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International Symposium on System and Software Reliability (ISSSR)

作者： Zhiyi Man Shaoping Wang Chao Zhang Yun Zhu School of Automation Science and electrical engineering Beihang University Beijing China AECC Aero-Engine Control System Institute Wuxi Jiangsu China

In the field of electric vehicles, the safety of power lithium-ion batteries is a socially important issue, and the study of thermal runaway of lithium batteries is now gradually becoming more in-depth. In this paper, the output parameters are up-dimensioned. Using a global sensitivity analysis method based on the Moms screening method, the influence of 11 parameters such as volume of the cell(vBt), mass of the cell(mBt) and electrolyte concentration $(\mathrm{W}_{\mathrm{e}})$ on the thermal runaway process of Li-ion batteries is analyzed, and the variation of 10 variables such as temperature(T) and carbon dioxide gas (CO 2 ) produced in the thermal runaway side reaction heat generation process and chemical reaction gas generation process over time is investigated under the influence of the parameter factors. In the study, the sensitivity of the parameters over a time interval is investigated in the rising dimension of the time domain. The results show that the overall effect of the parameters on the thermal process; for the output variables of the thermal process, they are mainly affected by the mass of the cell $(\mathrm{m}_{\mathrm{Bt}})$, the thickness of the SEI film $(\mathrm{t}_{\mathrm{sei}})$ and the specific heat capacity of the cell$(\mathrm{Cp}_{\mathrm{Bt}})$, while the chemical process is not significantly affected by the different parameters; among all the parameters, the volume $(\mathrm{V}_{\mathrm{Bt}})$ of the cell interacts most strongly with the other parameters for all the output variables. Finally, the results of the sensitivity analysis of the thermal runaway side reactions of the battery will be applied to the subsequent 3D simulation study.

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Recognition of Fixed Shape Boxes Based on the Improved YOLOv5 Algorithm 2

Recognition of Fixed Shape Boxes Based on the Improved YOLOv...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Li, Huiming Wang, Yuhao Du, Yuyuan Zou, Qingliang Tang, Shoufeng School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Institute for Safety Detection and Intelligent Control China University of Mining and Technology Xuzhou221116 China College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9798350334784

In recent years, industrial robotics has been making breakthroughs. Intelligent assembly robots can not only replace human labor and reduce safety hazards but can also replace some of the mental work of people. In order to improve the robustness of traditional vision robots in assembly application scenarios, this paper improves the YOLOv5 algorithm to achieve vision guidance of different scenarios by adding an attention mechanism. Experimental results show that the method can effectively improve the success rate of object detection. © 2022 IEEE.

关键词： Object detection

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ADRC Combined with IMC for Non-Minimum Phase Systems

ADRC Combined with IMC for Non-Minimum Phase Systems

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Chinese control Conference (CCC)

作者： Xiangyang Li Xindi Deng Senping Tian Wei Ai Key Laboratory of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

The integrated control method, active disturbance rejection control (ADRC) combined with internal model control (IMC), is proposed for non-minimum phase systems. The ADRC combined with IMC is to reduce the influence of zeros on right half plant (RHP), system uncertainty and outer disturbance. An improved IMC is put in the inner loop and ADRC is adopted in the outer loop. In order to enhance the system's stability, an approximate zero phase error model of controlled plant (ZPEM) is used as the internal model in the IMC control loop where the feedback of the inner loop is the dynamic weighted filter sum of the real output and the ZPEM output. The theoretical analysis and simulations verify effectiveness of the proposed method for non-minimum phase systems.

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Robust Vehicle Detection in High-Resolution Aerial Images With Imbalanced Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第3期2卷 238-250页

作者： Li, Xianghui Li, Xinde Li, Zhijun Xiong, Xinran Khyam, Mohammad Omar Sun, Changyin The Laboratory of Measurement Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China The Department of Automation University of Science Technology of China Hefei230027 China The Department of Faculty of Science McGill University MontréalQCH3A 0G4 Canada The School of Engineering and Technology Central Queensland University MelbourneVIC3000 Australia

Vehicle detection in images from unmanned aerial vehicles (UAVs) plays an important role in traffic surveillance and urban planning due to the popularity of UAVs. However, the class imbalance problem is an important factor that restricts the performance of vehicle detectors. There are two types of class imbalance in UAV images, i.e., foreground-background imbalance and foreground-foreground imbalance. For anchor-based single stage detector, as many ground truths cannot be assigned to corresponding anchors because of low intersection over union, it makes the foreground-background imbalance problem more severe. Therefore, we propose a novel bag-based single-stage detector, which treats each position on the feature map as a bag. A simple and adaptive definition of bags is proposed along with the positive sample definition method, which is utilized to ensure more ground truths can be assigned to proper bags. In addition, we utilize online hard example mining method to control the proportion of positive and negative samples during the training process. To address the foreground-foreground imbalance, we propose a novel data augmentation algorithm, which allows us to create appropriate visual context for under-represented class. Extensive experiments demonstrate the superiority of the proposed algorithm, compared with other state-of-the-art solutions. Impact Statement-Recently, unmanned aerial vehicles (UAVs) are widely used in intelligent transportation due to their low price and high flexibility, which makes vehicle detection in UAV images important for automatically gathering of traffic information. However, the class imbalance problem, which is common in object detection where some classes have far fewer frequencies in the dataset, has an adverse effect on the performance of vehicle detectors. The data augmentation method and deep learning based vehicle detector proposed in this article are able to reduce the negative impact and improve detection performance by at leas

关键词： Object detection

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Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

