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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2285-2295页

作者： Song, Ruiqi Ai, Yunfeng Tian, Bin Chen, Long Zhu, Fenghua Yao, Fei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China North Automatic Control Technol Inst Taiyuan 030006 Peoples R China

Accurate and reliable object detection is a fundamental component of perception system for autonomous driving. Specially, in some circumstances like autonomous driving in surface mine, there is a fact that the particularity of scene brings tremendous challenges for object detection with a series of problems caused by the multi-scale and camouflaged objects. In this paper, a multi-scale feature fusion and attention based multi-branches framework was proposed to improve the performance of object detection for above problems called MSFANet. In the proposed MSFANet, a multi-scale feature fusion module, which was used to capture the rich context features for multi-scale high level feature maps, and a multi-scale attention module, which was used to enhance the feature saliency of objects with different scales, were designed. What's more, to improve the performance of multi-scale object detection, we build 4 different prediction branches for large, medium small and smaller scale objects respectively. At last, we built our own dataset for automatic driving in surface mine called SurMine and test the model at our own datasets and KITTI benchmark. It achieved 82.7 mAP(%) and 92.57 mAP(%) in 32 36 ms on a TITAN RTX, compared to 80.2 mAP(%) and 87.83 mAP(%) in 28 similar to 34 ms by YOLOv7 on SurMine and KITTI benchmarks.

关键词： Feature extraction Object detection Autonomous vehicles Task analysis Detectors Semantics Real-time systems feature fusion object detection surface mine

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Hierarchical Neighbors Embedding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第6期35卷 7816-7829页

作者： Liu, Shenglan Yu, Yang Liu, Kaiyuan Wang, Feilong Wen, Wujun Qiao, Hong Dalian Univ Technol Fac Elect Informat & Elect Engn Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observation space. To address this issue, in this article, we propose hierarchical neighbors embedding (HNE), which enhances the local connections through hierarchical combination of neighbors. And three different HNE-based implementations are derived by further analyzing the topological connection and reconstruction performance. The experimental results on both the synthetic and real-world datasets illustrate that our HNE-based methods could obtain more faithful embeddings with better topological and geometrical properties. From the view of embedding quality, HNE develops the outstanding advantages in dealing with data of general distributions. Furthermore, comparing with other state-of-the-art manifold learning methods, HNE shows its superiority in dealing with sparsely sampled data and weak-connected manifolds.

关键词： Data sparsity hierarchical neighbors manifold learning topological and geometrical properties. the introduced to

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Spatial Repetitive Impedance Learning control for Robot-Assisted Rehabilitation

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第3期28卷 1280-1290页

作者： Yang, Jiantao Sun, Tairen Cheng, Long Hou, Zeng-Guang Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China

In robot-assisted rehabilitation and leg exoskeletons, humans and robots are required to collaboratively complete repetitive tasks with fixed periodic paths. In such applications, impedance learning control can provide variable impedance regulation for improving the performance of physical interactions;however, designing such control is highly challenging owing to the difficulty in modeling human time-varying dynamics. By exploiting the spatial periodicity characteristics of the desired trajectory and human impedance, we propose a novel spatial repetitive impedance learning control strategy to enhance interaction performance. First, a defined spatial operator serves as the mathematics foundation for constructing the robot dynamics in the spatial domain. Then, a spatial impedance learning controller is designed. In this article, time-varying impedance profiles are estimated using spatial full-saturation iterative learning laws, while robotic parameter uncertainties are estimated using the differential adaptation law with projection modification. We validate the uniform convergence of the tracking error through a Lyapunov-like analysis and demonstrate the control effectiveness using an illustrative example. Compared with related results on temporal repetitive learning control, the proposed control approach can enable human-robot system to complete a repetitive task with unspecified speeds according to the users' strengths and motion capacity.

关键词： Adaptive control impedance learning iterative learning control rehabilitation robot repetitive learning control robot control spatial periodicity

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Further Results on Adaptive Practical Tracking for High-Order Nonlinear systems With Full-state Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 9978-9985页

作者： Xie, Xue-Jun Wu, You Hou, Zeng-Guang Qufu Normal Univ Inst Automat Qufu 273165 Shandong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this article, an adaptive practical tracking control scheme is presented for full-state constrained high-order nonlinear systems. By skillfully introducing the adaptive gain, nonlinear transformed functions and sign functions into control design, a novel continuous state-feedback controller is constructed without imposing restrictive approximation techniques and feasibility conditions. Under mild assumptions, the boundedness of all the closed-loop signals can be guaranteed, full-state constraints are not transgressed for all time, and the tracking error tends to an arbitrarily small region of zero in a finite time.

