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Online health-aware energy management strategy of a fuel cell hybrid autonomous mobile robot under startup-shutdown condition

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Benarfa, Ghofrane Amamou, Ali Kelouwani, Sousso Hebert, Marie Zeghmi, Lotfi Jemei, Samir Univ Quebec Trois Rivieres Hydrogen Res Inst Dept Elect & Comp Engn Trois Rivieres PQ G8Z 4M3 Canada Univ Quebec Trois Rivieres Hydrogen Res Inst Dept Mech Engn Trois Rivieres PQ G8Z 4M3 Canada Univ Franche Comte UTBM CNRS FEMTO STFCLAB F-25030 Belfort France

In the age of Industry 4.0, the automation of industrial processes is essential for enhancing efficiency, productivity, and flexibility. Autonomous mobile robots are pivotal in this transformation, particularly in material handling and logistics operations within complex industrial environments. Fuel cell hybrid autonomous mobile robots, a type of autonomous mobile robot that functions with hybridization of battery and fuel cell, offer significant advantages in operational efficiency and sustainability. However, the commercialization of these vehicles is impeded by the limited lifespan of fuel cells and the adverse effects of frequent startup-shutdown cycles, which lead to significant fuel cell degradation and reduced operational efficiency. This study addresses these challenges by presenting an innovative, health-aware energy management strategy tailored for fuel cell hybrid autonomous mobile robots. The proposed strategy aims to balance hydrogen consumption with fuel cell degradation through a comprehensive two-step approach. First, the offline module employs digital modeling combined with a Markov Decision process to generate long-term power profiles. This step includes the use of Dynamic Programming to optimize power distribution, ensuring an efficient energy management strategy. Additionally, a transformer neural network is trained on this optimized data to accurately predict the fuel cell's power output. In the online step, a Model Predictive control technique is utilized to dynamically track the fuel cell's power output based on real-time predictions from the trained transformer model. This enables the system to adapt to changing operational conditions, maintaining optimal performance and extending the fuel cell's lifespan. Our comparative analysis, based on simulations and experimental tests conducted in a controlled laboratory environment, demonstrates that this approach enhances both fuel cell lifespan and hydrogen efficiency. Specifically, our strategy ex

关键词： Industry 4.0 Autonomous mobile robot Energy management strategy Transformer neural network Digital modeling Model predictive control

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An iterative optimization method for sustainable environmental improvement under mixed CAV-HDV traffic

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ENERGY 2025年 322卷

作者： Dong, Changyin Hu, Pei Li, Ni Chen, Wang Li, Ye Ni, Daiheng Xie, Ning Wang, Hao Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China China North Vehicle Res Inst Beijing 100072 Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410000 Peoples R China Univ Massachusetts Amherst MA 01003 USA Tech Univ Dresden Chair Traff Proc Automat D-01062 Dresden Germany Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Sch Transportat Jiangsu Key Lab Urban ITS Nanjing 211189 Peoples R China

This paper proposes an iterative optimization method for trajectory control of connected automated vehicles (CAVs) at an off-ramp bottleneck, aiming to improve energy consumption and pollutant emissions in the traffic system. The methodology integrates real-world data from the Next Generation Simulation (NGSIM) dataset and generated data from each iteration. A comprehensive cost function is developed to evaluate safety, efficiency, comfort, equilibrium, lane-changing, energy, and emissions. The lane-changing process is divided into two stages: lane-changing decision-making (LCD) and lane-changing execution (LCE), modeled using advanced artificial intelligence algorithms. Specifically, gcForest is applied to model LCD, while long short-term memory (LSTM) is used for LCE, allowing for more precise control of lane-changing behavior. The analysis considers fuel consumption and key vehicular emissions, including carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOC), and particulate matter (PM). The results indicate that energy consumption and pollutant emissions are reduced by 20 % after three iterations of optimization. Furthermore, the iterative method demonstrates significant environmental improvements, particularly when the CAV market penetration rate (MPR) reaches approximately 50 %. Higher MPR levels further enhance the sustainability benefits of CAVs, making them more advantageous for promoting sustainable traffic development.

