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Photovoltaic Fault Detection in Remote Areas Using Fuzzy-Based Multiple Linear Regression (FMLR)

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 243-249页

作者： Ardianto, Feby Ermatita, Ermatita Sofijan, Armin Doctoral Program in Engineering Science Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

This research focused on developing and implementing a fault detection model for photovoltaic (PV) systems in remote areas, utilizing a Fuzzy-Based Multiple Linear Regression (FMLR) approach. The study aimed to address the challenges of monitoring PV systems in locations with limited access to conventional power grids and technical resources. The fault detection system integrated environmental parameters such as solar radiation, temperature, wind speed, and rainfall, alongside PV system parameters like panel voltage, current, battery voltage, and inverter performance. Data collection and preprocessing were conducted over a specified period to identify operational patterns under both normal and faulty conditions, ensuring data accuracy through cleaning, normalization, and categorization. The research was conducted in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatera, Indonesia, contributing to the improvement of reliability and sustainability of renewable energy sources in isolated communities. The total number of data points for 276 rows with 6 attributes each was 1656 records. The MLR model was developed to predict the output power of the PV system, while fuzzy logic was employed to handle uncertainties in the data, offering a more flexible and adaptive decision-making process. The system applied fuzzy rules to determine the charging status (P3), categorizing it into Optimal Charging, Adjusted Charging, Charging Delay, or Fault Alert. The model was tested with real-time data, and its performance was validated through comparison with manual inspections. The results showed that the FMLR-based fault detection system effectively identified faults and optimized the performance of the PV system, making it suitable for remote areas in South Sumatera. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Multiple linear regression

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Establishing Utilization Decision Criteria for Open-Source Software Projects Based on Issue Resolution Time Evaluation 18

Establishing Utilization Decision Criteria for Open-Source S...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Inoue, Keisuke Tsuda, Kazuhiko University of Tsukuba Doctoral Program in Risk and Resilience Engineering Tsukuba Japan University of Tsukuba Faculty of Business Sciences Tsukuba Japan

ISBN: (纸本)9798331534677

In the face of growing demand for high-quality software solutions at reduced costs and development times, the use of open-source software (OSS) has become increasingly prevalent. This study evaluates the speed of OSS support activities - an essential quality factor for OSS users - and proposes a method for early-stage evaluation based on issue resolution time. We analyzed 100 popular OSS projects hosted on GitHub, collecting data on issue creation and completion dates. The results indicate a correlation coefficient of 0.587 between 30 and 180 days of observation, aligning with previous findings on OSS support continuity. Notably, a satisfactory correlation of 0.525 was achieved at just 21 days, suggesting that organizations can assess OSS support speed earlier and potentially accelerate their decision-making process. Our findings provide a practical criterion for evaluating the speed of OSS support activities, which may help organizations adopt OSS with greater confidence and enhance productivity in software-related projects. Future research will expand these criteria by exploring additional factors affecting issue resolution time, such as project scale and corporate support. © 2025 IEEE.

关键词： Open source software

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Modeling Syngas Fermentation for Ethanol Production under Fluctuating Inlet Gas Composition

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Bulletin of Chemical Reaction engineering and Catalysis 2025年第2期20卷 331-345页

作者： Istiqomah, Noviani Arifina Mukti, Rendy Kresnowati, Made Tri Ari Penia Setiadi, Tjandra Doctoral Program of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Food Engineering Faculty of Industrial Technology Institut Teknologi Bandung Indonesia

