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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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Analysis of effect of annealing at high temperature on nickel oxide and zinc oxide thin film for solar cell applications Iskandar Dzulkarnain Rummaja; Nur Afiqah Hani Senin; Muhammad Idzdihar Idris; Zarina Baharudin Zamani; Radi Husin Ramlee; Luke Bradley
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp777-787

Abstract

The use of thin films in solar cell technology has gained substantial interest because of their potential for cost-effective and efficient energy conversion. nickel oxide (NiO) and zinc oxide (ZnO) have been used as potential materials in solar cells application especially third generation solar cells because of their good characteristics, such as high electrical conductivity, chemical stability, resistance to degradation, and abundance and low cost. However, at high temperatures, both NiO and ZnO can undergo thermal decomposition and exhibit crystal defects and grain boundaries. This work investigates high temperature annealing on the morphology, structural, and optical properties of NiO and ZnO thin films. The deposited material was annealed at 500 ℃, 600 ℃, and 700 ℃ and be characterized via scanning electron microscopy (SEM), XRD, and UV-Vi’s spectroscopy. The results showed that inceasing the annealing temperature can improve both ZnO and NiO thin films in structure and appearance. For ZnO, higher temperatures made the grains bigger and more orderly, and for NiO, the process made the grains more organized, bigger in size, and spread out more evenly. However, annealing at high temperature yields a smaller bandgap energy value for both thin films.
Performance of low cost sensor temperature logger in double jacket reactor vacuum distillation Djoko Wahyudi; Wignyanto Wignyanto; Yusuf Hendrawan; Nurkholis Hamidi
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.pp1424-1435

Abstract

Heat transfer occurs due to the difference in temperature between the system and its surroundings. The effect of temperature can affect various aspects of the refining process, including efficiency, selectivity, reaction kinetics, and the quality of the final product. This study aims to analyze heat transfer in a double-jacket reactor by measuring temperature data taken using a temperature data logger system. Prototype low cost-effective temperature monitoring for double jacket reactor vacuum is this system integrates an Arduino Mega 2560, a type K thermocouple, amplifier MAX 6675 module, and an SD card data logger to measure and record reactor temperature for 30 minutes. The temperature data obtained is used to calculate the heat transfer and analyze the heat transfer characteristics of the reactor. Heat transfer analysis based on measured temperature data is able to provide insight into the characteristics of heat transfer in the observed system and can identify hot spots and heat transfer energy in the system. Thus, the temperature data logger used in the double jacket reactor in the vacuum distillation system can produce accurate data and information, and this system has broad application potential in temperature monitoring in various fields.
A quality control system for logistic ports goods movable harbor cranes based on internet of things and deep learning Ahmed Hatem Awad; Mohamed Sabry Saraya; Mohamed Shrief Mostafa Elksasy; Amr M. T. Ali-Eldin; Mohamed Moawad Abdelsalam
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.pp862-878

Abstract

The growth of commercial activity and the transportation of goods around the world has increased the challenges of stevedoring within ports. In case of loading and unloading ships safely, quickly, and efficiently, goods movable harbor cranes play an important role. This work aims to propose an industrial internet of things (IIoT)-based quality control system for logistic services ports goods movable harbor crane (QC-GMHC). The GMHC system based on using programmable logic controller (PLC), along with a multi-sensor data collecting system. Several operations have been done to establish the QC-GMHC system as: GMHC sensors real-time data storage, and data sharing; monitoring the GMHC status (remote-local); and the efficiency reporting. In order to validate the proposed system’s hardware, it was used in an already operational GMHC for six months, during which data were collected and analyzed. The results revealed that the proposed hardware system worked efficiently for 24 hours. To forecast the efficiency of the GMHC, a deep learning (DL) conventional long short-term memory (LSTM) and neural network model was trained and validated using synthetic data generated from acquired real data. The results showed that QC-GMHC can calculate efficiency with an accuracy of 80%, which is sufficient for our application.
Energy management enhancement of a smart home supplied by renewable energy system Shakir, Hasan Hammoodi; Salem, Fatma Ben
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.pp20-31

Abstract

Solar energy is a reliable and eco-friendly solution for power outages in Karbala, Iraq. This study presents a smart grid technology model for energy management in electrical systems, optimizing power schemes and economic benefits through a unique spatial distribution approach in Iraq, with the primary objective of ensuring consistent base loads for smart homes while achieving other economic goals. The algorithm’s effectiveness was tested in three different scenarios. The energy was supplied by the national grid and battery bank-powered base loads. Meteorological data, including temperature and solar radiation, was gathered from a station in Karbala city for testing and evaluation. The study found that energy consumption decreased by 85% in April, with solar energy accounting for 37% of the total consumption. Smart homes saved 48% of energy, reducing reliance on the grid to 15%, as well as the reduction of energy consumption reached up to 47% and 60% in January and July, respectively, with solar energy estimated at 14% and 26% in those months.
Fast region based convolutional neural network ResNet-50 model for on tree Mango fruit yield estimation Neethi Managali Vasanth; Raviraj PPandian
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.pp1084-1091

Abstract

The foundation of the Indian economy is agriculture, the amount of land available for agricultural activities has decreased due to numerous factors. To fulfill the demands of the expanding population, the maximum yield must be produced on the least amount of land that is accessible. To overcome the challenges of agriculture, many researches have been carried out to adopt technology into agriculture. As India is one of the world's top producers of Mangoes and has a vast market, and has encouraged extensive Mango farm development. Automatic yield estimation of Mangoes in the early stage is important to improve the quality and quantity of production which improves both domestic and export markets. The work proposes a fast region (FR) based convolutional neural network (CNN) residual network (ResNet)-50 model for efficient deep learning-based Mango crop yield estimation system to count the Mango fruit from the images of individual trees. A temporal Mango fruit database is used to estimate the yield of on tree Mango fruits, and a framework is provided to estimate Mango fruit yield in red, green, and blue (RGB) image. This experiment shows that the suggested FRCNN ResNet-50 model attained a better accuracy of 98.20% on the proposed dataset.
Automatic detection of solar cell surface defects in electroluminescence images based on YOLOv8 algorithm Drir Nadia; Chekired Fathia
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.pp1392-1404

