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Journal of Economy and Technology 2023年 1卷 29-47页

作者： Yunfan Zhang Yifan Su Yue Chen Feng Liu State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing China China Electric Power Planning & Engineering Institue Beijing China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Special Administrative Region China

The proliferation of plug-in electric vehicles (PEVs) advocates a distributed paradigm for coordinating PEV charging. Unlike existing primal-dual decomposition or consensus methods, this paper proposes a cutting-plane-based distributed algorithm considering a peer-to-peer (P2P) communication network, which enables asynchronous coordination among PEVs. First, an equivalent surrogate model is established by exploiting the duality of the original optimization problem, which masks the private information of individual users by equivalent transformation. Then, a cutting-plane based algorithm is derived to solve the surrogate problem in a distributed manner with intrinsic superiority to cope with various asynchrony. Critical implementation issues, including distributed initialization, distributed cutting-plane generation, and localized stopping criteria, are discussed in detail. Numerical tests on IEEE 37- and 123-node feeders with actual data illustrate that the proposed method is resilient to several asynchronies and admits the plug-and-play operation mode. The proposed methodology is expected to provide an alternative path toward the robust protocol for PEV charging.

关键词： Asynchronous algorithm Charging protocol Distributed optimization Plug-in electrical vehicles

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Prediction of second-order rate constants between carbonate radical and organics by deep neural network combined with molecular fingerprints

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Chinese Chemical Letters 2022年第1期33卷 438-441页

作者： Peizhe Sun Huixin Ma Shangyu Li Hong Yao Ruochun Zhang School of Environmental Science and Engineering Tianjin UniversityTianjin 300072China School of Civil Engineering Tianjin UniversityTianjin 300072China Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard Department of Municipal and Environmental EngineeringSchool of Civil EngineeringBeijing Jiaotong UniversityBeijing 100044China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Tianjin UniversityTianjin 300072China

Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined,and quantitative structural-activity relationships(QSARs)have not been established for rate *** study applied Max Min data processing method and used molecular fingerprints(MF)as the input of a deep neural network(DNN)to predict the rate constants between carbonate radical and organic *** parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate *** vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best *** optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods,including random data splitting,molecular descriptors,supporting vector machine,decision tree,*** showed that the MF-DNN model out-performed the other methods by more than 10%increase in prediction *** this MF-DNN model,we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine(1576)and veterinary practice(390).Among them,46 drugs were identified as fast-reacting compounds,suggesting the important relations of their environmental fate with carbonate radical.

关键词： Deep neural network Carbonate radical Molecular fingerprints QSAR Pharmaceuticals

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Investigation of a Simulated Robot Model when it is Trained to Set Trajectories in the Task of controlling Assembly Parameters at Remote Visualization

Investigation of a Simulated Robot Model when it is Trained ...

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2022 International Russian automation Conference, RusAutoCon 2022

作者： Malkarov, A.Yu. Arkhipov, M.V. Kocherevskaya, L.B. Moscow Polytechnic University Faculty of Mechanical Engineering Department of Automation and Control Moscow Russia Moscow Aviation Institute National Research University Foreign Language Department for Aerospase Specialities Moscow Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The science of robotics is becoming more and more relevant for industrial production. Visualization of the production system involves the exact modeling of its components as accurately as possible. For a robotic technological complex, such a main component is an industrial robot. The robot model should most accurately reflect what is happening in real production, since the robot is the basis of robotic production. Modeling allows you to perform a comprehensive analysis of the production system without direct design and start-up and, if possible, easily redesign it. An application was developed for building a virtual model of the robot and the workspace. An analysis of the behavior of the manipulator makes it possible to consistently improve the accuracy of modeling and the efficiency of designing a robotic technological complex. © 2022 IEEE.

关键词： Visualization

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Influence of the Kalman Filtering Initial Setting on SINS Initial Alignment and an Improved Method for the Alignment of Large Misalignment Angles 30

Influence of the Kalman Filtering Initial Setting on SINS In...

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30th Saint Petersburg International Conference on Integrated Navigation systems, ICINS 2023

作者： Ning, Xiaoge Huang, Jixun Li, Jianxun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Department of Automation Shanghai China Beijing Aerospace Times Optical-Electronic Co. Ltd Beijing China Beijing Institute of Aerospace Control Devices Beijing China

ISBN: (纸本)9785919950950

This paper analyzes the instability of the filtering system of the initial alignment under a quasi-static base and points out the great influence of Kalman filtering initial value setting on the filtering results. This is instructive for many conditions under initial alignment and integrated navigation. Meanwhile, an improved method for the alignment of large misalignment angles is proposed by replacing the body-frame bias state with the geographic-frame bias state. Simulation experiments show that it can improve the horizontal alignment accuracy by 0.1°and vertical alignment accuracy by 1.6°. © 2023 Concern CSRI Elektropribor, JSC.

