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2017 (vol. 27) - Number 2

W. Bozejko, M. Wodecki:

Discrete Systems: Theory and Applications. Special issue.

G. Bocewicz, Z. Banaszak, I. Nielsen:

Delivery-flow routing and scheduling subject to constraints imposed by vehicle flows in fractal-like networks

W. Bozejko, A. Gnatowski, R. Idzikowski, M. Wodecki:

Cyclic flow shop scheduling problem with two-machine cells

W. Bozejko, M. Uchronski,, Z. Chaczko, M. Wodecki:

Parallel patterns determination in solving cyclic flow shop problem with setups

J. Brodny, S. Alszer, J. Krystek, M. Tutak:

Availability analysis of selected mining machinery

K. Chmielewska, D. Formanowicz, P. Formanowicz:

The effect of cigarette smoking on endothelial damage and atherosclerosis development - modeled and analyzed using Petri nets

A. Galuszka, J. Krystek, A. Swierniak, T. Grzejszczak, C. Lungoci:

Information management in passenger traffic supporting system design as a multi-criteria discrete optimization task

M. Kardynska, J. Smieja:

Sensitivity analysis of signaling pathway models based on discrete-time measurements

J. Kasprzyk, P. Krauze, S. Budzan, J. Rzepecki:

Vibration control in semi-active suspension of the experimental off-road vehicle using information about suspension deflection

M. Koryl, D. Mazur:

Towards emergence phenomenon in business process management

M. Koryl:

Active resources concept of computation for enterprise software

H. Krawczyk, M. Nykiel:

Mobile devices and computing cloud resources allocation for interactive applications

W. Mitkowski, W. Bauer, M. Zagórowska:

Discrete-time feedback stabilization

J. Pempera:

An exact block algorithm for no-idle RPQ problem

K. Rzosinska, D. Formanowicz, P. Formanowicz:

The study of the influence of micro-environmental signals on macrophage differentiation using a quantitative Petri net based model

K. Skrzypczyk , M. Mellado:

Vehicle navigation in populated areas using predictive control with environmental uncertainty handling

W. Bozejko, J. Pempera, M. Wodecki:

A fine-grained parallel algorithm for the cyclic flexible job shop problem

ACS Abstract:

2010 (Volume 20)
Number 4
1. Hierarchical models of complex plants on basis of power boiler example
2. Improving energy compaction of a wavelet transform
3. Sensorless DTC of induction motor using neural network controller
4. Mixture model of NMR and its application
5. FACTS location and size for reactive power system compensation through the multi-objective optimization
6. Position weight matrix model as a tool for the study of regulatory elements distribution across the DNA sequence

Hierarchical models of complex plants on basis of power boiler exampleDownload full PDF article
W. Stanislawski, M. Rydel
(Opole University of Technology, Poland)

The methodology of hierarchical linearized mathematical models construction of complex plants to control purposes is presented in the paper. Thanks to the methodology, the one high order model (flat, one level model), is replaced by a collection of models, which are placed at different hierarchy levels. The models represent dynamic processes typical for each hierarchy level, and omit fast dynamic processes significant at lower levels. The higher is hierarchy level, the slower dynamic processes is described by the model and the lower is order of the mo\-dels. Multi-level model structure gives possibility of dynamic properties analysis by application of aggregation procedure. One of the principal aggregation procedure is reduction of models at individual levels of hierarchical structure. Such approach enables creating a reduced hierarchical model including a collection of models at every level of hierarchy, characterized by various adequacy scopes and accuracy of the plant features approximation. The paper presents methodology of hierarchical complex plants models creation on the example of evaporator of the BP--1150 boiler. Each of the subsystem at individual level of model hierarchy is a multi-input and multi-output causal systems.

keywords: once-through steam boiler, reduced hierarchical model, complex model reduction


Improving energy compaction of a wavelet transformDownload full PDF article
J. Stolarek
(Technical University of Lodz, Poland)

In this paper a new method for adaptive synthesis of a smooth orthogonal wavelet, using fast neural network and genetic algorithm, is introduced. Orthogonal lattice structure is presented. A new method of supervised training of fast neural network is introduced to synthesize a wavelet with desired energy distribution between output signals from low--pass and high--pass filters on subsequent levels of a Discrete Wavelet Transform. Genetic algorithm is proposed as a global optimization method for defined objective function, while neural network is used as a local optimization method to further improve the result. Proposed approach is tested by synthesizing wavelets with expected energy distribution between low-- and high--pass filters. Energy compaction of proposed method and Daubechies wavelets is compared. Tests are performed using image signals.

keywords: wavelet transform, neural networks, genetic algorithms, signal processing, lattice structure


Sensorless DTC of induction motor using neural network controllerDownload full PDF article
I. Messaif, N. Saadia
(Universite des Sciences et de la Technologie Houari Boumediene, Alger, Algerie)
E-M. Berkouk
(Ecole Nationale Polytechnique d'Alger, Algeria)