Syngas fermentation effectively converts CO, H2, and CO2 into valuable biofuels and chemicals. This study investigated the effects of fluctuating syngas composition and kLa as the critical operational parameters on microbial fermentation performance, with a focus on ethanol, acetic acid, and biomass production. Modeling results demonstrated that increasing CO concentration significantly enhanced metabolite production, whereas increases in H2 and CO2 concentrations yielded limited improvements. The findings revealed that a higher H2/CO ratio tent to reduce metabolite production, while a higher CO/CO2 ratio significantly improved fermentation outcomes. Additionally, higher kLa values were observed to promote metabolite production, though diminishing returns were evident at very high kLa levels. Further study on the impact of syngas composition disturbances (±5% to ±20%) and fluctuation durations (0.5, 1, 2, and 4 days) indicated that larger disturbances and longer fluctuation durations led to greater deviations in metabolite concentrations, with ethanol being the most sensitive, followed by acetic acid and biomass. Despite these fluctuations, the microbial system displayed resilience, stabilizing once gas composition returned to normal levels. These insights underscored the adaptability and robustness of syngas fermentation systems, making them viable for industrial applications where gas composition variability is inevitable. The ability to tolerate moderate fluctuations offers opportunities to reduce gas pretreatment costs and process syngas from diverse sources, benefiting industries such as steel manufacturing, oil refining, and biomass gasification. Copyright © 2025 by Authors,

关键词： Steelmaking

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Prediction Model for Track Quality Index Categories on the Northern and Southern Railway Lines of Java

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Periodica Polytechnica Transportation engineering 2025年第2期53卷 184-193页

作者： Yudariansyah, Hadi Ismiyati, Ismiyati Narendera, Alfa Doctoral Program of Civil Engineering Faculty of Engineering University of Diponegoro 5–7 Hayam Wuruk street Semarang50241 Indonesia Department of Civil Engineering Faculty of Engineering University of Diponegoro Prof. Sudarto Tembalang Street Semarang50275 Indonesia Department of Civil Engineering Faculty of Engineering Semarang State University E3–4 Kampus Sekaran Gunung Pati Street Semarang50229 Indonesia

Track quality index (TQI) is a quality metric that objectively measures the geometric condition of railway tracks for maintenance planning. The TQI categories serve as the basis for proposed track maintenance. The TQI measured by the EM120 track recording car on Java island currently covers only 78.84% of the 5,634.363 km of railway tracks, indicating that there are still track sections without TQI category values. This study aims to model the maintenance of railway infrastructure based on TQI categories derived from both track recording car results and manual measurements across various sections of railway lines on the northern and southern routes of Java island. The analysis used is based on the standard deviation of railway track geometry, including superelevation, levelling, lining, and track gauge. Factors such as turnouts, bridges, crossings, straight sections, and curves were then classified as predictive factors. Machine learning techniques were adopted, with 80% of the data set randomly used for training and the remaining for testing to generate TQI category predictions. A total of 233,175 TQI data points from 2019–2022 were used to build and validate the model. The results indicate that the multinomial regression model for TQI Categories 1, 2, 3, and 4 is highly accurate, the rest is influenced by other factors. These results imply that the model has an exceptional fit and excellent predictive capability for TQI on the northern and southern railway lines of Java island. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Prediction models

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Harnessing YOLO for Loose Fruits Detection: Boosting Productivity in Palm Oil Plantations

Harnessing YOLO for Loose Fruits Detection: Boosting Product...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Warni, Elly Indrabayu Achmad, Andani Syahsir, Aldilah Rezki Rhamadani Universitas Hasanuddin Doctoral Program of Electrical Engineering Department Informatics Department Makassar Indonesia Universitas Hasanuddin Electrical Engineering Department Makassar Indonesia Universitas Hasanuddin Informatics Department Makassar Indonesia

