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All Journal International Journal of Electrical and Computer Engineering IPTEK Journal of Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Gou Koutaki
Kumamoto University
Computer Science & IT Electrical & Electronics Engineering

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Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm I Gede Pasek Suta Wijaya; Keeichi Uchimura; Gou Koutaki
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3003.348 KB) | DOI: 10.11591/ijece.v8i1.pp246-253

Abstract

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.
Face Recognition Using Holistic Features and Linear Discriminant Analysis Simplification I Gede Pasek Suta Wijaya; Keiichi Uchimura; Gou Koutaki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 4: December 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i4.866

Abstract

This paper proposes an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified linear discriminant analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data is registered into the training data set. The holistic features of face image are proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment is proposed to decrease time complexity of retraining process. To know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D)2DLDA, 2DPCA, and (2D)22DPCA. The experimental results show that the proposed method can be solve the retraining problem of the conventional LDA indicated by requiring shorted retraining time and stable recognition rate.
Modified Convolutional Neural Network Architecture for Batik Motif Image Classification Ardian Yusuf Wicaksono; Nanik Suciati; Chastine Fatichah; Keiichi Uchimura; Gou Koutaki
IPTEK Journal of Science Vol 2, No 2 (2017)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.679 KB) | DOI: 10.12962/j23378530.v2i2.a2846

Abstract

Batik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they’re wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately.
Face Recognition Using Holistic Features and Simplified Linear Discriminant Analysis I Gede Pasek Suta Wijaya; Keiichi Uchimura; Gou Koutaki
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 4: August 2012
Publisher : Institute of Advanced Engineering and Science

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

Abstract

This paper proposed an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified Linear Discriminant Analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data was registered into the training data set. The holistic features of face image were proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment was proposed to decrease time complexity of retraining process. In order to know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D)2DLDA, 2DPCA, and (2D)22DPCA. The experimental results show that the proposed method can solve the retraining problem of the conventional LDA indicated by requiring short retraining time and stable recognition rate. DOI: http://dx.doi.org/10.11591/telkomnika.v10i4.1258
Co-Authors Ardian Yusuf Wicaksono Chastine Fatichah I Gede Pasek Suta Wijaya Keeichi Uchimura Keiichi Uchimura Nanik Suciati