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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
 625   626   627   628   629 
Artificial-neural-network based unified power flow controller for mitigation of power oscillations Vireshkumar Mathad; Gururaj Kulkarni
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.pp1323-1331

Abstract

The series and shunt control scheme of unified power flow controller (UPFC) impacts the performance and stability of the power system during power swing. UPFC is the most versatile and voltage source converter device as it can control the real and reactive power of the transmission system simultaneously or selectively. When any system is subjected to any disturbance or fault, there are many challenges in damping power oscillation using conventional methods. This paper presents the neural network-based controller that replaces the proportional-integral (PI) controller to minimize the power oscillations. The performance of the artificial neural network (ANN) controller is evaluated on IEEE 9 bus system and compared with a conventional PI controller.
Predicting temperature of Erbil City applying deep learning and neural network Sardar M. R. K. Al- Jumur; Shahab Wahhab Kareem; Raghad Z. Yousif
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.pp944-952

Abstract

One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and  generalized regression neural network (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.
The trends of supervisory control and data acquisition security challenges in heterogeneous networks M. Agus Syamsul Arifin; Susanto Susanto; Deris Stiawan; Mohd Yazid Idris; Rahmat Budiarto
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.pp874-883

Abstract

Supervisory control and data acquisition (SCADA) has an important role in communication between devices in strategic industries such as power plant grid/network. Besides, the SCADA system is now open to any external heterogeneous networks to facilitate monitoring of industrial equipment, but this causes a new vulnerability in the SCADA network system. Any disruption on the SCADA system will give rise to a dangerous impact on industrial devices. Therefore, deep research and development of reliable intrusion detection system (IDS) for SCADA system/network is required. Via a thorough literature review, this paper firstly discusses current security issues of SCADA system and look closely benchmark dataset and SCADA security holes, followed by SCADA traffic anomaly recognition using artificial intelligence techniques and visual traffic monitoring system. Then, touches on the encryption technique suitable for the SCADA network. In the end, this paper gives the trend of SCADA IDS in the future and provides a proposed model to generate a reliable IDS, this model is proposed based on the investigation of previous researches. This paper focuses on SCADA systems that use IEC 60870-5-104 (IEC 104) protocol and distributed network protocol version 3 (DNP3) protocol as many SCADA systems use these two protocols.
Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing Ali Abdul Wahhab Mohammed; Hussein Thary Khamees
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.pp846-853

Abstract

This paper has been utilized satellite Sentinel-2A imagery, this satellite is a polar-orbiting, multispectral high-resolution to cover Athens city, Greece that located at latitude (37° 58′ 46″) N, (23° 42′ 58″) E.,the work aims to measurement and study the wildfires natural resourcesbefore and after fire break out that happenedin forests of Athens city in Greece for a year (2007, 2018) and analysis the damage caused by these wildfiresand their impact on environment  and soil  by categorize the satellite images for the interested region before and after wildfires for a year (2007) and  a year (2018) and Discuss techniques that compute the area covered of each class and lessen  or limit the rapidly spreading wildfires damage.The categorizing utilizing the moments with (K-Means) grouping algorithm in RS (remote sensing). And the categorizing results show five unique classes (water, trees, buildings without tree, buildings with tree, bare lands) where, it can be notice that the region secured by each class before and after wildfires and the changed pixels for all classes.The experimental resulted of categorizing technique shows that the good performance exactness with a good categorizing and result analysisa bout the harms resulted from the fires in the forest Greece for a years (2007 and 2018).
Evaluation quality of service for internet of things based on fuzzy logic: a smart home case study Lairedj Aboubaker Saddik; Ben Ahmed Khalifa; Bounaama Fateh
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.pp825-839

Abstract

The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
A hybrid deep learning model for air quality time series prediction Samit Bhanja; Abhisek Das
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.pp1611-1618

Abstract

Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Convolutional neural network (CNN) and bidirectional long short-term memory (BDLSTM) network. Here we build a 3D input tensor so that CNN can extract the trends and spatial features more accurately within the input window. Here we also introduce a linking layer between CNN and BDLSTM to maintain the temporal ordering of feature vectors. In the end, our proposed HDNN framework is compared with the state-of-the-art models, and we show that HDNN outruns other models in terms of prediction accuracy.
Implementation of wheelchair controller using mouth and tongue gesture Rafia Hassani; Mohamed Boumehraz; Maroua Hamzi
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.pp1663-1671

