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15th Global Congress on Manufacturing and Management, GCMM 2021

作者： Yuan, Qiaoling Chen, Xun Wen, Donghui Key Lab. of Special Purpose Equipment and Advanced Manufacturing Technology of Ministry of Education Zhe-jiang University of Technology Hangzhou310014 China General Engineering Research Institute Faculty of Engineering and Technology Liverpool John Moores University LiverpoolL3 3AF United Kingdom

In view of the high-precision and low-damage requirements for surface polishing of hard and brittle components, an abrasive jet polishing process using a novel nozzle with specifically designed shroud was proposed. The removal mechanism of abrasive jet polishing was investigated by simulating the jet flow in the interaction area of the nozzle shroud and workpiece. The simulation results indicate that the velocity of the abrasive jet increases greatly by the shroud and the direction of the jet is aligned almost parallel to the workpiece surface. With such a design, the normal force of the abrasive jet is minimized to reduce the impact damage of the workpiece surface. Further, the stabilized abrasive jet insures a smooth machining. The machining experiments were performed on the quartz glass component, a typical hard and brittle material. The results of the shrouded abrasive jet polishing experiments show that the material removal mainly relied on the shearing and scratching of the workpiece surface rather than the mechanical shock impacts, which is consistent with the simulation findings. © Published under licence by IOP Publishing Ltd.

关键词： Glass

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Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation 39

Causal-Inspired Multitask Learning for Video-Based Human Pos...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation

arXiv

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arXiv 2025年

关键词： Self-supervised learning

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Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing

SSRN

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SSRN 2024年

作者： Li, Qi Guo, Dan Qian, Wei Zhou, Yuanen Zhao, Haifeng Sun, Xiao Wang, Meng School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

The remote photoplethysmography (rPPG) technique enables the estimation of vital signs such as heart rate by analyzing pulse-induced subtle skin color variation from facial videos. Robustly deriving cardiac pulse information with head motion (\eg, rotation and swings) interference, remains a critical challenge, as it inevitably introduces complex noise disturbances such as facial geometric deformation and head motions. Most existing approaches primarily focus on how to extract the weak pulse signals, overlooking the detrimental effects induced by such noises. In response to this, we propose a Temporal Gated Face Alignment Network (TGFAN) to adaptively counteract motion noise in long-term video sequences. As key modules in TGFAN, the bidirectional temporal face alignment block first captures inter-frame motion discrepancies and achieves temporal alignment, and then extracts motion-robust pulse features by spatiotemporal convolution. Furthermore, we also propose a temporal gating mechanism, which can dynamically guide motion-disturbed segments for face alignment to alleviate local temporal interference. Experimental evaluations on four public datasets demonstrate the superior performance of our proposed model under both intra-dataset and cross-dataset tests. Our codes will be availab.e at \url{https://***/Eadith/TGFAN}. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Superelastic Conical Friction Pendulum Isolator for Seismic Isolation of Bridges under Near-Fault Ground Motions

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Structural Control and Health Monitoring 2023年第1期2023卷

作者： Zheng, Wenzhi Tan, Ping Li, Jian Wang, Hao Liu, Yanhui Xian, Zhibin School of Civil Engineering Guangzhou University Guangzhou510006 China Key Lab. of Earthquake Resistance Earthquake Mitigation Structural Safety of Ministry of Education Guangzhou University Guangzhou510006 China Department of Civil Environmental and Architectural Engineering University of Kansas LawrenceKS United States Key Laboratory of C & PC Structures Ministry of Education Southeast University Nanjing210096 China

To improve the seismic performance of bridges, this study develops a novel superelastic conical friction pendulum isolator (SCFPI) by incorporating an improved conical friction pendulum isolator (CFPI) with shape memory alloy (SMA). The flat sliding function designed for the improved conical friction pendulum isolator is to enhance the adaptability under service loadings. The analytical model of the novel SCFPI is derived and the numerical model is developed within the OpenSees platform to investigate the hysteretic behavior under cyclic loadings. A cost-effective design method is proposed to design the SCFPI system for example bridges. The response mitigation efficacy of the novel SCFPI system is investigated using the optimum design parameters under near-fault ground motions. Several case studies are conducted to demonstrate the effectiveness of the novel SCFPI system and cost-effective design method. Results indicate that the cost-effective design method is particularly effective for parameter optimization of the novel SCFPI, which achieves effective mitigation for the displacement responses of the isolator and superstructure and also effectively controls the seismic force increment in piers. Case studies indicate the reliable re-centering capacity and mitigation efficacy of the SCFPI system for bridges under near-fault ground motions. The findings of this study contribute to upgrading structural resilience and promoting seismic applications as a reliable technical guidance. © 2023 Wenzhi Zheng et al.