ISBN: (纸本)9798331513320

This study explores the use of the YOLOv5 algorithm to detect loose fruits palm oil in plantations, improve collection efficiency, and reduce reliance on manual labor. Loose fruits have high economic value due to their oil content, but they are often not collected, resulting in financial losses and the growth of weeds around oil palm trees. Conventional manual methods are often ineffective;thus, automatic detection is required. The dataset comprises 525 images of loose fruits, covering complex conditions on plantations, and secondary data from Roboflow, including nontarget objects, such as rocks and dry leaves, to train the model to accurately distinguish loose fruits. During training, several parameters were optimized: batch size, learning rate, optimizer type, weight decay, and momentum, which showed that small batch sizes provided higher mAP50 values, whereas large batches increased mAP50:95, indicating the ability to detect objects at higher levels of complexity. Testing in a scenario with loose fruits clearly visible and partially covered with leaves or grass showed that YOLOv5 could detect loose fruits well despite some constraints in the obstructed environment. The results of this study show that, with appropriate parameter settings, YOLOv5 can be a reliable basis for automated loose fruit collection systems, potentially improving operational efficiency in oil palm plantations and contributing to the development of automation in the agricultural sector. © 2025 IEEE.

关键词： Fruits

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Djafar, Imran Indrabayu Syarif, Syafruddin Hasanuddin University Dipa Makassar University Doctoral Program of Electrical Engineering Department Makassar Indonesia Hasanuddin University Makassar Department of Informatics Indonesia Hasanuddin University Department of Electrical Engineering Makassar Indonesia

ISBN: (纸本)9798331513320

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities. © 2025 IEEE.

关键词： Convolutional neural networks

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Manufacturing process design for cellular structure used as impact energy absorbers 4

Manufacturing process design for cellular structure used as ...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Komara, Asep Indra Setiawan, Rachman Budiwantoro, Bagus Doctoral Program of Mechanical Engineering Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Manufacturing Design Engineering Department Politeknik Manufaktur Bandung Bandung Indonesia

The application of passive safety technology in vehicles is generally through impact-absorbing structures. This technology has been widely applied to trains, planes, and cars to protect passengers from fatal accidents. Cellular structures are defined by a unit cell that combines material, space, and unit cell repetition to obtain a large structure. It is selected as the base structure of the impact energy absorber for its potentially high specific energy absorption (SEA]. Such a structure poses a challenge in its manufacturing method. This study examines alternative manufacturing processes for cellular structures designed as impact absorbers, and then selects and develops an effective and efficient manufacturing method to produce these cellular structures. Several alternative manufacturing methods were chosen to exercise the use of a combined plaster molding technology and additive manufacturing for prototyping. Manufacturing process simulation studies were conducted using Altair Inspire Cast software to determine the success of the casting process. The final stage is to carry out the process of making cell structures and evaluating the results. This work results in an effective manufacturing process design to be implemented throughout the production stage. Furthermore, for mass production, mold patterns may be replaced by using injection molding. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Injection molding

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A Proposal of the Improved Method for Measuring Insulation Resistance between a Photovoltaic Module String and the Earth using the Self-biased Method

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IEEJ Transactions on Power and Energy 2025年第1期145卷 13-20页

作者： Kazama, Takuro Kato, Kazuhiko Doctoral Program in Risk and Resilience Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba305-8573 Japan Renewable Energy Research Center National Institute of Advanced Industrial Science and Technology 2-2-9 Machiikedai Koriyama963-0298 Japan

For safe operation of a photovoltaic generating system, an insulation monitoring function, which can immediately detect deterioration of insulation between photovoltaic module strings and the earth, is indispensable. This function requires accurate measurement, low leakage current and suppression of rising voltage against the earth. In this paper, a new insulation monitoring method of photovoltaic module strings is proposed by the improved existing self-biased method that has an inevitable voltage rise under measurement. In this new method, both terminals of photovoltaic module strings are always connected to the ground with appropriate resistors. Theoretical discussion and experiments for this new method showed precise measurement, and acceptable leakage current under various outdoor conditions. In addition, it was confirmed that this method could sufficiently decrease in the voltage rise compared with the existing self-biased method. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Photovoltaics

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The Influence of Silicon Dioxide as a Fuel Additive in a Diesel Engine Fuelled by Pure Palm Oil 4

