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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Huynh, Phat K. Huynh, Phi Yadav, Om P. Le, Chau Pirim, Harun Le, Trung Q. Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 419 GreensboroNC27411 United States School of Industrial Engineering and Management International University - VNU-HCM Quarter 6 Linh Trung Ward Thu Duc City Ho Chi Minh City721400 Viet Nam Department of Civil Engineering Technology & Construction Management University of North Carolina at Charlotte 9201 University City Boulevard CharlotteNC28223 United States Department of Industrial and Manufacturing Engineering North Dakota State University 1315 Centennial Blvd FargoND58102 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 E. Fowler Avenue TampaFL33620 United States

ISBN: (纸本)9798350367744

Smart power grids, pivotal to modern infrastructure, face increased vulnerability due to their complex and interconnected nature, often leading to disruptions that can result in widespread power outages and significant economic losses. Traditional grid monitoring systems frequently fall short in both predicting and mitigating these disruptions, creating a critical need for more advanced predictive methods that can foresee and interpret the progression of these anomalies into distinct failure modes. This paper introduces a transformer-based prognostic model based on Phasor Measurement Unit (PMU) data to enhance the predictive monitoring of smart grids. The method capitalizes on the advanced capabilities of transformer models to manage high-dimensional time-series data with complex dependencies. The methodology, validated rigorously against historical data, progresses through three main phases: (1) PMU data acquisition and preprocessing, (2) PMU-based feature engineering, and (3) prognostic modeling. In the data acquisition and preprocessing phase, high-resolution PMU data were collected, encompassing parameters such as voltage and current magnitudes, frequency, and phase angles. Next, we extracted 43 distinct features across four temporal windows, which were essential in characterizing the dynamic behavior of the grid. The prognostic modeling phase employed a transformer architecture with a sophisticated self-attention mechanism that efficiently handles the sequential PMU data, identifying subtle patterns indicative of potential grid failures. The prognostic model had a superior performance in predicting different types of grid failures-severe weather, lightning, and failed AC circuit equipment, which achieved an accuracy of up to 84.07% with low variability using five-fold cross-validation. This prognostic model enhances the predictive capabilities of smart grid monitoring systems and offers a robust framework for proactive management of grid health, potentially red

关键词： Phasor measurement units

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Enhancing energy efficiency through combined PEMFC and MH systems and advanced exhaust management strategies

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International Journal of Hydrogen Energy 2025年

作者： Miao, Shan Ezawa, Tomoya Honda, Koya Harano, Koki Sumita, Masami Katayama, Noboru Dowaki, Kiyoshi Department of Industrial and Systems Engineering of Science and Technology Tokyo University of Science Chiba Japan Department of Electrical Engineering Graduate of Science and Technology Tokyo University of Science Chiba Japan

Proton Exchange Membrane Fuel Cells (PEMFC) generate substantial heat during operation. This heat constitutes over 40% of the total energy produced from the cells' hydrogen fuel, making reusing this energy a focal point of research. An innovative system integrating PEMFC with Metal Hydrides (MH) was introduced. It utilizes the waste heat from the PEMFC to enhance hydrogen release from the MH, thereby improving overall energy efficiency. Two exhaust management strategies and experimental methods were designed to evaluate their impact on PEMFC operational efficiency and the hydrogen desorption rate from MH tanks. Temperature, pressure, and hydrogen flow rates were measured across various configurations. The results indicate that these strategies significantly stabilize operating temperatures and mitigate pressure drops. Additionally, under the new plan, the waste heat discharge rate increased from a baseline of 8.8%–10.4%, and then to 14.4%, demonstrating a marked improvement in thermal efficiency. The effective absorption rate of waste heat discharged by the FC into the MH tanks reached 8.3%, leading to a significant 13% increase in the hydrogen release rate. These improvements emphasize the effectiveness of the proposed strategies in alleviating temperature and pressure fluctuations while significantly enhancing the system's overall energy utilization. © 2025 The Authors

关键词： Energy efficiency

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Learning from failure - Development of a Culture of Failure Scale for Higher Education

Learning from failure - Development of a Culture of Failure ...

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2022 IEEE IFEES World engineering Education Forum - Global engineering Deans Council, WEEF-GEDC 2022

作者： Malschutzky, Michael M. Gurganus, Jamie R. Department of Management Sciences Germany Mechanical Engineering and Graduate School University of Maryland Baltimore County Engineering and Computing Education Program Baltimore United States

ISBN: (纸本)9781665475280

Benefits of a failure friendly culture, e.g., learning from failure, are widely known in occupational settings. Validated scales have been developed to measure organizational failure culture and individuals' mind-sets on failure. While research on learning from failure exists in secondary education, scales for higher education are lacking. To support both higher education engineering instructors and students, this contribution aims to develop a Culture of Failure Scale to assess students' and lecturers' handling of failures in engineering higher education settings. This data-driven approach helps inform instructors about aspects of educational settings that are safe and conducive to learning and supports their students to develop a mind-set where failures are seen as a source for improvements and learning. Exemplary application of the developed scale is shown by assessing the failure culture in different groupings, e.g., gender, between two participating German and US institutions. © 2022 IEEE.

