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SSRN 2022年

作者： Liu, Ding Zang, Chuanzhi Zeng, Peng Li, Wanting Wang, Xin Liu, Yuqi Xu, Shuqing Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang University of Technology Shenyang110870 China

Electric power grids are changing from traditional power systems to modern smart integrated power systems. The microgrid (MG) plays a vital role in this process through the integration of distributed renewable energy resources (RESs) and energy storage systems (ESSs). However, wind and solar radiation are characterized by intermittence, randomness and fluctuation. These uncertainties cannot be ignored in microgrids with high penetration of RES and random load demand, which challenges the effective and economic management of MGs. To achieve real-time economically optimal scheduling of microgrids, we proposed a real-time energy management strategy. Unlike traditional model-based approaches, this strategy is learning-based and does not need the knowledge of system uncertainties. The energy management problem of a microgrid is formulated as a Markov decision process (MDP) solved using policybased deep reinforcement learning (DRL). In the proposed RL-based approach, two different neural networks are used. One takes the real-time system state of the microgrid as inputs and makes the decision to obtain the optimal control policy © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Adaptive Backstepping control for Piezoelectric Micro Actuating system with Input Saturation

Adaptive Backstepping Control for Piezoelectric Micro Actuat...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Ying Li Ying Feng School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Piezoelectric actuators have been attached more importance in the field of high-precision micro actuating due to their excellent performance. However, the presence of the hysteresis nonlinearity and input saturation problems in this smart material actuators have caused adverse effects on their performance, including reduced operational accuracy and damage to the actuators. To eliminate the negative effects of hysteresis and compensate for input saturation, an adaptive backstepping controller with an anti-windup auxiliary compensation system is developed in this paper. The proposed approach shows good tracking performance for the piezoelectric micro actuating system introducing, which also proves that the anti-windup technology can handle the input saturation effects well.

关键词： Hysteresis Piezoelectric actuators Adaptation models Backstepping Adaptive systems

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FD-SLAM: Real-time tracking and mapping in dynamic environments

FD-SLAM: Real-time tracking and mapping in dynamic environme...

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2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Xu, Hua Yang, Chenguang Feng, Ying Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781728158594

A robot needs the ability of Simultaneous Localization and Mapping(SLAM) in an unknown environment. It can help the robot navigation and tracking. The traditional method only considers that the robot works in a static environment. But in most cases, the robot should work in a highly dynamic environment like people. This paper presents a novel SLAM system named FD-SLAM, building on ORB-SLAM2 [1], which adds the abilities of dynamic object detection and fusion of depth image and semantic image to better estimate the position of people in an image. To improve the real-time performance of FD-SLAM, it has five threads that run simultaneously in FD-SLAM: tracking, semantic segmentation, local mapping, loop closing, and point cloud mapping. FD-SLAM uses the deep neural network algorithm to detect the dynamic object and combine the depth image with the corresponding semantic image to obtain a more accurate position of a dynamic object thus reducing the impact of dynamic objects to more accurately estimate the pose of the camera. Finally, the point cloud thread uses each keyframe to create a point cloud map. We experiment with a public dataset. The results show that the absolute trajectory error of FD-SLAM is smaller than that of ORB-SLAM and DS-SLAM, and it has better real-time performance than other SLAM systems for a dynamic environment. © 2019 IEEE.

关键词： Semantics

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Multi-Loss Function for Distance-to-collision Estimation

Multi-Loss Function for Distance-to-collision Estimation

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Xiangzhu Zhang Lijia Zhang Ding Xu Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665402460

Estimation of the drone’s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.

关键词： Training Deep learning Navigation Estimation Predictive models Decoding Collision avoidance

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Cyber-Physical-Social systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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Neural network control using composite learning for USVs with output error constraints

Neural network control using composite learning for USVs wit...

