Vol. 24, No. 2

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Now showing 1 - 5 of 12

Particle Swarm Optimization with Distance Based Repulsivity
(Institute of Automation and Computer Science, Brno University of Technology, 2018-12-21) Pluhacek, Michal; Zelinka, Ivan; Senkerik, Roman; Viktorin, Adam; Kadavy, Tomas
In this study, we propose a repulsive mechanism for the Particle Swarm Optimization algorithm that improves its performance on multi-modal problems. The repulsive mechanism is further extended with a distance-based modification. The results are presented and tested for statistical significance. We discuss the observations and propose further directions for the research.
The Possibility of Interference Suppression by Correlation Receiver Applied to Marker Localization
(Institute of Automation and Computer Science, Brno University of Technology, 2018-12-21) Vestenicky, Peter; Vestenicky, Martin
The actual applications of the radiofrequency identification (RFID) systems in industry are focused not only on the identification but also on the localization of the RFID transponders. The special type of the RFID transponders is used to localize and to identify the underground facility networks such as pipes and cables. In such applications the RFID transponders are called markers. The paper describes an analysis of the correlation receiver for RSSI based localization of the inductive coupled RFID markers. The analysis is performed by the modeling of the localization device and the marker in Matlab – Simulink software. The aim of the analysis is to examine the ability of the correlation receiver to suppress the interfering signals from industrial sources, for example from the long wave telemetry transmitters which have their working frequencies in the same band as the working frequencies of the markers.
Fire Detection in Video Stream by Using Simple Artificial Neural Network
(Institute of Automation and Computer Science, Brno University of Technology, 2018-12-21) Janku, Peter; Kominkova Oplatkova, Zuzana; Dulik, Tomas; Snopek, Petr; Liba, Jiri
This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.
WECIA Graph: Visualization of Classification Performance Dependency on Grayscale Conversion Setting
(Institute of Automation and Computer Science, Brno University of Technology, 2018-12-21) Skrabanek, Pavel; Yayilgan, Sule Yildirim
Grayscale conversion is a popular operation performed within image pre-processing of many computer vision systems, including systems aimed at generic object categorization. The grayscale conversion is a lossy operation. As such, it can signicantly in uence performance of the systems. For generic object categorization tasks, a weighted means grayscale conversion proved to be appropriate. It allows full use of the grayscale conversion potential due to weighting coefficients introduced by this conversion method. To reach a desired performance of an object categorization system, the weighting coefficients must be optimally setup. We demonstrate that a search for an optimal setting of the system must be carried out in a cooperation with an expert. To simplify the expert involvement in the optimization process, we propose a WEighting Coefficients Impact Assessment (WECIA) graph. The WECIA graph displays dependence of classication performance on setting of the weighting coefficients for one particular setting of remaining adjustable parameters. We point out a fact that an expert analysis of the dependence using the WECIA graph allows identication of settings leading to undesirable performance of an assessed system.
Differential Evolution and Deterministic Chaotic Series: A Detailed Study
(Institute of Automation and Computer Science, Brno University of Technology, 2018-12-21) Senkerik, Roman; Viktorin, Adam; Zelinka, Ivan; Pluhacek, Michal; Kadavy, Tomas; Kominkova Oplatkova, Zuzana; Bhateja, Vikrant; Satapathy, Suresh Chandra
This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed.

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