关键词： Failure (mechanical)

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Modular Safety-Critical Control of Legged Robots

arXiv

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arXiv 2023年

作者： Tosun, Berk Samur, Evren Graduate Program in Systems and Control Engineering Bogazici University Istanbul Turkey Department of Mechanical Engineering Bogazici University Istanbul Turkey

Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive signals. A novel deep-learning model utilizing an efficient transformer architecture is used for the terrain estimation. A quadratic program combines the terrain estimations with inverse dynamics and a novel exponential control barrier function constraint to filter and certify nominal control signals. The result is an optimal controller that acts as a filter. The filtered control signal allows safe locomotion of the robot. The resulting approach is generalizable, and could be transferred with low effort to any other legged system. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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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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Clustering of Time-Varying Graphs Based on Temporal Label Smoothness

arXiv

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arXiv 2023年

作者： Fukumoto, Katsuki Yamada, Koki Tanaka, Yuichi Wai, Hoi-To Graduate School of Bio-Applications and Systems Engineering Tokyo University of Agriculture and Technology Japan Department of Electrical Engineering Tokyo University of Science Japan Graduate School of Engineering Osaka University Japan Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in many science and engineering fields including signal processing, machine learning, and data mining. Although most existing studies focus on the clustering of nodes in static graphs, we often encounter time-varying graphs for time-series data, e.g., social networks, brain functional connectivity, and point clouds. In this paper, we formulate a node clustering of time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels. We solve the problem with a primal-dual splitting algorithm. Experiments on synthetic and real-world time-varying graphs are performed to validate the effectiveness of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Velocity Estimation of an Underwater Vehicle Using Doppler Shift of Positioning Signals

Velocity Estimation of an Underwater Vehicle Using Doppler S...

引用

2025 IEEE Underwater Technology, UT 2025

作者： Tsuchiya, Atsushi Ebihara, Tadashi Wakatsuki, Naoto Yoshihara, Tohru University of Tsukuba Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan Aomi Construction Co. Ltd. Technology Development Department Tokyo Japan

ISBN: (纸本)9798331534189

This paper proposes a method for estimating the moving velocity of underwater vehicles by utilizing the Doppler shift in positioning signals employed for time-of-arrival (ToA) acoustic positioning. The Doppler shift generated in the received signal was measured by resampling, and the moving velocity in global coordinates was estimated by finding the least-squares solution using the Doppler shift generated in the direct wave and the position measured by ToA positioning. Field experiments conducted in a real marine environment demonstrated the feasibility and effectiveness of the proposed velocity estimation method. © 2025 IEEE.

关键词： Underwater acoustics

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Understanding preferences for autonomous trucks functions in China: Insights from drivers and organizational buyers

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Transport Policy 2025年 170卷 110-119页

作者： Fu, Hanlong Fu, Xiaowen Ye, Shi Wang, Kun Chen, Tiantian Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Behaviour and Knowledge Engineering Research Centre Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Korea Republic of Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

The transportation industry is undergoing a significant transformation as it integrates advanced driver assistance systems (ADAS) technologies. This shift is particularly important in the trucking industry. Driver assistance technologies offer a promising solution for improving safety and reducing traffic accidents. However, the trucking industry lags significantly behind passenger vehicles in the maturity and penetration rate of such technologies. This study uses a stated preference survey to explore the purchasing preferences for ADAS functions among truck drivers and organization buyers in China. Our findings show that truck drivers with a safe driving history prioritize reliability and assistance features such as automatic emergency braking, adaptive cruise control, and lane-centering control. In contrast, drivers with a record of unsafe driving favor more advanced ADAS functions, such as city or highway navigation on autopilot, owing to their ability to alleviate driving stress. Buyers from organizations, compared with individual truck drivers, are more averse to the additional costs of ADAS technologies, while larger companies seem more willing to invest in autonomous trucks than are smaller businesses and individuals. However, top management teams remain cautious, reflecting a lack of confidence in the operational and safety benefits of the current technology at Level 2 autonomy. Resistance to adopting autonomous trucks is also stronger among male (vs. female) drivers and older drivers, who comprise a large segment of the domestic market. The study recommends that autonomous vehicle system providers and governments prioritize active safety functions to further improve safety. Furthermore, it is suggested that extensive training and trials be provided to increase trust and confidence in autonomous truck technologies among industry stakeholders. © 2025 Elsevier Ltd

关键词： Highway accidents

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