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

IEEE Transactions on intelligent Transportation systems 2024年第5期25卷 4552-4564页

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan430063 China State Key Laboratory of Maritime Technology and Safety Wuhan430063 China National Taiwan University of Science and Technology Graduate Institute of Automation and Control and the Advanced Manufacturing Research Center Taipei10617 Taiwan Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems 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. © 2000-2011 IEEE.

关键词： Real time systems

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Matrix-injection-based transformation method for discrete-time systems with time-varying delay

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Science China(Information Sciences) 2023年第5期66卷 289-290页

作者： Chuan-Ke ZHANG Ke-You XIE Yong HE Jinhua SHE Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Engineering Tokyo University of Technology

Dear editor,In order to identify the influence of time-varying delays on the stability of control systems represented in a discretetime mode, developing stability criteria of discrete-time delayed systems has received serious attention in the past few decades (see [1–3] and their references).Consider the following linear discrete-time system with a time-varying delay:

关键词： Lyapunov functions

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Design of An intelligent control System for Underground Directional Drilling Trajectory in Coal Mines 41

Design of An Intelligent Control System for Underground Dire...

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第41届中国控制会议

作者： Wangnian Li Min Wu Ke Yao Quanxin Li Chengda Lu Sheng Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Directional drilling trajectory control technology is one of the key technologies to realize automation and intelligence of underground tunnel drilling in coal *** construction of directional drilling in underground coal mines mainly relies on manual observation of measurement-while-drilling parameters to control the drilling trajectory,which is highly dependent on manual *** paper designs an intelligent control system of underground directional drilling trajectory for coal *** the idea of hierarchical design and modular design,this system collects and displays geometric parameters,geological parameters,and engineering parameters of the directional drilling ***,an intelligent decision-making platform for directional drilling trajectory prediction and control is developed,laying a foundation for the research of directional drilling trajectory prediction and intelligent control for coal mines.

关键词： Trajectory prediction trajectory control geometric parameters geological parameters engineering parameters

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Design of an intelligent Monitoring System for Operating States in a Coal Mine Drilling Process 41

Design of an Intelligent Monitoring System for Operating Sta...

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第41届中国控制会议

作者： Xiao Yang Luefeng Chen Sheng Du Chengda Lu Wangnian Li Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development trend of intelligent drilling in coal mines,an intelligent monitoring system for operating states in a drilling process is developed to solve the problems of incomplete display of drilling parameters and ineffective *** system realizes the remote monitoring of drilling *** mainly adopts the Fuzzy C-Means clustering algorithm to cluster the drilling *** it combines expert empirical knowledge to classify coal seams into soft,hard and alternating soft and hard coal *** boundary values are calculated according to the boundary ***,three drilling operating states,rated,conservative and over-excited,are judged to provide guidance for the drilling process control.

关键词： Drilling process intelligent monitoring Fuzzy C-Means clustering Operating states

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Inverse Compensation Feedforward control Strategy for a Vertical Pneumatic Artificial Muscle System 8

Inverse Compensation Feedforward Control Strategy for a Vert...

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8th International Conference on control Science and systems Engineering, ICCSSE 2022

作者： Xiao, Huai Meng, Qingxin Lai, Xuzhi Yan, Ze Zhao, Shiying Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China

ISBN: (纸本)9781665484138

This paper proposes an inverse compensation feed-forward control strategy for a vertical pneumatic artificial muscle (PAM) system. Firstly, we conduct a preliminary experiment on the vertical PAM system, and on this basis, we analyze the motion characteristics of the system. Moreover, we clarify the control objective of this paper. Then, we establish a model that can describe the hysteresis characteristics of the system, and we further construct an inverse compensation feedforward controller by inversing this established model. Finally, we carry out some tracking control experiments based on the vertical PAM experimental platform to verify the effectiveness and superiority of the proposed control strategy, © 2022 IEEE.

关键词： Hysteresis

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Frequency Domain-Based Low-Rank Approximation for Magnetic Anomaly Detection

Frequency Domain-Based Low-Rank Approximation for Magnetic A...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yumeng Lv Congyu Liao Huan Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Department of Radiology Stanford University CA USA

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Current magnetic anomaly detection (MAD) methods prioritize signal-to-noise ratio (SNR) over signal features and edge information, leading to signal distortion. To address this issue, a new MAD approach utilizes structured lowrank approximation and block singular value decomposition based on the spatial frequency domain, dubbed FL-BSVD is proposed. First, the low-rankness structure of the magnetic anomaly signal is obtained through 2D Discrete Fourier Transform (2D-DFT) and structured Hankel transformation. Then, block singular value decomposition is applied to the Hankel matrix to reduce noise while preserving more signal edge features and enhancing detection accuracy. Finally, a field experiment comparing FL-BSVD with four commonly used methods is conducted. The experiment confirms that FL-BSVD can effectively recover magnetic anomaly signal features and edge information in a strong noisy environment.

关键词： Image edge detection Frequency-domain analysis Noise reduction Magnetic domains Feature extraction Noise measurement Magnetic anomaly detection Signal to noise ratio Singular value decomposition Periodic structures

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Correction of Images Projected on Non-White Surfaces Based on Deep Neural Network 14

Correction of Images Projected on Non-White Surfaces Based o...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Wang, Zewei Zhang, Jingjing Du, Xingzhuo Cao, Sihua Wei, Wenxuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781510657564

When projecting onto a non-white surface, the projected image is distorted or color mixing by complex luminance and chrominance information, which makes the projection result different from the visual perception of the human eye. The purpose of projection image correction is to remove these effects, and traditional solutions usually estimate parameters from the collected projection samples, compute an inverse model of the projection imaging process, and try to fit a correction function. In this paper, a deep neural network-based projection image correction network (PICN) is designed to implicitly learn complex correction functions. PICN consists of a U-shaped backbone network, a convolutional neural network that extracts projected surface features, and a perceptual loss network that optimizes the correction results. Such a structure can not only extract the deep features and surface interference features of the projected image, but also make the corrected projected image more in line with human visual perception. In addition, we built a projector-camera system under the condition of a fixed global illumination environment for verification experiment, and proved the effectiveness of the proposed method by calculating the evaluation metrics of projected images before and after correction. © 2022 SPIE.

关键词： Deep neural networks

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Drug-target interaction prediction using high order nonlinear features via neural factorization machines 4

Drug-target interaction prediction using high order nonlinea...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Qian, Quan Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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