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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 112 Documents
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Search results for , issue "Vol 12, No 3: June 2022" : 112 Documents clear
Optimization of power consumption in data centers using machine learning based approaches: a review Rajendra Kumar; Sunil Kumar Khatri; Mario José Diván
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3192-3203

Abstract

Data center hosting is in higher demand to fulfill the computing and storage requirements of information technology (IT) and cloud services platforms which need more electricity to power on the IT devices and for data center cooling requirements. Because of the increased demand for data center facilities, optimizing power usage and ensuring that data center energy quality is not compromised has become a difficult task. As a result, various machine learning-based optimization approaches for enhancing overall power effectiveness have been outlined. This paper aims to identify and analyze the key ongoing research made between 2015 and 2021 to evaluate the types of approaches being used by researchers in data center energy consumption optimization using Machine Learning algorithms. It is discussed how machine learning can be used to optimize data center power. A potential future scope is proposed based on the findings of this review by combining a mixture of bioinspired optimization and neural network.
A data quarantine model to secure data in edge computing Poornima Mahadevappa; Raja Kumar Murugesan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3309-3319

Abstract

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.
Denial of service attack: an analysis to IPv6 extension headers security nightmares Marlon A. Naagas; Anazel P. Gamilla
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2922-2930

Abstract

Dealing with scarcity issues of Internet protocol version 4 (IPv4), internet engineering task force (IETF) developed Internet protocol version 6 (IPv6) to support the needs of IP addresses for future use of the internet, however, one challenge that must be faced while transitioning to IPv6 is in the area of security. IPv6 is a new protocol that has many new probabilities for attackers to exploit the protocol stack and one of them is through IPv6 extension headers. Mishandling of extension headers are the security nightmares for network administrators, allowing for new security threats that will cause denial of service (DoS). As a result, the mishandling of IPv6 extension Headers creates new attack vectors that could lead to DoS–which can be exploited for different purposes, such as creating covert channels, fragmentation attacks, and routing header 0 attacks. Furthermore, this paper becomes proof of concepts that even to this day our well-known network devices are still exploitable by these attack vectors.
Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking Wisal Adnan Al-Musawi; Wasan A. Wali; Mohammed Abd Ali Al-Ibadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2414-2424

Abstract

This article presents a simple and efficient masking technique based on Chua chaotic system synchronization. It includes feeding the masked signal back to the master system and using it to drive the slave system for synchronization purposes. The proposed system is implemented in a field programmable gate array (FPGA) device using the Xilinx system generator tool. To achieve synchronization, the Pecora-Carroll identical cascading synchronization approach was used. The transmitted signal should be mixed or masked with a chaotic carrier and can be processed by the receiver without any distortion or loss. For different images, the security analysis is performed using the histogram, correlation coefficient, and entropy. In addition, FPGA hardware co-simulation based Xilinx Artix7 xc7a100t-1csg324 was used to check the reality of the encryption and decryption of the images.
A novel fault location approach for radial power distribution systems Tarek Hamdouche; Omar Bendjeghaba; Brakta Noureddine; Ahriche Aimad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2242-2255

Abstract

Power distribution systems (PDS) are increasingly complex and spread over long distances and in different locations. Given their radial configuration, the loads could be inserted at the same distances from the substation. This leads to multiple estimation of fault location (FL) and yields time consuming for iterative FL algorithms. In this article, we provide a novel practical approach to fault localization in order to defeat these limitations. The central idea of the proposed approach is to divide the multilateral distribution system into a possible number of mono-lateral sub systems (MLS) using a proposed communicating sensor. Next, we apply two different fault locator algorithms only to the defective MLS. The first variant of the approach is based on the impedance method, while the second variant is non-parametric used only when there is lack in the line data. To test the proposed technique in practice, we used the IEEE 13 Node test feeder, and a real Algerian PDS. The results obtained clearly show the contribution of the dedicated method for locating faults in the PDS.
Smart hydroponic based on nutrient film technique and multistep fuzzy logic Prabadinata Atmaja; Nico Surantha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3146-3157

Abstract

Automation in hydroponics is have been a great change. Research with fuzzy logic control it’s designed to add to each parameter one by one. In a way microcontroller will activate one by one relay to regulate the parameters with fuzzy logic. While parameter calibration is done, calibration is needed for the next checking if the parameter were not optimal, until its parameter optimal. Multistep fuzzy is used to counter measure the same activation of the relay. With adding real time data monitoring to the system. From test result evaluating multistep fuzzy logic method were 100% works as expected. with another testing approach for best module for sending real time data monitoring for hydroponics. From the real time data transmission method, the success of sending data is 30% from the ESP82166 and 75% of the NRF24L01 with a shortage of the NRF24L01 data loss. For the relay activation can be accommodate with dynamic programming. As for multistep fuzzy logic for hydroponic tested to reach optimal water condition for kale crops resulting in average 12.8 iterations calibration from condition where researches add water only from the start.
A multithreaded hybrid framework for mining frequent itemsets Jashma Suresh Ponmudiyan Poovan; Dinesh Acharya Udupi; Nandanavana Veerappareddy Subba Reddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3249-3264

Abstract

Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures while mitigating the problems of relying on either of them alone. However, limited efforts have been made to reinforce the efficiency of such frameworks. To address these issues this paper proposes a novel multithreaded hybrid framework comprising of NegNodesets and N-list structure that uses the multicore feature of today’s processors. While NegNodesets offer a concise representation of itemsets, N-lists rely on List intersection thereby speeding up the mining process. To optimize the extraction of frequent items a hash-based algorithm has been designed here to extract the resultant set of frequent items which further enhances the novelty of the framework.
Control of shunt-wound motors with DC/DC converters using the pole placement technique Nadheer Abdulridha Shalash; Wameedh Riyadh Abdul-Adheem; Yasir Khaldoon Abdul Jabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2335-2345

Abstract

Many techniques have been developed for the speed manipulation of shunt-wound direct current motors (SWDCMs) established based on armature and field control. The current research proposes a controller based on the pole placement (PP) control technique and compares it with a proportional integral (PI) controller for trajectory speed control of SWDCM with uncertainty. The circuit of the DC/DC converters energizes the DC motor. The responses are analyzed according to the dynamic mathematical model of the implemented controllers and the model of the DC/DC converters. Comparison of the motor dynamical response of the conventional PI and proposed PP controllers indicates that the PP controller exhibits improved performance in terms of rise time and steady-state error
Simulation for predictive maintenance using weighted training algorithms in machine learning Chanintorn Jittawiriyanukoon; Vilasinee Srisarkun
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2839-2846

Abstract

In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach Loan Thi-Thuy Ho; Somjit Arch-int; Ngamnij Arch-int
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2944-2954

Abstract

A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DL-LiteR KBs in the presence of priority. More specifically, we propose a new variant of Dung’s argumentation frameworks, which corresponds to a given inconsistent DL-LiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs.

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