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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Deng, Haoran Yang, Bo Zhu, Dafeng Yao, Gang Guan, Xinping Srinivasan, Dipti Shanghai Jiao Tong University Department of Automation Shanghai China Kth Royal Institute of Technology Electric Power and Energy Systems Division Stockholm Sweden Shanghai Jiao Tong University Department of Electrical Engineering Shanghai China National University of Singapore Department of Electrical & Computer Engineering Singapore

ISBN: (纸本)9798350376067

At the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), there is a strong causal relationship between HFCV parc and hydrogen refueling stations (HRSs) planning, also known as indirect network effects. This is a crucial factor that hasn't been considered and cannot be ignored in the hydrogen supply infrastructure planning (HSIP) problem. To model the indirect network effects, we quantify the effect of HRS planning on HFCV parc in detail by proposing the calculation method of drivers' fuel buffer, defining hydrogen refueling convenience, and incorporating coupled constraints between the hydrogen network and transportation network. Further, we characterize the effect of HFCVs on HRS planning by constructing a decision-dependent distributionally robust optimization problem considering the HFCV parc uncertainty. We innovatively quantify the upper bound of the ambiguity set of Wasserstein associated with both the endogenous and exogenous factors. We propose effective linearization methods to deal with the nonlinear constraints in the indirect network effects model and transform the two-stage optimization problem into a solvable MILP problem. The simulation results show that the proposed method can obtain more economical investment decisions and achieve higher energy utilization efficiency. More importantly, in this planning framework, investors can set model parameters more flexibly according to the situation of the HFCV market, thereby promoting the popularization of HFCVs. © 2024 IEEE.

关键词： Hydrogen fuels

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JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization

arXiv

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arXiv 2025年

作者： Fan, Yixuan Xu, Haotian Liu, Mengqiao Zhuo, Qing Zhang, Tao Department of Automation Tsinghua University China Beijing Institute of Astronautical Systems Engineering China

The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of traditional methods, such as heuristic approaches, which often overlook field geometry and entrance constraints, we propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP. The network uses an encoder-decoder architecture with graph transformers and attention mechanisms to model routing as a Markov Decision Process, and is trained via reinforcement learning for efficient and rapid end-to-end planning. Experimental results indicate that JPDS-NN reduces travel distances by 48.4–65.4%, lowers fuel consumption by 14.0–17.6%, and computes two orders of magnitude faster than baseline methods, while demonstrating 15–25% superior performance in dynamic arrangement scenarios. Ablation studies validate the necessity of cross-attention and pre-training. The framework enables scalable, intelligent routing for large-scale farming under dynamic constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Modular ISRU systems as a Building Block for Sustainable Space Exploration 73

Modular ISRU Systems as a Building Block for Sustainable Spa...

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73rd International Astronautical Congress, IAC 2022

作者： Fälker, Svenja Dorau, Tim Viedt, Isabell Mädler, Jonathan Urbas, Leon Tajmar, Martin Bach, Christian Institute of Aerospace Engineering Space Systems TU Dresden Marschnerstraße 32 Dresden01307 Germany Institute of Automation Process Control Systems and Process Systems Engineering Group TU Dresden Helmholtzstraße 14 Dresden01069 Germany

Leading space agencies and companies identified the utilization of local resources as foundation for the continuous long-term exploration of the solar system within the Global Exploration Roadmap. In-Situ Resource Utilization (ISRU) will be one of the key technologies for sustainable space exploration and habitation missions to Moon, Mars and beyond. Defined production systems based on hydrogen value chains are utilized as essential building blocks to transform the available resources into valuable elements for energy, propulsion, life support or production systems. The current research at TU Dresden addresses the engineering of ISRU systems using a modular plant concept of standardized, interchangeable and reusable modules. The development, production and automation processes are defined based on the existing standards on modular process plants (VDI 2776) and modular automation (VDI/VDE/NAMUR 2658) for terrestrial application. Therein, a module called Process Equipment Assembly (PEA) realizes unit operations which are interconnected to form different plant configurations that enable flexible production paths. These standardized architectures and concepts are adapted to the requirements of space utilization. Compared to currently developed highly specialized single use systems, a modular plant approach according to these standards allows the flexible application to different mission requirements and environmental conditions. In advance, modular plants minimize the development effort and risk, lead to a lowered market entry threshold for small and medium-sized enterprises from diverse market sectors and encourage commercial competition between manufacturers. This paper includes initial research work as a foundation for the future development process of modular ISRU process plants. A major input for the holistic development of modular ISRU plants are top-level mission characteristics and derived sub-level requirements. They represent a valuable input for the developme

