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第40届中国控制会议

作者： Xuxin Zhang Shiyu Chen Bingya Zhao Ya Zhang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the clock synchronization problem of wireless sensor networks with time-varying noises and malicious denial of service（Do S） attacks. A robust and secure clock synchronization scheme based on filter is proposed in intermittent Do S attack case, which is composed of three steps: relative skew estimation, clock skew compensation and software clock reading compensation. Through the clock skew compensation, the synchronization error of the clock skew can be constrained into a bounded range. Through the software clock reading compensation, the possible error of the software clock reading can be compensated and the clock synchronization can be achieved completely. In addition, extensive simulations are conducted which validate that the proposed scheme outperforms the existing ones.

关键词： clock synchronization wireless sensor networks DoS attacks bounded noises filter

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An Improved Frequency Hopping Algorithm Based on Transmission Scheduling against Jamming Attacks 36

An Improved Frequency Hopping Algorithm Based on Transmissio...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Wang, Chenyuan Kong, Xiangying Chen, Xuebing Cao, Xianghui Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Jiangsu Automation Research Institute Jiangsu China Southeast University School of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9781665437127

In wireless networked systems, jamming attacks are destructive and easy to be launched. To mitigate the jamming attack impact, frequency hopping is an effective way, in which a defender side can switch channels to avoid using the ones under jamming attack. However, the resilient ability of anti-jamming of traditional frequency hopping algorithm can't meet the requirement of communication without channel strategy and jamming detection under the circumstance of various forms of jamming attacks. In this paper, an improved frequency hopping algorithm with higher transmit success rate (TSR) has been proposed based on channel selection and adjustment of transmission time to defend different kinds of attacks such as reactive jamming, static jamming and scanning jamming. Our algorithm judges whether to adjust data transmission time according to the result of transmit success rate and use channel selection strategy to avoid most of interfered channels. Finally, our proposed algorithm is evaluated as a resilient scheme under different forms of jamming attacks through simulations. © 2021 IEEE.

关键词： Frequency hopping

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Adaptive tracking control of unknown state target for CPSs subjected to cyberattacks

Adaptive tracking control of unknown state target for CPSs s...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Yizhou Sun Ya Zhang School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665476881

In this study, an adaptive controller for Cyber-Physical systems(CPSs) subjected to cyberattacks is proposed to track a class of target system with unknown inputs and states. Firstly, an estimation strategy is proposed to adaptively estimate the unknown inputs and states of target on the basis of finite impulse response (FIR) filter. Secondly, due to the existence of cyberattacks in CPSs, an adaptive tracking control scheme with designed compensation signal is constructed to stabilize the system performance which might be damaged by attacks, where Lyapunov function related to adaptive estimated factor is adopted to prove that the CPSs are able to track the target under attacks. In the last part, a numerical experiment is implemented to demonstrate the validity of the theoretical results.

关键词： Target tracking Adaptive systems Finite impulse response filters System performance Estimation Lead control systems

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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network 4

Hot Spot Detection of Photovoltaic Module Infrared Near-fiel...

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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Fan, Fu Na, ZhiXiong Zhang, ChangZhi Li, HaoRan Tong, ChaoLiu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300010 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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CrossNet: Computing-Friendly Lightweight Anchor-Free Detector 4

CrossNet: Computing-Friendly Lightweight Anchor-Free Detecto...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Yao, Yuncong Wang, Qiang Mai, Jiren Yang, Wankou Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjin210096 China School of Automation Southeast University Nanjin210096 China Jiangsu Automation Research Institute Lianyungang221116 China

ISBN: (纸本)9780738131702

Many scenarios such as edge and mobile scenarios are very sensitive to computing complexity and parameter size. Most object detection models cannot be directly deployed without specific modifications. In this paper, we introduce CrossNet, a compute-friendly lightweight anchor-free detector based on CenterNet. We abandon DCN module and design the network structure according to lightweight design principles. A new sample matching strategy based on neighbor points is adopted for improving its performance greatly. Weighted Focal Loss and Balanced L1 Loss further reduce the performance gap between CrossNet and CenterNet. CrossNet possesses a model size of 4.3MB (12.8× smaller than original CenterNet, respectively) and requires 1.51B operations for inference (11.7× smaller than original CenterNet, respectively) while still achieving mAP of 74.2% on VOC dataset. We hope our work could provide some inspiration for lightweight detector design. © 2021 IEEE.

