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International Carpathian Control Conference (ICCC)

作者： Vinícius Berno Da Silva Paulo César Barros Rodrigues Murillo Ferreira Dos Santos Paolo Mercorelli Milena Faria Pinto Wolmar Araújo Neto Department of Electroelectronics CEFET-MG Leopoldina Brasil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Germany Graduate Program in Electrical Engineering CEFET-RJ Rio de Janeiro Brasil Federal University of Espírito Santo UFES Vitória Brazil

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Gain scheduling has been successfully applied in many different areas due to the positive results achieved when applied to nonlinear control. Its utilization enables the controller to respond rapidly to changes in operating conditions. This study demonstrates the application of gain scheduling to a SMAR didactic level system to implement adaptive control. The results exhibited a strong response with minor errors using a Proportional and Integral (PI) controller, which facilitated the development of two generic equations for determining the values of K p (proportional gain) and K i (integral gain) based on the desired set point.

关键词： Adaptive scheduling PI control Liquids System dynamics Integral equations Noise Gain measurement

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Techno-economic and life-cycle assessment of electrochemical captured CO2 conversion process in monoethanolamine

Techno-economic and life-cycle assessment of electrochemical...

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2022 AIChE Annual Meeting

作者： Lee, Suhyun Choi, Woong Kim, Jae Hyung Hwang, Yun Jeong Na, Jonggeol Ewha Womans University School of Chemical Engineering and Materials Science Korea Republic of Ewha Womans University Graduate program in System Health Science and Engineering Korea Republic of Clean Energy Research Center Korea Institute of Science and Technology Korea Republic of Seoul National University School of Chemistry Korea Republic of Korea Republic of Ewha Womans University Dept. of Chemical Engineering & Material science Advanced Manufacturing Systems Laboratory Korea Republic of

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing systems

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IFAC-PapersOnLine 2022年第2期55卷 548-553页

作者： Nicolas Nebelung Mario D.S. de Oliveira Santos Sofia T. Helena Athon F.C.S. de Moura Leite Matheus B. Canciglieri Anderson L. Szejka Control and Automation Engineering Department Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Junior Scientific Initiation Program at the Nossa Senhora de Medianeira School and Pontifical Catholic University of Paraná Curitiba – Paraná Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

关键词： Industry 4.0 Smart System Artificial Intelligence Machine Learning Ontology Machine Failure Forecast

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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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Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

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Strategic legislation for the promotion of university–industry collaborations: a case study of Taiwan

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Journal of Technology Transfer 2025年第1期50卷 304-344页

作者： Truong, Thanh Hue Lin, Bou-Wen Tung, Ching-Pin International Degree Program in Climate Change and Sustainable Development National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Research Center for Future Earth National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Graduate Institute of Business Administration College of Management National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan National Applied Research Laboratories No. 106 Sec. 2 Heping E. Rd. Da’an Dist. Taipei City106214 Taiwan Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan

The strategic utilization of technology to enhance industrial competitiveness and product quality is pivotal for economic growth. As universities own more inventions, collaborative efforts between industry and academia become essential for regional competitiveness and overall economic advancement. This study explores Taiwan’s systematic approach to upgrading its infrastructure, emphasizing the significance of University–Industry Collaborations (UIC) in realizing Industry 4.0. Government strategies and policy decisions emerge as key drivers in fostering successful collaborative projects between industry and university teams. The relaxation of restrictions, particularly related to part-time teachers and technology transfer, along with the promotion of corporate programs, effective use of public funding, and the development of an innovative digital platform system for UIC, collectively contribute to establishing a robust university–industry cooperation mechanism. This mechanism has contributed to Taiwan’s move towards new digital advances in innovation and entrepreneurship, fostering industrial leadership and efficient governance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Technology transfer

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Rainfall Frequency Analysis Using Event-Maximum Rainfalls – an Event-Based Mixture Distribution Modeling Approach

SSRN

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SSRN 2023年

作者： Cheng, Ke-Sheng Chen, Bo-Yu Lin, Teng-Wei Nakamura, Kimihito Ruangrasamee, 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 an 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. All rights reserved.

关键词： Poisson distribution

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Analysis of Non-fatal Occupational Accidents in a Ready-mixed Concrete Company

Analysis of Non-fatal Occupational Accidents in a Ready-mixe...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Sario, Maria Cristina Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Sittiwatethanasiri, Thaninrat Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

The production and delivery of ready-mixed concrete consist of several activities which expose employees to considerable safety hazards that lead to occupational accidents. The study aimed to determine the significant factors contributing to non-fatal occupational accidents in the ready-mixed concrete industry in Qatar. A total of 84 non-fatal occupational accidents were collected from a ready-mixed concrete company in Qatar. A coding scheme was developed based on available literature and the company's existing classification to analyze each accident in terms of employee, injury, and accident characteristics. Cross-tabulation analysis was used to determine the associations between categorical factors. Frequency distribution showed that regular employees aged between 25 and 44 years old were mostly involved in accidents, with hand/arm and leg/foot being mostly injured. The most common accident types were struck by an object (44%), slip, trip or fall on the same level (23.8%), and caught in between objects (17.9%). The majority of work-related accidents (71%) could be attributed to unsafe behavior, particularly due to improper use of PPE. Strong significant associations were observed between activity type and accident root cause, as well as injured body type and accident root cause. The significant factors contributing to non-fatal occupational accidents were determined through the study. The insights obtained from this study could be used by the company in designing a comprehensive safety management program with the goal of driving occupational injuries to a lower level. © 2022 IEEE.

关键词： Accidents

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