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Proceedings of the Winter Simulation Conference

作者： Yining Huang Hong Wan Xi Chen Operations Research Graduate Program North Carolina State University Raleigh USA Department of Industrial and Systems Engineering North Carolina State University Raleigh USA Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg USA

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.

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Product lifecycle management and sustainable development in the context of industry 4.0: A systematic literature review 28

Product lifecycle management and sustainable development in ...

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28th ISTE International Conference on Transdisciplinary engineering, TE 2021

作者： De Oliveira, Michele Marcos Andreatta, Luisa Geraldi Stjepandić, Josip Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba - Paraná Brazil

ISBN: (纸本)9781643682082

The Fourth Industrial Revolution and its disruptive technologies are emerging massively. With various motivations for its implementation such as elevation of speed, reducing costs, mitigating errors, and other different factors, more and more technology are being employed to solve business problems and increase profit. Through the years, product lifecycles have been shorted as a good reason to reduce production costs and give to consumers the experience of replacing goods in shorter periods. This linear cycle of 'produce-use-dispose' reached a massive success in the last decades, but have started to show lack of sustainability for consumer and industry. This work aims to conduct a Systematic Literature Review about the future challenges for Product Lifecycle Management and Sustainable Development, considering the context of Industry 4.0. The expected outcome for this study is to find elements that can support the use of 4.0 technologies in Product Lifecycle Management with a strong background for Sustainable Development. © 2021 The authors and IOS Press.

关键词： Industry 4.0

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Classification of Multivariate Time Series Signals Using Self-Supervised Representation Learning for Condition Monitoring

Classification of Multivariate Time Series Signals Using Sel...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Koichi Sumiya Sumika Arima Doctoral Program in Policy and Planning Sciences University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

In the analysis of real-world data, two significant challenges often arise: high-dimensional signals and their temporal interactions. To address these issues and identify transitions in process conditions through end-to-end learning, we propose a self-supervised representation learning framework that conceptualizes signals as images. This straightforward approach classifies imaged signals as time-specific conditions by maximizing mutual information within a domain-specific feature space. We applied this methodology to two labeled open datasets and one unlabeled real-world process dataset, yielding promising results.

关键词： Representation learning Furnaces Time series analysis Self-supervised learning Glass Medical services Semiconductor device manufacture Turning Wool Mutual information

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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

Teaching Control Theory using Mobile Robot Obstacle Followin...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana J. Bautista Jose Mendoza-Peñaloza L. H. Oliveira D. A. Mendes Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

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Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Data-Driven Approaches to Detecting Misdeliveries in Truck L...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hidaka, Ayumu Shin, Ryota Tsuchiya, Atsushi Nakabayashi, Norihiko Okada, Yukihiko Shida, Yohei University of Tsukuba Master's Program in Service Engineering Tsukuba Japan University of Tsukuba Doctoral Programs in Policy and Planning Sciences Tsukuba Japan Yamato Transport Co. Ltd. Minato Japan University of Tsukuba Yamato Transport Co.Ltd. Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan

ISBN: (纸本)9798350324457

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages. © 2023 IEEE.

关键词： anomaly detection GPS misdelivery trajectory method transport data truck logistics

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Space Division Multiplexing Optical Coherence Tomography for Wide-field Angiographic Imaging of the Human Retina

Space Division Multiplexing Optical Coherence Tomography for...

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2024 Frontiers in Optics, FiO 2024

作者： Adkins, Aaron J. Hao, Senyue Ren, Chao Song, Andrew Zhou, Chao Department of Biomedical Engineering Washington University in St. Louis St. LouisMO United States Imaging Science PhD Program Washington University in St. Louis St. LouisMO United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States

We have constructed a parallelized 4-channel multi-beam optical coherence tomography system capable of achieving high speed and wide field of view angiographic imaging of the retina. © Optica Publishing Group 202... 详细信息

关键词： Optical coherence tomography

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A New Tree Structure for Local Diagnosis

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Journal of Information Science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information Science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Distributionally Robust Performative Optimization

arXiv

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arXiv 2024年

作者： Jia, Zhuangzhuang Wang, Yijie Dong, Roy Hanasusanto, Grani A. Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States Graduate Program in Operations Research and Industrial Engineering The University of Texas Austin United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States

In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework facilitates safe decision-making in scenarios with incomplete information about the underlying decision-dependent distributions, relying instead on accessible reference distributions. To tackle the challenge of decision-dependent uncertainty, we introduce an algorithm named repeated robust risk minimization. This algorithm decouples the decision variables associated with the ambiguity set from the expected loss, optimizing the latter at each iteration while keeping the former fixed to the previous decision. By leveraging the strong connection between distributionally robust optimization and regularization, we establish a linear convergence rate to a performatively stable point and provide a suboptimality performance guarantee for the proposed algorithm. Finally, we examine the performance of our proposed model through an experimental study in strategic classification. © 2024, CC BY.

关键词： Optimization

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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A Review on Active Customers'Participation in Smart Grids

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Journal of Modern Power systems and Clean Energy 2023年第1期11卷 3-16页

作者： Konrad Schmitt Rabindra Bhatta Manohar Chamana Mahtab Murshed Ilham Osman Stephen Bayne Luciane Canha Electrical and Computer Engineering Department Texas Tech UniversityLubbockTX79409USA Wind Energy Program Texas Tech UniversityLubbockTX79409USA Electromechanical and Power Systems Department Federal University of Santa MariaSanta MariaRS97105-900Brazil Design Release Engineer at Electrification and Power conversion General MotorsGM Tech Center RoadWarrenMI48092USAl IEEE

Industrial,commercial,and residential facilities are progressively adopting automation and generation *** having flexible demand and renewable energy generation,traditional passive customers are becoming active participants in electric power system *** profound coordination among grid operators and active customers,the facilities’capability for demand response(DR)and distributed energy resource(DER)management will be valuable asset for ancillary services(ASs).To comply with the increasing demand and flexible energy,utilities urgently require standards,regulations,and programs to efficiently handle load-side resources without trading off stability and *** study reviews different types of customers’flexibilities for DR,highlighting their capabilities and limitations in performing local ancillary services(LASs),which should benefit the power grid by profiting from it through incentive *** financial incentives and techniques employed around the world are presented and *** potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

关键词： Active distribution network(ADN) ancillary services energy management flexible demand resiliency

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