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arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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The role of demographic compensation in stabilising marginal tree populations in North America

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ECOLOGY LETTERS 2022年第7期25卷 1676-1689页

作者： Yang, Xianyu Angert, Amy L. Zuidema, Pieter A. He, Fangliang Huang, Shongming Li, Shouzhong Li, Shou-Li Chardon, Nathalie I. Zhang, Jian East China Normal Univ Zhejiang Tiantong Forest Ecosyst Natl Observat & Res Ctr Global Change & Complex Ecosyst Sch Ecol & Environm Sci 500 Dongchuan Rd Shanghai 200241 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai Peoples R China Univ British Columbia Biodivers Res Ctr Vancouver BC Canada Univ British Columbia Dept Bot Vancouver BC Canada Wageningen Univ Forest Ecol & Forest Management Grp Wageningen Netherlands Univ Alberta Dept Renewable Resources Edmonton AB Canada Govt Alberta Dept Agr Forestry & Rural Econ Dev Edmonton AB Canada Fujian Normal Univ Sch Geog Sci Key Lab Subtrop Mt Ecol Minist Sci & Technol & Fujian Prov Funded Fuzhou Peoples R China Lanzhou Univ State Key Lab Grassland Agroecosyst Lanzhou Peoples R China Lanzhou Univ Coll Pastoral Agr Sci & Technol Lanzhou Peoples R China

Demographic compensation-the opposing responses of vital rates along environmental gradients-potentially delays anticipated species' range contraction under climate change, but no consensus exists on its actual contribution. We calculated population growth rate (lambda) and demographic compensation across the distributional ranges of 81 North American tree species and examined their responses to simulated warming and tree competition. We found that 43% of species showed stable population size at both northern and southern edges. Demographic compensation was detected in 25 species, yet 15 of them still showed a potential retraction from southern edges, indicating that compensation alone cannot maintain range stability. Simulated climatic warming caused larger decreases in lambda for most species and weakened the effectiveness of demographic compensation in stabilising ranges. These findings suggest that climate stress may surpass the limited capacity of demographic compensation and pose a threat to the viability of North American tree populations.

关键词： demoraphic compensation integral projection model plant demography population growth rate range limit vital rate

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Graph matching based on fast normalized cut and multiplicative update mapping

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PATTERN RECOGNITION 2022年 122卷 108228-108228页

作者： Yang, Jing Yang, Xu Zhou, Zhang-Bing Liu, Zhi-Yong China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China TELECOM SudParis Comp Sci Dept F-91011 Evry France Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Point correspondence is a fundamental problem in pattern recognition and computer vision, which can be tackled by graph matching. Since graph matching is basically an NP-complete problem, some approximate methods are proposed to solve it. Continuous relaxation offers an effective approximate method for graph matching problem. However, the discrete constraint is not taken into consideration in the optimization step. In this paper, a fast normalized cut based graph matching method is proposed, where the discrete constraint is introduced into the optimization step. Specifically, first a semidefinite positive affinity matrix based form objective function is constructed by introducing a regularization term which is related to the discrete constraint. Then the fast normalized cut algorithm is utilized to find the continuous solution. Last, the discrete solution of graph matching is obtained by a multiplicative update algorithm. Experiments on both synthetic points and real-world images validate the effectiveness of the proposed method by comparing it with the state-of-the-art methods. 0 2021 Elsevier Ltd. All rights reserved.

关键词： Graph matching Fast normalized cut Discrete constraint Multiplicative update

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A Fast and Robust Seam Tracking Method for Spatial Circular Weld Based on Laser Visual Sensor

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷

作者： Ma, Yunkai Fan, Junfeng Yang, Huizhen Yang, Lei Ji, Zhaohui Jing, Fengshui Tan, Min Chinese Acad Sci 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 Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Yaskawa Shougang Robot Co Ltd Shanghai 201815 Peoples R China

