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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
 464   465   466   467   468 
Interest Excitation Propagation Model for Information Propagation on micro-blogging Hongtao Liu; Hongfeng Yun; Hui Chen; Zhaoyu Li; Yu Wu
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

An Information propagation model of micro-blogging is proposed for distinguishing normal and non-normal micro-blogging based on users’ interest excitation, which is abbreviated as IEPM(Interest Excitation Propagation Model for Information).The parameters of the model are clearly associated with the actual propagation and can reflect the characteristics of the propagation feature. The model can distinguish users’ non-autonomous behavior in the propagation process of micro-blogging, which can preliminarily judge that it is a non-normal marketing micro-blogging when it doesn’t meet the general propagation in the model. Quantitative analysis and experiment is performed with the dataset from the representative and typical non-normal micro-blogging in Sina micro-blogging, one of the most popular micro-blogging in China. The results show that the model can better reveal the general propagation laws of micro-blogging, and can distinguish normal and non-normal micro-blogging, which will have theoretical and practical significance to a certain degree. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.2734
Modeling of a planar SOFC performances using artificial neural network N. A. Zambri; Norhafiz Salim; A. Mohamed; Ili Najaa Aimi Mohd Nordin
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1645-1652

Abstract

The Planar Solid Oxide Fuel Cell (PSOFC) is one of the renewable energy technologies that is important as the main source for distributed generation and can play a significant role in the conventional electrical power generation. PSOFC stack modeling is performed in order to provide a platform for the optimal design of fuel cell systems. It is explained by the structure and operating principle of the PSOFC for the modeling purposes. PSOFC model can be developed using Artificial Neural Network approach. The data required to train the neural net-work model is generated by simulating the existing PSOFC model in the MATLAB/ Simulink software. The Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks are the most useful techniques in many applications and will be applied in developing the PSOFC model. A detailed analysis is presented on the best ANN network that gives the greatest results on the performances of the PSOFC. The simulation results show that Multilayer Perceptron (MLP) gives the best outcomes of the PSOFC performance based on the smallest errors and good regression analysis.
A Lyapunov Approach to Control Design for Grid-Connected Inverters Vu Tran; Mufeed MahD
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 5: May 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

This paper develops a Lyapunov approach to control design for grid connected inverter. The control objective is to track a reference current which is proportional to the fundamental harmonic of the grid voltage. By using the internal model principle, the grid voltage and the reference current are described as the outputs of an autonomous linear oscillatory system. A state space description for the whole system is obtained by combining the state of the inverter circuit and that of the oscillatory system for the grid voltage. Based on the state space description, a Lyapunov approach is developed to design a state-feedback controller for tracking a reference current with minimal tracking error. The Lyapunov approach ensures internal stability and makes efficient use of the structural information, such as the total harmonic distortion (THD) of the grid voltage, and the magnitude/phase of the reference current. The effectiveness of the Lyapunov approach is validated via SimPower simulation. A real circuit is built using microcontroller ezDSP28335, the output current obtained is in phase with the grid voltage and has small THD, as we expected. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5362
Impact of engineering parameters on performance of relay-assisted network Issam Maaz; Jean-Marc Conrat; Jean-Christophe Cousin; Samer Alabed
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp248-255

Abstract

This paper compares the performance of a relay assisted network to the performance given by a classical macrocell network without the presence of relay node schemes. The capacity enhancement provided by a relaying system as a function of the relay antenna height and the propagation environment surrounding the relay nodes is analyzed and discussed in details. The analysis in this work is based on the theoretical Shannon capacity where both measured/experimental path loss and calibrated path loss models are taken into consideration. In this work, we assume a decode and forward scheme, a full-duplex relaying protocol and an optimized relay location is investigated. A 30 % of improvement in the macrocell capacity is achieved with the usage of relaying scenario compared to a classical macrocell network. Furthermore, increasing the relay antenna height from 4 meters to 12 meters can significantly increase the relay capacity to more than 20 % in suburban and moderate urban environments.
Distribution Network Fault Diagnosis Method Based on Granular Computing-BP CHEN Zhong-xiao; CUI Ke; CHEN Xing-yu; LI Yan-fang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: March 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