关键词： Iterative optimization Trajectory control Artifical intelligence Lane-changing modeling

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Research on Antenna Inverse modeling Method Based on Attention Mechanism CNN-BiLSTM

Research on Antenna Inverse Modeling Method Based on Attenti...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Cao, Yuan Hu, Xiao Yin, Songfeng School of Electrical and Control Engineering Liaoning Technical University Huludao125105 China

ISBN: (纸本)9789819620494

In order to solve the problems of long simulation time and many parameters to be optimized in traditional antenna design methods, this paper proposes an antenna inverse modeling method based on the attention mechanism CNN-BiLSTM, which extracts the multidimensional features of the data through convolutional neural network (CNN), and captures the mapping relationship between the electromagnetic response and the geometric parameters of the microwave device by using bidirectional long and short-term memory network (BiLSTM). The introduction of the self-attention mechanism allows the model to notice the correlation between different inputs in the global, maximizing the retention of features and thus improving the robustness of the model. In this paper, the model is applied to the design process of printed dipole antenna. Through comparison, the inverse modeling method shows good prediction and generalization ability, which can be applied to the optimal design of antennas. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Evaluation of Enhanced Drilling Dynamic Sensor Positioning in Drill String Resulted in Substantial Improvements in Operational Efficiency and Cost Reduction: North Sea Case Study analysis

Evaluation of Enhanced Drilling Dynamic Sensor Positioning i...

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2025 International Petroleum Technology conference, IPTC 2025

作者： Pourtangestani, Mehdi Alemi, Hassan Maliekkal, Harish My, Lien Ta Khoobyari, Hamid SLB Aberdeen United Kingdom SLB Ankara Turkey

ISBN: (纸本)9781959025436

Industry has been actively integrating advanced solutions to enhance drilling efficiency and reduce NonProductive Time (NPT), which are critical to any drilling project. As a result, use of downhole drilling sensors has become increasingly common, from vertical to complex well profiles. These sensors enable real-time monitoring of downhole drilling dynamics, allowing for parameter adjustments that extend bit life, reduce Bottomhole Assembly (BHA) wear, and improve overall operational performance. This approach aimed to minimize drill string dysfunctions caused by interactions between drill string components and formation layers of varying compressive strengths, which can lead to dynamic vibrations. To determine ideal placement of real-time downhole sensors in the drill string, Finite Element analysis (FEA) was employed for simulations and modeling, incorporating field operational criteria to evaluate BHA configurations. The simulations produced high-frequency measurements, which were validated using offset data from actual field runs, confirming the accuracy of the results. The results highlighted that real-time sensor placement near cutting elements in the BHA, particularly above the BHA steering drive and close to the drill bit, could significantly improve drilling performance. This optimized configuration, enriched by real-time downhole data, enhanced the drilling process without compromising the steering efficiency required to meet trajectory goals as demonstrated in the presented case study for the reliability and consistency of this engineering analysis, by early detection of drill string dysfunctions, including shock and vibration, led to higher mitigation capabilities, resolving string hanging issues in rotary mode and improving overall control and effectiveness. Field results showed a remarkable 50% increase in the rate of penetration (ROP) and a reduction in total bit runs, resulting in enhanced operational productivity and saving over $2 million in overal

关键词： Bottom-hole assembly

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Construction and analysis of Wooden Pallets Recycling Model Based on Closed-Loop Supply Chain

Construction and Analysis of Wooden Pallets Recycling Model ...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Zha, Meng Zhang, Meizhou Zhang, Hanting Zhou, Min Xima Steel Logistics Co. Ltd. Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As an important tool for material stacking, handling and transportation in enterprises, wooden pallets have many problems such as large loss, low recovery rate and high procurement cost. Therefore, improving the recycled rate and recycling utilization rate of wooden pallet, and reducing its failure rate, is an effective means to reduce the operation cost of enterprises. Starting from the actual situation, this paper first establishes the closed-loop supply chain model of Company J, then concretizes and generalizes the entire supply chain model by constructing the closed-loop supply chain mathematical model. Finally, through analyzing and comparing the data related to wooden pallets of the company in two years, it proves that wooden pallets adopt the closed-loop supply chain recycling mechanism can greatly reduce the operating costs of enterprises, and also has important reference significance for realizing resource protection and green production. © 2025 SPIE.

关键词： Packaging Mathematical modeling Manufacturing Transportation Engineering Metals Systems modeling Failure analysis Inspection Stochastic processes

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International Workshops which were held in conjunction with the 6th International conference on process Mining, ICPM 2024

International Workshops which were held in conjunction with ...