Spatial circular welding is an important application in industrial production. However, some feature points extraction methods for spatial circular welds have disadvantages such as large amounts of calculation and poor anti-interference ability. In addition, due to the welding thermal deformation, the spatial circular weld needs to be tracked. Therefore, a fast and robust seam tracking method for spatial circular weld based on laser visual sensors is proposed in this article. First of all, a self-updating template matching (SUTM) method is proposed to get weld feature points. Compared with the method based on geometric features, the proposed SUTM method has the advantages of rapidity and robustness. What is more, an algorithm for spatial circle center fitting based on random sampling consensus (RANSAC) is proposed, which overcomes the interference of outlier points and improves the robustness of circle center detection. At the same time, the tracking error model of the spatial circular weld is established and the welding deviation from the x-, y-, and z-directions is obtained. By correcting the deviation in real-time, the fast and robust real-time seam tracking of the spatial circular weld is realized. Through welding test, the proposed method can obtain the position of the spatial fillet circular weld quickly and robustly and complete the high-precision seam tracking.

关键词： Laser visual sensor robot welding seam tracking self-updating template matching (SUTM) spatial circular weld

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Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks

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KNOWLEDGE-BASED systems 2021年 215卷 106744-106744页

作者： Zhu, Yifan Lin, Qika Lu, Hao Shi, Kaize Qiu, Ping Niu, Zhendong Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Lib Beijing 100081 Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15260 USA

Tremendous academic information causes serious information overload problems while supporting scientific research. Scientific paper and citation recommendation systems have been developed to relieve this problem and work as a filter to furnish only relevant papers to researchers. Although previous studies have made comparative progress, this problem is still challenging because current paper recommendation systems rely on heterogeneous and multi-sourced features, thereby requiring a unified learning representation to cover different types and modalities of information. Additionally, the implicit influence of scholars' previous preferences of writing and citing on his/her new manuscript has not been well considered in the previous studies. Facing the issue from these two aspects, in this paper, a heterogeneous knowledge embedding-based attentive RNN model is proposed to recommend scientific paper citations. First, the preparation of features consists of two parts: (1) building a unified learning representation of structural entities and relations for recommending paper citations;and (2) defining and constructing a bibliographic network comprising five types of entities and five relations. The bibliographic network enables learning a unified representation so that all graphical entities and relations can be vectorized using TransD. To establish textual representations, the PV-DM model is utilized to generate numeric features for the title of each paper. Second, by combining structural and textual representations focusing on the ``author-text query" scenario, an attentive bidirectional RNN is constructed to recommend paper and citation based on an user's identity with a length-limited inquiry to capture the scholars' previous writing and citing preferences, thereby reducing recommendation error. Through the DBLP dataset, our experiment results show the feasibility and effectiveness of our method, both in terms of the number as well as the quality of the first few recom

关键词： Paper recommendation Heterogeneous knowledge Attentive recurrent neural networks Recommendation systems E-learning

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Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and Rhythmic Development

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FRONTIERS IN PLANT SCIENCE 2018年 9卷 1688-1688页

作者： Kang, Mengzhen Hua, Jing Wang, Xiujuan de Reffye, Philippe Jaeger, Marc Akaffou, Selastique Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst LIAMA Beijing Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Agr Qingdao Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Univ Montpellier CNRS Amap Unit CIRADINRAIRD Montpellier France Univ Jean Lorougnon Guede Dept Seeds & Seedlings Prod Daloa Ivory Coast Cote Ivoire

Functional-structural plant models (FSPMs) generally simulate plant development and growth at the level of individual organs (leaves, flowers, internodes, etc.). Parameters that are not directly measurable, such as the sink strength of organs, can be estimated inversely by fitting the weights of organs along an axis (organic series) with the corresponding model output. To accommodate intracanopy variability among individual plants, stochastic FSPMs have been built by introducing the randomness in plant development;this presents a challenge in comparing model output and experimental data in parameter estimation since the plant axis contains individual organs with different amounts and weights. To achieve model calibration, the interaction between plant development and growth is disentangled by first computing the occurrence probabilities of each potential site of phytomer, as defined in the developmental model (potential structure). On this basis, the mean organic series is computed analytically to fit the organ-level target data. This process is applied for plants with continuous and rhythmic development simulated with different development parameter sets. The results are verified by Monte-Carlo simulation. Calibration tests are performed both in silico and on real plants. The analytical organic series are obtained for both continuous and rhythmic cases, and they match well with the results from Monte-Carlo simulation, and vice versa. This fitting process works well for both the simulated and real data sets;thus, the proposed method can solve the source-sink functions of stochastic plant architectures through a simplified approach to plant sampling. This work presents a generic method for estimating the sink parameters of a stochastic FSPM using statistical organ-level data, and it provides a method for sampling stems. The current work breaks a bottleneck in the application of FSPMs to real plants, creating the opportunity for broad applications.

