Nemlineáris, dinamikus rendszerek hatékony optimalizálásának egy lehetősége

Evolution as a process has remarkable problem-solving abilities. Today the available technical items enable the successful use of genetic algorithms as a sort of model of evolu-tion is various fields of science. The procedure presented in this work combines the advan-tages of well-known soft techniques. Let it be assumed that the criterion function to be minimized has a global minimum. Let [!Beilleszthetetlen képlet, nézze meg a tanulmány szövegében!]  be a criterion function. The aim is to determine the [!Beilleszthetetlen képlet, nézze meg a tanulmány szövegében!] global minimum. The use of the genetic algorithm in the stochastic-based search will most likely result in finding this minimum. In the further part of this work 9 will dente the free parameters of the system. The algorithms presented in this paper can be made parallel to a great extent and thus can be largely im-plemented in grid system

Wireless sensor network based localization in industrial environments

The use of wireless devices has greatly increased in the last decade, and it has been one of the most widely used medium of information transmission. Within the wireless devices the wireless sensor networks are the most contemporary and most commonly researched field. The work deals with the industrial use of wireless sensor networks and more precisely with monitoring and controlling industrial assembly lines. The focus of this study is localization by the use of wireless technology in the above mentioned environment. In the experiment wireless sensors are placed on the base elements of currently being assembled products. The developed system is able to specify the precise place of the product in the assembly line and record the time of localization. By the use these information the time of assembling the product can be monitored. For determining the place of the product the Received signal strength indication – RSSI has been used. The current position of the product is calculated by a neural network. The use of these sensors makes possible the measuring and recording of the influences on the product during the assembly, such as the effects of temperature, humidity, or if the product has been hit or damaged. By the use of these wireless sensor networks the quality of the assembled products can be improved and the process of assembly can be optimized

Real-Time Vehicle Classification System Using a Single Magnetometer

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set

Indoor Localization Simulation Framework for Optimized Sensor Placement to Increase the Position Estimation Accuracy

Indoor position estimation is an important part of any indoor application which contains object tracking or environment mapping. Many indoor localization techniques (Angle of Arrival – AoA, Time of Flight – ToF, Return Time of Flight – RToF, Received Signal Strength Indicator – RSSI) and technologies (WiFi, Ultra Wideband – UWB, Bluetooth, Radio Frequency Identification Device – RFID) exist which can be applied to the indoor localization problem. Based on the measured distances (with a chosen technique), the position of the object can be estimated using several mathematical methods. The precision of the estimated position crucially depends on the placement of the anchors, which makes the position estimate less reliable. In this paper a simulation framework is presented, which uses genetic algorithm and the multilateral method to determine an optimal anchor placement for a given pathway in an indoor environment. In order to make the simulation more realistic, the error characteristics of the DWM1001 UWB ranging module were measured and implemented in the simulation framework. Using the proposed framework, various measurements with an optimal and with a reference anchor placement were carried out. The results show that using an optimal anchor placement, a higher position estimation accuracy can be achieved

Identification of the place and materials of knocking objects in flow induced vibration

Flow induced vibration can be found and identified by acoustic methods. Using acoustic sensors, the first task, the event detection has been solved using the sequential probability ratio test after autoregressive filtering the measured signal. The LABView program and actual test of the event recognition technique are presented. The signals were recorded in a 100 m long test loop having artificially placed flow induced vibrating objects hitting the wall. Time delay between the fronts of detected events has been used to localize the actual place of the acoustic source. The recognition of the material of the knocking object is based traditionally on the spectrum estimation. However, this is rather time consuming task by naked eyes. We are proposing to introduce the skeleton method for event identification

Indoor localization simulation framework for optimized sensor placement to increase the position estimation accuracy

Indoor position estimation is an important part of any indoor application which contains object tracking or environment mapping. Many indoor localization techniques (Angle of Arrival – AoA, Time of Flight – ToF, Return Time of Flight – RToF, Received Signal Strength Indicator – RSSI) and tech- nologies (WiFi, Ultra Wideband– UWB, Bluetooth, Radio Frequency Identification Device – RFID) exist which can be applied to the indoor localization problem. Based on the measured distances (with a chosen technique), the position of the object can be estimated using several mathematical methods. The precision of the estimated position crucially depends on the placement of the anchors, which makes the position estimate less reliable. In this paper a simulation framework is presented, which uses genetic algorithm and the multilateral method to determine an optimal anchor placement for a given pathway in an indoor environment. In order to make the simulation more realistic, the error characteristics of the DWM1001 UWB ranging module were measured and implemented in the simulation framework. Using the proposed framework, various measurements with an optimal and with a reference anchor placement were carried out. The results show that using an optimal anchor placement, a higher position estimation accuracy can be achieved