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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Recent developments of the reconstruction in magnetic particle imaging

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 290-302页

作者： Lin Yin Wei Li Yang Du Kun Wang Zhenyu Liu Hui Hui Jie Tian CAS Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Medical Imaging Center the First Affiliated HospitalJinan UniversityGuangdong 510632China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100083China

Magnetic particle imaging(MPI)is an emerging molecular imaging technique with high sensitivity and temporal-spatial *** reconstruction is an important research topic in MPI,which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration *** reconstruction primarily involves system matrix-and x-space-based *** this review,we provide a detailed overview of the research status and future research trends of these two *** addition,we review the application of deep learning methods in MPI reconstruction and the current open sources of ***,research opinions on MPI reconstruction are *** hope this review promotes the use of MPI in clinical applications.

关键词： Magnetic particle imaging Image reconstruction System matrix X-space

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Parallel vision and learning for intelligent perception in smart driving 1

Parallel vision and learning for intelligent perception in s...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhang, Hui Li, Yidong Li, Xuan Wang, Fei-Yue Beijing Jiaotong University Beijing China Peng Cheng Laboratory Shenzhen China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Modern vision models are built upon large-scale diversified dataset, which is labor-intensive and cost-ineffective to collect and label. They are usually effective under simple constrained conditions, while the performance gets worse under actual traffic scenes. To address aforementioned problems, we propose a visual analysis framework based on parallel vision and learning for intelligent perception in smart driving. Specifically, our framework first generate and select data exploiting artificial image system. Then, computational experiments are conducted with predictive learning for model design, learning, and evaluation. Our framework is powered with virtual-actual interactive ability through parallel execution, integrating information from different scenes. The difficulty of the visual model in the actual scene can be applied to guide the model training in the artificial scene. We also carried out a case study to preliminarily demonstrate the effectiveness of the proposed framework. © 2021 IEEE.

关键词： Computer vision

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Environmental monitoring system based on hybrid mode network structure

Environmental monitoring system based on hybrid mode network...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Li, Zhishuo Yang, Guodong Li, En Liang, Zize Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems 95 Zhongguancun East Road Beijing100190 China Yuquan Road Beijing100049 China

ISBN: (纸本)9781665436786

To achieve large-scale and high bandwidth environmental monitoring, this paper proposes a hybrid network structure based on the ZigBee network and Mesh network, which consists of the backbone network and nodes, branch network and nodes, and monitoring center. Both the backbone network node and branch network node with sensor information collection and wireless communication functions are designed. And a monitoring center is developed to display environmental information in real time. With the hybrid-mode network, the environment monitoring experiments were carried out to verify the feasibility of the system. The results show that compared to traditional environmental monitoring systems, the proposed system has the advantage of strong networking ability, large bandwidth, long transmission distance, and low cost. © 2021 IEEE.

关键词： Bandwidth

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Neural-Dynamics-Based Active Steering control for Autonomous Vehicles with Noises

Neural-Dynamics-Based Active Steering Control for Autonomous...

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IEEE Symposium on Intelligent Vehicle

作者： Ying Liufu Long Jin Fei-Yue Wang School of Information Science and Engineering Lanzhou University Lanzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper introduces a neural-dynamics-based active steering control (NDASC) scheme developed under artificial systems, computational experiments, and parallel execution (ACP) framework, aimed at enhancing the stability and reliability of autonomous vehicles in noisy environments. Based on the Taylor expansion theorem, noises can be represented in the form of polynomials for the desired accuracy, and therefore polynomial noises can be viewed as a more generalized representation of noises. Then, the proposed NDASC scheme includes a model predictive active steering control (MPASC) strategy solved by a polynomial noise resilience neural dynamics (PNRND) model. Computational experiments parallelly implemented upon the CarSim-Simulink platform substantiate the effectiveness and robustness of the proposed NDASC scheme, providing significant theoretical and practical insights for control strategies of autonomous vehicles under various noisy environments.

关键词： Noise Predictive models Polynomials Stability analysis Robustness Noise measurement Vehicle dynamics

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Snoring detection based on a stretchable strain sensor

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Science China(Information Sciences) 2021年第7期64卷 224-226页

作者： Zhengwei LI Qingkun SONG Long CHENG Min TAN School of Automation Harbin University of Science and Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Snoring is caused by the stenosis of the upper respiratory tract, which can lead to the vibration of the palatine *** brings not only harm to the snorer [1] but also sleep deprivation to those around them [2]. Furthermore, studies show that loud snorers have a 67%greater chance of suffering from a stroke and a 34%higher risk of having a heart attack. To identify people at risk from these diseases,snoring recording and analysis become highly important [3].

关键词： Information systems and Communication Service

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Vehicle Trajectory Prediction Based on Graph Attention Network 6th

Vehicle Trajectory Prediction Based on Graph Attention Netwo...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Chaochen, Zhuolei Zhang, Qichao Li, Ding Li, Haoran Pang, Zhonghua North China University of Technology Beijing100144 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hunan University Changsha410082 China

ISBN: (纸本)9789811692468

Trajectory prediction is one of the main challenges to autonomous vehicles. Except for the predicted vehicle historical trajectory information, viable solutions for this task must also consider the static geometric context, such as lane centerlines, and the dynamic interaction information among traffic participants, such as the influence of surrounding vehicles or pedestrians on the predicted vehicle. Recently, mainstream methods are mostly based on recurrent neural networks, which have achieved state-of-the-art (SOTA) performance on distance-based prediction metrics. In order to consider the influence of static geometric context and dynamic interaction information better for the vehicle trajectory prediction, we propose a two-stage prediction framework and a combined VAE-GAT model. Stage I estimates the future destination point of the predicted vehicle using a destination point estimation module based on VAE. In stage II, it produces multiple trajectories using a multiple trajectories prediction module based on GAT which consists of an LSTM-based encoder-decoder and a graph attention network. Finally, one specific trajectory is selected from multiple trajectories conditioned on the estimated destination point. The experimental results and performance comparison based on the Argoverse motion forecasting dataset are given to prove the effectiveness of the proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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A Study on Decentralized Autonomous Organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

A Study on Decentralized Autonomous Organizations Based Inte...

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2021 China automation Congress, CAC 2021

作者： Hou, JiaChen Ding, WenWen Liang, Xiaolong Zhu, FengHua Yuan, Yong Wang, FeiYue Institute of Systems Engineering Macau University of Science and Technology China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system's complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work. © 2021 IEEE

关键词： Smart contract

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng The Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology Wuhan430063 China The State Key Laboratory of Maritime Technology and Safety Wuhan430063 China The Automation and Control Center The Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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