关键词： Computer software reusability

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Distributed Least-Squares Optimization Solvers with Differential Privacy

Distributed Least-Squares Optimization Solvers with Differen...

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American control Conference (ACC)

作者： Weijia Liu Lei Wang Fanghong Guo Zhengguang Wu Hongye Su Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the distributed least-squares optimization problem with differential privacy requirement of local cost functions, for which two differentially private distributed solvers are proposed. The first is established on the distributed gradient tracking algorithm, by appropriately perturbing the initial values and parameters that contain the privacy-sensitive data with Gaussian and truncated Laplacian noises, respectively. Rigorous proofs are established to show the achievable trade-off between the (E, 8)-differential privacy and the computation accuracy. The second solver is established on the combination of the distributed shuffling mechanism and the average consensus algorithm, which enables each agent to obtain a noisy version of parameters characterizing the global gradient. As a result, the least-squares optimization problem can be eventually solved by each agent locally in such a way that any given (E, 8)-differential privacy requirement can be preserved while the solution may be computed with the accuracy independent of the network size, which makes the latter more suitable for large-scale distributed least-squares problems. Numerical simulations are presented to show the effectiveness of both solvers.

关键词： Differential privacy Privacy Accuracy Laplace equations Gaussian noise Distributed databases Cost function

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Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation

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Journal of Leather Science and engineering 2022年第1期4卷 90-110页

作者： Praveen Kumar Moganam Denis Ashok Sathia Seelan Department of Design Automation Cyber Physical Systems LaboratorySchool of Mechanical EngineeringVellore Institute of TechnologyVellore 632014India

Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.

关键词： Convolution neural networks Machine learning classifier Leather defects Multi class classification Class activation map Segmentation

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Electrical Tracking,Erosion and Fire Retardancy Performance of Silicone Rubber Insulation Containing Aluminum Trihydrate,Graphene and Glass Fiber Additives

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CSEE Journal of Power and Energy systems 2023年第5期9卷 1918-1925页

作者： M.Tariq Nazir Arslan Khalid B.Toan Phung Arup K.Das Peter Cheetham Ashish Paramane Shakeel Akram Khoi Loon Wong School of Engineering RMIT UniversityMelbourneVIC 3000Australia. the Center for Advanced Power Systems(CAPS) Florida State UniversityFL 32310USA the Flame Security International Pty Limited Australia the School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW 2052Australia Department of Electrical Engineering National Institute of TechnologySilcharAssam 788010India the College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjingJiangsu 211816China

Silicone rubber composite is a priority electrical insulating material used in high-voltage outdoor insulation *** electrical tracking/erosion and poor flame resistance performance of silicone rubber once ignited,substantially reduce its working *** paper attempts to investigate tracking/erosion performance of room temperature vulcanized(RTV)silicone rubber along with flame retardant parameters using aluminum trihydrate(ATH),graphene nanosheets(GN)and milled glass fiber(GF)*** inclined plane test(IPT)was performed in line with criteria defined in IEC 60587 using step-up tracking voltage method while flame retardancy is evaluated according to ASTM E 1354.0 using a cone *** suggest 30% of ATH assists in improving physical tracking/erosion resistance of pristine silicone elastomer rubber by impeding development of leakage current and a great reduction in maximum average temperatures on the surface of *** improvement in performance of RTV2 is achieved through introduction of 1%of GN and 5% of GF as seen in ***,30% of ATH reduces heat release rate and smoke production rate,and this trend is improved with the introduction of GN/***4 has pop up as the most promising silicone rubber composite with excellent electrical tracking,erosion,and flame resistance performance relative to its counterparts in this study.