The Influence of Silicon Dioxide as a Fuel Additive in a Die...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Prahmana, Rico Aditia Darmanto, Prihadi Setyo Reksowardojo, Iman Kartolaksono Prakoso, Tirto Juangsa, Firman Bagja Doctoral Program of Mechanical Engineering Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung40132 Indonesia Program Study of Mechanical Engineering Faculty of Industrial Technology Institut Teknologi Sumatera South Lampung35365 Indonesia Thermal Science and Engineering Research Group Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia Department of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia Program Study of Mechanical Engineering Faculty of Industrial Technology Universitas Pertamina Jakarta12220 Indonesia

In the last decade, the Indonesian government has implemented a program to use biodiesel as a substitute for fossil diesel to reduce dependence on fossil fuels. On the other hand, the development of biodiesel as an alternative fuel to replace fossil diesel has several challenges, one of which is the production of biodiesel, which is more complicated and requires a long process. However, the direct use of crude palm oil (CPO) in diesel engines is not recommended because it has many drawbacks. Research recently shows that fuel additives in diesel engines have promising prospects. Therefore, to solve the problem, this study used pure palm oil (PPaO) as the primary fuel, with the addition of silicon oxide (SiO2) as a fuel additive in diesel engines. The results showed that using SiO2 additive in PPaO can increase thermal efficiency by 18.8% at 60% load compared to diesel fuel (DF). Nevertheless, using this fuel increased specific fuel consumption (SFC) by 3.9% on average and increased the opacity by 153% compared to diesel fuel. These results indicate that SiO2 has the potential to be developed as an effective additive to PPaO fuel that can improve the efficiency and performance of diesel engines. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Biodiesel

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Current advances in bioethanol synthesis from lignocellulosic biomass: sustainable methods, technological developments, and challenges

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Journal of Umm Al-Qura University for Applied Sciences 2025年 1-18页

作者： Novia, Novia Melwita, Elda Jannah, Asyeni Miftahul Selpiana, Selpiana Yandriani, Yandriani Afrah, Bazlina Dawami Rendana, Muhammad Department of Chemical Engineering Faculty of Engineering Universitas Sriwijaya Jl. Raya Palembang-Prabumulih KM 32 Inderalaya Sumatera Selatan Ogan Ilir 30662 Indonesia Master Program of Environmental Management Graduate School Universitas Sriwijaya Jl. Padang Selasa 524 Bukit Besar Sumatera Selatan Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya South Sumatra Palembang 30139 Indonesia

Bioethanol production from lignocellulosic biomass has become an economical and environmentally friendly substitute to current petroleum-based fuels. This is due to its ability to decrease carbon dioxide emissions and make use of plentiful natural resources. Current advances in this area concentrate on improving the effectiveness and expandability of the conversion procedures by using inventive pretreatment approaches, enhanced enzyme compositions, and refined fermentation technologies. Consolidated bioprocessing (CBP) and utilizing genetically modified microbes have expedited the decomposition of intricate biomass complexes and enhanced overall productivity. Furthermore, process intensification developments, such as the adoption of hybrid reactors and continuous production systems, have considerably decreased production expenses and energy usage. Nevertheless, there are still obstacles to overcome, including the raw materials inconsistency, the demand for efficient recovery and purifying techniques, and the financial viability of extensive processes. This study examines the latest developments in bioethanol production from lignocellulosic biomass. It focuses on ecological approaches, technical advancements, and the main obstacles that need to be overcome to exploit its promise as a biofuel fully. This review primarily focused on outcomes documented in the last five years from 2019 to 2024. The first segment of this work focuses on the second-generation bioethanol production process, which includes: the properties and composition of lignocellulosic biomass, pretreatment, enzymatic saccharification, and fermentation. The subsequent portion of this paper examines a techno-economic analysis, the obstacles and the prospective technology outlook. It finishes by discussing findings from research fields that haven't been investigated yet, besides the obstacles faced in bioethanol manufacturing methods. © The Author(s) 2025.

关键词： Bioethanol Enzymatic hydrolysis Fermentation Lignocellulosic biomass Pretreatment

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