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A Real-time Fire Segmentation Method Based on A Deep Learning Approach

IFAC-PapersOnLine 2022年第6期55卷 145-150页

作者： Mengna Li Youmin Zhang Lingxia Mu Jing Xin Ziquan Yu Shangbin Jiao Han Liu Guo Xie Yi Yingmin Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an Shaanxi 710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec H3G 1M8 Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics (NUAA) Nanjing Jiangsu China

As a kind of the forest “fault”, fire is highly destructive and difficult to rescue. Fire segmentation is helpful for firefighters to understand the fire scale and formulate a reasonable fire-fighting plan. Therefore, this paper proposes a real-time fire segmentation method based on deep learning. This method is an improved version of deeplbav3+, which is an encoder-decoder structure network. Encoder network is composed of deep convolutional neural network and atrous spatial pyramid pooling. Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy. Therefore, in order to compensate for the loss of segmentation accuracy, on the basis of the original decoder network, this paper adds two different shallow features to make the network contain rich fire feature information. Experimental results show that the comprehensive performance of this method is better than the original deeplabv3+, especially the segmentation speed of the network is greatly improved, which is about 59 FPS.

关键词： Forest fire Fire segmentation Atrous spatial pyramid pooling Mobilenetv3 Deeplabv3+

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Local Path Planning Algorithm for UGV Based on Improved Covariance Matrix Adaptive Evolution Strategy

Local Path Planning Algorithm for UGV Based on Improved Cova...

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第33届中国控制与决策会议

作者： Jiangbo Zhao Jiaquan Zhang Junzheng Wang Xin Zhang Yanlong Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology Technical center Xinxing Cathay International Group Co.Ltd. Techenology Center of Special Vehicle Manufacture and Evaluation Engineering of Beijing Beijing North Vehicle Group Co.Ltd.

Local path planning algorithm is one of the key technologies for unmanned ground vehicle(UGV).In order to reduce the computational complexity of the local path planning algorithm and ensure the real-time performance of the algorithm,a non-uniform column grid lines modeling method is introduced,and on this basis,a modeling method for planning paths is *** at the path planning problem in a multi-obstacle environment,the evaluation function of the path is constructed from four aspects,and the covariance matrix adaptive evolution strategy(CMA-ES)is used to solve the nonlinear optimization *** order to further reduce the amount of calculation,the CMA-ES algorithm is improved by the dynamic adjustment strategy of population *** results show that the path planning algorithm can effectively realize the local path planning in complex environment.

关键词： Local path planning CMA-ES Unmanned ground vehicle

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Deep Learning-Based LOS and NLOS Parameters Estimation, Tracking, and Identification

Deep Learning-Based LOS and NLOS Parameters Estimation, Trac...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Kai Chen Jun Wang Jie Yang Le Liang Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Millimeter-wave (mmWave) communication systems have large bandwidths and, combined with large-scale antenna arrays and beamforming technology, facilitate higher resolution of angle and delay measurements. This, in turn, provides opportunities for integrated sensing and communication (ISAC). Accurate and reliable estimation of mmWave channel state information is crucial for ISAC. In this paper, we propose a deep learning-based method for estimating channel multipath parameters, tracking, and identifying line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Specifically, we leverage a network named “you only look once (YOLO)”, a highly efficient object detection network in computer vision, to detect the angles of arrival (AOAs) and angles of departure (AODs) of multipaths from received radio frequency signals. Moreover, we use a tracking-by-detection algorithm, ByteTrack, to track the variation of each path's AOA and AOD under a mobile scenario. Thanks to these vision-based channel estimation approaches, we reveal that LOS and NLOS paths exhibit different trajectory properties, allowing us to precisely identify LOS and NLOS paths, a challenge originally in the context of ISAC. We validate the proposed estimation, tracking, and identification methods on real measurement data and demonstrate that the proposed estimation method shows better performance compared to existing methods.

关键词： Antenna measurements Computer vision Simultaneous localization and mapping Millimeter wave measurements Estimation Channel estimation Transforms Integrated sensing and communication Millimeter wave communication Antenna arrays

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Neural based PID control for networked processes

Neural based PID control for networked processes

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International Symposium on Advanced control of Industrial Processes (ADCONIP)

作者： J. Chen N. Cheng J. Zhang Department of Chemical Engineering Chung Yuan Christian University Chungli Taiwan Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China National Institute for Standards and Technology Boulder CO USA

A neural based PID feedback control method for networked process control systems is presented. As there are some uncertain factors such as external disturbance, randomly delayed measurements or control demands in real networked process control systems, the proposed PID controller is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed loop systems. To demonstrate the potential applications of the proposed strategy, an example of a simulated batch reactor is provided. The proposed design method is shown to be useful and effective in dealing with network process control systems.