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2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Xu, Puyong Yang, Chenguang Dai, Shi-Lu Mao, Zhaoyong Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Unmanned System Research Institute Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728158594

In this paper, by focusing on trajectory tracking control of unmanned surface vessel (USV), we present a control method considering uncertain dynamics and output error constrains. Firstly, by using the properties of tan-type barrier Lyapunov function (BLF), the output tracking error can be constrained. Secondly, we use radical basis function neural network (RBF NN) to approximate the uncertain dynamics. Considering that the estimated parameters convergence cannot be achieved in the absence of persistent excitation (PE) conditions, the composite learning update law of the weight matrix in the NN is adopted to guarantee the parameters convergence under interval excitation (IE) conditions which is easier to reach. In simulation studies, it is proven that the USV have good ability to follow the pre-designed trajectory with small tracking error and the parameters convergence can be ensured. © 2019 IEEE.

关键词： Lyapunov functions

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Ddos attack detection based on one-class svm in sdn 6th

Ddos attack detection based on one-class svm in sdn

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhao, Jianming Zeng, Peng Shang, Wenli Tong, Guoyu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811581007

Software Defined Networking (SDN) is a new type of network architecture, which provides an important way to implement automated network deployment and flexible management. However, security problems in SDN are also inevitable. DDoS attack belongs to one of the most serious attack types, which is fairly common for today’s Internet. In SDN security fields, DDoS attack detection research has been received more and more attention. In this paper, a DDoS attack detection method based on one-class SVM in SDN is proposed, which provides a better detection accuracy. Furthermore, two new feature vectors, including middle value of flow table item’s duration and protocol data traffic percentage, are extracted to integrate into the item of 11 feature vectors. Additionally, basing on selection and construction method of the 11 feature vectors, a DDoS attack behavior model is established by using one-class SVM algorithm, and the self-adaptation genetic algorithm is designed to optimize the corresponding parameters of the Gaussian kernel of one-class SVM. The experimental results in SDN show that, the proposed new feature vectors are shown to more better detection accuracy, and the proposed method is more feasible by comparing with the BP neural network and RBF neural network algorithms under the same 11 features vectors. © Springer Nature Singapore Pte Ltd 2020.

关键词： Genetic algorithms

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Deep mining of temporal information for lithium-ion battery health status assessment based on wavelet-time-frequency fusion model and multi-attention mechanism

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Journal of Energy Storage 2025年 130卷

作者： Tian-E Fan Han-Yu Li Fei Feng College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 PR China Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Automation Chongqing University Chongqing 400044 China Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing University Chongqing 400044 China

State-of-health (SOH) of lithium-ion batteries plays an important role in battery management system, because accurate SOH estimation can ensure the safety and reliability of electric vehicles. However, because the measured voltage and current data contains abundant temporal periodic information and frequency components, which leads to the current SOH estimation methods either fail to fully display the inherent information hide in the data or adequately integrate time -frequency domain information for accurate SOH estimation. Therefore, to address this issue, an improved wavelet time-frequency fusion model (IWTFFM) and multi-attention mechanism is proposed for SOH estimation of lithium-ion batteries. Also, the noise interference in the measured data is considered. Firstly, the voltage/current data in frequency domain is extracted from charging stage by employing wavelet decomposition and threshold denoising techniques. Then, multi-attention mechanisms are applied to effectively enhance the information fusion and improve the model's ability in capture essential features. Finally, the effectiveness and accuracy of our proposed method is verified on NASA dataset, the results show that, compared to traditional machine learning methods, our approach improves the RMSE by 2 %–3 % when considering the interference of Gaussian noise. The proposed IWTFFM method offers a robust solution for accurate SOH estimation of lithium-ion batteries.

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： autonomous vehicles

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Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

Evaluating Image Quality of DIBR-synthesized Views in Textur...

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Chinese Automation Congress (CAC)

作者： Guangcheng Wang Nan Guo Hongyan Liu Yonghui Zhang Yazhen Zhou Ke Gu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426480

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality.

关键词： Image quality Visualization Wavelet domain Image color analysis Fuses Databases Predictive models

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