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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
 587   588   589   590   591 
Two-terminal fault detection and location for hybrid transmission circuit Muhd Hafizi Idris; Mohd Rafi Adzman; Hazlie Mokhlis; Mohammad Faridun Naim Tajuddin; Haziah Hamid; Melaty Amirruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp639-649

Abstract

This paper presents the algorithms developed to detect and locate the faults ata hybrid circuit. First, the fault detection algorithm was developed using the comparison of total positive-sequence fault current between pre-fault and fault times to detect the occurrence of a fault. Then, the voltage check method was used to decide whether the fault occurred at overhead line (OHL) or cable section. Finally, the fault location algorithm using the impedance-based method and negative-sequence measurements from both terminals of the circuit were used to estimate the fault point from local terminal. From the tests of various fault conditions including different fault types, fault resistance and fault locations, the proposed method successfully detected all fault cases at around 1 cycle from fault initiation and with correct faulted section identification. Besides that, the fault location algorithm also has very accurate results of fault estimation with average error less than 1 km and 1%. 
V2X communication system with non-orthogonal multiple access: outage performance perspective Tu-Trinh Nguyen; Dinh-Thuan Do
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp854-864

Abstract

To achieve low-latency and high-reliability (LLHR) for applications in the vehicle-toeverything (V2X) networks, the non-orthogonal multiple access (NOMA) based Long Term Evolution (LTE)-based is introduced a promising technology. NOMA-V2X provides higher spectrum efficiency compared with the orthogonal multiple access (OMA) based V2X. This study propose two-way relay assisted NOMA-V2X transmission by exploiting amplify-and-forward (AF) and full-duplex. We derive expressions of outage probability to evaluate performance of two vehicles and to improve the quality of service (QoS) for the device with the poor channel conditions. These expressions are further verified by Monte-Carlo simulations.
Design and implementation of an S-band transmitter for nanosatellites with new configuration Bui Thi Ha; Tran Chinh Doan; Bach Gia Duong
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1067-1077

Abstract

In this paper, the author presents the design and implementation of an Sband transmitter for nanosatellites. By combining heterostructure field effect transistors (HFET) and laterally diffused metal–oxide–semiconductor (LDMOS) technology and using flexible structure and flexible control method, this research obtained 60 dB gain power when input is -14 dBm, output power is 46 dBm (more than 25 W) in 2,1 GHz -2,3 Ghz frequency; phase noise is -80 dBc/Hz at 100 KHz offset frequency. Unlike other traditional transmitters, this transmitter was designed with multi-stages which have multi-peaks resonance to expand bandwidth to respond to the requirement of generation of the complex signal in wide band. Moreover, the phase locked loop (PLL) in frequency synthesizer makes the frequency conversion more flexible and output frequency more stable; thermal problem in module also was solved by using thermistor and operation mode. Measurement results prove that the design does not only satisfy the requirements of nanosatellites but also can be applied to other satellites together with their ground station because it has open configure with flexible structure and flexible control method.
Efficient reconfigurable architecture for moving object detection with motion compensation Sridevi N.; M. Meenakshi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp802-810

Abstract

The detection and tracking of object in large data surveillance requires a proper motion estimation and compensation techniques which are generally used to detect accurate movement from video stream. In this paper, a novel hardware level architecture involving motion detection, estimation, and compensation is proposed for real-time implementation. The motion vectors are obtained using 16×16 sub-blocks with a novel parallel D flip flop architecture in this work to arrive at an optimised architecture. The sum of absolute difference (SAD) is then calculated by optimized absolute difference and adder blocks designed using kogge-stone adder which helps in improving the speed of the architecture. The controller block is designed by finite state machine model used for synchronization of all the operations. Further, the comparator and compensation blocks are optimized by using basic logical elements and the Kogge-stone adder. Finally, the proposed architecture is implemented on Zynq Z7-10 field-programmable gate array (FPGA) and simulated using System Generator tool for real time traffic signal. The hardware and software parameters are compared with the existing techniques which shows that the proposed architecture is efficient than existing methods of design.
PSO-ANN in preventing traffic collisions: a comparative study Md. Ashikuzzaman; Wasim Akram; Md. Mydul Islam Anik; Mahamudul Hasan; Md. Sawkat Ali; Taskeed Jabid
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1796-1803