The paper deals with development of sensorless Direct Torque Control (DTC) system based on neural network. This network is built to solve the task of proper switching states selection based on information about electromagnetic torque and stator flux (position and magnitude) of induction motor. In fact, this technique which uses conventional switching table is not convenient for one-line and real time control for its high computation time. In order to avoid this problem a solution based on neural network is proposed. Well trained Artificial Neural Network structure can replace successfully the switching table. However, in the Neutral-Point-Clamped topology, it has an inherent problem of Neutral Point Potential (NPP) variation. In this way, a Neural Network-Direct Torque Control technique has been applied and the estimated value of the Neutral Point Potential is used, which is calculated by motor currents. This control strategy offers the possibility of selecting appropriate switching state to achieve the control of Neutral Point Potential. Simulation results verify the validity of the proposed method.

keywords: direct torque control, NPC three-level inverter, switching table, neural point potential, neural network, induction motor


Mixture model of NMR and its applicationDownload full PDF article
F. Binczyk, J. Polanska
(Silesian University of Technology, Gliwice, Poland)
R. Tarnawski
(Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland)

Nuclear Magnetic Resonance (NMR) is widely used technique in cancer diagnosis and treatment planning. It is employed to search for the high concentration regions of particular metabolites, which are directly related to the concentration of cancer cells. NMR signal maybe be characterized by a set of peaks which are representation of every distinct metabolite. Area under peak must be calculated in order to obtain proper information about metabolite amount. Commercially available software allows for the analysis of one-peak-in-time only. The proposed technique, based on Gaussian Mixture Model (GMM), allows for modeling all-peaks-in-time, and corrects after the neighboring peaks giving more accurate estimates of metabolite concentration. The resulting software processes NMR signal from the very beginning up to the final result, which is given in a form of so called metabolite map.

keywords: GMM, EM algorithm, BIC, NMR, Savitzky-Golay filter


FACTS location and size for reactive power system compensation through the multi-objective optimizationDownload full PDF article
M. Belazzoug
(University of Saad Dahleb Road of Soumaa - Blida, Algeria)
M. Boudour
(Electrical and Industrial Systems Laboratory, USTHB, Algeria)
K. Sebaa
(University of Medea, Algeria)

The problem of the FACTS (Flexible Alternative Current Transmission System Devices) location and size for reactive power system compensation through the multi-objective optimization is presented in this paper. A new technique is proposed for the optimal setting, dimension and design of two kinds of FACTS namely: Static Volt Ampere reactive (VAR) Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) handling the minimization of transmission losses in electrical network. Using the proposed scheme, the type, the location and the rating of FACTS devices are optimized simultaneously. The problem to solve is multi criteria under constraints related to the load flow equations, the voltages, the transformer turn ratios, the active and reactive productions and the compensation devices. Its solution requires the the advanced algorithms to be applied. Thus, we propose an approach based on the evolutionary algorithms (EA) to solve  multi-criterion problem. It is similar to the NSGA-II method (Ellitist Non Dominated Sorting Genetic Algorithm). The Pareto front is obtained for continuous, discrete and multiple of five MVArs (Mega Volt Ampere reactive) of compensator devices for the IEEE 57-bus test system (IEEE bus test is a standard network).

keywords: reactive dispatch, multi-objective optimization, NSGA-II, SVC, TCSC, FACTS


Position weight matrix model as a tool for the study of regulatory elements distribution across the DNA sequenceDownload full PDF article
R. Jaksik, J. Rzeszowska-Wolny
(Silesian University of Technology, Gliwice, Poland)

Ab initio methods of DNA regulatory sequence region prediction known as transcription factor binding sites (TFBS) are a very big challenge to modern bioinformatics. Although the currently available methods are not perfect they are fairly reliable and can be used to search for new potential protein-DNA interaction sites.  The biggest problem of {\em ab initio} approaches is the very high false positive rate of predicted sites which results mainly from the fact that TFBS are very short and highly degenerate. Because of that they can occur by chance every few hundred bases making the task of computational prediction extremely difficult if one aims to reduce the high false positive rate keeping highest possible sensitivity to predict biologically meaningful sequence regions. In this work we present a new application that can be used to predict TFBS regions in very large datasets based on position weight matrix models (PWM's) using one of the most popular prediction methods.
The presented application was used to predict the concentration of TFBS in a set of nearly 2.2 thousand unique sequences of human gene promoter regions. The study revealed that the concentration of TFBS further than 1kbp from the transcription initiation site is constant but it decreases rapidly while getting closer to the transcription initiation site. The decreasing TFBS concentration in the vicinity of genes might result from evolutionary selection which keeps only sites responsible for interactions with proteins being part of a specific regulatory mechanism leading to cells survival.

keywords: transcription factors, TFBS, regulation of gene expression, regulatory sequence elements, DNA, position-weight matrix, PWM


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