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Transfer Matrix Method-Based Dynamic Model to Compensate the Pulsating Pressure in Strain-Based Nonintrusive Pressure Measurement for the L-Shaped Pipe

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SSRN

SSRN 2023年

作者： Wang, Ze-Chao Qu, Yong-Zhi Yan, Wang-Ji Yuen, Ka-Veng Zhou, Zude Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Mechanical and Industrial Engineering University of Minnesota Duluth United States State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau China School of Mechanical Engineering Wuhan University of Technology Wuhan China

Since dynamic strain-pressure sensitivity coefficient (DSSC) is not always equal to the static SSC(SSSC), errors could be introduced. The present study introduces a transfer matrix method (TMM)-based dynamic model to compensate the pulsating pressure in strain-based nonintrusive pressure measurement for the L-shaped pipe to avoid the use of the shock tube. Firstly, the baseline dynamic model is calibrated with the tested SSSC and natural frequencies using model updating. Secondly, to select reasonable parameters to calibrate the baseline analytical model, the parametric sensitivity analysis is conducted with the derived closed-form sensitivities of the DSSC and natural frequency. The uniqueness of the field transfer matrix is also proven strictly regardless of whether the eigenvectors of the coefficient matrix of the governing equation are normalized. The proposed compensation method for pulsating pressure is finally validated in a hydraulic pipe system, which demonstrates that the proposed method can reduce measurement error effectively. © 2023, The Authors. All rights reserved.

关键词： Natural frequencies

Characterization of exploratory patterns and hippocampal–prefrontal network oscillations during the emergence of free exploration

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Science Bulletin 2021年第21期66卷 2238-2250,M0004页

作者： Wenxiu Dong Hongbiao Chen Timothy Sit Yechao Han Fei Song Alexei L.Vyssotski Cornelius T.Gross Bailu Si Yang Zhan Guangdong Provincial Key Laboratory of Brain Connectome and Behavior CAS Key Laboratory of Brain Connectome and Manipulationthe Brain Cognition and Brain Disease Institute(BCBDI)Shenzhen Key Laboratory of Translational Research for Brain DiseasesShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsShenzhen 518055China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169 University of Chinese Academy of Sciences Beijing 100049China Institute of Neuroinformatics the University of Zürich and Swiss Federal Institute of Technology(ETH)Zurich CH-8057Switzerland European Molecular Biology Laboratory(EMBL) Monterotondo 00015Italy School of Systems Science Beijing Normal UniversityBeijing 100875China

During free exploration,the emergence of patterned and sequential behavioral responses to an unknown environment reflects exploration traits and ***,the behavioral dynamics and neural substrates underlying the exploratory behavior remain poorly *** developed computational tools to quantify the exploratory behavior and performed in vivo electrophysiological recordings in a large arena in which mice made sequential excursions into unknown *** entropy was calculated to characterize the cumulative and moment-to-moment behavioral dynamics in explored and unexplored *** field potential analysis revealed that the theta activity in the dorsal hippocampus(d HPC)was highly correlated with the occupancy *** d HPC and prefrontal cortex(PFC)oscillatory activities could classify various aspects of free *** of exploration was accompanied by a coordinated decrease and increase in theta activity in PFC and d HPC,*** results indicate that d HPC and PFC work synergistically in shaping free exploration by modulating exploratory traits during emergence and visits to an unknown environment.

关键词： Exploration Prefrontal cortex Hippocampus Neural oscillations Entropy

Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

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arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation. © 2022, CC BY.

关键词： Three dimensional computer graphics

Weakly Aligned Feature Fusion for Multi-modal Object Detection

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arXiv 2022年

作者： Zhang, Lu Liu, Zhiyong Zhu, Xiangyu Song, Zhan Yang, Xu Lei, Zhen Qiao, Hong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100086 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China The Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, depth, etc. However, multi-modal data often suffer from the position shift problem, i.e. the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multi-modal features and puzzles the CNN training. In this paper, we propose a general multi-modal detector named Aligned Region CNN (AR-CNN) to tackle the position shift problem. Firstly, a region feature alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal region features. Secondly, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns. Thirdly, we present a new multi-modal feature fusion method, which selects the more reliable feature and suppresses the less useful one via feature re-weighting. Additionally, by locating bounding boxes in both modalities and building their relationships, we provide novel multi-modal labelling named KAIST-Paired. Extensive experiments on 2D and 3D object detection, RGB-T and RGB-D datasets demonstrate the effectiveness and robustness of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

Signal Feature Analysis of Contact Force at the Tip of a Flexible Ureteroscope 42

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Signal Feature Analysis of Contact Force at the Tip of a Fle...

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Yinan, Deng Tangwen, Yang Shaotao, Dai Song, Guoli Beijing Jiaotong University Inst. of Info. Sci. and the Beijing Key Lab. of Advanced Information Science and Network Technology Beijing China Beijing Jiaotong University School of Electrical Engineering Beijing China Shenang Institute of Automation Cas State Key Laboratory of Robotics Shenyang China

ISBN: (纸本)9781728119908

This paper presents a signal analysis approach to identify the contact objects at the tip of a flexible ureteroscope. First, a miniature triaxial fiber optic sensor based on Fiber Bragg Grating(FBG) is devised to measure the interactive force signals at the ureteroscope tip. Due to the multidimensional properties of these force signals, the principal components analysis(PCA) method is introduced to reduce dimensions. The signal features are then extracted from the representative principal component signals using the wavelet transform(WT) method. Experimental results show that the contact objects at the tip of a ureteroscope are readily discriminated from the measured force signals with the proposed *** Relevance - This work commits to analyze the contact force signals at the tip of a flexible ureteroscope for the purpose of contact objects identification. © 2020 IEEE.

关键词： Fiber Bragg gratings

Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape

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arXiv 2022年

作者： Zhu, Xiangyu Yu, Chang Huang, Di Lei, Zhen Wang, Hao Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing100190 China Beijing100049 China Beijing Advanced Innovation Center for BDBC Beihang University Beijing China Beijing100049 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Engineering Westlake University Hangzhou China

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape. Copyright © 2022, The Authors. All rights reserved.

关键词： Geometry

FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Wu, Jinxing Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China

Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds. However, there still present many challenges for efficient and robust feature fusion in the field of 3D object detection. In this paper, first, unstructured 3D point clouds are filled in the 2D plane and 3D point cloud features are extracted faster using projection-aware convolution layers. Further, the corresponding indexes between different sensor signals are established in advance in the data preprocessing, which enables faster cross-modal feature fusion. To address LiDAR points and image pixels misalignment problems, two new plug- and-play fusion modules, LiCamFuse and BiLiCamFuse, are proposed. In LiCamFuse, soft query weights with perceiving the Euclidean distance of bimodal features are proposed. In BiLiCamFuse, the fusion module with dual attention is proposed to deeply correlate the geometric and textural features of the scene. The quantitative results on the KITTI dataset demonstrate that the proposed method achieves better feature-level fusion. In addition, the proposed network shows a shorter running time compared to existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Optomechanical dissipative solitons (vol 600, pg 75, 2021)

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NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China