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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 9,199 Documents
 137   138   139   140   141 
Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control S.Y. Sim; C.K. Chia; W.M. Utomo; H.H. Goh; Y.M.Y. Buswig; A.J.M.S. Lim; S.L. Kek; A.A. Bohari; C.L, Cham
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp105-112

Abstract

This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved.  Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance.
A comparative review on deep learning models for text classification Muhammad Zulqarnain; Rozaida Ghazali; Yana Mazwin Mohmad Hassim; Muhammad Rehan
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp325-335

Abstract

Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.
Optimization Design of Cantilever Beam for Cantilever Crane Based on Improved GA Shufang Wu; Tiexiong Su
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

Based on in depth study of optimization design methods, according to analyzing the forces of cantilever beam for Cantilever Crane, with the feature that I-beams used in cantilever beam mostly, combining structural optimization technique with discrete variables with GA, an optimization design method of cantilever beam for Cantilever Crane based on improved GA was proposed for the problems of huge material redundancy and high production cost. Mathematical model and fitness function of the structural optimization with discrete variables were built, optimization design of cantilever beam structure was achieved, the efficiency of this optimization design method was validated and the consumption of steel for production was reduced. This method had a certain guiding significance for engineering application. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4807
A Statistical Multiplexing Method for Traffic Signal Timing Optimization in Smart Cities Ben Ahmed Mohamed; Boudhir Anouar Abdelhakim; Bouhorma Mohammed; Ben Ahmed Kaoutar
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2015
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Urban road traffic is the heart of many problems: more recent years, this critical aspect involved every day is unfavorable to many fields, such as economics or ecology. For these reasons, the Intelligent Transportation Systems (ITS) have emerged to best optimize the expenditure of the user on often complex road networks. In this paper, after studying the backgrounds of such systems, we propose a system of control of traffic lights through the use of statistical multiplexing technique based on fixed and vehicular networks of wireless sensors. We will see that this architecture can be flexible within the framework of ITS and participate in low cost to obtain interesting results. The simulation results prove the efficiency of the traffic system in an urban area with an adaptable and dynamic traffic road, because the average waiting time of cars at the intersection is sharply dropped when the red light duration is 65 s and the green light time duration is 125 s. DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8092  
A Sub-pixel detection algorithm of the MEMS dynamic fuzzy image Yuan LUO; Chao JI; Yi Zhang; Zhangfang HU
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 8: December 2012
Publisher : Institute of Advanced Engineering and Science

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

Abstract

The testing image have a certain degree of ambiguity when Application of machine vision method for MEMS dynamic parameters were measured.This paper presents a sub-pixel algorithm: Using self-similar characteristics of fractal interpolation to overcome the problem ,that can not be accurate interpolation and the edge of the image reconstruction. Then because of abilities of high resolution and anti-noise,after that using wavelet transform to obtain the image edge detection.The experimental results show that the algorithm can reach 0.02 pixel accuracy. DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1643 
Audio Sensing Aid based Wireless Microphone Emulation Attacks Detection Wang Shan-shan; Luo Xing-guo; Li Bai-nan
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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

Abstract

The wireless microphone network is an important PU network for CRN, but there is no effective technology to solve the problem of microphone evaluation attacks. Therefore, this paper propose ASA algorithm, which utilizes three devices to detect MUs, and they are loudspeaker audio sensor (LAS), environment audio sensor (EAS), and radio frequency fingerprint detector (RFFD). LASs are installed near loudspeakers, which have two main effects: One is to sense loudspeakers’ output, and the other is to broadcast warning information to all SUs through the common control channel when detecting valid output. EASs are pocket voice captures provided to SU, and utilized to sense loudspeaker sound at SU’s location. Utilizing EASs and energy detections in SU can detect primary user emulation attack (PUEA) fast. But to acquire the information of attacked channels, we need explore RFFDs to analyze the features of PU transmitters. The results show that the proposed algorithm can detect PUEA well. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3424 
An Adaptive Scheme to Achieve Fine Grained Video Scaling S Safinaz; A. V. Ravi Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 1: October 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i1.pp43-58

Abstract

A robust Adaptive Reconstruction Error Minimization Convolution Neural Network ( ARemCNN) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.
Performance evaluation of cloud service with hadoop for twitter data Ganesh Panatula; K Sailaja Kumar; D Evangelin Geetha; T V Suresh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp392-404

Abstract

In the era of rapid growth of cloud computing, performance calculation of cloud service is an essential criterion to assure quality of service. Nevertheless, it is a perplexing task to effectively analyze the performance of cloud service due to the complexity of cloud resources and the diversity of Big Data applications. Hence, we propose to examine the performance of Big Data applications with Hadoop and thus to figure out the performance in cloud cluster. Hadoop is built based on MapReduce, one of the widely used programming models in Big Data. In this paper, the performance analysis of Hadoop MapReduce WordCount application for Twitter data is presented. A 4-node in-house Hadoop cluster was setup and experiment was carried out for analyzing the performance. Through this work, it was concluded that Hadoop is efficient for BigData applications with 3 or more nodes with replication factor 3. Also, it was observed that system time was relatively more compared to user time for BigData applications beyond 80GB. This experiment had also thrown certain pattern on actual data blocks used to process the WordCount application. 
Optimal volt/var control of distribution system using MOPSO Ramesh Babu M; C. Venkatesh Kumar; R. Sreekanth
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1088-1095

Abstract

This paper presents a novel method for solving multi-objective Volt/Var control of radial distribution system. The Volt/Var control is formulated as a multi-objective optimization problem which consists of the following objectives: minimization of real power loss, minimization of total voltage deviation and minimization of number of OLTC’s and capacitor operation and voltage fluctuations for a day-a-head in Distribution system.The Proposed MOPSO Algorithm is used to find the optimal settings of control variables such as On-Load tap changer (OLTC) and Shunt Capacitor. The proposed MOPSO algorithm is tested on a standard IEEE33-bus and 69-bus distribution system.
A Study of Cognitive Technology OFDM System and Frame Structure Hua Hou; Wei Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5514-5521

Abstract

For mobile communication system with multi-service of multiple users, spectrum utilization rate is low and user's QoS requirements need to be improved. The paper proposed a cross-layer based on cognitive radio technology and OFDM technology model and designed the core technology of this frame structure model  such as service division, spectrum sensing and spectrum aggregation in detail. Comprehensive judgment through the service needs of different users, and the spectrum holes judgment, not only meet different user’s QoS, but also improve spectrum utilization and increase the system suitability. Finally, the simulation analysis of the model’s performance shows that the model can not only reasonable use of the spectrum holes, and better meet the user’s QoS requirements.

Page 139 of 920 | Total Record : 9199

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