关键词： Alignment

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Robust Platoon control of Industrial Heavy-Load Autonomous Vehicles 12

Robust Platoon Control of Industrial Heavy-Load Autonomous V...

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12th International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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Motor Fault Diagnosis Under Complex Working Conditions Based on Signal Fusion and CA-CNN

Motor Fault Diagnosis Under Complex Working Conditions Based...

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Chinese automation Congress (CAC)

作者： Mingxuan Cai Qi Liu Yuxiang Liu Bo Yang Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Ministry of Education of China Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multiclass motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method.

关键词： Fault diagnosis Employee welfare Wavelet transforms Accuracy Fast Fourier transforms Motors Sensors Numerical models Velocity measurement Matrix converters

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Precise LiDAR SLAM in Structured Scene Using Finite Plane and Prior Constraint

Precise LiDAR SLAM in Structured Scene Using Finite Plane an...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xie, Yuhui Zhao, Wentao Wang, Jiahao Wang, Jingchuan Chen, Weidong Institute of Medical Robotics and Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Ministry of Education Shanghai200240 China

ISBN: (纸本)9781665481090

In this paper, we propose a precise LiDAR SLAM in optimization framework using plane-like object as landmark. Compared to general methods, finite plane feature is used to represent landmark and a new residual model is designed, making constraint from edge of landmark can be used to limit the parallel position between LiDAR and landmark, leading to a more accurate result. Moreover, floor plan is used to provide global pose for reduction of drift, and additionally feature orientation prior is used to prevent map distortion when updating the inaccurate part of floor plan. Experiments are conducted using data collected in real environment. The result qualitatively shows that proposed method can build a corrected map from floor plan without distortion, and quantitatively verifies that proposed method can outperform other baseline methods in accuracy. © 2022 IEEE.

关键词： Optical radar

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Evaluating the Influence of Object Detection Models and Dataset Selection on Traffic Accident Prediction Performance

Evaluating the Influence of Object Detection Models and Data...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Wen-Hui Chen Yu-Hung Huang Yu-Chen Lin Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Traffic accidents pose significant social and economic challenges, making effective accident prediction systems essential. Traditional machine learning methods often struggle to capture the complex, nonlinear patterns in visual accident data. Recent advancements suggest that deep learning offers a promising solution to this challenge. These vision-based systems rely on object detection as a foundational step, identifying key entities in traffic scenes captured by cameras to improve prediction accuracy. Despite its importance, the impact of object detection accuracy on overall accident prediction effectiveness remains underexamined. This study investigates this relationship through cross-dataset analysis. Experimental results show that object detection accuracy directly impacts accident prediction performance, with training dataset choice playing a critical role. Enhancing model generalization is essential to stabilizing accident prediction systems.

关键词： Training Economics Visualization Accuracy Transportation Object detection Predictive models Safety Object recognition Accidents

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Anti-swing Coordinated control of the UAV Wing Folding During the Aerial Recovery Process

Anti-swing Coordinated Control of the UAV Wing Folding Durin...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Liu, Yiheng Wang, Honglun Wang, Yanxiang Fan, Jiaxuan School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Astronautical System Engineering Beijing100076 China Shenyuan Honors College of Beihang University Beijing100191 China The Science and Technology On Aircraft Control Laboratory Beihang University Beijing100191 China Research and Development Center of China Academy of Launch Vehicle Technology Beijing100071 China

ISBN: (纸本)9789811966125

In the process of UAV aerial recovery, the UAV needs to fold its wings, which will lead to a sharp drop in the lift and cause the cable to sway. Aiming at this problem, an anti-swing coordinated control method of wing folding based on quadratic programming control allocation is proposed. It can make full use of the grid rudder of the drogue to compensate the lift lost caused by wing folding and use quadratic programming to allocate the rudder deflection angle that can not only satisfy the moment balance, but also satisfy the force balance, so as to realize the coordinated control of the drogue grid rudder and the UAV rudder surface. Simulations are conducted to verify the effectiveness of the proposed control method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFICATION USED IN NUCLEAR POWER PLANTS 29

A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFI...

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2022 29th International Conference on Nuclear engineering, ICONE 2022

作者： Zhang, Pengbin Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Wang, Hongrui State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886472

Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power plants to deal with the turbine's load tracking problem. A setpoint-shifting method is adopted to achieve the informativity of the closed-loop system, and the PID parameters are designed through an optimization method. For validating the proposed method, this paper modeled a pressurized-water reactor (PWR) nuclear power plant, a complex system consisting of three circuits, through the first principle. The primary circuit contains three main devices: a nuclear reactor, a pressurizer, and a steam generator. The models of these three devices are provided by previous literature separately. The whole model is generated through mass and energy balance. This mechanism model is used as the plant model, while the identified model is used as the control model. The initial PID parameters are set through open-loop identification using an external excitation. Then the proposed method works online to track the turbine's load in time. The effectiveness of the proposed method is illustrated by simulations. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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