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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Hou, Bingchang Wang, Dong Xie, Min Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Systems Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9783031494208

Rotating machines are widely used in various domains and its condition monitoring can help to gain more economic profits and prevent unexpected accidents. Vibration signals usually contain sufficient health information, but they are also severely contaminated by heavy background noises including random noise and fixed fundamental vibration components. Therefore, it is of vital importance to effectively extract fault components for effective machine condition monitoring. Considering that the fault components of rotating machines usually exist in some narrow frequency bands, blind bandpass filtering methods such as fast kurtogram, blind deconvolution, and adaptive signal decomposition have been successively proposed. However, it is observed that these methods might be easily influenced by random impulsive noise and low-frequency components. Moreover, these methods also cannot simultaneously extract fault components distributed in different frequency bands. To solve these problems, a new machine fault components extraction methodology based on fully interpretable optimized weights is proposed. The interpretable optimized weights are generated by convex optimization by using healthy and faulty vibration signals, and they could provide sufficient frequency information of fault components. Simulated and experimental vibration signals demonstrate the effectiveness and superiority of the proposed method in solving the aforementioned problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convex optimization

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Influence of Keywords Color on Time Pressure in Synchronous Online Learning for Non-Native English Students: An Eye-tracking Approach 23

Influence of Keywords Color on Time Pressure in Synchronous ...

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7th International Conference on Education and Multimedia Technology, ICEMT 2023

作者： Mariñas, Klint Allen Amblon Sato, Jun Jou, Yung-Tsan Mariñas, Alana Gianne Amblon Department of Industrial and Systems Engineering Chung Yuan Christian University Taiwan Engineering and Engineering Management Mapua University Manila Philippines

ISBN: (纸本)9798400709142

The impact of COVID-19 has forced learning institutions to shift from physical to online methods. Due to the changes in learning environments, instructors and students must adapt to the online learning setup. Synchronous online learning has been utilized as an alternative;however, due to sudden changes, students, especially non-native English students using unfamiliar language, need to follow the course in real-time, which cause time pressure problem this affect learning effectiveness. The current research recruited twenty non-native English university students. Reading materials with text keywords and color assistance was utilized through eye-tracking. A post-test was conducted to analyze the relationship between the eye movement parameters and post-test results in different scenarios using analysis of variance. The experimental results indicated that reading materials with colored keywords could help non-native English students to improve reading comprehension and learning outcomes effectively. The red-colored keywords were significant under no time pressure, while the green-colored keywords were substantial in the context of time pressure. This research could provide important information in designing learning materials in synchronous online learning. © 2023 ACM.

关键词： Students

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Surface modification techniques of membranes to improve their antifouling characteristics:recent advancements and developments

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Frontiers of Chemical Science and engineering 2023年第12期17卷 1837-1865页

作者： Muhammad Tawalbeh Haya Aljaghoub Muhammad Qasim Amani Al-Othman Sustainable and Renewable Energy Engineering Department University of SharjahSharjah 27272United Arab Emirates Sustainable Energy&Power Systems Research Centre RISEUniversity of SharjahSharjah 27272United Arab Emirates Industrial Engineering and Engineering Management Department University of SharjahSharjah 27272United Arab Emirates Department of Chemical and Biological Engineering American University of SharjahSharjah 26666United Arab Emirates Materials Science and Engineering Program College of Arts and SciencesAmerican University of SharjahSharjah 26666United Arab Emirates

Extensive research efforts are currently devoted to developing and improving conventional technologies for water treatment. Membrane-based water treatment technologies are among the most preferred options due to their commercial success, simple operation, low energy and space requirements, and high separation efficiency. Despite the advances made in membrane-based technologies, fouling remains a critical challenge. Fouling occurs upon the accumulation of unwanted impurities on the membrane surface and within the membrane pores which results in a significant decline in the membrane permeate flux. To alleviate the operational challenges from fouling, surface modification to develop antifouling membranes appears to be an effective technique. A comprehensive review of the surface modification techniques for the development of antifouling membranes is provided in this paper. Chemical surface modification techniques (grafting and plasma treatment), physical modification techniques (blending, coating, adsorption, and thermal treatment), and combined physical and chemical modification techniques have been discussed. Moreover, the challenges related to surface modification and the future research directions are addressed.