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International Workshops which were held in conjunction with the 6th International conference on process Mining, ICPM 2024

ISBN: (纸本)9783031822247

The proceedings contain 56 papers. The special focus in this conference is on process Mining. The topics include: One Language to Rule Them All: Behavioural Querying of process data Using SQL;EVErPREP: Towards an Event Knowledge Graph Enhanced Workflow Model for Event Log Preparation;representative Sampling in process Mining: Two Novel Sampling Algorithms for Event Logs;root Cause analysis Using Rule Mining on Object-Centric Event Logs;the Jensen-Shannon Distance for Stochastic Conformance Checking;a Dynamic Programming Approach for Alignments on process Trees;constructive Alignment in process Mining;understanding Student Behavior Using Active Window Tracking and process Mining;measuring Skill Acquisition and Retention: A Case Study of Math Fluency;assessing the Impact of Exam Preparation process on Students’ Careers;evaluation of Study Plans Using Partial Orders;towards Standardized modeling of Collaboration processes in Collaboration process Discovery;revealing One-to-Many Event Relationships in Event Knowledge Graphs;on the Impact of Low-Quality Activity Labels in Predictive process Monitoring;Towards Accurate Predictions in ITSM: A Study on Transformer-Based Predictive process Monitoring;predictions in Predictive process Monitoring with Previously Unseen Categorical Values;differentially Private Event Logs with Case Attributes;caLenDiR: Mitigating Case-Length Distortion in Deep-Learning-Based Predictive process Monitoring;CC-HIT: Creating Counterfactuals from High-Impact Transitions;multivariate Approaches for process Model Forecasting;enhancing Predictive process Monitoring Using Semantic Information;a Classification of data Quality Issues in Object-Centric Event data;analyzing the Evolution of Boards in Collaborative Work Management Tools;extending process Intelligence with Quantity-Related process Mining;ranking the Top-K Realizations of Stochastically Known Event Logs;framework for Extracting Real-World Object-Centric Event Logs from Game data;object-Centric

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Analyzing Machining Cycle Time Anomalies via CNC and Operational data 20

Analyzing Machining Cycle Time Anomalies via CNC and Operati...

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20th CIRP conference on modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Vlaminck, Noémie Nicolas, Michel Benamara, Tariq Raddoux, Hervé Cenaero Rue des Frères Wright 29 Gosselies6041 Belgium Sonaca Route Nationale 5 Gosselies6041 Belgium

As manufacturers are transitioning to Industry 4.0, real-Time monitoring of manufacturing processes has become more prevalent. This paper presents an intuitive anomaly analysis tool designed to support data-driven decision-making for multiple Computer Numerical control (CNC) machines in a production environment. By leveraging internal Programmable Logic controller (PLC) signals enriched with tool information from G-code, and operational scheduling data, the proposed tool identifies anomalies in machining process durations without relying on theoretical cycle time estimates from G-code or CNC interpolation strategies. Instead, it computes actual program duration distributions after applying rigorous filtering to address operational disruptions such as server issues, maintenance, or shift changes. Results reveal a near-linear correlation between these computed statistics and theoretical machining durations predicted by a machining simulator, validating the pipeline's reliability. The developed decision-support application integrates cycle time statistics into comprehensive tables and visualizations, allowing users to analyse data at program, tooling, and G-code levels. This approach facilitates the detection of specific anomalies or bottlenecks in the process, including significant cycle time variance in tools or G-code sections, tool sequencing issues, recurrent anomalies in subprograms or G-code ranges linked with axes and table movements, unoptimized machining parameters or other causes. By focusing on robust data preparation, this work highlights the critical role of preprocessing in achieving actionable insights for production optimization. © 2025 The Author(s).

关键词： Machining centers

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Multi-flow process Mining for Comprehensive Simulation Model Discovery 24

Multi-flow Process Mining for Comprehensive Simulation Model...

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14th International conference on Information Communication and Management, ICICM 2024

作者： Khodadadi, Atieh Lazarova-Molnar, Sanja AIFB Karlsruhe Institute of Technology Karlsruhe Germany University of Southern Denmark Odense Denmark

ISBN: (纸本)9798400717475

process mining has proven effective in explaining the underlying processes of systems, thereby improving systems' understanding, analysis, and operational efficiency. process mining, however, often falls short in addressing multiple dimensions of systems' behaviors, limiting its ability to provide comprehensive insights for systems' performance and optimization opportunities. In this paper, we introduce an enhancement to conventional process mining that we term Multi-flow process Mining (MFPM), which effectively extracts process flows across different system dimensions, such as time, energy, waste, and carbon footprint. MFPM enables a more comprehensive view of a system's dynamics, enabling holistic decision-making for enhanced system efficiency. We detail the framework of MFPM, outline corresponding data requirements, and introduce an expanded version of Petri nets-used here as a modeling formalism to describe and analyze multi-flow system processes. Through a detailed case study, we demonstrate the practical application of MFPM in capturing and analyzing multifaceted aspects of systems. © 2024 Copyright held by the owner/author(s).