关键词： Nonlinear systems Adaptive systems Trajectory Safety Robots Lyapunov methods Fuzzy systems Adaptive practical tracking control feasibility conditions full-state constraints high-order nonlinear systems

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Adaptive Fuzzy Asymptotic Tracking control of state-Constrained High-Order Nonlinear Time-Delay systems and Its Applications

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1671-1680页

作者： Wu, You Xie, Xue-Jun Hou, Zeng-Guang Qufu Normal Univ Inst Automat Qufu 273165 Shandong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article discusses the adaptive fuzzy asymptotic tracking control for high-order nonlinear time-delay systems with full-state constraints. Fuzzy-logic systems and a separation principle are utilized to relax growth assumptions imposed on unknown nonlinearities. The adverse effect caused by unknown time delays is eliminated by choosing appropriate Lyapunov-Krasovskii functionals. By integrating nonlinear-transformed functions with a key coordinate transformation into the control design and constructing a specific compact set on the initial values of system states, the desired trajectory and parameter estimates, it is rigorously proved that all closed-loop signals are semiglobally bounded, the fuzzy approximation is valid, the full-state constraints are not violated without feasibility conditions on virtual controllers, and asymptotic tracking is achieved. The effectiveness and advantages of this control scheme are confirmed by two examples including a single-link robotic system.

关键词： Adaptive fuzzy asymptotic tracking control feasibility conditions full-state constraints high-order nonlinear time-delay systems

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Shared control of Tendon-Driven Continuum Robots Using Visibility-Guaranteed Optimization for Endoscopic Surgery

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2024年第2期6卷 487-497页

作者： Deng, Zhen Wei, Xiaoxiao Pan, Chuanchuan Li, Guotao Hu, Ying Fuzhou Univ Dept Mech Engn & Automat Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Tendon-driven continuum robots (TDCRs) with mechanical compliance have gained popularity in natural orifice transluminal endoscopic surgery (NOTES). Teleoperation problems of the TDCRs involve performance objectives in addition to the visibility constraint. Handling the coupling between potentially conflicting objectives and the visibility constraint remains challenging for surgeons when operating TDCRs. This paper presents a shared control method to assist in the teleoperation of the TDCRs, which guarantees visual targets remain within the field of view (FoV) of the TDCR. The visibility constraint is explicitly defined using a zeroing control barrier function, which is specified in terms of the forward invariance of a visible set. To ensure accuracy, the Jacobian matrix of the system is approximated online using sensing data. Then, the visibility constraint, along with the robot's physical constraints, is integrated into a quadratic program (QP) framework. This allows for the optimization of the control input of the operator subject to constraints, thus preserving visibility. Finally, simulations and experiments were conducted to demonstrate the effectiveness of the proposed approach under two teleoperation modes. The results show that the proposed method achieved a reduction of approximately 70% in ITP and 43% in MAE compared to direct teleoperation.

关键词： Robotic endoscopic surgery continuum robot optimal visual control control barrier function

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Enhancing Representation Power of Deep Neural Networks With Negligible Parameter Growth for Industrial Applications

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第11期54卷 6837-6848页

作者： Chen, Liangming Jin, Long Shang, Mingsheng Wang, Fei-Yue Chinese Acad Sci Chongqing Key Lab Big Data & Intelligent Comp Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In industrial applications where computational resources are finite and data noises are prevalent, the representation power of deep neural networks (DNNs) is crucial. Traditional network structures often require a significant increase in the parameter amount to enhance the representation power, making it difficult to achieve effective representation under parameter amount constraints. In order to alleviate this problem, this work leverages the ordinary differential equation (ODE) interpretation of deep residual networks, elucidating the relationship between the fine-grained connectivity modes of blocks in DNNs and the representation power. We build a bridge from the order of numerical methods and the order of ODEs to the representation power of DNNs. Besides, we show that higher-order ODEs can be approximated by k-step methods incorporating trainable coefficients. Empirically, we validate our theoretical insights by demonstrating the superior representation power of our proposed network structures through enhanced performance on industrial tasks, such as surface defect detection, critical temperature prediction of superconductors, and image classification under noises. The proposed method provides a new approach to the design of network structures for robust and accurate DNNs, enhancing the representation power with a negligible number of additional parameters. The code is publicly available at https://***/LongJin-lab/Order-and-Representation-Power.