关键词： fouling antifouling membrane surface modification membrane pretreatment

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Differentially Private Online Federated Learning with Correlated Noise 63

Differentially Private Online Federated Learning with Correl...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (, δ) DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Federated learning

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Filho, Jose Oniram De A. Limaverde Oliveira, Luiz Henrique Bautista, Jaider Fortaleza, Eugenio Munoz, Daniel M. Department of Mechanical Engineering Brasília Brazil University of Brasília Department of Electrical Engineering Brasília Brazil University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Colombia

ISBN: (纸本)9798350374575

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Study on Purchasing Behavior Analysis Method by Comparing Difference in Latent Class Distributions between Membership Stages

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Journal of Japan Industrial management Association 2022年第2期73卷 54-69页

作者： Yang, Tianxiang Yamashita, Haruka Goto, Masayuki Major of Industrial and Management Systems Engineering Graduate School of Creative Science and Engineering Waseda University Japan Department of Information and Communication Sciences Faculty of Science and Technology Sophia University Japan Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan

A marketing policy called the"Membership Stage System" is widely used in retail business. A membership stage provides benefits to customers such as shopping points when a customer's annual cumulative purchase amount exceeds a certain threshold and the customer's stage is raised a level. As a result, the company is not only able to promote the customer's willingness to purchase, but it can also obtain the purchasing history data, thereby enabling high-quality customer analysis. The most fundamental analysis is to infer the difference of purchasing characteristics between member stages and to construct different clustering models for each member stage. However, when the clustering models are learned independently for each membership stage, it is not possible to compare the obtained clusters between membership stages. In this study, we propose a new analytical method and its learning algorithm to analyze differences in cluster distribution between membership stages. Through demonstrating the proposed model applied to an actual data set of purchasing history data on a membership stage system, the effectiveness of our proposal is clarified. © 2022 Japan Industrial management Association. All rights reserved.

关键词： Sales

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Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots

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engineering 2024年第9期40卷 202-210页

作者： Peng Jiang Christian Sonne Wangliang Li Fengqi You Siming You Department of Industrial Engineering and Management Business SchoolSichuan UniversityChengdu 610064China Department of Ecoscience Aarhus UniversityRoskilde DK-4000Denmark Chinese Academy of Sciences Key Laboratory of Green Process and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Systems Engineering Cornell UniversityIthacaNY 14853USA James Watt School of Engineering University of GlasgowGlasgow G128QQUK

Intelligent chatbots powered by large language models(LLMs)have recently been sweeping the world,with potential for a wide variety of industrial *** frontier technology companies are feverishly participating in LLM-powered chatbot design and development,providing several alternatives beyond the famous ***,training,fine-tuning,and updating such intelligent chatbots consume substantial amounts of electricity,resulting in significant carbon *** research and development of all intelligent LLMs and software,hardware manufacturing(e.g.,graphics processing units and supercomputers),related data/operations management,and material recycling supporting chatbot services are associated with carbon emissions to varying *** should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change *** this work,we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent *** on a life-cycle and interaction analysis of these phases,we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related *** anticipating the enormous potential of this advanced technology and its products,we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.

