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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yen, Show-Jane Lee, Yue-Shi Lee, Wen-Hao Department of Computer Science and Information Engineering Ming Chuan University Taipei Taiwan Department of Management Information Systems National Chiayi University Chiayi City Taiwan

ISBN: (纸本)9789819606948

Mining quantitative association rules involves identifying the relationships between items based on their purchased quantities from a transaction database. In general, items with varying quantities are treated as distinct new items, making it challenging to meet the criteria for these new items. Previous methods have often relaxed these criteria to uncover quantitative association rules;however, excessive relaxation can lead to the discovery of many irrelevant rules, while insufficient relaxation may result in few or no rules being found. Additionally, the quantity ranges associated with items can be divided into intervals, and some of these intervals need to be combined, potentially leading to information loss during the process. In this article, we present new definitions and an approach for discovering quantitative association rules that address the issues of user-defined criteria, range segmentation, and interval integration, allowing us to identify the quantitative association rules that genuinely interest users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data mining

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AI-BASED OPEN PLATFORM FOR SMART MANUFACTURING: EMPOWERING SMES IN MACHINE TOOL INDUSTRY 51

AI-BASED OPEN PLATFORM FOR SMART MANUFACTURING: EMPOWERING S...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Tsai, Ming-Shun Chou, Che-Wei Chien, Chen-Fu International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Systems Management Feng Chia University Taiwan Department of Industrial Engineering & Engineering Management National Tsing Hua University Hsinchu Taiwan

This research addresses the urgent requirement for digital transformation within small and medium-sized enterprises (SMEs) in the Taiwanese machine tool and machining industry. Recognizing the barriers to AI adoption, including high implementation costs and a lack of specialized expertise, this study proposes an AI-based open platform for smart manufacturing. This platform leverages adaptable foundation models with fine-tuning using limited data samples, generated through deep learning, to enable rapid production line changes and provide SMEs with accessible AI solutions. The platform integrates data-driven insights, deep learning algorithms, and other AI technologies to tackle critical challenges in the industry. These include high testing costs, tool-related workpiece damage, challenges in maintaining manufacturing precision, and difficulties in adapting to diverse product demands. This research offers practical AI solutions for SMEs in flexible manufacturing and guides tool machine developers in AI integration, it contributes to the digital transformation of SMEs and bridges gaps in academic literature and industry applications. The study establishes a novel AI-based Open Platform architecture tailored for SMEs, advancing smart manufacturing accessibility and actionable in machine tool industry. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： AI-Based Open Platform Machine Tool industry Machining Quality Smart Manufacturing Tool Wear Diagnostic System

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Manufacturing management of productivity in the steel industry using system dynamics modelling and statistical evaluation

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International Journal of Industrial and systems engineering 2023年第4期43卷 491-528页

作者： Munyai, Thinandahva Thomas Ayomoh, Michael Kweneojo O. Makinde, Olasumbo Ayodeji Nesamvuni, A. Edgar Ramatsetse, Boitumelo I. Department of Operations Management Tshwane University of Technology Pretoria South Africa Department of Industrial and Systems Engineering University of Pretoria Pretoria South Africa Faculty of Management Sciences Tshwane University of Technology Pretoria South Africa Educational Information and Engineering School University of the Witwatersrand Pretoria Johannesburg South Africa

This paper has identified and analysed various drivers capable of influencing the level of productivity in steel manufacturing. The South African Steel Manufacturing Industries (SASMI) was used as a case study for this research. In order to achieve this, a comparative analysis of factors that could influence the productivity of SASMI was conducted using the exploratory factor analysis. Next was the creation of an integrated network of the systemic factors and lastly, the development of a system dynamics model for insight into the sensitivity of productivity dynamics per factor over a period of 30 months. Multiple regression analysis (MRA) was used to establish the relationship between productivity and its drivers. The results of the MRA, showed that competitiveness (in terms of production strategy, speed, cost, quality monitoring strategy and market share), facility layout and government support with weights of 0.319, 0.249 and 0.153, respectively, are critical to the productivity of SASMI. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Steelmaking

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The role of digital twins in shaping aviation’s circular economy transformation 12

The role of digital twins in shaping aviation’s circular ec...

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12th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2024

作者： Pattan, Akram Bhandigani, Madhura Carpitella, Silvia Abolghasem, Sepideh Department of Manufacturing Systems Engineering and Management California State University Northridge 18111 Nordhoff St NorthridgeCA91330 United States

ISBN: (纸本)9791281988101

This study addresses the challenges and benefits of Digital Twin (DT) applications in the aviation industry. We conducted a systematic literature review and employed a Multi-Criteria Decision Making (MCDM) approach to identify key factors for developing a DT aligned with the circular business model, specifically for supply chain systems, production, and operations optimization. Our analysis synthesizes the major benefits and challenges, which were applied to a real-world case study involving aviation industry stakeholders. The results provide valuable insights for enhancing aviation processes through DT technology. © 2024 The Authors.