关键词： Residual neural networks Vectors Noise Biological neural networks Medical services Defect detection Agriculture Deep neural networks (DNNs) industrial applications ordinary differential equation (ODE) representation power

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Dependency-Aware Vehicular Task Scheduling Policy for Tracking Service VEC Networks

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2400-2414页

作者： Li, Chao Liu, Fagui Wang, Bin Chen, C. L. Philip Tang, Xuhao Jiang, Jun Liu, Jie South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518066 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

In this paper, we study a tracking service vehicular edge computing (VEC) network that provides computation offloading service for Intelligent vehicles, where computational tasks with different urgency and dependency are required to be completed efficiently within strict time constraints. We consider the actual scenario where the environmental parameters fluctuate randomly and their distributions are unknown, thus, a long-term scheduling policy optimization problem needs to be solved. For this motivation, we first define a queueing criterion to sort the subtasks into a scheduling queue, and then model a specific Markov decision process (MDP) according to the scheduling queue. Furthermore, we propose our vehicular task scheduling policy optimizing (VTSPO) algorithm based on the most advanced policy-based deep reinforcement learning (DRL). The experimental results compared with known value-based DRL algorithms verify the advantages of the proposed VTSPO algorithm.

关键词： Task analysis Intelligent vehicles Optimization Processor scheduling Vehicle dynamics Heuristic algorithms Costs Deep reinforcement learning (DRL) scheduling policy tracking service vehicular edge computing (VEC)

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Dynamic Multiaction Recognition and Expert Movement Mapping for Closed Pelvic Reduction

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第8期19卷 8667-8678页

作者： Pan, Ming-Zhang Deng, Ya-Wen Li, Zhen Chen, Yuan Liao, Xiao-Lan Bian, Gui-Bin Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Pelvic fractures are one of the most serious traumas in orthopedic care, and reduction during routine surgery is a significant challenge. Because there are so many vital organs, blood vessels, and nerves around the pelvis, and the reduction force is large, the operational requirements for the surgeon are extremely strict and require extensive experience and surgical skills. This article proposes a method for collecting and digitizing doctors' reduction movements, which aims to help intelligent devices recognize surgeons' reduction actions and provides a means to learn from expert experience to improve the accuracy of surgery. First, the convolutional bidirectional long short-term memory algorithm with multilayer cross-fused features is proposed. It extracts time and spatial correlations between multimodal data in a hierarchical manner. Second, discrete dynamic motion primitives are adopted for mapping the surgeon's palm movement trajectory. Finally, this article constructs a data acquisition platform and collects data from surgeons with varying proficiency in closed reduction. Experiment results show that the closed reduction action recognition accuracy is 99% and posture recognition accuracy is 95.5%. The recognition algorithm proposed by this article is significantly higher than the commonly used algorithms in terms of Accuracy, Precision, Recall, and F1-Score. This article provides methods and means for the digitization of surgical expertise and transfers learning for robot-assisted surgery.

关键词： Action recognition conv-BiLSTM closed pelvic reduction dynamic movement primitives multimodal data fusion surgeon movement mapping

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Hybrid neural network-aided strong wind speed prediction along rail network

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JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS 2024年 252卷

作者： Liu, Yuhang Zhang, Zhipeng Huang, Yujie Zhao, Wenqiang Dai, Lei Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Urban Complex Risk Control & Resilience Go Shanghai Emergency Management Shanghai 200240 Peoples R China Zhejiang Commun Investment Grp Co Ltd Hangzhou 310000 Peoples R China

With the advent of networked rail operations and increasing risks caused by extreme weather conditions, there is an urgent need to enhance the safety early warning and emergency protection for rail under extreme wind environments. By deploying anemometers along the railway and monitoring the surrounding wind speeds, dispatchers can proactively take protective measures to mitigate the impact of severe weather on rail transportation. In particular, accurate time-series forecasting during periods when wind speeds exceed the threshold is critical for railway anomaly monitoring. This study employs Extreme Value Theory (EVT) to determine the threshold for strong winds. In order to identify high wind speeds, memory networks are used to memorize strong wind events in historical data. By combining the Gated Recurrent Unit (GRU) module with the attention mechanism, this study ultimately develops a hybrid neural network model in the prediction of wind speed. Based on real-world datasets of wind speeds along the Hangzhou-Haining intercity railway and along the Norway rail network via meteorological stations, the effectiveness of the hybrid neural network model has been verified by comparing base models. The proposed strong wind speed prediction has practical applications in enhancing early warning and rail safety under complex wind climate.

关键词： Strong wind Rail safety Prediction Neural network Attention mechanism

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