关键词： Dry band arcing electrical tracking heat release rate leakage current silicone rubber smoke production rate

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Optimization of Cylindrical Blades for Wind Turbine Based on Magnus Effect

Optimization of Cylindrical Blades for Wind Turbine Based on...

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2023 International Conference on Electromechanical and Energy systems, SIELMEN 2023

作者： Lukin, Aleksandr Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Anuchin, Alecksey ITMO University Faculty of Control Systems and Robotics Saint-Petersburg Russia Moscow Power Engineering Institute Electric Drives Department Moscow Russia

ISBN: (纸本)9798350315240

Wind turbines based on the Magnus effect open new prospects in terms of energy generation for areas with low average wind speeds. The main feature of these turbines is rotating cylinders which are used to create lift instead of blades. Due to this unconventional design, different parameters of the cylinder, such as mass and moment of inertia, can greatly affect the dynamic characteristics of such energy systems. The goal of this paper was to present an approach to structural optimization of a cylinder flange of the experimental Magnus effect-based wind turbine. The proposed approach, presented as an algorithm, allows us to determine input data for the preferred structural optimization software and achieve lightweight, low-inertia parts. control simulation has shown that optimized parts ensure a high enough factor of safety while also significantly reducing the moment of inertia. © 2023 IEEE.

关键词： Finite element method

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Bayesian Optimization with Clustering for Sparse Map Construction 42

Bayesian Optimization with Clustering for Sparse Map Constru...

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42nd Chinese control Conference, CCC 2023

作者： Li, Chengxi Gong, Wei Li, Li Tongji University The Department of Control Science and Engineering Shanghai China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China

ISBN: (纸本)9789887581543

High-quality sparse maps (e.g., radio maps, noise maps, and hydrographs) are essential in environmental monitoring, decision-making, path navigation, etc. However, constructing high-quality maps requires intensive data sampling, which is a repetitive and tedious task. To address this issue, we propose a new sparse map construction method based on Bayesian optimization in this paper. First, by exploiting the idea of spatial clustering, a novel acquisition method is proposed, which considers potential correlations between different points. This method further guides intelligent agents to collect samples at the most informative points. Second, by designing inference models, high-precision and stable maps can be gradually constructed as the number of sampling points increases. Next, a customized dataset, which contains Wi-Fi signal strength in a complex indoor environment, is collected to support the performance validation of our proposed method. Finally, we test the performance of our methods on this customized dataset. Experimental results show that our proposed methods outperform other existing methods including random search, grid search, and maximum predictive error sampling. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Decision making

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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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Trajectory Planning of Autonomous Mobile Robot using Model Predictive control in Human-Robot Shared Workspace 3

Trajectory Planning of Autonomous Mobile Robot using Model P...

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3rd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Chen, Jieming Chen, Xiang Liu, Steven The Hong Kong Polytechnic University Department of Electrical Engineering Hong Kong Hong Kong University of Kaiserslautern Institute of Control Systems Kaiserslautern Germany

ISBN: (纸本)9798350398410

This paper proposes an integrated framework of trajectory planning and control for autonomous mobile robots (AMRs) in an intra-logistic scenario, where humans and robots share the same indoor workspace. The proposed framework consists of perception and trajectory planning. To perceive human motion, information from the RGB-D camera is used to detect human positions. Then a model-based tracking approach is employed to track multiple people's motion and estimate their speed over a short time horizon. For trajectory planning, a Model Predictive control (MPC) based scheme is adopted to generate motor commands while taking into account energy efficiency, safety, and addressing human-aware proximity constraints. To convexify the nonlinear collision avoidance constraints, Sequential Convexification Programming is employed. The simulation and experimental results demonstrate that the proposed method can be implemented in real-time and efficiently avoid people in advance. © 2023 IEEE.

关键词： Model predictive control

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