关键词： Petri nets

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Classifying Land Cover Using Hyperspectral Image and LiDAR data 13

Classifying Land Cover Using Hyperspectral Image and LiDAR D...

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13th International conference on Intelligent control and Information processing, ICICIP 2025

作者： Zhang, Nian Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across numerous spectral bands, providing unique spectral signatures that differentiate various land cover types. On the other hand, LiDAR technology offers high-resolution elevation data, enabling accurate characterization of surface structures. This paper details the process of converting unorganized LiDAR point clouds into organized formats for effective analysis, followed by a robust data fusion technique that combines spectral and spatial information. Utilizing a land cover classification methodology on the MUUFL Gulfport dataset, collected in November 2010, we evaluate the effectiveness of our approach by comparing classification accuracies of multiple machine learning algorithms, including kernel classification model, support vector machines, and others. Our results demonstrate significant improvements in classification accuracy when hyperspectral and LiDAR data are fused, highlighting the efficacy of this integrated methodology for complex land cover analysis. © 2025 IEEE.

关键词： Support vector machines

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Coupling adsorption and solar photocatalysis using CuO/zeolite for amoxicillin removal: Mathematical modeling and optimization study

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INORGANIC CHEMISTRY COMMUNICATIONS 2025年 177卷

作者： Belaissa, Yahia Chabane, Ahmed Sarrai, Abdelaziz Berrabeh, Ismail Hentabli, Mohamed Trari, Mohamed USTHB Lab Storage & Valorizat Renewable Energies Fac Chem BP 32 Algiers Algeria Univ Medea Lab Biomat & Transport Phenomena LBMPT Medea 26000 Algeria A MIRA Univ Bejaia Fac Technol Lab Adv Polymer Mat LMPA Bejaia 06000 Algeria Univ Chlef Fac Technol Proc Engn Dept POB 151 Hay Essalem 02000 Chlef Algeria Univ Hassiba BenBouali Lab Vegetal Chem Water Energy BP 151 Chlef 02000 Algeria EURL TELEGHMA PLAST Lab Anal & Conform Control Ind Prod LACCIP Mila 43000 Algeria

In this study, we have evaluated the efficiency of amoxicillin (AMX) removal by adsorption on the Y-Zeolite (Y-Z) followed by photocatalysis using CuO impregnated into the Zeolite by chemical route (CuO/Y-Z). Both CuO and CuO/Y-Z were synthesized and characterized by X-Ray Diffractometry (XRD), Scanning Electron Microscopy (SEM) and FT-IR spectroscopy. The operational parameters of adsorption and photocatalysis under sunlight onto CuO/Y-Z were investigated and optimized step by step. The results showed that for an initial AMX (Co) concentration of 100 ppm, the adsorption reached a reduction of 40 %, accompanied by AMX release phenomenon. Heterogeneous solar photocatalysis not only addressed this phenomenon but also enhanced the removal efficiency to 98 %. TOC analysis revealed 65 %, indicating a high AMX mineralization under optimized conditions. The photodegradation kinetics were successfully described by the Langmuir-Hinshelwood (L-H) model. The kinetic analysis parameters revealed that the disappearance of AMX follows a first-order kinetic, with a rate constant of 2.42 x 10- 2 min- 1 (t1/2 = 28 min), Additionally, a proposed empirical model for degradation kinetics demonstrated excellent agreement with experimental data, achieving a coefficient (R2) of 0.98 and a low RMSE. This model outperformed others under optimal conditions. Incorporating initial concentration as a variable enhanced generalization, simplified modeling, and improved predictive accuracy. This unified framework provides robust insights into elimination kinetics, optimizing processes in diverse industrial scenarios. In conclusion, the hybrid process using CuO/Y-Z offers a promising strategy for developing high-efficiency materials for wastewater treatments.

关键词： Adsorption Solar photocatalysis Coupling two processes Amoxicillin removal Copper oxide CuO Zeolite (Y-Z) Photocatalysis Mineralization Mathematical modeling

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