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arXiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Robot Operating System

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MCMFuse:a Multi-level Co-selection Model in the Frequency Domain to Enhance EEG Signal Analysis for Emotion Recognition

MCMFuse:a Multi-level Co-selection Model in the Frequency Do...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yuxin Yang Youhan Li Tianyu Zeng Tianqi Fan Hongyu Zhao Sen Qiu School of Future Technology School of Artificial Intelligence Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China The Key Laboratory of Intelligent Control Optimization for Industrial Equipment of Ministry of Education and the School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Electroencephalogram signals, as physiological signals unaffected by subjective consciousness and physiological dysfunctions, can objectively reflect a patient's physiological data and serve as a reliable basis for emotion recognition. Prior research has highlighted the temporal and spatial features of EEG signals. However, extracting frequency-domain features is difficult due to EEG's non-stationary. This study introduces an inception structure to reuse more EEG features. Then, we carry out an enhanced co-selection in the frequency domain by combining the shallow features autonomously selected by the inception structure with the deep features extracted from the temporal and spatial domains. Ultimately, deep features and co-selected features are fused. Our model MCMFuse was tested on the DEAP dataset with cross-validation, showing promising results in emotion recognition.

关键词： Emotion recognition Frequency-domain analysis Neural networks Feature extraction Brain modeling Information retrieval Electroencephalography

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Active Power control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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PV powered neuromorphic sensors with optical connectivity

PV powered neuromorphic sensors with optical connectivity

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： George-Iulian Uleru Alexandru Barleanu Mircea Hulea Zabih Ghassemlooy Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Photonics Technology Laboratory Optical Communications Research Group. FEE Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

The spiking neural networks (SNN) benefits from low power consumption, very good signal-to-noise ratio and the ability to model rigorously the physiology of the biological neural areas. In robotics, the SNN can be used in different applications including motion control where the neural module drives the actuators based on the information from sensors. The most suitable type of sensing devices in biomimetic robotics are the neuromorphic sensors (NS) with spiking output which can include an optical transmitter for wireless connectivity. Considering that the reduced energy consumption is a critical characteristic for the SNN, in this work we evaluate the possibility of using photovoltaic (PV) panels to power the NS with optical output. The focus is on the recently introduced type of NS with integrated force sensing resistor (FSR) that uses a module based on a light emitting diode (LED) to generate optical pulses. We measured the responses of this NS with the load mass when it is powered by a PV panel, and the results show that the NS operates in nominal conditions despite the slight variations of the used supply voltage. Moreover, the NS is able to transmit optical pulses which frequency depends on the load mass.

关键词： Biomedical optical imaging Neuromorphics Robot sensing systems Optical network units Optical receivers Sensors Optical sensors

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations

Optimal denial-of-service attack scheduling for remote state...

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第43届中国控制会议

作者： Xinxin Yang Yuqing Ni Xiaomeng Chen Shan Chen Fei Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things EngineeringJiangnan University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems(CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR(Signal-to-Interference-plus-Noise Ratio)model. It should be noted that the same attack power can lead to different packet loss rates in different network *** the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process(SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process(MDP). The simulation results support our theoretical findings.

关键词： SOMDP remote state estimation packet loss channel CPS security

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on computer and Information Processing technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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Intelligent Construction and Analysis of a Complex Network for Determining Risk-Influencing Factors in Ship Self-Sinking Accidents

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ASCE-ASME Journal of Risk and Uncertainty in engineering Systems, Part A: Civil engineering 2025年第3期11卷

作者： Ma, Jun Wang, Liguang Xu, Luhui Zhao, Jiong Lin, Rui School of Computer Science Xijing Univ. Xi'An Key Laboratory of Human-Machine Integration and Control Technology for Intelligence Xi'an710123 China School of Computer Science Xijing Univ. Xi'an710123 China Xi'An Senke Construction Engineering Co. Xi'an710043 China

One of the most effective methods for analyzing risk-influencing factors (RIFs) in maritime accidents is the complex network approach. However, there is limited research specifically focused on RIFs in ship self-sinking accidents. Moreover, challenges remain in the accurate ontology construction of RIFs based on the data set, intelligent extraction of RIF propagation paths, RIF importance ranking, and robustness analysis of complex networks. To address these issues, an intelligent method is proposed to construct a ship self-sinking accident RIF complex network (SSARIFCN) based on 204 accident reports, yielding 247 nodes and 1,872 edges derived from 488 RIF propagation paths. This approach significantly improves the objectivity and accuracy of RIF identification while enhancing the intelligence level of the information extraction process. The three-weight LeaderRank algorithm is applied for node importance ranking and network robustness analysis, outperforming eight traditional ranking models and five attack algorithms, enhancing the accuracy of key RIF identification. Based on this algorithm, the top five RIFs for ship self-sinking accidents were identified, concluding that "unfit for maritime navigation"is a frequently overlooked key RIF. The experiment validates the method's effectiveness in identifying key RIFs, controlling RIF propagation, and enhancing accident prevention and navigational safety. © 2025 American Society of Civil Engineers.

关键词： Risk assessment

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Adaptive Stiffness control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic Systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators computer aided software engineering Automation Adaptive systems control systems End effectors Hardware Dynamical systems Manipulator dynamics

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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