Abstract

In this paper, a simple human-machine interface allowing people with severe disabilities to control a motorized wheelchair using mouth and tongue gesture is presented. The development of the proposed system consists of three principal phases: the first phase is mouth detection which performed by using haar cascade to detect the face area and template matching to detect mouth and tongue gestures from the lower face region. The second phase is command extraction; it is carried by determining the mouth and tongue gesture commands according to the detected gesture, the time taken to execute the gestures, and the previous command which is stored in each frame. Finally, the gesture commands are sent to the wheelchair as instruction using the Bluetooth serial port. The hardware used for this project were; laptop with universal serial bus (USB) webcam as a vision-based control unit, Bluetooth module to receive instructions comes from the vision control unit, standard joystick used in case of emergency, joystick emulator which delivers to the control board signals similar to the signals that are usually generated by the standard joystick, and ultrasonic sensors to provide safe navigation. The experimental results showed the success of the proposed control system based on mouth and tongue gestures.
An improved vigener algorithm based on circular-left-shift key and MSB binary for data security Aso Ahmed Majeed; Banaz Anwer Qader
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.pp431-437

Abstract

Cryptography is a significant study area at present since it can be vital to protect exceedingly sensitive and secret information from illegal fraud during network transmission. One of the basic cryptographic algorithms is the Vigenere cipher, which is a very easy encryption method to be used as an alternative to Caesar cipher for encrypting the text of the message. In this paper, we enhance the Vigenere algorithm and propose a new method by shifting the key in each message to prevent repeating the messages. Also, it converts the messages into binary form rather than an alphabet. Furthermore, it adds a few bits of random padding to each block of outputs to send a series of bits. The proposed algorithm is named “Circular-Left-Shift Key-based Vigener Algorithm using MSB Binary (CLS-V-MSB)”. Finally, this technique slightly raises the size of the ciphertext, but substantially increases the cipher's protection, achieves the security objectives (authentication, confidentiality, integrity, freshness, and non-repudiation), and avoids Kasiski and Friedman.
K-means method for clustering learning classes A. D. Indriyanti; D. R. Prehanto; T. Z. Vitadiar
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.pp835-841

Abstract

Learning class is a collection of several students in an educational institution. Every beginning of the school year the educational institution conducts a grouping class test. However, sometimes class grouping is not in accordance with the ability of students. For this reason, a system is needed to be able to see the ability of students according to the desired parameters. Determination of the weight of test scores is done using the K-Means method as a grouping method. Iteration or repetition process in the K-Means method is very important because the weight value is still very possible to change. Therefore, the repetition process is carried out to produce a value that does not change and is used to determine the ability level of students. The results of the class grouping test scores affect the ability of students. Application of K-Means method is used in building an information system grouping student admissions in an educational institution. Acceptance of students will be grouped into 3 groups of learning classes. The results of testing the system that applies K-Means method and based on data on the admission of prospective students from educational institutions have very high accuracy with an error rate of 0.074. 
Design of high scalability multi-subcarrier rof hybrid system based on optical CDMA/TDM Ahmed Ghanim Wadday Ghanim Wadday; Faris Mohammed Ali; Hayder Jawad Mohammed Albattat
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.pp927-937

Abstract

The technology of radio over fiber (RoF) regard a crucial point to solve problems in wireless communication system. As well as, the growth of internet applications also reveals a tremendous increase in bandwidth for different applications. Therefore, the development of optical networks is very important that have maximum bandwidth by using different multiple access techniques. Optical code division multiple access (OCDMA) technique has considered as a good solution for high bandwidth network. Hybrid optical systems of OCDMA and time division multiplexing (OTDM) has been proposed in this paper to increase the number of simultaneous users. The results of hybrid OCDMA and OTDM system demonstrate that this system can make a considerable increase in the network scalability while ensuring sufficient data rate and an acceptable bit error rate. Where M-user OCDMA signals can be transmitted in different channels of an OTDM system. Due to its wide band facility compared with other access techniques, OCDMA used here. In addition to its high scalability for our radio network, the OTDM and SCM utilized. The combination of these efficient access technique and powerful time-sharing media are lead to increase the framework system scalability.

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