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arXiv 2023年

作者： Lee, Duksang Overman, William Lee, Dabeen Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of Graduate School of Business Stanford University StanfordCA94305 United States

This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function and stochastic non-stationary reward and resource consumption functions. We propose the observe-then-decide regime and improve the existing decide-then-observe regime, while the two settings differ in how the observations and feedback about the reward and resource consumption functions are given to the decision-maker. We develop an online dual mirror descent algorithm that achieves near-optimal regret bounds for both settings. For the observe-then-decide regime, we prove that the expected regret against the dynamic clairvoyant optimal policy is bounded by (Equation presented) where ρ ∈ (0, 1) is the budget parameter, H is the length of the horizon, S and A are the numbers of states and actions, and T is the number of episodes. For the decide-then-observe regime, we show that the regret against the static optimal policy that has access to the mean reward and mean resource consumption functions is bounded by (Equation presented) with high probability. We test the numerical efficiency of our method for a variant of the resource-constrained inventory management problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

Advancing Precision Aquaculture Through Big Data Analytics a...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Bravo, Francisco Amorim, Joana Amirkandeh, Melika Besharati Bodorik, Peter Cerqueira, Vitor Gomes, Nuno R.C. Korus, Jennie Oliveira, Mariana Parent, Marianne Pimentel, João Reilly, Derek Sclodnick, Tyler Grant, Jon Filgueira, Ramón Whidden, Christopher Universidad Adolfo Ibañez Facultad de Ingenieria y Ciencias Santiago Chile Dalhousie University Faculty of Computer Science Halifax Canada University of Porto Faculty of Engineering Portugal Barcelona Spain Innovasea Marine Systems Bedford Canada Charlottetown Canada Dalhousie University Department of Oceanography Halifax Canada Marine Affairs Program Dalhousie University Halifax Canada

ISBN: (纸本)9798331540081

The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

关键词： Aquaculture

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Low Carbon Charging Schedule of Electric Buses: Case Study in Denmark and Japan

Low Carbon Charging Schedule of Electric Buses: Case Study i...

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15th International Conference on Power, Energy, and Electrical engineering, CPEEE 2025

作者： Osaki, Fumiaki Pastorelli, Francesco Santos, David Menchaca Zepter, Jan Martin Marinelli, Mattia Iino, Yutaka Fujimoto, Yu Hayashi, Yasuhiro Graduate School of Advanced Science and Engineering Waseda University Tokyo Japan Department of Wind and Energy Systems Roskilde Denmark Advanced Collaborative Research Organization for Smart Society Waseda University Tokyo Japan

ISBN: (纸本)9798331520090

This study examines regional differences in time-varying carbon dioxide (CO2) emissions from power systems in Denmark and Japan, influencing on the low-carbon charging schedules of electric buses in public transportation systems. The time-varying CO2 emissions differ significantly due to variations in the penetration rates of variable renewable energy (VREs) sources, particularly wind power. Numerical experiments reveal that under an optimal charging schedule, total CO2 emissions in Denmark are 81% lower in summer and 71% in winter compared to Japan when chargers are available only at their depot. Additionally, distributing chargers across all bus terminals ensures the feasibility of transportation in winter. Furthermore, distributed chargers at bus terminals have potential to reduce emissions by up to 4% further by expanding the flexibility of charging times to align with VRE utilization throughout the day. This international comparison highlights the critical importance of optimizing charging schedules in high VRE penetration systems, where CO2 emissions exhibit significant daily fluctuations. © 2025 IEEE.

关键词： Mass transportation

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Blockchain-based Edge Computing Service with Dynamic Entry and Exit Mechanism

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IEEE Transactions on Mobile Computing 2025年

作者： He, Qiang Feng, Zheng Fang, Hui Wang, Xingwei Zhao, Liang Yu, Keping Choo, Kim-Kwang Raymond Northeastern University College of Medicine and Biological Information Engineering China Shanghai University of Finance and Economics Research Institute for Interdisciplinary Science School of Information Management and Engineering China Northeastern University College of Computer Science and Engineering Shenyang110169 China Shenyang Aerospace University College of Computer Science and Engineering Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Gaussian Process Port-Hamiltonian systems: Bayesian Learning with Physics Prior

arXiv

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arXiv 2023年

作者： Beckers, Thomas Seidman, Jacob Perdikaris, Paris Pappas, George J. Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States graduate program in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct. We propose Gaussian Process Port-Hamiltonian systems (GP-PHS) as a physics-informed Bayesian learning approach with uncertainty quantification. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Hamiltonians instead of a single point estimate. Due to the underlying physics model, a GP-PHS generates passive systems with respect to designated inputs and outputs. Further, the proposed approach preserves the compositional nature of Port-Hamiltonian systems. © 2023, CC BY-NC-ND.

