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43rd Chinese control Conference, CCC 2024

作者： Wang, Guoqing Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Aerodynamic force prediction is an important application of deep learning in the field of fluid mechanics. In this paper, a light graph neural network model with residual connections named LRGCN is designed to speed the computational process of the aerodynamic prediction. Comparing with other graph convolutional networks, the LRGCN reduces redundant operations and introduces skip connections, overcomes the limitations of traditional machine learning methods that require geometric airfoil parameters. By using CFD simulation, a dataset of aerodynamic forces for airfoil profiles was generated, where the nodes and edges of the flow field grids are abstracted to build a graph structure for aerodynamic force prediction. This study takes variations of the airfoil shape and pitch angle into consideration, enabling the prediction of lift and drag to be fast. In the numerical experiments, the LRGCN achieves the result that normalized test set loss is around 10-3 and the R2 is higher than 90%, which exhibits stronger robustness and higher accuracy than three other current convolutional models. And the LRGCN runs 60 times faster than the normal CFD method for the same prediction task, which significantly reduces time costs. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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A Novel Radar Emitter Fingerprint for Multiple Path Propagation Environment

A Novel Radar Emitter Fingerprint for Multiple Path Propagat...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Xuefei Zhu, Mengtao Zhang, Ruibin Li, Yunjie School of Information and Electronics Beijing Institute of Technology Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

ISBN: (纸本)9781839539954

Mining additional modulation information of signal propagation environment as a new fingerprint feature is more important for modern radar reconnaissance systems. This paper proposes a definition and processing method of a new radar emitter fingerprint from multiple path propagation environment, and demonstrates its application in an improved radar pulses deinterleaving task. For the fingerprint generation, the proposed method reconstructs a reference radar pulse by pulse description words (PDW) and correlate it to intercepted pulse sequence firstly. Then an adaptive threshold is utilized to pick out the main peaks belonging to the same radar. Next all the selected range profiles take a procedure of self-focusing and coherent accumulation among different pulses. Finally, propagation environment fingerprint (PEFP) is obtained after eliminating main-peak by CLEAN in the accumulated results. For the deinterleaving application, a sparse auto-encoder (AE) is used to extract high dimensional features from the generated PEFPs, then the features are combined into traditional PDW as extended features to implement an improved deinterleaving task. Simulation results verify the validity and effectiveness of the proposed fingerprint. The PEFP enhanced deinterleaving processing can improve the precision by 6% in non-cooperative conditions compared with traditional methods. © The Institution of Engineering & technology 2023.

关键词： Radar signal processing

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Solar evaporation for simultaneous oil-water separation and electricity generation with Janus wood-based absorbers

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Frontiers of Environmental science & Engineering 2024年第2期18卷 13-23页

作者： Yue Yang Ze Fu Qi Zhang Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological EngineeringNanjing University of Science&TechnologyNanjing 210094China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesKey Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)Jiangsu Key Laboratory of Artificial Functional MaterialsNanjing UniversityNanjing 210093China

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m2 is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.

关键词： Oily wastewater Carbonized wood Salinity gradient Electricity generation Solar irradiation

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Research on Path Planning for Satellite Formation Configuration Changes 3

Research on Path Planning for Satellite Formation Configurat...

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3rd China International SAR Symposium, CISS 2022

作者： Wu, Han Han, Fei Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai China

ISBN: (数字)9798350398717

ISBN: (纸本)9798350398717

For different observation tasks, synthetic aperture radar(SAR) satellite formation needs to adopt different configurations. In this case, how to realize the path planning is an important research problem for configuration transition. In this paper, a sequential convex programming(SCP) is proposed for the swarm reconfiguration that involve avoiding collisions and minimizing fuel. The quadratic performance index of the control variable represents the optimization index of path planning to ensure the optimal fuel consumption. Then, a linear discrete relative orbital dynamic model under the J2 perturbation is constructed. Regarding the dynamic constraints and the prevention of inter-satellite collisions as basic constraints, the path planning problem of configuration transformation is solved as a discretized convex programming problem. The SCP is introduced to realize iterated optimal planning. This algorithm reduces the computational complexity while developing the high control precision. Through experimental analysis, this method can ensure that there is no collision risk between satellites, and the planning converges to complete configuration reconstruction. © 2022 IEEE.

关键词： Convex optimization

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Flow Field Prediction of Variable Geometry using a Novel Deep CNN Model with Dual-Feature of Flow Regions 43

Flow Field Prediction of Variable Geometry using a Novel Dee...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Mai J., Cao Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Flow field prediction of variable geometry aims for the precise prediction of varying flow fields with complex geometric shapes and their variations. By combining a novel deep convolutional neural network (CNN) model with a Computational Fluid Dynamics (CFD) method, the solution of the Navier-Stokes equation can be effectively approximated with a significant reduction in the corresponding computation time cost. This paper proposes an approach named AM UNet-3 for predicting the flow field of variable geometry, which can directly predict the expected flow field of new shapes after the training of deep CNN through extracting and identifying dual features of flow field regions: geometric information features and multi-class channel features. The AM UNet-3 is a convolutional neural network model that combines a U-Net variant structure with three independent decoders and attention mechanisms. The framework of U-Net can learn the complete numerical solutions of the N-S equations to velocity and pressure fields from ground-truth data. The three independent decoders are used to output the pressure field and the velocity components in the x-direction and y-direction respectively. The embedded channel attention and spatial attention mechanisms can enhance the ability of AM UNet-3 to extract fast-changing boundary layer characteristics. The performances of AM UNet-3, U-Net and UNet-3 are tested and compared on a dataset consisting of flow field data with 981 different geometric shapes. Experiment results show that AM UNet-3 gets the fastest inference and the lowest prediction error in the three methods. Compared with the standard CFD method, AM UNet-3 is able to speed up the computation more than three orders of magnitude. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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Motion Path Planning of Agent Based on Proximal Policy Optimization Algorithm 5

