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SSRN 2024年

作者： Khan, Sadique Masood, Mudassir Medina, Mario Alzahrani, Fahad Mechanical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Mechanical Engineering Department California State University Los Angeles United States

Compression ignition engines when operated on gasoline fuels cause significant reduction in NOx and particulate emissions. In such advanced combustion strategy, the fuel-oxidiser mixing process, intensified by the prolonged ignition delays of gasoline fuels, directly affects the stability and efficiency of combustion. Thus, optimising fuel spray characteristics leads to optimisation of injector design, engine performance and subsequent decrease in emissions. Since spray development is a complex process that involves wide range of length and time scales, computationally expensive modelling techniques can be replaced with machine learning (ML) models. These ML models are employed to predict spray characteristics utilizing datasets generated from comprehensive spray studies. In this work, a dataset of about 5400 instances taken from non-evaporating gasoline fuel spray imaging experiments under Gasoline Compression Injection (GCI) engine conditions is used to train various ML models with data split of 70% and 30% for training and testing, respectively, with five-folds cross-validation performed within the training. The fuel injection pressure (60 – 150 MPa), chamber pressure (0.1 – 2 MPa), nozzle diameter, nozzle hole conicity and injection duration are used as input features to the models for predicting the spray tip penetration and spray angle. The performance of four ML models was evaluated and compared under default and tuned hyperparameters against experimental data and available physics-based correlations in the literature. The models include random forest, extreme gradient boosting, multilayer perceptron, and elastic-net. The results show that the hyperparameter-tuned extreme gradient boosting model performs best in predicting the spray parameters. The overall model performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE), resulting in values of 0.884, 0.651, and 1.571, respectively. This

关键词： Machine learning

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation

Creating Mathematical Models to Help Formulate Company Strat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Widhilaga Gia Perdana Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies.

关键词： Performance evaluation Knowledge acquisition Collaboration Companies Knowledge management Mathematical models Regression analysis Information management Problem-solving System implementation

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Load Frequency Control of Hybrid Power System Using Marine Predator Algorithm Tuned Cascaded Controller

Load Frequency Control of Hybrid Power System Using Marine P...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Muhannnad Inshal Shahzad Muhammad Majid Gulzar Aqsa Shahzad Ahlam Jameel Adnan Shakoor Salman Habib Department of Control & Instrumentation Engineering Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) Dhahran Saudi Arabia Comsats Institute of Information Technology Abbottabad Pakistan Department of Electrical Engineering Lahore College for Women University Lahore Pakistan Interdisciplinary Research Center for Biosystems and Machines (IRC-BSM) Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The load frequency Control (LFC) problem in multi-area power systems is very common whenFtable RES are added to the power grid because of their irregular behavior. Renewable energy combined with a thermal generator makes a multi-area power system. As renewable sources are not stable, that's why they are attached to conventional sources to complete the load demand. This paper implemented a sophisticated control technique known as the Marine Predator Algorithm (MPA) in conjunction with PID n for LFC of the interconnected power system. By using the MPA control technique, their behavior to locate and capture prey helps to effectively encounter frequency fluctuations in the power system. Different controllers, like GA-PI, FFA-PI, and ABC-PID n , have been analyzed in this study and compared with the proposed technique to show the superiority of the proposed technique.

关键词： Hands Renewable energy sources Accuracy Systematics Sensitivity Generators Power grids Thermal loading Frequency control Convergence

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Comprehensive Performance Evaluation of Topcon, Hit, and Perc Tandem Solar Cells: Analysing Performance and Stability

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SSRN 2024年

作者： Yi, Junsin Rahman, Rafi Ur Khokhar, Muhammad Quddamah Madara, Polgampola Chamani Aida, Maha Nur Jony, Jaljalalul Abedin Alamgeer Yousuf, Hasnain Chu, Mengmeng Park, Sangheon Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Future Energy Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of

The evolution of photovoltaic technologies has led to significant advancements in tandem solar cells, notably TOPCon, HIT, and PERC cells. Historically, these technologies have demonstrated substantial improvements in efficiency and stability. This study conducts a comprehensive performance evaluation of TOPCon, HIT, and PERC tandem solar cells by measuring light current-voltage (LIV) characteristics in both series and parallel connections under varying sunlight intensities (0 to 0.5 suns). The maximum efficiency recorded for the TOPCon/TOPCon cells in series connection was 6.63%, while in parallel, it was 25.78%. For HIT/HIT cells, series and parallel connections yielded maximum efficiencies of 27.00% and 6.58%, respectively. PERC/PERC cells achieved efficiencies of 17.81% in series and 21.38% in parallel. These results were obtained by systematically varying the sunlight intensity from 0 to 0.5 suns, revealing that TOPCon technology excels under low-light conditions. Future work will optimize material properties and interconnection methods to enhance performance and commercial viability. © 2024, The Authors. All rights reserved.

关键词： Current voltage characteristics

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Kamal Shahid Muhammad Majid Gulzar Muhammad Ali Raza Salman Habib Hasnain Ahmad Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings.

关键词： Meters Load monitoring Reactive power Artificial neural networks Smart homes Machine learning User interfaces

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Factors Affecting Willingness to Use Solar Photovoltaic Technology: A Systematic Literature Review

Factors Affecting Willingness to Use Solar Photovoltaic Tech...

