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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Secure Routing Protocol Based on FANET

Secure Routing Protocol Based on FANET

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： I. I. Naumov R. R. Ibadov D. G. Chebotarev Faculty of Automation Mechatronics and Control Don State Technical University Rostov-on-Don Russia Department of Electrical Engineering and Electronics Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The flying ad-hoc network (FANET) technology-based secure routing protocol described in this study functions between a ground station and a UAV network. This protocol employs a hybrid authentication technique to guarantee communication dependability. The system is based on an asymmetric method that ensures secure connections between the UAV and the ground control station, as well as between the UAVs themselves. A symmetric scheme with digital signatures is used to encrypt the routing. Before joining the network, the UAV must register. During the registration process, the Key Distribution Center provides the necessary public and private keys for each node that wants to connect. Furthermore, crucial routing data is safeguarded using the TWINE algorithm. Wormhole attacks are avoided by using the packet binding method. Even in the event of an aggressor attack, simulations show that this routing system can sustain effective network functioning, ensuring a high packet delivery ratio and optimal network costs.

关键词： Industries Network topology Authentication Autonomous aerial vehicles Routing Routing protocols Ad hoc networks Safety Registers Grippers

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Data-Based Soft Fault Diagnosis of a Three-Phase DC/AC Converter

Data-Based Soft Fault Diagnosis of a Three-Phase DC/AC Conve...

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China International electrical and Energy Conference (CIEEC)

作者： Cen Chen He Du Xi Cai Haoran Wang Guofu Zhai Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China China Three Gorges Construction Engineering Corporation Chengdu China Three Gorges Intelligent Industrial Control Technology Company Wuhan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

With the widespread use of power electronic equipment, accurately identifying soft faults in power electronic converters is crucial for the stable and reliable operation of power electronic equipment. This paper proposed a soft fault diagnosis method for three-phase DC/AC converter using a data-based approach. Firstly, simulation analysis is conducted on the soft faults of three-phase DCI AC converter, and appropriate test signals are selected as data sources. Then, the LSTM neural network and its variants are used to obtain fault diagnosis models. Finally, the method is validated through experiments, and the results show that it has good performance in soft fault diagnosis of the three-phase DCI AC converter.

关键词： Fault diagnosis Training Accuracy Soft sensors Neural networks White noise Power electronics

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Individual pitch controller with static inverted decoupling for periodic blade load reduction on monopile offshore wind turbines

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Ocean engineering 2025年 334卷

作者： Lara, Manuel Ruz, Mario L. Mulders, Sebastiaan Paul Vázquez, Francisco Garrido, Juan Department of Electrical Engineering and Automation University of Cordoba Campus of Rabanales Cordoba14071 Spain Department of Mechanics University of Cordoba Campus of Rabanales Cordoba14071 Spain Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Individual pitch control (IPC) is a technique used to reduce periodic blade loads in wind turbines. It generally uses the multiblade coordinate transformation to convert blade load measurements from a rotating frame into a two-axes non-rotating frame. Although these non-rotating axes are assumed to be decoupled, studies reveal persistent interactions. Reducing this coupling, such as by introducing an azimuth offset, enhances IPC performance. This study explores the impact of static inverted decoupling, which decouples the process in the steady state, on IPC performance. The proposed IPCs are adaptive, scheduling controller and decoupling gains based on operational conditions. In such IPC designs, the integral gains of the diagonal controllers and the decoupling elements can either be the same or different. These methods were validated on a simulated 15 MW wind turbine. controller parameter optimization was accomplished through genetic algorithms to minimize blade fatigue loads, measured via the damage equivalent load (DEL). Results indicate that incorporating static inverted decoupling into IPC improves blade load reduction without increasing pitch actuator effort. IPCs with similar integral gains and matching absolute values in decoupling elements achieved the best balance between DEL reduction and complexity with minimal actuator effort, while additional optimization parameters provided negligible improvements. © 2025 Elsevier Ltd

关键词： Genetic algorithms

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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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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Rong Zheng, Song Wang, Jinlong Xu, Zhezhuang Wen, Weixiang Chen, Boyu Zhang, Qingdong Fuzhou University College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming Fujian353000 China

ISBN: (纸本)9781728119489

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. © 2022 IEEE.

关键词： Trucks

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Secure Routing Protocol Based on FANET

Secure Routing Protocol Based on FANET

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Naumov, I.I. Ibadov, R.R. Chebotarev, D.G. Don State Technical University Faculty of Automation Mechatronics and Control Rostov-on-Don Russia Don State Technical University Department of Electrical Engineering and Electronics Rostov-on-Don Russia

ISBN: (纸本)9798331511241

关键词： Cooperative communication

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Study of 5 MW grid connected PMSG wind turbine under voltage dip: Case study 1

Study of 5 MW grid connected PMSG wind turbine under voltage...

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1st International Conference on Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition, ICRSEtoSET 2023

作者： Bouhedda, Ali Naceri, Billel Ahmia, Abderrahmane Mazari, Ali Aoues, Kamel Abbas, Hamou Ait Laroussi, Kouider University of Bouira Electrical Engineering Department Bouira Algeria University of Biskra Laboratory of Identification Commande Control and Communication [LI3CUB Biskra Algeria University of Djelfa Laboratory of the Applied and Automation and Industrial Diagnostic Djelfa Algeria Biskra University of Bouira Department of Mechanical Engineering Bouira Algeria University of Bouira Laboratory of Materials and Durable Development-LM2D Bouira Algeria University of Djelfa Laboratory of the Applied and Automation and Industrial Diagnostic Djelfa Algeria

ISBN: (纸本)9798350346336

The study and simulation presented in this article provide a comprehensive analysis of the dynamic and control performance of a wind turbine connected to the electrical network during a voltage dip. The wind turbine setup utilizes a Permanent Magnet Synchronous Generator (PMSG) with the goal of optimizing the power output from the variable speed wind system in these circumstances. The suggested control approach employs a converter on the generator side to regulate the DC bus voltage, and a converter on the network side to manage the active and reactive power flows into the grid. This approach aims to provide a good regulation of the DC bus voltage despite any disturbances caused by the drive train and to satisfy the requirements for active and reactive powers necessary to achieve optimal operation of the wind conversion system. The network side converter plays a crucial role in managing the power injection into the electrical network. During a voltage dip, it can be used to control the active and reactive powers injected into the network, thus reducing the effects of the voltage dip on the system. Additionally, the control strategy also takes into account the need to ensure that the system is operating at its most efficient state, and the active and reactive powers are optimized to meet this requirement. © 2023 IEEE.

关键词： Wind turbines

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Asad Ali Sarah Gul Tallat Mahmood Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) Rawalpindi Pakistan Department of Electrical Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.

关键词： Deep learning Location awareness Industries Uncertainty Service robots Computational modeling Reinforcement learning

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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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