关键词： greenlab inverse method source-sink parameters functional-structural plant model stochastic development parameter estimation

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Underwater image matching by incorporating structural constraints

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC systems 2017年第6期14卷

作者： Yang, Xu Liu, Zhi-Yong Qiao, Hong Song, Yong-Bo Ren, Shu-Nan Ji, Da-Xiong Zheng, Sui-Wu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Zhongguancun East Rd 95 Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhejiang Univ Ocean Coll Zhoushan Peoples R China Huizhou Adv Mfg Technol Res Ctr Co Ltd Huizhou 516000 Peoples R China

Underwater robot plays an important role in underwater perception and manipulation tasks. Vision information processing is essential for the intelligent perception of an underwater robot, in which image matching is a fundamental topic. Feature-based image matching is suitable for the underwater environment. However, current underwater image matching usually directly applies those methods with a general purpose or designed for images obtained from the land to underwater images. The problem is that the blurring appearance caused feature descriptor ambiguity, which may greatly deteriorate the performance of these methods on underwater images. Aiming at problem, this article provides an underwater image matching framework by incorporating structural constraints. By integrating the appearance descriptor and structural information by a graph model, the feature correspondence-based image matching is formulated and solved by a graph matching method. Particularly, to solve the outlier feature problem, the graph matching method is applicable to the case where outlier features exist in both underwater images. Experiments on both synthetic points and real-world underwater images validate the effectiveness of the proposed method.

关键词： Underwater image image matching underwater robot feature correspondence

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68-3: DeepFatigueNet: A Model for Automatic Visual Fatigue Assessment Based on Raw Single-Channel EEG

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SID Symposium Digest of Technical Papers 2019年第1期50卷

作者： Yaguang Song Danli Wang Kang Yue Nan Zheng The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Three-dimension (3D) display has become increasingly popular in many fields. However, watching 3D content continuously can lead to visual fatigue that is harmful to users' vision system. Visual fatigue assessment aims at monitoring users' brain states based on the electroencephalogram (EEG) signals to identify different fatigue levels and avoid severe fatigue. Most of existing studies on the modeling of visual fatigue assessment rely on manual features extracted from EEG, which is time-consuming and needs prior knowledge. Convolutional Neural Networks (CNNs) which have been used in computer vision, speech recognition have attracted increasing interest. There is still a lack of attempts to employ end-to-end EEG analysis on visual fatigue assessment. In this paper, we propose a deep learning model DeepFatigueNet to perform automatic feature extraction and classification from raw single-channel EEG. The DeepFatigueNet is evaluated on our own visual fatigue dataset and compared with the state-of-the-art deep learning methods for EEG-based tasks. The overall accuracy of DeepFatigueNet reaches 75.9% on the three-classification task exceeding other models. The experimental results demonstrate the effectiveness of our model and show the potential of deep convolutional neural networks for end-to-end visual fatigue assessment.

关键词： Visual Fatigue Assessment EEG Deep Learning

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Research on Trajectory Planning and Motion control of 7-DoF Sawyer Manipulator

Research on Trajectory Planning and Motion Control of 7-DoF ...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Kun Luo Gang Xiong Yuxiao Wang Bin Hu Sheng Liu Shichao Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

Regarding human-machine collaborative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collaborative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others.

关键词： Interpolation Trajectory planning Collaboration Aerospace electronics Manipulators Trajectory Task analysis

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

arXiv

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arXiv 2022年

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo The University of Science and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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