关键词： Object detection

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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Chinese Automation Congress (CAC)

作者： Tao Zhong Yahui Gan Zao Han Huan Gao Ang Li Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments.

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ISAC Prototype System for Multi-Domain Cooperative Communication Networks

arXiv

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arXiv 2024年

作者： Yang, Jie Que, Hang Tao, Du Liang, Le Li, Xiao Wen, Chao-Kai Jin, Shi The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan

Future wireless networks are poised to transform into integrated sensing and communication (ISAC) networks, unlocking groundbreaking services such as digital twinning. To harness the full potential of ISAC networks, it is essential to experimentally validate their sensing capabilities and the role of sensing in boosting communication. However, current prototype systems fall short in supporting multiple sensing functions or validating sensing-assisted communication. In response, we have developed an advanced ISAC prototype system that incorporates monostatic, bistatic, and network sensing modes. This system supports multimodal data collection and synchronization, ensuring comprehensive experimental validation. On the communication front, it excels in sensing-aided beam tracking and real-time high-definition video transmission. For sensing applications, it provides precise angle and range measurements, real-time angle-range imaging, and radio-based simultaneous localization and mapping (SLAM). Our prototype aligns with the 5G New Radio standard, offering scalability for up to 16 user equipments (UEs) in uplink transmission and 10 UEs in downlink transmission. Real-world tests showcase the system’s superior accuracy, with root mean square errors of 2.3 degrees for angle estimation and 0.3 meters (m) for range estimation. Additionally, the estimation errors for multimodal-aided real-time radio SLAM localization and mapping are 0.25 m and 0.8 m, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooperative communication

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TravelNet: Self-supervised Physically Plausible Hand Motion Learning from Monocular Color Images

TravelNet: Self-supervised Physically Plausible Hand Motion ...

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International Conference on Computer Vision (ICCV)

作者： Zimeng Zhao Xi Zhao Yangang Wang Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665428132

This paper aims to reconstruct physically plausible hand motion from monocular color images. Existing frame-by-frame estimating approaches can not guarantee the physical plausibility (e.g. penetration, jittering) directly. In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space. Our key idea is to adopt a self-supervised learning strategy to train a novel encoder-decoder, named TravelNet, whose training motion data is prepared by the physics engine using discrete pose states. TravelNet captures key pose states from hand motion sequences as compact motion descriptors, inspired by the concept of keyframes in animation. Finally, it manages to extract those key states out of perturbations without manual annotations, and reconstruct the motions preserving details and physical plausibility. In the experiments, we show that the outputs of the TravelNet contain both finger synergism and time consistency. Through the proposed framework, hand motions can be accurately reconstructed and flexibly re-edited, which is superior to the state-of-the-art methods.

关键词： Training Computer vision Perturbation methods Color Manuals Space exploration Reliability

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Data-Driven Distributed MPC for Load Frequency control of Networked Nonlinear Power systems

Data-Driven Distributed MPC for Load Frequency Control of Ne...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Yubin Jia Jun Zhou Panxiao Yong Jun Guo School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing P. R. China

ISBN: (纸本)9781665476881

In this paper we proposed a data-driven distributed model predictive control (MPC) for the load frequency control of the multi-area interconnection nonlinear power system. The data-driven Koopman operator method is used to deal with the nonlinear wind power generation system in each area. The distributed Koopman operator algorithm is proposed to deal with the huge amount of data in the large-scale distributed system. Based on the distributed linear model obtained by the distributed Koopman operator algorithm, the distributed MPC is applied to realize the load frequency control (LFC) of the multi-area power system. The cooperation-based cost function is optimized by the MPC controller in each area, and the global optimal solution can be achieved by the proposed method. The effectiveness and practicability of the proposed algorithm are demonstrated by the simulation.

关键词： Distributed databases Wind power generation Prediction algorithms Cost function Power systems Nonlinear systems Frequency control

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