关键词： Process control Delay Artificial neural networks control systems Stochastic processes Inductors Entropy

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Extended Disturbance Observer Based Execution Adjustment Estimation Method of Dynamic Demand Response

Extended Disturbance Observer Based Execution Adjustment Est...

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International Conference on control, Automation and Information Sciences (ICCAIS)

作者： Yaqing Zhao Xin Shan Wenjun Bi Ying Wang Kun Yuan Kaifeng Zhang School of Cyber Science and Engineering Southeast University Nanjing China System R&D Center NARI Group Corporation (State Grid Electric Power Research Institute) Nanjing China Key Laboratory of Measurement and Control of CSE Southeast University Nanjing China

ISBN: (数字)9781728123110

ISBN: (纸本)9781728123127

Since Dynamic demand response (DDR) has the characteristics of large quantity and uncertainty, it is difficult to get accurate response power in real time. At the same time, it is very important to know the execution adjustment of DDR accurately and quickly for updating DDR control strategy and frequency regulation of power system. In this paper, we design an extended disturbance observer (EDO) to estimate the execution adjustment of DDR. Firstly, in order to facilitate the design of estimation scheme, a simplified load frequency control (LFC) model is obtained by using model fitting method. Then, by combining the load variation with tie-line power exchange as an extended disturbance, a reduced-order LFC model of power system is derived. Finally, based on the reduced-order LFC model, EDO can be designed by pole-assignment method. The execution adjustment of DDR can be obtained by subtracting the measured value of tie-line power from the estimated value of the observer. From the simulation results of a multi-area LFC control system, the effectiveness of the proposed scheme is demonstrated.

关键词： Frequency control Power system dynamics Load modeling Disturbance observers

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Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

arXiv

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arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

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New Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing Processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao Qingbo He Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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A Hierarchical Graph Search Method for Path Planning of Unmanned Ground Vehicle for Freight Transportation

A Hierarchical Graph Search Method for Path Planning of Unma...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Jianing Hu Zhen Li Sen Guo Weiran Yao School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China CRRC Nanjing Puzhen Co. Ltd Nanjing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

For the problem of collision volume change when forklift unmanned vehicles work in complex storage environments, a multi-level graph search path planning method is proposed. The first level is the path planning of the Unmanned Ground Vehicle (UGV) body, which uses the grid map and the A * algorithm to plan the path of the UGV transferring in the scene; the second level is the joint path planning of the UGV and the object being carried, to avoid overcomplicating this issue, the unmanned vehicle will be limited to pushing the cargo only forwards. The third level is the path planning when the unmanned vehicle carries multiple objects, which decomposes the whole scene into multiple sub-scenes, plans the paths of the unmanned vehicle in the sub-scenes separately, and searches for the optimal combination of sub-scenes. Higher level searches will invoke lower level algorithms and provide a virtual scene and target as input. The experimental results show that this multilevel path planning method is optimal and complete in solving the forklift unmanned vehicle for handling a single object, and can give a feasible solution for handling multiple objects.

关键词： Costs Transportation Search problems Path planning Land vehicles complexity theory Autonomous vehicles

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images

Interpretable analysis of feature importance and implicit co...

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Industrial Cyber-Physical systems (ICPS)

作者： Xiaohu AO Feng Wang Juan Zhao Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. There have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. The interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： Deep learning Analytical models Correlation Image recognition Neural networks Thumb Gray-scale

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Reactive power compensation in microgrids via distributed control strategy

Reactive power compensation in microgrids via distributed co...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xiong Hu Hong Zhou Zhi-Wei Liu Zhi-Hong Guan Ming Chi Department of Automation Wuhan University P. R. China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing P. R. China College of Automation Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781467384155

To minimize power losses in microgrids, we concentrate on reactive power compensation by microgenerators connected to microgrid via electronic interfaces. Comparing exits works, not only the constraint of power balance, but also the bounded capacity of microgenerator is taken into account. We propose a distributed algorithm for the reactive power compensation problem with a fully connected communication network, to solve the optimal reactive power compensation problem in microgrids. Case study illustrates the proposed algorithm can effectively solve the reactive power compensation problem.

关键词： Reactive power Microgrids Distributed algorithms Topology Linear programming Algorithm design and analysis Mathematical model

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