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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 9,174 Documents
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Estimation of Voltage Sag Loss Based on Blind Number Theory Fan Li-Guo; Zhang Yan-Xia
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp5932-5937

Abstract

Serious power quality issues and huge economic loss can be caused by voltage sag. It’s helpful for grid corporation to estimate voltage sag loss. Voltage sag and influence of sensitive equipment are analyzed in the paper. An estimation method of voltage sag loss based on blind number theory is proposed, which takes the sag magnitude and duration as its main characteristic parameters. First euclid distance and relative close degree between the sag magnitude and duration of voltage sag samples and threshold values is calculated based on TOPSIS. According to relative similarity degree，probable value, credibility and mean value of voltage sag loss are then calculated and influence of uncertainty factor can be considered. Example analysis shows that loss estimation method is a feasible and applicable for most sensitive equipments.
The Research of Digital Algorithm Based on Frequency-Dependent Transmission Lines Yongqing Liu; Baina He; Yunwei Zhao; Hengxu Ha; Xinhui Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 5: May 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

The algorithm for obtaining the discrete response of popagation function for frequency dependent parameter line is presented. Consider a minimum sampling period Tsm, that is, the highest frequency fH=1/(2Tsm) in the signal is taken into account. The impedance z(w) and the admittance y(w) are obtained in the frequency range of [0,fH] by employing the Carson’s formula. The propagation function at each frequency point is subsequently obtained, the impulse response in discrete time domain is then obtained using Poision Sum Formula. In order to avoid the long length of impulse reponse under the higher sampling frequency, the poles and zeros of z transform of discrete propagation function are evaluated by the Prony’s method. Subsequently, the coeffcients of the discrete infinite impulse response of propagation function are obtained. Using these coefficients the wave transfer sources can be easily computed by discrete convolution operation. The simulation tests show that the results using the propsed method is accurate, the error is not more than 1% in contrast of the results generated by EMTP. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2491
Fusion of Random Projection, Multi-Resolution Features and Distance Weighted K Nearest Neighbor for Masses Detection in Mammographic Images Viet Dung Nguyen; Minh Dong Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i3.pp1030-1035

Abstract

Breast cancer is the top cancer in women both in the developed and the developing world. For early detection of the disease, mammography is still the most effective method beside ultrasound and magnetic resonance imaging. Computer Aided Detection systems have been developed to aid radiologists in diagnosing breast cancer. Different methods were proposed to overcome the main drawback of producing large number of False Positives.  In this paper, we presented a novel method for masses detection in mammograms. To describe masses, multi-resolution features were utilized. In feature extraction step, we calculated multi-resolution Block Difference Inverse Probability features and multi-resolution statistical features. Once the descriptors were extracted, we deployed random projection and distance weighted K Nearest Neighbor to classify the detected masses. The result is quite sanguine with sensitivity, false positive reduction and time for carrying out the algorithm
A Breeding Estimated Particle Filter Research Xiong Fang
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

As the normal particle filter has an expensive computation and degeneracy problem, a propagation-prediction particle filter is proposed. In this scheme, particles after transfer are propagated under the distribution of state noise, and then the produced filial particles are used to predict the corresponding parent particle referring to measurement, in which step the newest measure information is added into estimation. Therefore predicted particle would be closer to the true state, which improves the precision of particle filter. Experiment results have proved the efficiency of the algorithm and the great predominance in little particles case. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6712 
Building Segmentation of Satellite Image based on Area and Perimeter using Region Growing Ervin Yohannes; Fitri Utaminingrum
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp579-585

Abstract

A building can be known by look shape, color, and texture. Building can be detected by using many method. Region growing is one simple segmentation method because only use seed point. Before segmentation, the image must be preprocessing include sharpening, binerization by otsu method. Sharpening for clarify image and otsu method changed image valued 0 and 1. Next step is post-preprocessing include segmentation using region growing and opening closing operation. And the last process is detection building where building of detection will be signed. In this research, we present region growing for building segmentation by using both area and perimeter as a important variable in the region growing. Value of area more than 10 and perimeter is more than 50 are produced most of building.
Near Optimal Convergence of Back-Propagation Method using Harmony Search Algorithm Abdirashid Salad Nur; Nor Haizan Mohd Radzi; Siti Mariyam Shamsuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

Training Artificial Neural Networks (ANNs) is of great significanceand a difficult task in the field of supervised learning as its performance depends on underlying training algorithm as well as the achievement of the training process. In this paper, three training algorithms namely Back-Propagation Algorithm, Harmony Search Algorithm (HSA) and hybrid BP and HSA called BPHSA are employed for the supervised training of Multi-Layer Perceptron feed  forward type of Neural Networks (NNs)  by giving special attention to hybrid BPHSA. A suitable structure for data representation of NNs is implemented to BPHSA-MLP, HSA-MLP and BP-MLP. The proposed method is empirically tested and verified using five benchmark classification problemswhich are Iris, Glass, Cancer, Wine and Thyroid dataset on training NNs. The MSE, training time, and classification accuracy of hybrid BPHSA are compared with the standard BP and meta-heuristic HSA. The experiments showed that proposed method has better results in terms of convergence error and classification accuracy compared to BP-MLP and HSA-MLPmaking the BPHSA-MLPa promising algorithm for neural network training. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7233
Overlapping Communities Detection Based on Link Partition in Directed Networks Qingyu Zou; Fu Liu; Tao Hou; Yihan Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach based on link partition to detect overlapping communities structure in directed networks. In contrast to previous researches focused on grouping nodes, our algorithm defines communities as groups of directed links rather than nodes with the purpose of nodes naturally belong to more than one community. This approach can identify a suitable number of overlapping communities without any prior knowledge about the community in directed networks. We evaluate our algorithm on a simple artificial network and several real-networks. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in directed networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3304
A multi-color based features from facial images for automatic ethnicity identification model Mohd Zamri Osman; Mohd Aizaini Maarof; Mohd Foad Rohani; Nilam Nur Amir Sjarif; Nor Saradatul Akmar Zulkifli
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1383-1390

Abstract

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.
A Dynamic Multi-nest Ant Colony Algorithm for Aircraft Landing Problem Feng Xiao-rong; Feng Xing-jie; Liu Dong
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Aircraft landing problem is an NP-hard problem. The article presents a static method for measure of the distance between flights, defines the distance as the pheromone of flights and analyzed experimentally firstly. Then proposes a dynamic multi-nest ant colony optimization algorithm for solving this problem, by dynamically calculates the pheromone between flights. The experimental results show that the algorithm has better global search ability and relatively fast convergence rate and compared with traditional first come first serve, genetic algorithm and particle swarm algorithm, this method can quickly give the better flight approach and landing order to help controllers make efficient aircraft scheduling policy and reduce flight delays. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4487 
Pavement Image Segmentation Based on FCM Algorithm Using Neighborhood Information Xinsong Wang; Guofeng Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 7: November 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Standard FCM algorithm takes the pixel gray-scale information into account only, while ignoring the spatial location of pixels, so the standard FCM algorithm is sensitive to noise. This paper present a pavement image segmentation algorithm based on FCM algorithm using neighborhood information. The presented algorithm introduces neighborhood information into membership function to improve the standard FCM algorithm. It can eliminate noise effectively and retain the boundary information. The experiments by synthetic images and real pavement images show that the presented algorithm in this paper performs more robust to noise than the standard FCM algorithm and retain the boundary information effectively. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1551

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