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2023 International Symposium on Ultrafast Phenomena and Terahertz Waves, ISUPTW 2023

作者： Guan, Hanxiao Qiu, Weihang Ma, Yehao Tian, Liangfei Cao, Yuqi Zhang, Guangxin College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou China Ningbo University of Technology Robotics Institute Ningbo China

ISBN: (纸本)9798350317473

Terahertz technology is a non-invasive and label-free inspection method, promising for applications in various fields, especially in biomedical diagnosis. Terahertz radiation is capable for cell physiological and pathological status recognition due to its sensitivity to cell composition and structural information of biomacromolecular. In this paper, the transmission properties of glass, polymethyl methacrylate and polypropylene in THz regime were compared and polypropylene was selected as the petri dish material for cell adhesion due to its high transmission of terahertz wave. The time-domain and frequency-domain spectra, refractive index and absorption coefficient of hepatoma cell line Huh7 and normal cell line MIHA were analyzed. The experimental results showed significant distinction between the terahertz characteristic spectra of hepatoma cells and normal cells. This work can be helpful for applying terahertz technology in cancer detection at the cellular level. © 2023 IEEE.

关键词： Terahertz waves

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Numerical analysis on the transport properties and residence time distribution of ribbon biomass particles in a riser reactor based on CFD-DEM approach

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Particuology 2024年第4期87卷 133-146页

作者： Haichao Zhao Conghui Gu Bingyang Xu Yuan Liu Mingpu Du Kaiyuan Deng Jingyu Zhu Iana Voronina Zhulin Yuan School of Energy and Power Jiangsu University of Science and TechnologyZhenjiang212100China China Tobacco Jiangxi Industrial Co.Ltd. Nanchang330096China Institute of Mechanics and Power Engineering Ogarev Mordovia State UniversitySaransk430005Russia Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China

A bended ribbon biomass particle model was developed to explore the dynamic transport properties inside a riser *** time distribution(RTD)of the particles was analyzed by using the Eulerian-Lagrange *** effects of sampling height,particle density,particle size and gas-to-solid mass ratio on RTD were *** coupled Computational Fluid Dynamics and Discrete Element Method(CFD-DEM)model was verified firstly by experimental data on pressure drop and residence time distribution density *** simulation results demonstrated that the ribbon biomass particles display a typical annular-core spatial distribution during *** RTD of particles exhibit an approximate single-peaked normal *** mean residence time(MRT)can reach up to 0.7 s when the particle density is 1200 kg/m^(3).Particle with higher density has longer mean residence *** flow patterns are closer to plug flow if particle length over 12 *** particle flow pattern is not sensitive to changes in particle density and size,while the gas-to-material mass ratio has a significant impact on it.

关键词： Riser Gas-solids two-phase flow Ribbon biomass particles Residence time distribution CFD-DEM

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Research on cooperative scheduling of AGV transportation and charging in intelligent warehouse system based on dynamic task chain

Research on cooperative scheduling of AGV transportation and...

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IEEE International Conference on Networking, Sensing and control

作者： Chao Li Weimin Wu The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665472449

Multi-task assignment of multiple Automated Guided Vehicles (AGV) is one of the main processes in an intelligent warehouse. This paper models a dynamic task chain scheduling to improve the energy and time utilization of the system. The charging pile competition prevention mechanism is introduced to solve the problem of unbalanced charging resources allocation in the system. A joint scheduling strategy of charging tasks is proposed to make full use of the information of the destination of the task and shorten the driving distance of AGV. Aiming at the shortcoming of poor local search ability of genetic algorithm, an improved genetic algorithm based on genome is designed. Numerical and simulation experiments show that the proposed algorithm and model perform better than traditional methods.

关键词： Transportation Predictive models System recovery Dynamic scheduling Search problems Multitasking Numerical models

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Research on real-time export container allocation considering loading efficiency

Research on real-time export container allocation considerin...

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IEEE International Conference on Networking, Sensing and control

作者： Jianlin Qian Weimin Wu Yangfei Zhu The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665472449

Determining where to stack the containers on the storage yard of a container terminal is an important problem, because it will make a great importance on the loading efficiency. In order to minimize the container relocation times and the average transportation distance of containers, we propose a penalty based strategy for real-time container allocation. When the sequence of containers arriving at the storage yard is uncertain, this strategy can allocate the location for a single container when it arrives, so as to minimize the container relocation times and transportation distance during shipment as a whole. Experiments show that this strategy can get a relatively good result.