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Technology & engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Nanang Kurniawan Soeprijanto Reny Nadlifatin Interdisciplinary School of Management & Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Industrial Chemical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331515881

ISBN: (纸本)9798331515898

The purpose of this research is to create an organized review of the literature on the variables influencing the intention to adopt solar photovoltaic technology. Data collection for systematic literature reviews follows the “Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)” guidelines. Eighty-five articles fulfil the criteria. According to an analysis of these chosen primary papers, it is found that most of the research contexts are on household usage. Factors that majority affect the willingness to use solar photovoltaic technology in that context could be classified as economy, social & community, technology, supporting policies & government incentives, personal, environmental concern and other factors. Economy, involving cost & benefit consideration is still the major factor influencing the willingness to use solar photovoltaic. Furthermore, certain inconsistencies regarding the impact of environmental concerns on the factors influencing the adoption of solar photovoltaic systems have been identified. This emphasizes the necessity for further scientific analysis to validate these findings and serve as a reference and direction for future researchers to investigate in different research contexts, particularly in non-household (industrial or business) contexts.

关键词： Photovoltaic systems Costs Government Asia Data collection Systematic literature review Guidelines Business

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Ontologies in Design: How Imagining a Tree Reveals Possibilities and Assumptions in Large Language Models 25

Ontologies in Design: How Imagining a Tree Reveals Possibili...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Haghighi, Nava Yu, Sunny Landay, James A. Rosner, Daniela Computer Science Stanford University StanfordCA United States Symbolic Systems Program Stanford University StanfordCA United States Computer Science Department Stanford University StanfordCA United States Human Centered Design & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713941

Amid the recent uptake of Generative AI, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-based analyses are crucial, we argue that ontologies - concerning what we allow ourselves to think or talk about - is a vital but under-recognized dimension in analyzing these systems. Proposing a need for a practice-based engagement with ontologies, we offer four orientations for considering ontologies in design: pluralism, groundedness, liveliness, and enactment. We share examples of potentialities that are opened up through these orientations across the entire LLM development pipeline by conducting two ontological analyses: examining the responses of four LLM-based chatbots in a prompting exercise, and analyzing the architecture of an LLM-based agent simulation. We conclude by sharing opportunities and limitations of working with ontologies in the design and development of sociotechnical systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ontology

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Systematic Control of Electro-hydraulic Velocity Servomechanism using Fractional Order Technique

Systematic Control of Electro-hydraulic Velocity Servomechan...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Muhammad Majid Gulzar Fahad Usman Khan Daud Sibtain Adnan Shakoor Muhammad Khalid Department of Control & Instrumentation Engineering Department of Electrical Engineering University of Central Punjab Lahore Pakistan Energy Center of Excellence (ECoE) Harbin Electric International Harbin China Electrical Engineering Department Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Electro-Hydraulic ServoMechanism (EHSM) is a nonlinear system with tentative dynamics, whose control is a difficult task. In this research, an alternative to the existing technique, a fractional order PID control-based approach, is presented. Initially, a SIMULINK based nonlinear model is constructed. The linear model is obtained by approximating the non-linearity using the bilinear transformation. Pole placement is blended with the Ziegler-Nichols tuning method to carry out the controller design and controller parameter tuning. The results are compared with conventional PID, and stability analysis is also analyzed. The results of the fractional order controller show satisfactory improvement than the results of conventional PID.

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Electric Vehicles Limit Equitable Access to Essential Services During Blackouts

arXiv

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arXiv 2024年

作者： Essus, Yamil Rachunok, Benjamin Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States Operations Research Program North Carolina State University RaleighNC United States

Electric vehicles (EVs) link mobility and electric power availability, posing a risk of making transportation unavailable during blackouts. We develop a computational framework to quantify the impact of EVs on mobility and access to services and find that existing access issues are exacerbated by EVs. Our results demonstrate that larger batteries reduce mobility constraints but their effectiveness is dependent on the geographic distribution of services and households. We explore the trade-offs between mobility and quality-of-life improvements presented by Vehicle-to-Grid technologies and the feasibility and trade-offs of public charging infrastructure as a solution to access inequalities. Equitable access to essential services (e.g. supermarkets, schools, parks, etc.) is the most important aspect of community resilience and our results show vehicle electrification can hinder access to essential services unless properly incorporated into policy and city-scale decision-making. © 2024, CC BY.

关键词： Electric vehicles

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Rainfall frequency analysis using event-maximum rainfalls – An event-based mixture distribution modeling approach

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Weather and Climate Extremes 2024年 43卷

作者： Cheng, Ke-Sheng Chen, Bo-Yu Lin, Teng-Wei Nakamura, Kimihito Ruangrassamee, Piyatida Chikamori, Hidetaka Department of Bioenvironmental Systems Engineering National Taiwan University Taipei Taiwan Master Program in Statistics National Taiwan University Taipei Taiwan Graduate School of Agriculture Kyoto University Kyoto Japan Department of Water Resources Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Environmental Management Engineering Okayama University Okayama Japan

Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the use of annual maximum series for rainfall frequency analysis may yield design rainfall estimates of higher uncertainties. Moreover, for regions with cyclostationary climate patterns, the annual maximum rainfalls may be caused by different prevalent storm types, which differ in terms of their occurrence frequency and storm rainfall characteristics. In this study, we propose a novel event-maximum-rainfall-based mixture distribution modeling approach for rainfall frequency analysis. By considering the event-maximum rainfalls of individual storm events, the sample size for parameter estimation increases, and the uncertainty of design rainfall estimates reduces. Mixture distribution modeling enables a thorough investigation of the contributing probabilities of different storm types to the annual maximum rainfall. Through rigorous stochastic simulation, we demonstrated the superiority of the proposed approach over the conventional annual maximum rainfall approach. The proposed approach was applied to four representative rainfall stations in Taiwan, and the results revealed that the proposed approach is more robust than the conventional annual maximum rainfall approach. The results provide insights into the contributions of individual storm types to the annual maximum rainfall. © 2023 The Authors

关键词： Annual maxima Event-maximum rainfalls Frequency analysis Mixture distribution Negative binomial distribution Poisson distribution

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