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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 9,138 Documents
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Integration of statistical methods and neural networks for temperature regulation parameter optimization Kaddar, Leila Benaissa; Khelifa, Said; Zareb, Mohamed El Mehdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp124-132

Abstract

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.
A novel 7-level reduced-switch MLI topology fed PMSM drive for electric vehicle system Chinta Anil Kumar; Kandasamy Jothinathan; Lingineni Shanmukha Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp746-756

Abstract

The compact and efficient design motivates the renewable energy-powered permanent magnet synchronous motor drive for electric vehicle applications. The available renewable energy is interfaced with a power drive by employing an electronic commutator such as a conventional 3-level inverter. But, the multilevel inverter produces favorable merits by producing staircase output voltage from several input direct current (DC) sources. The cascaded H-bridge multilevel inverter plays a significant role in many applications, but it was developed only for limited voltage levels. The major problem in cascaded h-bridge multilevel inverter (CHBMLI) requires more switching devices for higher voltage levels, which increases the size, cost and space of the electric vehicle (EV). To overcome above-mentioned problems, a new objective has been developed by employing the novel Reduced-switch multilevel inverter topology for higher voltage levels. This improves the voltage quality and reducing the harmonic level, and common-mode voltage issues. The main contribution of this work is, developing the novel 5-level, 7-level reduced-switch multilevel inverter (RSMLI) topologies with reduced switching devices with favourable merits over CHBMLI topology. Finally, the performance of proposed novel 5-level and 7-level RSMLI topologies fed PMSM drive for EV system has been verified, by using MATLAB/Simulink computing tool, and simulation results are presented with comparisons.
Design of matrix, distributive round robin, ping pong and enhanced ping lock arbiter for shared resources systems Nagaiyanallur Lakshminarayanan Venkatara; Subramanian Sumithra; Ramaiah Purushothaman; Subramani Suresh Kumar; Kathiresan Kokulavani; Velankanni Gowri
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1337-1345

Abstract

Arbiter is one of the main core elements in the network scheduler. The significant goal of this work is to design a high-speed and low execution-time arbiter with lock free and fair arbitration scheme. In this work, four types of arbiters such as matrix arbiter (MA), ping pong arbiter (PPA), distributive round-robin arbiter (DRRA) and enhanced ping lock arbiter (EPLA) are designed and analyzed area, delay, and speed of arbiters. MA is worked in square matrix format and matrix transition is performed for effective routing. The DRRA is designed by using a multiplexer and counter. Hence an, effective scheduling is carried out in DRRA. Binary tree format is used in PPA. The PPA provides low chip size and high speed than existing MA and DRRA. The PPA limits fair arbitration during uniformly distributed active request patterns. To overcome this problem, PPA is improved with some lock systems to create an EPLA. A new ping lock arbiter (PLA) leaf and PLA inter structure is proposed at the gate level to reduce the execution delay, improve the speed and achieve fair arbitration over all other existing arbiters. Resource allocation, execution delay, and speed are analyzed using the Xilinx Integrated Software Environment (ISE) tool.
Empowering geological data analysis with specialized software GIS modules Dossan Baigereyev; Syrym Kasenov; Laura Temirbekova
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1953-1964

Abstract

This research is devoted to the development of a geographic information system (GIS) for the analysis of geological data. It presents two specialized software modules designed to solve complex geological problems related to potential progress to disturbed masses and magnetotelluric sounding. These modules are integrated into the QGIS environment, offering efficient data processing and analysis capabilities, contributing to a deeper understanding of geological structures. The study presents a mathematical model for the problem of magnetotelluric sounding (MTS) and the continuation of potentials towards the perturbed masses, demonstrating numerical results using the developed algorithm. To confirm the accuracy of the model, a comparative analysis was carried out with empirical data for various chemical elements, which showed high accuracy, especially at shallow depths, with an error rate of less than 2%. In addition, the study highlights the importance of powerful GIS for the analysis and interpretation of geological data, including geochemical, geophysical and remote sensing information. The advanced functionality of QGIS simplifies data processing and visualization, which makes it an invaluable tool for geologists and researchers.
Optimal proportional-integral speed control for closed-loop engine timing system Saher Albatran; Salman Harasis
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp128-133

Abstract

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.
Islanded microgrid: hybrid energy resilience optimization Gopu, Veeranjaneyulu; Nagaraj, Mudakapla Shadaksharappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp693-703

Abstract

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.
Bayesian decision model based reliable route formation in internet of things Mohanavel Jothish Kumar; Suman Mishra; Elangovan Guruva Reddy; Madasamy Rajmohan; Subbiah Murugan; Narayanasamy Aswin Vignesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1665-1673

Abstract

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.
A new hybrid parallel genetic algorithm for multi-destination path planning problem Luthfiansyah Ilhamnanda Yusuf; Aina Musdholifah
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp584-591

Abstract

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.
Major depressive disorder: early detection using deep learning and pupil diameter Mohamed, Islam Ismail; El-Wakad, Mohamed Tarek; Shafie, Khaled Abbas; Aboamer, Mohamed A.; Rahman Mohamed, Nader A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp916-932

Abstract

Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was employed to distinguish between individuals diagnosed with depression and those who are considered mentally healthy. Pupillometric recordings were collected from a total of 58 individuals, comprising 29 healthy individuals and 29 individuals diagnosed with depression. Pupil size was recorded every 4 ms. The performance of three pretrained convolutional neural networks (GoogLeNet, SqueezeNet, and AlexNet) was evaluated for depression classification using the pupil size data. The highest accuracy of 98.28% was obtained with AlexNet. This finding highlights the potential of utilizing pupil diameter as a reliable indicator for objectively measuring depression.
An improved post-hurricane building damaged detection method based on transfer learning Guangxing Wang; Seong-Yoon Shin; Gwanghyun Jo
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1546-1556

Abstract

After a natural disaster, it is very important for the government to conduct a damaged assessment as soon as possible. Fast and accurate disaster assessment helps the government disaster relief departments allocate resources and respond quickly and effectively to minimize the losses caused by the disaster. Usually, the method of measuring disaster losses is to rely on manual field exploration and measurement, and then calculate and label the damaged buildings or land, or rely on unmanned collections to remotely collect pictures of the disaster-stricken area, and compare the original pictures to carry out the disaster annotation and calculation. These methods are time-consuming, labor-intensive, and inefficient. This paper proposes a post-hurricane building damage detection method based on transfer learning, which uses deep learning image classification algorithms to achieve post-disaster satellite image damage detection and classification, thereby improving disaster assessment efficiency and preparing for disaster relief and post-disaster reconstruction. The proposed method adopts the theory of transfer learning, establishes a disaster image detection model based on the convolutional neural network model, and uses the 2017 Hurricane Harvey data as the experimental data set. Experiments have proved that our proposed model accuracy of disaster detection reaches 97%, which is 1% higher than other models.

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