关键词： Loading Stacking Decision making Transportation Containers Real-time systems Sensors

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Efficient Trajectory Planning for Coordinated Arrival of Fixed-Wing UAV Swarm 1

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Zhong, Yuhang Hu, Yangxiu Chen, Yang He, Ningyu Xu, Guangtong Xu, Chao Gao, Fei Huzhou Institute Zhejiang University Huzhou313000 China College of Artificial Intelligence Nankai University Tianjin300350 China Shanghai Aerospace Control Technology Institute Shanghai201109 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

This paper proposes an efficient centralized trajectory planning algorithm for fixed-wing swarms to address the problem of coordinated arrival. A Dubins-based initial path sampling method is designed in the front end to provide a good initial guess considering kinodynamic feasibility. Then a joint optimization approach is carried out in the back end where constraints are formulated as a penalty in the cost function. Moreover, a well-formed trajectory representation is utilized to realize spatial-temporal optimization with few variables, leading to the high computational efficiency. A replanning strategy is also introduced to handle the situation when the target trajectory changes. Numerous simulation results demonstrate the efficiency and feasibility of the algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Fixed wings

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Efficient Autonomous Exploration of Unknown Environment Using Regions Segmentation and VRP 1

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Xue, Chaoyu Zhao, Tianhao Xie, Anhuan Zhu, Qiuguo Wu, Jun Xiong, Rong College of Control Science and Engineering Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Autonomous exploration of unknown environments is a critical application scenario in robotics. However, existing studies have strived to generate an efficient tour plan that enables the robots to fully explore the environment. This is due to the greedy strategies and the lack of utilizing the information of the unexplored area. To address the above issues, we propose a new exploration strategy. Our approach involves partitioning the unexplored area into distinct regions and utilizing their estimated workload and spatial structure to construct a region graph. Subsequently, a VRP (Vehicle Routing Problem) planner allocates regions to robots and generates a global tour for each robot in a coordinated manner, which is then refined and provided to the robot’s navigation module. Experimental results demonstrate that that our method outperforms existing approaches in terms of exploration speed, total distance, and overlap ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robots

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Novel vision-LiDAR fusion framework for human action recognition based on dynamic lateral connection

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IET cyber-systems and Robotics 2024年第4期6卷 21-31页

作者： Fei Yan Guangyao Jin Zheng Mu Shouxing Zhang Yinghao Cai Tao Lu Yan Zhuang The School of Control Science and Engineering Dalian University of TechnologyDalianChina Technology Centre of Dalian Customs Dalian CustomsDalianChina ZX‐YZ School of Network Science Haikou University of EconomicsHainanChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina

In the past decades,substantial progress has been made in human action ***,most existing studies and datasets for human action recognition utilise still images or videos as the primary ***-based approaches can be easily impacted by adverse environmental *** this paper,the authors propose combining RGB images and point clouds from LiDAR sensors for human action recognition.A dynamic lateral convolutional network(DLCN)is proposed to fuse features from *** RGB features and the geometric information from the point clouds closely interact with each other in the DLCN,which is complementary in action *** experimental results on the JRDB-Act dataset demonstrate that the proposed DLCN outperforms the state-of-the-art approaches of human action *** authors show the potential of the proposed DLCN in various complex scenarios,which is highly valuable in real-world applications.

关键词： neural network-based pedestrian identification sensor fusion

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On extended state Kalman filter-based identification algorithm for aerodynamic parameters

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control Theory and technology 2024年第2期22卷 235-243页

作者： Wenyan Bai Ruizhe Jia Peng Yu Wenchao Xue Beijing Aerospace Automatic Control Institute Beijing100854China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately identify aerodynamic parameters, and has desired convergence speed and convergence precision.

关键词： Aerodynamic parameters Parameter identification Extended state Kalman filter

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TCPP: A Benchmark Platform for Traffic Congestion Prediction with Multi-Source Auxiliary Data

TCPP: A Benchmark Platform for Traffic Congestion Prediction...

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Chinese Automation Congress (CAC)

作者： Yusong Zhou Xinmin Zhang Xiaoyu Jiang Zhihuan Song State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zhejiang China Huzhou Institute of Industrial Control Technology Huzhou Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

With rapid urbanization, traffic congestion has become a challenge to efficient urban transportation. To address this challenge, we develop a novel traffic congestion prediction platform (TCPP) which is a unified benchmark tool for traffic congestion prediction with multi-source auxiliary data. By combining data integration, model selection and evaluation modules, TCPP provides a standardized framework for researchers to compare and evaluate different models and datasets. Meanwhile, through a comprehensive and detailed configuration syntax, TCPP allows users to flexibly schedule tasks and execute multiple tasks continuously and uninterruptedly. In addition, two datasets collected from California highways are enriched with multi-source weather data and temporal features. We evaluate the performance of TCPP on these two datasets and demonstrate its effectiveness in predicting traffic congestion. The results demonstrate that TCPP can contribute to future research on traffic congestion prediction and the development of intelligent transportation systems and urban traffic management.

关键词： Schedules Diversity reception Data integration Predictive models Benchmark testing Syntactics Data models Forecasting Traffic congestion Meteorology

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