Conference
International Conference on Electric Information and Control Engineering
About: International Conference on Electric Information and Control Engineering is an academic conference. The conference publishes majorly in the area(s): Control system & Algorithm design. Over the lifetime, 1768 publications have been published by the conference receiving 4255 citations.
Papers
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15 Apr 2011
TL;DR: In this paper, a chaotic particle swarm optimization algorithm (CPSO) was presented for extracting solar cell model parameters, in which the global search performance and local convergence of PSO were improved by introducing a chaos search.
Abstract: Utilizing the numerical analysis and optimization method for extracting solar cells model parameters, one recurrent issue refers to the difficulty in initializing the parameters. Moreover, those methods using solar cells exponential model are sensible to small changes in the data measured. A chaotic particle swarm optimization algorithm (CPSO) was presented for extracting solar cell model parameters, in which the global search performance and local convergence of particle swarm optimization (PSO) were improved by introducing a chaos search. The CPSO searched for optimal parameters without strict limitation on the search ranges. The procedure is illustrated by applying it to parameters extraction using the current-voltage data measured from a silicon cell and a solar module. The results demonstrate that the method can reduce the influence of experimental data measurement accuracy, and the statistical analysis data of fitting (I–V) characteristics curves are better than that of other published methods.
155 citations
15 Apr 2011
TL;DR: Three A-star algorithms are studied to compare the maze searching capacity and efficiency of their different heuristic functions, and the depth-first search algorithm is adopted as a benchmark to judge the usefulness of heuristic function.
Abstract: Robots can be widely used to fulfill the task of search and rescue trapped persons in some dangerous situations, which can be abstracted as a maze. Three A-star algorithms are studied in this paper to compare the maze searching capacity and efficiency of their different heuristic functions, and the depth-first search algorithm, which has no heuristic information, is also adopted as a benchmark to judge the usefulness of the 3 heuristic functions of A-star algorithms. Experiments validated the usefulness of heuristic function with the results that the A-star algorithms outperform the depth-first search algorithm in most cases, and the A-star algorithm with the Euclidean distance from the father point of current point to the target point included in the heuristic function shows the best performance.
76 citations
27 Apr 2017
TL;DR: Automatic brain tumor stage classification is done by using probabilistic neural network (PNN), which is fastest technique and also provide the good classification accuracy.
Abstract: Brain tumour is a group of tissue that is prearranged by a slow addition of irregular cells. It occurs when cell get abnormal formation within the brain. Recently it is becoming a major cause of death of many people. The seriousness of brain tumor is very big among all the variety of cancers, so to save a life immediate detection and proper treatment to be done. Detection of these cells is a difficult problem, because of the formation of the tumour cells. It is very essential to compare brain tumor from the MRI treatment. Brain tumor is classified into three types: Normal, Benign and Malignant. The neural network will be used to classify the phase of brain tumor that is benign, malignant or normal. Feature extraction by using the Gray Level Co-Occurrence Matrix (GLCM). Image recognition and image compression is done by using the Principal Component Analysis (PCA) method and also large dimensionality of the data is reduced. Automatic brain tumor stage classification is done by using probabilistic neural network (PNN). Segmentation process is done by using K-means clustering algorithm and also detects the brain tumor spread region. Numbers of defect cells are finding in the spreaded region. PNN is fastest technique and also provide the good classification accuracy. Simulation is done by MATLAB 2013 software.
50 citations
15 Apr 2011
TL;DR: An automatic alarm device for traffic accidents is introduced in this paper and can automatically detect a traffic accident, search for the spot and then send the basic information to first aid center within two seconds covering geographical coordinates, the time and circumstances in which a traffic accidents takes place.
Abstract: An automatic alarm device for traffic accidents is introduced in this paper. It can automatically detect a traffic accident, search for the spot and then send the basic information to first aid center within two seconds covering geographical coordinates, the time and circumstances in which a traffic accident takes place. By means of satellite navigation system, first aid rescuers can accurately locate the place with maximum error controlled by 10 meters, so that they can save the injured people as soon as possible.
47 citations
15 Apr 2011
TL;DR: It can be found that the proposed neural network model can accurately forecast the generated electrical power and output current under different weather conditions.
Abstract: The purpose of this paper is to forecast the electrical energy generated by photovoltaic systems using the method of neural network. A database, which includes the actual measured electrical energy and the parameters of weather conditions that can influence the electrical energy generated by the photovoltaic system (PV system), is established in advance in order to be used in electrical energy forecasts. The Matlab/Simulink software is used in this paper to set up a neural network model with the learning algorithm of back-propagation network in order to forecast the generated electrical energy of the PV system. After observing the results of electrical energy forecast and divergence evaluation, it can be found that the proposed neural network model can accurately forecast the generated electrical power and output current under different weather conditions. The feasibility and accuracy of the proposed forecast system is then validated.
43 citations