关键词： Supply chains

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Design of Navigation Satellite Constellation for Global Lunar PNT

Design of Navigation Satellite Constellation for Global Luna...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Kang, Minjae Jeong, Hojoon Park, Jaeuk Kee, Changdon Interdisciplinary Program in Space Systems Seoul National University Korea Republic of Department of Aerospace Engineering and SNU-IAMD Seoul National University Korea Republic of

ISBN: (纸本)9780936406404

This study presents the design of a lunar navigation satellite constellation to provide global Positioning, Navigation, and Timing (PNT) services, beginning with coverage at the lunar South Pole and gradually expanding to the entire Moon. Building upon an initial Phase 1 constellation that ensures reliable PNT at the South Pole, Phase 2 extends this coverage globally, addressing the challenges of providing continuous navigation services across the lunar surface. The proposed Hybrid Frozen Constellation combines Elliptical Lunar Frozen Orbits (ELFOs) and Circular Frozen Orbits to offer a cost-effective and efficient solution, leveraging the stable South Pole-oriented orbits while minimizing the number of satellites required. The study explores various satellite configurations to achieve best Position Dilution of Precision (PDOP) across the entire lunar surface. By carefully analyzing the geometry and phasing of the orbits, the design aims to maximize navigation performance while reducing the satellite volume. Simulation results confirm that the full constellation achieves over 98% PDOP availability, ensuring PDOP values remain below 6, meeting the GPS standards for reliable navigation. This performance guarantees accurate positioning, even in challenging regions of the lunar surface, and supports both robotic and crewed exploration missions. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Lunar missions

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Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition 8

Super-Resolution Based ROI Enhancement Approaches for Palmpr...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： MacAn, Huseyin Furkan Bingol, Ozkan Turk, Salih Ekinci, Murat Gumushane University Department of Management Information Systems Gümüşhane Turkey Gumushane University Department of Software Engineering Gümüşhane Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331531492

The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes. © 2024 IEEE.

关键词： Biometrics

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OPTIMIZING HYPERPARAMETERS OF RANDOM FOREST MODEL FOR TRAFFIC INCIDENT DETECTION 50

OPTIMIZING HYPERPARAMETERS OF RANDOM FOREST MODEL FOR TRAFFI...

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50th International Conference on Computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Elsahly, O. Abdelfatah, A. Alshraideh, H. Engineering Systems Management PhD Program American University of Sharjah Sharjah United Arab Emirates Department of Civil Engineering American University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781713886952

Traffic incidents are non-recurring events that disrupt normal traffic flow and can have negative impacts on traffic operations, safety, and economy. As a result, there is a growing need for developing effective and reliable Automatic Incident Detection (AID) systems to detect incidents as early as possible to minimize their impacts. Machine Learning models, such as Neural Networks and Random Forests, have been utilized to develop AID models. However, the performance of machine learning models is significantly determined by its hyperparameters that need to be tuned to improve its performance in detecting incidents accurately and quickly. In this study, incident and normal traffic scenarios were generated using traffic simulator data, which is used to train and test a Random Forest incident detection model. The proposed methodology uses VISSIM microsimulation software to generate a diverse dataset of incident and normal conditions scenarios. Traffic parameters such as flow rate, speed, and occupancy at upstream and downstream stations were used as input variables for the model. The Random Forest model was trained and tested through RapidMiner software with 5-fold cross validation. Model performance was enhanced through the model hyperparameters tunning. An improvement of 0.35% in the F-score and 0.15% in the overall accuracy was achieved compared to the default model parameters. Findings of this study suggest that the tunning of hyperparameters of Random Forest models is crucial for developing accurate AID systems. The proposed methodology and optimized hyperparameter values provided by this study can be used as a guideline for developing Random Forest models for incident detection in transportation systems. Overall, this study contributes to the development of accurate and efficient traffic incident detection systems, which could help to mitigate the negative impacts of traffic incidents and improve transportation operations, safety, and economy. © 2023 Computers an

关键词： Software testing

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Optimisation of combined cooling, heating and power (CCHP) systems incorporating the solar and geothermal energy: a review study

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International Journal of Ambient Energy 2022年第1期43卷 42-60页

作者： Asadi, Reza Assareh, Ehsanolah Moltames, Rahim Olazar, Martin Nedaei, Mojtaba Parvaz, Farzad Department of Mechanical Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Energy Engineering Energy Systems Engineering Sharif University of Technology Tehran Iran Department of Chemical Engineering University of the Basque Country Bilbao Spain Department of Management and Engineering University of Padova Padova Italy Department of Mechanical Engineering Semnan University Semnan Iran

Recently, numerous studies have focused on simulation and optimisation of combined cooling, heat, and power (CCHP) systems. This research, from a different perspective, aims to conduct a comprehensive review of the studies performed in the field of solar, geothermal or combined sources, and subsequently analysing the multi-objective evolutionary algorithms to identify the most efficient situation, which satisfy researchers' needs in order to attain a better performance in their ongoing or future research projects. It is worth noting that multi-objective optimisation in these cycles is based on optimising a thermodynamic term (exergy efficiency, thermal efficiency, etc.) and an economic term (the cost of consumables, maintenance costs, etc.), with the thermodynamic functions accounting for energy resources. Results of different studies in terms of design parameters, operation process, optimisation techniques, and other obtained results are compared. Additionally, a summary of typical performance characteristics and cost for the CCHP systems with different prime movers was presented. Results showed that the utilisation of CCHP systems is more efficient than simple single objective production systems. Furthermore, the use of Nano-fluids in solar collectors instead of water increases the heat transfer efficiency and specifically those of SiO2, CuO, AL2O3 and TiO2 lead to high performance. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Multiobjective optimization

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Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xia Jiang Linglingzhi Zhu Taoli Zheng Anthony Man-Cho So Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of ${\mathcal{O}}\left({{\varepsilon ^{ - 4}}}\right)$, surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations.

关键词： Smoothing methods Machine learning algorithms Signal processing algorithms Signal processing Probabilistic logic Numerical simulation Complexity theory Speech processing Optimization Convergence

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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