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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yingxiu Xu, Qimin Zhang, Jinglong Xu, Lei Li, Lingzhi Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments. © 2022 IEEE.

关键词： Ethernet

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Escalator passenger posture anomaly detection based on improved SSD model 12

Escalator passenger posture anomaly detection based on impro...

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

With the widespread use of escalators in daily life, escalator-related injury factors are threatening public health. To secure escalator system, most of existing studies focus on post hoc statistics and trend to fix these deficiencies to reduce escalator-related potential risks. However, they are not designed for early-stage detection of impending escalator unsafe factors. To solve this problem, this paper proposes an improved Single Shot MultiBox Detector (SSD) algorithm for anomaly detection of passenger posture on escalators. Firstly, we replace the widely used VGG16 backbone of SSD network by simpler MobileNetV2 to accelerate model inference. Secondly, the distance intersection over Union (DIoU) loss function is introduced to further improve performance. Based on the characteristics of IoU and considering the shortcomings of GIoU, DIoU directly regresses the Euclidean distance between the center points of the two boxes to accelerate the convergence. The experimental results show that the improved SSD escalator passenger posture anomaly detection algorithm can efficiently identify the dangerous posture of escalator passengers and start relevant safety protection measures. According to inference of model, the escalator system can be controlled in practice to formulate the corresponding safety protection measures, and improve the safety performance of the escalator operation. © 2022 IEEE.

关键词： Anomaly detection

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A Distributed Model Predictive control for Multi-zone HVAC with Global Resource Constrained

A Distributed Model Predictive Control for Multi-zone HVAC w...

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第34届中国过程控制会议

作者： Shunjian MA Yuanyuan ZOU Shaoyuan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Heating,Ventilation and Air Conditioning(HVAC) for multi-zone buildings includes centralized plants and remote equipment to jointly regulate indoor air temperature of every zone to satisfy difference *** to the limited capacity of the total supply air in centralized equipment,two conflicts are raised when controlling indoor air temperature for all *** is between meeting diverse demands to meet user's preferences and limited global resources,and another conflict is between distributed regulation facilities and centralized air supply *** this paper,a distributed model predictive control(DMPC) with global resource constrained is proposed for multi-zone HVAC ***,a centralized MPC with energy savings and thermal comforts is formulated to balance the demands of each zone and the limited resource ***,this centralized MPC is decomposed into subsystems by dual *** Lagrange function is integrated in the resulting optimization to deal with global constraints,so that a fully distributed solvable DMPC can be solved by a distributed primal-dual *** effectiveness of the proposed method is proved by simulation examples.

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Uncertainty-Aware Reactive control for Signal Temporal Logic with Unknown Obstacles

Uncertainty-Aware Reactive Control for Signal Temporal Logic...

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Asian control Conference

作者： Chen Chen Shaoyuan Li Xiang Yin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing the Ministry of Education of China Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we investigate the challenge of con-trol synthesis for Signal Temporal Logic (STL) specifications within workspaces harboring unknown obstacles. We consider a sensing-based control setting, where robots are equipped with sensors with a limited sensing range to acquire obstacle locations with prior information. The objective is to synthesize a control strategy that maximize the robustness degree of the STL tasks while simultaneously minimizing the probability of collisions with obstacles during its operation. To address this, we present a receding horizon reactive control framework, tackling the problem by solving an uncertainty-aware optimal planning problem at each time instant. A key aspect of our approach is the avoidance of explicit computation of collision probability. Instead, we transform the uncertainty-aware synthesis problem into a non-probabilistic optimization problem, and the results of which still provide probabilistic guarantees. Simulation results are provided to illustrate the effectiveness of our approach.