To deal with the complexity and uncertainty of distribution network fault information, a method of fault diagnosis based on granular computing and BP is proposed. This method uses attribute reduction advantages of granular computing theory and self-learning and knowledge acquisition ability of BP neural network. It put granular computing theory as the front-end processor of the BP neural network, namely simplify primitive information making use of granular computing reduction, and according to the concepts of relative granularity and significance of attributes based on binary granular computing are proposed to select input of BP, thereby reducing solving scale, and then construct neural network based on the minimum attribute sets, using BP neural network to model and parameter identify, reduce the BP study training time, improve the accuracy of the fault diagnosis. The distribution network example verifies the rationality and effectiveness of the proposed method.DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2184
Load Balancing Based on the Specific Offset of Handover Liu Zhanjun; Ma Qichao; Ren Cong; Chen Qianbin
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.pp6281-6290

Abstract

Load Balancing technology in mobile wireless communication networks has been discussed largely. The current Load balancing algorithms have mainly adjusted the handover parameters without considering the inherent relationship of the handover parameters. In the paper, by considering the internal relationship of specific offset of handover, the constraint of the specific offser of handover was simplified, so the process of mobility load balancing algorithm was improved. With the improved mobile load balancing algorithm, the number of handover parameters was reduced and the signal process was simplified. Simulation results showed that the congestion rate  was reduced, the resource utilization rate was  improved and the Qos was improved.
Ultrasonic Flaw Signal Classification using Wavelet Transform and Support Vector Machine Yu Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 12: December 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

This paper presents a ultrasonic flaw signal classification system by using wavelet transform and support vector machine (SVM). A digital flaw detector is first used to acquire the signals of defective carbon fiber reinforced polymer (CFRP) specimen with void, delamination and debonding. After that, the time domain based ultrasonic signals can be processed by discrete wavelet transform (DWT), and informative features are extracted from DWT coefficients representation of signals. Finally, feature vectors selected by PCA method are taken as input to train the SVM classifier. Furthermore, the selection of SVM parameters and kernel function has been examined in details. Experimental results validate that the model coupled with wavelet transform and SVM is a promising tool to deal with classification for ultrasonic flaw signals. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3673
Calcification detection using convolutional neural network architectures in Intravascular ultrasound images Hannah Sofian; Joel Than Chia Ming; Suraya Muhammad; Norliza Mohd Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1313-1321

Abstract

Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture.
Study on the Nonstationary jammer Suppression for DSSS Receiver Zhe-jun Diao; Ji-ning Feng; Xiao-bo Yang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Using TVAR(Time Varying AR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in DSSS(Direct Sequence Spread Spectrum). The performance of TVAR model for IF (Instantaneous Frequency) estimation will be affected by some factors such as basis functions. The DSSS (Direct Sequence Spread Spectrum) is sensitive to non-stationary jammer, especially the LFM (Frequency Modulation) jammer. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the SINR of correlation output to the narrow band jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4733
Experimental investigation of photovoltaic thermal solar air collector with exergy performance comparison Bahtiar Bahtiar; Muhammad Zohri; Ahmad Fudholi
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp652-658

Abstract

The integration of photovoltaic technology and solar air collector is named a photovoltaic thermal (PVT) system. PVT system generates electricity as pumping power to fan DC and produces thermal energy together with to cooling PV panel. The experimental with the indoor and outdoor evaluation of PVT solar air collector have been compared at the chosen solar intensity of 820 W/m2. The mass flow rate is range from 0.01 kg/s to 0.05 kg/s. The exergy and efficiency exergy accuracy of PVT solar air collector between indoor and outdoor evaluation are 98.42%, 98.11% respectively. The exergy and exergy efficiency comparison results indicated that the indoor and outdoor investigation is suitable. 

Page 466 of 918 | Total Record : 9174

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