Motion Path Planning of Agent Based on Proximal Policy Optim...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Wang, Jiangtao Sun, Zewen Li, Peng Sun, Liang University of Science and Technology Key Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education School of Intelligent Science and Technology Beijing100083 China University of Science and Technology Beijing Key Laboratory of Urban Underground Space Engineering Beijing100083 China

ISBN: (纸本)9798350325294

Aiming at the problems of insufficient intelligence level, computational complexity, and lack of autonomous learning capability of traditional path planning algorithms, this paper proposes a solution for implementing the motion path planning of agent using proximal policy optimization algorithm based on deep reinforcement learning. The solution can provide a universal method for the path planning of agent moving in a two-dimensional plane, which splits the task into two subtasks: path generation and path tracking, where the agent generates a feasible path based on proximal policy optimization algorithm, and the path tracking task can be implemented by a traditional control algorithm. Finally, the proposed algorithm is simulated and verified in training and testing environments. The results show that the proposed algorithm can effectively complete the path generation task, and the proximal policy optimization algorithm has advantages over the A∗ algorithm in terms of intelligence level and environmental adaptability. Furthermore, the algorithm is generalizable to all movable objects. © 2023 IEEE.

关键词： Motion planning

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Guidance Navigation and control for Chang’E-5 Powered Descent

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space(science & technology) 2021年第1期2021卷 173-187页

作者： Honghua Zhang Ji Li Zeguo Wang Yifeng Guan Beijing Institute of Control Engineering Beijing 100190China Science and Technology on Space Intelligent Control Laboratory Beijing 100190China

To achieve the goal of collecting lunar samples and return to the Earth for the Chang’E-5 spacecraft,the lander and ascender module(LAM)of the Chang’E-5 spacecraft successfully landed on the lunar surface on 1 Dec.,*** guidance,navigation,and control(GNC)system is one of the critical systems to perform this *** GNC system of previous missions,Chang’E-3 and Chang’E-4,provides the baseline design for the Chang’E-5 LAM,and the new characteristics of the LAM,like larger mass and liquid sloshing,also bring new challenges for the GNC *** GNC design for the descent and landing is presented in this *** guidance methods implemented in the powered descent are presented in detail for each *** consumption and hazard avoidance should be particularly considered in the design.A reconfigurable attitude control is adopted which consists of the quaternion partition control,phase and gain stabilization filter,and dual *** controller could provide fast attitude maneuver and better system *** the navigation,an intelligent heterogeneous sensor data fusion method is presented,and it is applied for the inertial measurement unit and velocimeter ***,the flight results of the LAM are *** sensors were able to provide valid measurement data during descent,and the thrusters and the main engine operated well as ***,a successful soft lunar landing was achieved by the LAM.

关键词： return stabilization partition

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Network synchronizability enhancement viaadding antagonistic interactions

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Autonomous intelligent Systems 2024年第1期4卷 1-7页

作者： Yue Song Xiaoqin Liu Dingmei Wang Pengfei Gao Mengqi Xue Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai201210China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai201210China Department of Control Science and Engineering Tongji UniversityShanghai201210China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji UniversityShanghai201210China Lanzhou Longneng Electric Power Technology Company Lanzhou730070China

We discover a“less-is-more”effect that adding local antagonistic interactions(negative edge weights)can enhance the overall synchronizability of a dynamical network *** explain this seemingly counterintuitive phenomenon,a condition is established to identify those edges the weight reduction of which improves the synchronizability index of the underlying *** further reveal that this condition can be interpreted from the perspective of resistance distance and network community *** obtained result is also verified via numerical experiments on a 14-node network and a 118-node *** brings new thoughts and inspirations to the future directions of optimal network design problems.

关键词： Synchronizability Complex network Laplacian matrix Antagonistic interaction

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Multi-frame Temporal Modeling for Multi-object Tracking in Satellite Video 2

Multi-frame Temporal Modeling for Multi-object Tracking in S...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Chai, Bingqian Qiao, Tingting Xie, Baorong Wang, Jue Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (纸本)9798331515669

With the advancement of remote sensing technology, video satellites have played an increasingly important role in both military and civilian domains, and they are widely used in areas such as military guidance, intelligent transportation, and disaster response. Multi-object tracking (MOT) in satellite video is a crucial research topic in these fields and has made certain progress. However, due to the difficulty of tracking objects in complex scene and low resolution, current MOT methods face issues of low accuracy in satellite video. In this work, we propose a joint-detection-and-tracking method called multi-frame temporal MOT (MFT-MOT). Firstly, a heatmap guided multi-frame temporal modeling method is introduced to extract features with temporal semantic information while suppressing background noise. Then, we utilize a weights-shared encoder-decoder network to facilitate interaction of temporal and spatial information and capture inter-frame object motion tracks at low resolution. Experiments on SAT-MTB dataset shows the effectiveness of our method. © 2024 IEEE.

关键词： Object tracking

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