关键词： Hamiltonians

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An overview on GNSS carrier-phase time transfer research

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Science China(Technological Sciences) 2020年第4期63卷 589-596页

作者： ZHANG Ming Lü JinHu BAI ZhengDong JIANG ZhiQi CHEN BoBo The Key Laboratory of Systems and Control Institute o f Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Institute of Geomatics of Tsinghua University Beijing 100084China Management and Department of Energy and Resources Engineering College of EngineeringPeking UniversityBeijing 100871China

Global navigation satellite system(GNSS)carrier phase observations are two orders of higher accuracy than pseudo-range observations,and they are less affected by multipath *** a result,the time transfer accuracy can reach 0.1 ns,and the frequency transfer stability can reach 1×10^-15 with carrier phase(CP)method,therefore CP method is considered the most accurate and promising time transfer *** focus of this paper is to present a comprehensive summary of CP method,with specific attention directed toward day-boundary clock jump,ambiguity resolution(AR),multi-system time transfer and real-time time ***-boundary clock jump is essentially caused by pseudo-range *** approaches were proposed to solve the problem,such as continuously processing strategy,sliding batch and bidirectional filtering methods which were compared in this ***,researches on AR in CP method were *** scholars attempted to fix the single-difference ambiguities to improve the time transfer result,however,owing to the uncalibrated phase delay(UPD)was not considered,the current studies on AR in CP method were still ***,because four GNSS systems could be used for time-transfer currently,which was helpful to increase the accuracy and reliability,the researches on multi-system time transfer were ***’s more,real-time time transfer attracted more attention nowadays,the preliminary research results were presented.

关键词： carrier phase(CP) time transfer day-boundary clock jump ambiguity resolution(AR) multi-system time transfer real-time time transfer

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Two-phase cost-sensitive-learning-based framework on customer-side quality inspection for TFT-LCD industry

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Journal of Intelligent Manufacturing 2024年

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu30013 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

The Covid-19 outbreak in 2020 boosted the stay-at-home economy, causing a surge in electronics industry demand, especially benefiting the LCD panel sector. However, as the pandemic situation improved, countries revised policies, leading to the gradual discontinuation of remote work arrangements in various industries. This resulted in declining dividends for the stay-at-home economy. The decreased demand created intense competition within the TFT-LCD industry, urging panel companies to prioritize product quality enhancement to meet customer expectations. Panel quality inspection heavily relied on manual labor, causing varying inspection levels due to subjective judgments. Understanding and aligning with customer expectations regarding product quality inspections became imperative. Identifying defective products during inspection led to additional costs for the companies. Balancing customer product quality requirements and re-inspection costs became crucial for optimal benefits. This study addresses the binary classification problem of customer-side quality inspection through cost-sensitive learning. The predictive model considers panel process yield, production history, customer feedback, inspection capacity constraints, and cost minimization to predict panel quality as accepted or defective. To tackle the highly imbalanced data, a two-phase cost-sensitive-learning-based framework is proposed, combining data preprocessing methods and models, while considering re-inspection capacity constraints and costs to enhance accuracy. The model’s evaluation uses key performance indicators like AUC and G-mean. Actual inspection cost and defective parts per million (DPPM) are calculated based on the company’s practical assessment. Two products are used for experimentation to validate the proposed model, demonstrating over 50% reduction in inspection cost and over 10% improvement in DPPM. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Spr

关键词： Inspection

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A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

A DEA-CCR Model Application in Clustered Stocks Portfolio wi...

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IEEE International Conference on Industrial engineering and engineering management

作者： M.M. Navarro M. N. Young Y.T. Prasetyo J.L. Camino B.B. Navarro V.T. Ramos School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Department of Industrial Engineering Technological Institute of the Philippines Quezon City Philippines International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Civil Engineering Technological Institute of the Philippines Quezon City Philippines Department of Electrical Engineering Technological Institute of the Philippines Quezon City Philippines

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform.

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Performance Assessment of Food Logistics Service Under SERVQUAL Model Using Analytic Hierarchy Process Approach

Performance Assessment of Food Logistics Service Under SERVQ...

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IEEE International Conference on Industrial engineering and engineering management

作者： Poonyawat Kusonwattana Yogi Tri Prasetyo Jui-Hao Liao Omar Paolo Benito Michael Nayat Young Nattakit Yuduang Thanatorn Chuenyindee Satria Fadil Persada School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines International Bachelor Program in Engineering Yuan Ze University Chung-Li Taiwan Department of Industrial Engineering and Management Yuan Ze University Chung-Li Taiwan Logistics and Supply Chain Management Program Faculty of Business Administration Suvarnabhumi Institute of Technology Samut Prakan Thailand Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok Thailand Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

The food supply chain at present is important to the human and there have a lot of transportation modes to distribute food from manufacturer to customer also business to business. The customers there need fresh food and the providers always find the best way in logistics that send their food to customers with the best quality and fresh, that is why the cold chain of logistics is popular and needs to improve more for the customers. However, there is no clear way to improve the performance and assessment of food logistics service, the aim of this study focused on service quality of food logistics and identify a set of service quality dimensions that developed from the SERVQUAL model for measuring their logistics process after that researcher prioritized the dimensions and sub-dimensions by Analytic Hierarchy Process (AHP) and finalize the local weight and global weight from AHP result. Finally, this research has shown the providers and the customers were prioritized for punctuality in food logistics service. It should be concern and improvement about punctuality in a food logistics service at the same time, the worst dimension of the provider's perspective is tangible and the customer perspective is empathy, that they did not pay attention it should be improved later.

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Blockchain based Public Auditing Outsourcing for Cloud Storage 27

Blockchain based Public Auditing Outsourcing for Cloud Stora...

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27th IEEE International Conference on Parallel and Distributed systems, ICPADS 2021

作者： Lin, Yangfei Li, Jie Kimura, Shigetomo Zhang, Yongbing Ji, Yusheng Yang, Yang Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Tsukuba Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan School of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665408783

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user. © 2021 IEEE.

关键词： Smart contract

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