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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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Smart actuator for IM speed control with F28335 DSP application Abidaoun H. Shallal; Assaad F. Nashee; Aws Ezzaldeen Abbas
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.pp1421-1431

Abstract

In the industrial application, the induction motors (IMs) and the digital signal processing (ZQ28335) combination are very important in the scientific field. Two thirds of consumption of electricity is due to motor driven equipment. The direct torque control (DTC) is the standard of the industry and it has fast response control system applications. The drawback of DTC is the flux and torque ripples in the measurements. The scalar control can be considered as a solution to this drawback but with poor response. Torque and speed of IM are controlling individually, the variable speed drive (VSDs) is used. This occurs with variation of the voltage and frequency of IM supply. To decrease the levels of flux and torque ripples, 3-level inverters represent an attractive technique. The compromise of a huge flux and torque at the beginning level and low values at steady state of operation is crucial to ensure better stability with feedback linearization of the nonlinear behavior. In this paper, VSD with DTC IM with multilevel inverter with the newest version of ZQ28335 digital signal processor (DSP) is proposed. Emulation and the results of experiment through DSP ZQ28335 make certain correct dynamic response to the operations of torque and flux.
Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool Sangeeta Rani; Nasib Singh Gill; Preeti Gulia
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.pp1041-1051

Abstract

Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.
The trend malware source of IoT network Susanto Susanto; M. Agus Syamsul Arifin; Deris Stiawan; Mohd. Yazid Idris; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp450-459

Abstract

Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.
Dynamic state estimation of multi-machine power system with UPFC using EKF algorithm Meera R Karamta; Jitendra G Jamnani
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.pp642-646

Abstract

Estimation of dynamic state variables in a multi-machine power system connected with UPFC is presented in this paper, using Extended Kalman filter (EKF) algorithm. A two-generator test case is used to estimate the generator rotor angle and rotor speed. The DC link voltage of the UPFC is the additional state variable to be estimated. Dynamic mathematical modeling of the multi-machine system with UPFC is explained in this work. DSE is done under transient condition of three-phase fault.
Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms Shaymaa Mahmood Mahdi; Omar Farouq Lutfy
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.pp847-856

Abstract

Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
S-CDCA: a semi-cluster directive-congestion protocol for priority-based data in WSNs Marwan Ihsan Shukur
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp438-444

Abstract

The internet of things (IoT) protocols and regulations are being developed forvarious applications includes: habitat monitoring, machinery control, general health-care, smart-homes and more. A great part of I0T comprised of sensors nodes in connected networks (i.e. sensor networks.). A sensor network is a group of nodes with sensory module and computational elements connected through network interfaces. The most interesting type of sensor networks are wireless sensor networks. The nodes here are connected through wirless interfaces. The shared medium between these nodes, creates different challenges. Congestion in such network is ineavitable. Different models andmethods were proposed to alleviate congestion in wireless sensor networks.This paper presents a semi-cluster directive congestion method that allivatenetwork congestion forpriority-baseddata transmission. The method simprove the network performance by implementing temporary cluster forlow level priority data packets while providing a clear link between highpriority data source node and the network base station. Simulation resultsshow that. The proposed method outperformes ad hocOn-demand distance vector (AODV) reactive procotol approach and priority-based congestion control dynamic clustering (PCCDC) a cluster-based methodin network energy consumption and control packets overhead during network operation.The proposed method also shows comparative improvments in end-to-enddelays versus PCCDC.
Enhancement of gain using a multilayer superstrate metasurface cell array with a microstrip patch antenna ِAli Khalid Jassim; Malik Jasim Farhan; Fadia Noori Hummadi Al-Nuaimy
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.pp1564-1570

Abstract

This research presents a new idea in the use of wireless communication antennas: it uses a multi-layered array of cells called a superstrate multi-layer metasurface (MTM) and is placed in front of a patch of microstrip antenna to absorb surface waves and prevent them from passing through the insulating material, which reduces the permeability of the insulator and thus improves the Antenna properties, The proposed hexagonal cell with resonators is placed on the flame resistant (FR4) substrate, with a relative permittivity of 4.3 and an area (14×14) mm2 . It was tested when the metasurface layer is 4 mm in front of the patch and the distance between the metasurface layers is 2 mm. The optimum distances were calculated by the sweep parameter, and the improved antenna gain and the input reflection coefficient were obtained together. (S11) has been improved from -31.217 to -38.338 dB and, the gain from 3.28 dB to 6.554 dB.
Deepenz: prediction of enzyme classification by deep learning Hamza Chehili; Salah Eddine Aliouane; Abdelhafedh Bendahmane; Mohamed Abdelhafid Hamidechi
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.pp1108-1115

Abstract

Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests  show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.
Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery Karina Djunaidi; Herman Bedi Agtriadi; Dwina Kuswardani; Yudhi S. Purwanto
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.pp795-800

Abstract

One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a histogram which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.
Grid reactive voltage regulation and cost optimization for electric vehicle penetration in power network Farrukh Nagi; Aidil Azwin; Navaamsini Boopalan; Agileswari K. Ramasamy; Marayati Marsadek; Syed Khaleel Ahmed
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.pp741-754

Abstract

Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.

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