Abstract

Traffic accident is a global threat which causes health and economic casualties all around the world. Due to the expansion of transportation systems, congestion can lead to spike road accident. Every year thousands of people have died due to traffic accidents. Various technologies have been adopted by modern cities to minimize traffic accidents. Therefore, to ensure people’s safety, the concept of the smart city has been introduced. In a smart city, factors like road, light, and weather conditions are important to consider to predict traffic mishap. Several machine learning models have been implemented in the existing literature to determine and predict traffic collision. But the accuracy is not enough and there exist a lot of challenges in determining the accident. In this paper, an approach of particle swarm optimization with artificial neural network (PSO-ANN) has been proposed to determine traffic collision using the dataset of the transport department of United Kingdom. The performance of PSO-ANN outperforms the existing machine learning model. PSO-ANN model can be adopted in the transportation system to counter traffic accident issues. Random Forest, Naıve Bayes, Nearest Centroid, K-Nearest Neighbor classification have been used to compare with the proposed PSO-ANN model.
ArSL-CNN a convolutional neural network for Arabic sign language gesture recognition Ali A. Alani; Georgina Cosma
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1096-1107

Abstract

Sign language (SL) is a visual language means of communication for people who are Deaf or have hearing impairments. In Arabic-speaking countries, there are many Arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSL-CNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the ArSL-CNN model returned a train and test accuracy of 98.80% and 96.59%, respectively. The results also revealed the impact of imbalanced data on model accuracy. For the second set of experiments, various re-sampling methods were applied to the dataset. Results revealed that applying the synthetic minority oversampling technique (SMOTE) improved the overall test accuracy from 96.59% to 97.29%, yielding a statistically signicant improvement in test accuracy (p=0.016,  α<0=05). The proposed ArSL-CNN model can be trained on a variety of Arabic sign languages and reduce the communication barriers encountered by Deaf communities in Arabic-speaking countries.
Improved incentive pricing-based quasi-linear utility function of wireless networks Fitri Maya Puspita; Bella Juwita Rezky; Arden Naser Yustian Simarmata; Evi Yuliza; Yusuf Hartono
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1467-1475

Abstract

The model of the incentive pricing scheme-based quasi-linear utility function in wireless network was designed. Previous research seldom focusses on user’s satisfaction while using network. Therefore, the model is then attempted to be set up that is derived from the modification of bundling and models of reverse charging and maintain the quality of service to users by utilizing quasi-linear utility function. The pricing schemes then are applied to local data server traffic. The model used is known as mathematical programming problem that can be solved by LINGO 13.0 program as optimization tool to get the optimal solution. The optimal results show that the improved incentive pricing can achieve better solution compared to original reverse charging where the models will be obtained in flat fee, usage-based, and two-part tariff strategies for homogeneous consumers.
Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier Firas Saaduldeen Ahmed; Noha Abed-Al-Bary Al-jawady
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1180-1187

Abstract

Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.
Effects of intensity of magnetic field generated by neodymium permanent magnet sheets on electrical characteristics of monocrystalline silicon solar cell Piyapat Panmuang; C. Photong
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp18-27

Abstract

In this research, the effects of magnetic field intensity on electrical characteristics of a monocrystalline silicon solar cell were investigated. The experimental test-rig under Standard Test Condition was set up and tested to observe the respective effects. The electrical characteristics in terms of current-voltage-power curves, critical solar cell parameters and fill factor were then examined and analyzed. The outcome of this study demonstrates that the external magnetic field has a positive impact on electrical parameters, the experimental results showed that applying magnetic intensity of 60-260mT significantly affected the electrical characteristics of the cell; i.e., maximized cell current, voltage and power by 12.20, 7.12 and 23.60%, respectively. In addition, this positive impact consequencely happened on the i-v and p-v electrical characteristics curves of the solar cell; reflected by 3.69% increasing in the fill factor. 
Development of a new linearizing controller using Lyapunov stability theory and model reference control Mfoumboulou, Yohan Darcy; Mnguni, Mkhululi Elvis Siyanda
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1328-1343

Abstract

One of the most challenging aspects in the nonlinear control of a magnetic levitation (Maglev) system is to find an efficient control algorithm to achieve the stability and accuracy of the closed-loop system. The challenge is then to develop a linearizing control algorithm to maintain a steel ball at a desired position. In this paper, a novel linearizing control algorithm is proposed, which consists of the Lyapunov direct method (LDM) and the model reference control (MRC). The Lyapunov function is developed using the nonlinear equations of the magnetic levitation system, and the reference model is a linear second order system. Two control methods are developed to guarantee system robustness and output stability. Firstly, a new integral linear quadratic regulator (ILQR) is designed for the reference model. Then, an additional innovative proportional gain is combined with the linearizing controller to make the nonlinear control signal stronger. The simulation results indicate that the proposed linearizing controller has excellent set-point tracking, no time delay, fast rising and settling times, and achieves states stability.

Page 589 of 918 | Total Record : 9174

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