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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 102 Documents
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Search results for , issue "Vol 12, No 4: April 2014" : 102 Documents clear
Applying SVM for Foot Pressure Pattern Classification Pan Wentao; Peng Zhaoqin; Caochun Li Bo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An automatic loading simulation and testing system of steering servo was designed in this paper. Design of hardware and software of the testing system were illustrated, and the Load testing principle and test strategy were analyzed. Torque servo motor was used to simulate the loading added to the steering servo. The torque magnitude was controlled proportional to the angular displacement of the steering servo to simulate elastic load. Fuzzy PID control algorithm was used to make torque servo motor follow the change of angular displacement quickly and accurately. The effectiveness of the automatic testing system was proved by the experimental results. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4277
A Neural Network based Intelligent Method for Mine Slope Surface Deformation Prediction Considering the Meteorological Factors Sunwen Du; Jin Zhang; Zengbing Deng; Jingtao Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate mine slope surface deformation forecasting can provide reliable guidance for safe mining production and mine construction planning, which is also important for the personnel safety of the mining staffs. The mine slope surface deformation forecasting is a non-linear problem. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is still a challenge of how to determine the appropriate spread parameter in using the GRNN for deformation forecasting. In this paper, a mine slope surface deformation forecasting model combining artificial bee colony optimization algorithm (ABC) and generalized regression neural network was proposed to solve this problem. The effectiveness of this proposed forecasting model was proved by experiment comparisons. The test results show that the proposed intelligent forecasting model outperforms the BP neural network forecasting model, BP neural network with genetic algorithm optimization (GA-BPNN) and the ordinary linear regression (LR) forecasting models in the mine slope surface deformation forecasting. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4815

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