiency of symbolic modeling both as for possible bifurcation pattern forecasting, as for bifurcation type identification.
PRECISION DATA ACQUISITION (DAQ) MODULE WITH REMOTE REPROGRAMMING
Authors: Kochan Roman , Kochan Orest
Published: February 2021

These papers are designed precision data acquisition unit based on a 24- bit ADC type AD7712. It is designed for use in distributed information-measuring systems and networks. Flow data collection allows remote reprogramming - a replacement program of work through the serial interface during operation ( after the identification of the appropriate command ). Flow data collection to automatically perform the following procedures designed to improve the accuracy of the measurement result of : (i) the zero ; (ii) the calibration of exemplary source voltage; (iii ) correction of temperature coefficient of exemplary source voltage; (iv ) correction of quadratic integral non-linearity error of the ADC. Implementation procedures improve the accuracy of measurement error provides acceptable DC voltage ± 0.01% in working conditions and non-linearity of the transformation of not more than ±0.0003%.
SYNTHESIS OF STRUCTURES OF OPERATING DEVICES IMPLEMENTATION CRYPTOGRAPHIC ALGORITHMS IPSEC OF OPTIMIZED FOR MEDIA PACKAGES PROCESSIN
Authors: Korkishko Tymoor , Shevchuk Ruslan
Published: February 2021

In the paper the operating device of IPSec protocol optimized for treatment media of packages is investigated. Analytical expressions which describe time of media packages processing depending on the parameters of operating device structure are offered, the mathematical model of operating device of IPSec processor is developed. On the basis of mathematical model, with the purpose of reduction of delay and jitter, which rise up during the generation of media package, software is developed for optimization of structures descriptions of processor IPSec operating device. The row of the optimized structures of operating device is got for different services of given IPSec treatment at different technological descriptions of component base. The analysis of results allowed to set that in most cases, the least time of treatment media of packages is observed at iterative and iterative-conveyer realization of IPSec operating device.
AN INFLUENCE OF PARAMETER VARIATION ON MODEL-BASED EMERGENCY FORECASTIING IN THE PULSE SYSTEM
Authors: Kolokolov Yury , Monovskaya Anna
Published: February 2021

Practical realization of model-based emergency forecasting in pulse systems for the case of multi-parameter variation in a possible wide range associates with the problem of a-priori forming of single-statement information on system dynamics. The fractal diagram is proposed to use for the purpose of system dynamics presentation. This diagram allows one-to-one correspondence between the both, parameter and phase,spaces establishing by line up stages.
COMPRESSION OF THE TECHNOLOGICAL DATA IN TERMS OF GALOIS BASIC FUNCTIONS
Authors: Yatskiv Nataliya
Published: February 2021

In this article data compression method on the basis of Galois basic functions of zero and first rank is proposed, efficiency of coding with the usage of this method is investigated.
SOURCES OF RANDOMNESS FOR USE IN RANDOM NUMBER GENERATION
Authors: Fragopoulos A. G. , Serpanos D. N.
Published: February 2021

Efficient generation of random numbers plays significant role in cryptographic applications. Such a generator has to produce unpredictable and un-correlated random bits. Random number generators are classified as pseudo-random number generators (PRNGs) and true random number generators (TRNGs). The first ones have the disadvantage that they can be proven predictable, while the latter ones can produce true random bits but it is not easy to re-produce specific sequences or implement them in constrained environments and there may exist correlations and biases of produced sequences. A third class of random number generators has been introduced, called hybrid-random number generators (h-RNGs), where there is a combination of a cryptographically strong PRNGs or TRNGs which are seeded, and possibly re-seeded, through a source of randomness with high entropy. In this paper, we present an overview of various sources of randomness that can be used either as direct random number generators or as seed sources in h-RNGs, for application in embedded systems.
INTEGRATED SYSTEMS OF INFORMATION SECURITY IN COMPUTER NETWORKS
Authors: Loutsky Georgy , Shyrotchin Valerij , Mukhin Vadim
Published: February 2021

In this paper we suggest the integrated security systems for computer networks, which are adaptive to certain network parameters. Also is suggested the approach to adaptive security systems parameters evaluation based on the analysis of information value changing in time. The main requirements to adaptive security systems are developed.
FAST ALGORITHMS AND COMPUTING MEANS OF CRYPTOLOGICAL FUNCTIONS
Authors: Horpenyuk Andriy
Published: February 2021

The problems of speed of asymmetric cryptology computational algorithms are analyzed in the article. There has been offered for the greater speed to apply computing facilities of analytical principle of functioning together with algorithmic computing facilities. There have been given fundamentals of synthesis of such facilities, considered problems that limit domain of their application in cryptography. There have been given research results, which expose the prospects of application domain expansion of such facilities.
APPLICATION OF OBJECT ORIENTED NEURAL NETWORK TO CONTROL MOTION OF THE LOAD OF A SEA CRANE
Authors: Falat Pawel , Brzozowska Lucyna , Brzozowski Krzysztof
Published: February 2021

The paper presents object oriented approach to design of neural networks. The second part of the article presents an application of the object oriented neural network to control the load of the sea crane of an A-Frame type. The control algorithm has to stabilize load position and compensate the weaving. The model of the A-Frame dynamics were developed and used to achieve the optimal winch drive functions for various sea conditions. Those functions have been used to teach the network.
ALTERATION CORRECTION IN LYMPHOCYTE IMAGE FOR MICRO NUCLEUSES DETECTION
Authors: Carni Domenico Luca , Grimaldi Domenico , Lamonaca Francesco
Published: February 2021

The paper presents a method pointed out to detect and to correct the alterations of (i) under-exposure, (ii) overexposure, (iii) out of focus, (iv) Gaussian noise, affecting the images acquired into the flow cytometer. These alterations reduce the image quality and interfere with the correct micro nucleus detection in lymphocyte. The objective of the proposed correction is to make the image able to be correctly processed by the pattern matching algorithm (i) to detect the micronucleus into human lymphocytes, (ii) to minimize the doubtful detections, and (iii) to enhance the confidence that in the rejected images are not including the micro nucleuses. The results of numerical tests confirm the validity of the proposed correction method.