Abstract

In the last few years, the development of renewable energies has increased on a large scale. At least, to guarantee the security and stability of the photovolataic system's production, it is imperative that the photovoltaic modules exhibit a high level of reliability. Therefore, the development of an intelligent detection environment to enable the identification of defects in solar cells during manufacturing has become an important issue for the growth of the photovoltaic (PV) sector. This work proposed a fault diagnosis of surface solar cells using deep learning methods for computer vision, using the eighth version of the you only look once (YOLOv8) algorithm. This detection method was applied to a dataset of electroluminescence (EL) images containing twelve PV cell defects on a publicly available heterogeneous background. Then, using this dataset, we trained, validated, and tested the YOLOv8, YOLOv5 models. The results show that YOLOv8 provides a high level of accuracy in fault diagnosis compared with YOLOv5, and also improves the detection speed of the model. Indeed, the average precision achieves 90.5% This suggested approach ensures high accuracy in fault identification which demonstrates the effectiveness of computer vision to identify multi-object cell defects.
A novel FFNN-AHO hybrid predictive model for enhancing the performance of jet-cooled PVT system Mohamed A. Essa; Alaa M. Rashad; Ahmed Y. Hatata
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp711-725

Abstract

Photovoltaic-thermal (PVT) systems are common in the conversion of solar energy to electrical and thermal energy. The performance of such systems depends on the environmental conditions in which these systems are applied. This paper presents a parametric study of a jet-cooling PVT system with a staggered distribution of the jets. A feedforward neural network (FFNN) is proposed as a novel predictive model for analyzing the characteristics of the PVT system and its thermal and electrical performance. Moreover, a novel optimization algorithm called archerfish hunting optimizer (AHO) is applied to obtain the optimal structure and elements of the proposed FFNN. The PVT system variables considered as inputs to the FFNN-AHO model are flow rate, wind speed, solar irradiance, and ambient temperature. The average temperature of the PV reaches a maximum of 45.84 ºC, and the maximum temperature un-uniformity reaches to 3.59 ºC. The studied PVT system achieved maximum electrical, thermal, and overall efficiencies of 14.23%, 54.43%, and 68.1%, respectively. Moreover, the results demonstrate that the FFNN-AHO hybrid model provides highly accurate PVT system performance prediction. The correlation coefficient between the actual and predicted data is close to 1, indicating a strong correlation and confirming the reliability and effectiveness of the FFNN-AHO model.
Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm Wiwit Agus Triyanto; Kusworo Adi; Jatmiko Endro Suseno
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.pp407-418

Abstract

The chicken farming industry is one of the biggest food industries that supports the achievement of food security internationally. Farmers need an independent tool that can monitor the welfare conditions of chickens in cages. Using their tools, farmers can ideally detect the condition of chickens. Lameness chickens, can be known for activity and dredging of their location in the cage. Occlusion, and background in the cage are interesting challenges. By observing behavior, image handling practices can be used to identify tainted chicks and provide an early warning of sickness in chickens. In this study, you only look once, version 8 (YOLOv8) which is a convolutional neural network (CNN) network model was chosen to perform the detection, tracking, and mapping of chicken locations. YOLOv8 was combined with various algorithm optimizers to improve training performance, such as root mean square (RMS) Prop, stochastic gradient descent (SGD), ADAM, and ADAMW. Multi-object tracking algorithms such as BOT-sort and ByteTrack are also used to improve tracking performance. Based on the results, YOLOv8 with combinations of optimizer algorithms ADAMW has the best mAP, support, precision and F1-score values compared to the others, with 0.936, 0.993, 0.990, 0.991. Meanwhile, for multi object tracking, ByteTrack is faster in inference time(s) values compared to the others, with 0.2.
Automating cloud virtual machines allocation via machine learning Kamoun-Abid, Ferdaous; Frikha, Hounaida; Meddeb-Makhoulf, Amel; Zarai, Faouzi
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.pp191-202

Abstract

In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.
Wireless internet of things solutions for efficient photovoltaic system monitoring via WiFi networks Himri Yacine; Kadri Boufeldja
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.pp901-910

Abstract

The imperative for sustainable energy production has necessitated the significant expansion of renewable energy sources, particularly photovoltaic (PV) systems. The utilization of real-time monitoring and data analysis is imperative to enhance the efficiency and performance of photovoltaic systems. This abstract presents developing and deploying a wireless monitoring system for a photovoltaic system. The system utilizes a Raspberry Pi device connected to a WiFi network and an SD card for data storage to enable remote monitoring and management of PV systems. The proposed monitoring system comprises a Raspberry Pi equipped with sensors to measure various parameters such as voltage, current, temperature, and the ambient conditions of the solar panels; the monitoring system can be remotely accessible through the wireless capabilities of the Raspberry Pi, which are activated by establishing a connection to an existing WiFi network. The proposed configuration facilitates the placement of the monitoring station in any desired location, hence eliminating the requirement for intricate wiring connections. These real-time data enable solar system managers to quickly identify anomalies, anticipate breakdowns, and optimise energy production. The paper presents a wireless monitoring system with a cost-effective and scalable solution for monitoring photovoltaic systems.

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