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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 64 Documents
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Search results for , issue "Vol 25, No 2: February 2022" : 64 Documents clear
A review on techniques and modelling methodologies used for checking electromagnetic interference in integrated circuits Tamana Baba; Nurul Arfah Che Mustapha; Nurul Fadzlin Hasbullah
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.pp796-804

Abstract

The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques Mohammed Berrahal; Mostafa Azizi
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.pp972-979

Abstract

The advancements in artificial intelligence research, particularly in computer vision, have led to the development of previously unimaginable applications, such as generating new contents based on text description. In our work we focused on the text-to-image synthesis applications (TIS) field, to transform descriptive sentences into a real image. To tackle this issue, we use unsupervised deep learning networks that can generate high quality images from text descriptions, provided by eyewitnesses to assist law enforcement in their investigations, for the purpose of generating probable human faces. We analyzed a number of existing approaches and chose the best one. Deep fusion generative adversarial networks (DF-GAN) is the network that performs better than its peers, at multiple levels, like the generated image quality or the respect of the giving descriptive text. Our model is trained on the CelebA dataset and text descriptions (generated by our algorithm using existing attributes in the dataset). The obtained results from our implementation show that the learned generative model makes excellent quantitative and visual performances, the model is capable of generating realistic and diverse samples for human faces and create a complete portrait with respect of given text description.
Modular reduction with step-by-step using of several bits of the reducible number Sakhybay Tynymbayev; Yevgeniya Aitkhozhayeva; Dana Tananova; Sairan Adilbekkyzy
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.pp1087-1093

Abstract

Although public key cryptography is known to solve the problem of physically secure key exchange, the main drawback of this system is its low performance during encrypting and decrypting data. One of the ways to solve this issue is to increase the speed of the modular reduction operation, one of the basic operations of asymmetric cryptoalgorithms. A new method of step-by-step reduction by the N-bit module P using several bits of the 2Nbit reducible number A in one step is proposed in this paper. The method is based on using multiples of the P and reducing modulo at each step not the entire initial number, but its parts (A1, A2… Ai), which allows to reduce the bit capacity of A. A structural diagram of the hardware implementation of this method are developed. The main unit of the modular reduction device is a block of partial remainder formers, in which the partial remainder is computed using multiples of the P. The circuits are modeled in the Vivado Design Suite computer aided design (CAD) on base Artix-7 Fieldprogrammable gate array (FPGA) device from Xilinx. Optimization of hardware costs is achieved by applying the same comparison circuits to compare different multiples of P with Ai
Intelligent fault diagnosis for power distribution system-comparative studies Thi Thom Hoang; Thi Huong Le
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.pp601-609

Abstract

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
Renewable energy based dynamic tariff system for domestic load management Kuheli Goswami; Arindam Kumar Sil
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.pp626-638

Abstract

To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
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.
Model predictive controller for a retrofitted heat exchanger temperature control laboratory experiment Noureddine Mansour; Mohamed Bin Shams; Hussain Ismail
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.pp857-866

Abstract

This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost interface using X-transposed-region (XTR) converter, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience
Decentralised optimal deployment of mobile underwater sensors for covering layers of the ocean Valimohammad Nazarzehi; Rasoul Damani
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.pp840-846

Abstract

This paper presents the problem of sensing coverage of layers of the ocean in three dimensional underwater environments. We propose distributed control laws to drive mobile underwater sensors to optimally cover a given confined layer of the ocean. By applying this algorithm at first the mobile underwater sensors adjust their depth to the specified depth. Then, they make a triangular grid across a given area. Afterwards, they randomly move to spread across the given grid. These control laws only rely on local information also they are easily implemented and computationally effective as they use some easy consensus rules. The feature of exchanging information just among neighbouring mobile sensors keeps the information exchange minimum in the whole networks and makes this algorithm practicable option for undersea. The efficiency of the presented control laws is confirmed via mathematical proof and numerical simulations.
Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier Millee Panigrahi; Dayal Kumar Behera; Krishna Chandra Patra
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.pp884-891

Abstract

Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.
A novel fast-qualitative balance test method of screening for vestibular disorder patients Tran Anh Vu; Hoang Quang Huy; Le Van Tuan; Pham Thi Viet Huong
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.pp910-919

Abstract

Body balance test is one of the methods of assessing vestibular level. However, the results are still qualitative, depending on the subjectivity of the doctor. This study proposes a new, low-cost method to quantitatively determine the degree of body imbalance. The proposal includes a low-cost laser source, a proposed rectangular paper frame, a camera, and a computer. The rectangular frame is mounted on the patient. The laser source is fixed and projected onto this rectangular frame. The laser projection point is taken as the origin point to evaluate the movement of the frame, which is also the movement of the patient’s body. This rectangular frame is pre-marked with points to get more accuracy of the position of the laser point. Therefore, this measurement is not affected by the position of the camera during recording. The video is then procecced by computer to determine the position of laser point, it is also presented the movement of the patient’s body. Initial trials were conducted on vestibular and normal patients. The results show that there is a clear difference in the balance of the vestibular and healthy people. The proposed method can be used to support quantitative screening for vestibular disease.

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