The frequent occurrence of cyber-attacks has made webshell attacks and defense gradually become a research hotspot in the field of network security. However, the lack of publicly available benchmark datasets and the over-reliance on manually defined rules for webshell escape sample generation have slowed down the progress of research related to webshell escape sample generation and artificial intelligence (AI)-based webshell detection. To address the drawbacks of weak webshell sample escape capabilities, the lack of webshell datasets with complex malicious features, and to promote the development of webshell detection, we propose the Hybrid Prompt algorithm for webshell escape sample generation with the help of large language models. As a prompt algorithm specifically developed for webshell sample generation, the Hybrid Prompt algorithm not only combines various prompt ideas including Chain of Thought, Tree of Thought, but also incorporates various components such as webshell hierarchical module and few-shot example to facilitate the LLM in learning and reasoning webshell escape strategies. Experimental results show that the Hybrid Prompt algorithm can work with multiple LLMs with excellent code reasoning ability to generate high-quality webshell samples with high Escape Rate (88.61% with GPT-4 model on VirusTotal detection engine) and Survival Rate (54.98% with GPT-4 model). Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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A dynamic event-triggered transmission strategy for distributed convex optimization

A dynamic event-triggered transmission strategy for distribu...

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第42届中国控制会议

作者： Pengfei Liu Kun-Zhi Liu Dianfeng Zhang Shengli Du Academy of Control Science and Engineering Dalian University of Technology School of Mathematics and Informational Science Yantai University College of Automation Faculty of Information TechnologyBeijing University of Technology

A dynamic event-triggered transmission strategy is considered for the continuous-time distributed convex optimization algorithm over a strongly connected and weight-balanced directed *** auxiliary variable is introduced in the dynamic event-triggered control(DETC) that is free of Zeno *** model the DETC system into a hybrid *** Lyapunov stability theory,we give a detailed proof for convergence of the continuous-time distributed convex optimization ***,a numerical simulation shows that the DETC can effectively save network communication resources compared with a static event-triggered control(SETC).

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Semi-Global Robust Practical Output Voltage Tracking for Single-Phase UPS Inverters via Sampled-Data control

Semi-Global Robust Practical Output Voltage Tracking for Sin...

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Chinese control Conference (CCC)

作者： Kaiji Zeng Wei Liu Weijie Sun Weidong Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

This paper deals with the sampled-data semi-global robust practical output voltage tracking problem for single-phase voltage source uninterruptible power supply inverters. We construct an aperiodic sampled-data state feedback control law in an internal model principle framework. Lyapunov analysis shows that as long as the upper bound of the sampling time interval of the controller is less than an explicitly computable threshold, our problem can be handled by the proposed sampled-data control law. And the tracking error is guaranteed to approach a given neighborhood of the origin asympotically.

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Time-varying group formation tracking for UAV swarm systems with collision avoidance

Time-varying group formation tracking for UAV swarm systems ...

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

作者： Jintong Yu Yongzhao Hua Jianglong Yu Xiwang Dong Zhang Ren Institute of Artificial Intelligence Beihang University Beijing China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing China

Aiming at the tracking and control problem of UAV swarm formation, it has important application value in civil fields such as environmental monitoring, disaster rescue, regional logistics Based on the consensus control theory, this paper proposes an unmanned aerial vehicle (UAV) group formation tracking control method with a collision avoidance mechanism. The designed control approach is verified on the Matlab simulation platform respectively. A physical verification is built by using motion capture as external positioning system, and the physical flight of the Tello UAV swam system is carried out. The results verify the effectiveness of the designed UAV group formation tracking control method with a collision avoidance mechanism.

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A Novel Trajectory Tracking controller for UAV with Uncertainty Based on RBF and Prescribed Performance Function 3rd

A Novel Trajectory Tracking Controller for UAV with Uncert...

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3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Qi, Xuelei Li, Chen Ma, Hongjun College of Information Science and Engineering Northeastern University Shenyang110819 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong Guangzhou China

ISBN: (纸本)9789811961410

This paper proposes a novel trajectory tracking controller based on RBF neural network and fractional-order sliding mode control (FO-SMC). First, the prescribed performance control (PPC) is introduced into the system to make the tracking error converge to the predefined set. Then, the fractional-order calculus is introduced into SMC to alleviate the chattering of the system. Considering that RBF neural network can compensate for the uncertainty of the UAV motion model, RBF is introduced into the design of the controller. Besides, the Lyapunov theorem proves the stability of the system, and all signals in the closed-loop system are stable. Finally, a case study is carried out through simulation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Reinforcement Learning of Threshold Policy for Dynamic Wireless Charging with Distributed Energy Resources

Reinforcement Learning of Threshold Policy for Dynamic Wirel...

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IEEE General Meeting Power& Energy Society

作者： Liangliang Hao Yunjian Xu Lang Tong Department of Control Science and Engineering Shandong University Jinan China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong SAR School of Electrical and Computer Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

We study dynamic wireless charging for Electric Vehicles (EVs) on electrified highways where EVs are charged while in motion. Our focus is on the cost-minimization scheduling of EV charging based on EV mobility states, charging demands, travel plans, available distributed renewables, and power constraints of the distribution systems. As a Markov decision process with uncountable action space that couples stochasticities in charging demands, vehicle mobilities, and renewable resources, standard dynamic programming and approximation approaches face exponentially growing complexity and modeling uncertainties. By exploiting the linear network topology when EVs are on a highway, this work reveals a threshold structure of the optimal charging policy and develops a reinforcement learning approach for uncertainty models, resulting in a scalable EV charging solution. The proposed EV charging algorithm outperforms multiple charging benchmarks by 4.7-52.4% cost reduction.

关键词： Road transportation Renewable energy sources Uncertainty Costs Markov decision processes Inductive charging Dynamic scheduling Electric vehicle charging Distributed power generation Vehicle dynamics

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