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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Analytical Center of Mass Trajectory Generation for Humanoid Walking and Running with Continuous Gait Transitions

Analytical Center of Mass Trajectory Generation for Humanoid...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tobias Egle Johannes Englsberger Christian Ott Faculty of Electrical Engineering and Information Technology TU Wien Tobias Egle and Christian Ott are with the Automation and Control Institute Vienna Austria Institute of Robotics and Mecha-tronics German Aerospace Center (DLR) Wessling Germany

ISBN: (纸本)9798350309805

We present an analytical trajectory generation framework for the combined computation of multiple walking and running sequences with continuous gait transitions. This framework builds on the Divergent Component of Motion (DCM)-based walking algorithm and the spline-based trajec-tory generation of the Biologically Inspired Deadbeat (BID) control for running. We describe our approach to generating closed-form center of mass (CoM) trajectories for walking and running by alternately linking the two gaits through continuity constraints. Thereby, we distinguish between vertical and horizontal planning. The vertical trajectory is computed in a forward recursion from the first to the last gait sequence. Due to the coupling of the gait sequences in the horizontal direction, we show the efficient generation of the horizontal CoM trajectory in a single matrix calculation. Subsequently, we unify the control strategies using a DCM tracking controller for the complete trajectory and integrate the proposed framework into an inverse dynamics-based whole-body controller. Finally, the presented approaches are validated in simulations with the humanoid robot Toro.

关键词： Legged locomotion Couplings Humanoid robots Biology Trajectory Planning Splines (mathematics)

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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Distributed Prescribed-Time Observer for Nonlinear Systems in Block-Triangular Form

arXiv

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital arithmetic

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Cooperative planning ofmulti-agent systems based on task-oriented knowledge fusion with graph neural networks

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Frontiers of information technology & Electronic engineering 2022年第7期23卷 1069-1076页

作者： Hanqi DAI Weining LU Xianglong LI Jun YANG Deshan MENG Yanze LIU Bin LIANG Department of Automation Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Office of Science and Technology Tianjin UniversityTianjin 300350China School of Aeronautics and Astronautics Sun Yat-sen UniversityShenzhen 518107China Faculty Office of Electrical and Electronics Engineering University of NottinghamNingbo 315154China

Cooperative planning is one of the critical problems in the field of multi-agent system *** work focuses on cooperative planning when each agent has only a local observation range and local *** propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling *** main contributions of this paper are based on the comparisons with previous work:(1)we realize feasible and dynamic adjacent information fusion using GraphSAGE(i.e.,Graph SAmple and aggreGatE),which is the first time this method has been used to deal with the cooperative planning problem,and(2)a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation,to obtain an effective and stable training process in our *** results demonstrate the good performance of our proposed method.

关键词： Multi-agent system Cooperative planning GraphSAGE Task-oriented knowledge fusion

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Optimization of Stacking Location for Mixed Loading of Steel Products

Optimization of Stacking Location for Mixed Loading of Steel...

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IEEE International Conference on Industrial technology (ICIT)

作者： Rong Wang Song Zheng Jinlong Wang Zhezhuang Xu Weixiang Wen Boyu Chen Qingdong Zhang Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Fujian Sanming China

ISBN: (纸本)9781728119496

In the steel logistics park, the operational efficiency is repeatedly compromised due to excessive truck turnaround. For solving this problem fundamentally, the mixed loading relationship of multi-type steel products is excavated and the optimum solution for storage yard is proposed in the paper. Due to the limited stacking space, some products with small outbound demand have to be stacked together. A two-stage model is established to meet the stacking requirements for different outbound quantity of steel products. In the first stage, products with a small amount of stock will be mixed and stacked together according to certain rules. Then all products are allocated to specific yards in the second stage with the goal of maximizing the mixed correlation of steel products in the same yard to reduce the number of transitions for pick-up trucks. At the end, products in large quantities are placed in the stacking position close to the pick-up position. Experiments demonstrate that the proposed scheme is of great benefit to improve the operation efficiency of the logistics park.

关键词： Stacking Loading Layout Excavation Mathematical models Data models Steel

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Vehicle Scheduling for Steel Logistics Based on Vehicle Batching

Vehicle Scheduling for Steel Logistics Based on Vehicle Batc...

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IEEE International Conference on Industrial technology (ICIT)

作者： Weixiang Wen Kang Chen Jinlong Wang Zhezhuang Xu Rong Wang Boyu Chen Qingdong Zhang Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China Sansteel Minguang Co. Ltd. Fujian Sanming China

ISBN: (纸本)9781728119496

The vehicle scheduling in the logistics system of the steel park is a critical factor determining the scheduling efficiency and logistics throughput. The limited space resources at the entrance of the steel park make vehicles shall be arranged in reasonable batches to guarantee the scheduling consistency. To achieve the purpose, the paper first formulates the model of vehicle batching. Then, the algorithm CCVB (constrained clustering of vehicle batching) is proposed to solve the problem from a clustering perspective. Comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed mechanism can sufficiently improve the scheduling efficiency of steel logistics under limited time and space resources.

关键词： Space vehicles Job shop scheduling Processor scheduling Computational modeling Clustering algorithms Throughput Data models

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Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

arXiv

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arXiv 2023年

作者： Dong, Yongqi Lu, Xingmin Li, Ruohan Song, Wei van Arem, Bart Farah, Haneen Faculty of Civil Engineering and Geosciences Delft University of Technology Delft2628 CN Netherlands School of Electrical and Control Engineering North China University of Technology Beijing100144 China Department of Civil and Environmental Engineering College of Engineering Villanova University VillanovaPA19085 United States School of Information Science and Technology North China University of Technology Beijing100144 China

The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can mislead human drivers and consequently contribute to unsafe driving. In response to this concern to accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline: data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy based loss with label smoothing, and post-processing. Leveraging state-of-the-art deep learning techniques, especially those involving Transformer models, the pipeline demonstrates superior performance verified through various experiments. Notably, the self-supervised pre-training with MiM can greatly enhance the detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) yielded a higher accuracy of 94.77% and an improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an AUC of 0.9498. Furthermore, fine-tuning epochs were dramatically reduced to 41 from the original 280. Ablation study regarding techniques to alleviate the data imbalance between normal and abnormal instances further reinforces the model's overall performance. In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems. © 2023, CC BY.

关键词： Image enhancement

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment

Identifying Appropriate Probability Density Function for Win...

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Emerging Trends in electrical, control, and Telecommunication engineering (ETECTE), International Conference on

作者： Ghulam Abbas Arshad Ali Muhammad Zubair Zohaib Mushtaq Umar Farooq Irfan A. Khan Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan Intelligent Systems Lab & Automation Facility (ISLAF) IEECE University of the Punjab Lahore Pakistan Clean and Resilient Energy Systems (CARES) Lab Texas A&M University Galveston TX USA

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures-Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2 )-are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology.

关键词： Wind energy generation Electric potential Wind energy Wind speed Urban areas Sea measurements Wind power generation Probability density function Probability distribution Weibull distribution

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