关键词： Measurement Simulation Transforms Probability Robot sensing systems Probabilistic logic Robustness

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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Amplification of high-order azimuthal mode based on a ring-core Yb-doped fiber

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Optoelectronics Letters 2022年第4期18卷 222-226页

作者： OU Nanxian LI Wei QIU Runzhou ZHANG Bin GAO Shecheng LIU Weiping Department of Electronic Engineering College of Information Science and TechnologyJinan UniversityGuangzhou 510632China Fiberhome Telecommunication Technologies Co. Ltd.Wuhan 430074China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems and School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510275China

In order to increase the number of amplified azimuthal modes in Yb-doped fiber(YDF),a multiple azimuthal modes amplifier based on a ring-core Yb-doped fiber(RC-YDF)was proposed and demonstrated.A home-made RC-YDF which can support 6 azimuthal mode groups was employed to amplify the signal mode at 1064 nm,using a core pump *** amplification characteristics of 5 high-order azimuthal linear polarization(HA-LP)mode groups(LP_(11),LP_(21),LP_(31),LP_(41),LP_(51))were studied comprehensively.A more than 8 d B gain is obtained for each signal mode with 5 d Bm input power,and the associated differential modal gain between all modes is less than 1 d *** intensity profiles of all modes are stable and well preserved during the process of amplification.

关键词： mode order modal

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A Multi-Gas Detection System Based on Convolutional Neural Network

A Multi-Gas Detection System Based on Convolutional Neural N...

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第43届中国控制会议

作者： Si-Wen Chen Hong-Yue Li Hui-Rang Hou Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The emission of carbon oxides（COX）, nitrogen oxides（NOX）, sulfur compounds, and volatile organic compounds（VOCs） from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared（NDIR） multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network（1D-CNN） and depthwise separable convolutional neural network（DS-CNN）. The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust.

关键词： Non-Dispersive Infrared Multi-gas Detection System Pattern Recognition Convolutional Neural Network

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MOSFormer: A Transformer-based Multi-Modal Fusion Network for Moving Object Segmentation

MOSFormer: A Transformer-based Multi-Modal Fusion Network fo...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zike Cheng Hengwang Zhao Qiyuan Shen Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene diversity. Meanwhile, the prevalent methods are projection-based, struggling with the challenge of blurred boundaries. To tackle the dataset issue, we introduce a nuScenes-based MOS dataset, which provides richer scenes and more dynamic instances. To alleviate the boundary blur-ring issue and further improve accuracy and generalizability, we propose a dual-branch multimodal fusion MOS network, MOSFormer. The Transformer structure is incorporated to extract spatio-temporal information better, while image semantic information is utilized to refine the boundaries of moving objects. Finally, experiments on two datasets show that our method achieves state-of-the-art performance, and a mapping experiment with our method confirms its effectiveness in downstream tasks such as mapping and localization.

关键词： Location awareness Three-dimensional displays Autonomous systems Semantics Object segmentation Transformers Real-time systems Data mining Faces Intelligent robots

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Security analysis of simultaneous classical communication and CVQKD with a local local oscillator under the phase reference pulse intensity attack

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Optics express 2025年第11期33卷 23893-23906页

作者： Weiqi Liu Minghui Zhang Jin Qi Tao Wang College of Information Science and Technology Northwest University Xi’an 710127 Shaanxi China State Key Laboratory of Advanced Optical Communication Systems and Networks Center of Quantum Information Sensing and Processing Shanghai Jiao Tong University Shanghai 200240 China tonystar@***

The simultaneous classical and continuous variable quantum communication can allow classical communication and continuous variable quantum key distribution with a local local oscillator (LLO CVQKD) to be implemented simultaneously using the same communication architecture. Such scheme enables LLO CVQKD to be integrated with classical communication at the minimum cost, but the existence of reference pulses will bring security loopholes to the simultaneous communication system, allowing eavesdroppers to launch attacks. In this paper, we analyze and simulate the performance of the system by introducing the trusted noise model under the phase reference pulse intensity attack, where Eve can hide her attack by manipulating the intensity of the reference pulse using a phase insensitive amplifier. A reconstructed noise model is proposed to solve this problem. Moreover, we show that the reconstructed noise model can strengthen the security of the practical simultaneous communication system.

关键词： Optical signals Phase contrast Phase noise Quantum key distribution Raman scattering Signal transmission

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