关键词： Large language models Intelligent chatbots Carbon emissions Energy and environmental footprints Life-cycle assessment Global cooperation

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Organizational Transformation for Business Sustainability and Growth in Public Service Corporations 1

Organizational Transformation for Business Sustainability an...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Widiastuti, Rini Rusdiansyah, Ahmad Widyaningrum, Retno Doctoral of Management Technology Interdisciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Industrial and Systems Engineering Faculty of Industrial Technology and Systems Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

Public service companies are essential in society, providing reliable, affordable, high-quality services focused on customer satisfaction while meeting stakeholder expectations and regulations for sustainable business growth. Strategy innovation is necessary to achieve these goals. In Indonesia, public service companies are particularly consolidated by state-owned enterprises (SOEs), which the government fully owns. SOEs innovate by forming holding companies, which the government sees as the most effective way to create synergy. Studies show that 60%-70% of organizational changes fail, highlighting the importance of successful transformation in public companies. Ensuring smooth transitions with minimal disruption while maintaining service quality is crucial for good organizational performance in public service corporations. Efficiency in transformations and transitions can be achieved through change management. This study evaluates various change management factors and frameworks, examining their impact on organizational performance using the 7s McKinsey Change Model. The research involves a literature review, surveys, and descriptive statistical analysis. The results indicate that change management factors in the McKinsey model influence organizational performance. However, the optimal change management model for public companies remains undetermined, presenting opportunities for further research. © 2024 IEEE.

关键词： Service industry

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Ontology-Based Functional Analysis of an Aerospace System

Ontology-Based Functional Analysis of an Aerospace System

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Johnson, Hamilton E. Sureshkumar, Mayuranath Kannan, Hanumanthrao Thomas, L. Dale Mechanical and Aerospace Engineering Department University of Alabama in Huntsville HuntsvilleAL35899 United States Industrial and Systems Engineering and Engineering Management Department University of Alabama in Huntsville HuntsvilleAL35899 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

systems engineering often employs "-ilities", such as affordability, to represent various aspects of a system. While these "-ilities" are widely used, exact definitions for the terms can vary between individuals. While most definitions of affordability include some cost notion, only a subset balance cost against a benefit. Additionally, some definitions treat affordability as a binary constraint, a system either is affordable or it is not. Others define it in more continuous terms which could be treated as an objective to be maximized. While multiple definitions can be readily observed from different sources, the degree to which definitions for a concept such as affordability differs within a single organization has not been thoroughly studied. This paper presents results from a survey completed by 172 employees of NASA Marshall Space Flight Center. Definitions of affordability from each participant are categorized ac-cording to how cost and benefit notions are used in the definition. Eight different categories of definitions were observed;however, 86 of the 172 definitions were categorized as involving a cost constraint with no clear benefit component. Due to the variety of definitions of affordability observed, practitioners should be cautious about assuming what stakeholders and fellow decision makers mean when using the term affordability. The use of affordability definitions categorized as involving only a cost constraint may result in the elimination of alternatives in a design space, rather than driving decision makers to continuously improve affordability. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence 41

EMC2: Efficient MCMC Negative Sampling for Contrastive Learn...

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41st International Conference on Machine Learning, ICML 2024

作者： Yau, Chung-Yiu Wai, Hoi-To Raman, Parameswaran Sarkar, Soumajyoti Hong, Mingyi Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Amazon Web Services United States Department of Electrical and Computer Engineering University of Minnesota United States

A key challenge in contrastive learning is to generate negative samples from a large sample set to contrast with positive samples, for learning better encoding of the data. These negative samples often follow a softmax distribution which are dynamically updated during the training process. However, sampling from this distribution is non-trivial due to the high computational costs in computing the partition function. In this paper, we propose an Efficient Markov Chain Monte Carlo negative sampling method for Contrastive learning (EMC2). We follow the global contrastive learning loss as introduced in (Yuan et al., 2022), and propose EMC2 which utilizes an adaptive Metropolis-Hastings subroutine to generate hardness-aware negative samples in an online fashion during the optimization. We prove that EMC2 finds an O(1/√T)-stationary point of the global contrastive loss in T iterations. Compared to prior works, EMC2 is the first algorithm that exhibits global convergence (to stationarity) regardless of the choice of batch size while exhibiting low computation and memory cost. Numerical experiments validate that EMC2 is effective with small batch training and achieves comparable or better performance than baseline algorithms. We report the results for pre-training image encoders on STL-10 and Imagenet-100. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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