关键词： Bridges

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High Crystalline Quality A-Axis Oriented Al0.56Sc0.44N Films for High Coupling Saw Applications 22

High Crystalline Quality A-Axis Oriented Al0.56Sc0.44N Films...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Xuan, Weipeng Xie, Weilun Huang, Xiwei He, Xingli Cao, Zhen Jin, Hao Dong, Shurong Luo, Jikui Hangzhou Dianzi University Ministry Of Education Key Lab. Of Rf Circuits And Systems College Of Electron. & Info Hangzhou310018 China Soochow University School Of Electronic And Information Engineering Suzhou China Zhejiang University Key Lab. Of Adv. Micro/Nano Electron. Dev. & Smart Sys. Of Zhejiang College Of Info. Sci. & Electronic Eng Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China

ISBN: (纸本)9784886864352

High crystalline quality Al0.56Sc0.44N ( 112¯0) (a-plane) films were prepared on Al2O3 ( {11¯02) substrates by magnetron reaction sputtering. X-ray diffraction (XRD) analysis indicates that the grown Al0.56Sc0.44N films are single-phased and a-plane oriented. The full width at half maximum (FWHM) of the rocking curve is only 0.4°, showing the high crystalline quality of the films. Effects of the normalized thickness hAlScN/λ on the acoustic wave propagation properties of the SAW resonator are evaluated by simulations. SAW resonators based on the sputtered non-polar (112¯0) Al0.56Sc0.44N films were prepared, which present a maximum electromechanical coupling coefficient ({keff2) of 5.7%. © 2023 IEEJ.

关键词： High Coupling coefficient Non-polar AlScN SAW

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Robust Visual Odometry Using Semantic Information in Complex Dynamic Scenes 5th

Robust Visual Odometry Using Semantic Information in Complex...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hao Wang, Le Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230009 China Department of Computer Science and Technology Hefei University of Technology Hefei Anhui230009 China

ISBN: (纸本)9789811623356

Traditional vision-based simultaneous localization and mapping (SLAM) technology cannot obtain the semantic information of the surrounding environment, which will cause the robot to fail to complete intelligent grasping, human-computer interaction, and other advanced decision tasks. There are many challenges to solve this problem, such as obtaining less semantic information of the environment with low precision and being unable to deal with dynamic objects in a real environment quickly and effectively. In this paper, a robust visual odometry using semantic information in complex dynamic scenes was designed. Specifically, we present a refined instance segmentation method based on the contextual information of the frame to improve the accuracy of segmentation. On this basis, a feature detection and elimination algorithm for dynamic objects based on instance-level semantic information is proposed to improve the localization accuracy of camera pose. We extensively evaluate our system on the public TUM data set and compare it with ORB-SLAM2 and other methods. Experiments show that our methods greatly improve the localization accuracy of the camera pose and the robustness of the system, which verifies that our system is effective in complex dynamic scenes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Social Group Query Based on Multi-Fuzzy-Constrained Strong Simulation

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ACM Transactions on knowledge Discovery from data 2021年第3期16卷 1–27页

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) and Hefei University of Technology Macquarie University NSW Australia Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education and Mininglamp Academy of Sciences Mininglamp Technology

Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interactions between members of the group. Generally, Graph Pattern Matching (GPM) technique is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this article, first, a social group query problem that needs to consider the trust between members of the group is proposed. Then, to solve this problem, a multi-fuzzy-constrained strong simulation matching model is proposed based on multi-constrained simulation, and a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence is proposed. Aiming at the inefficiency of the NTSS algorithm when pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.

关键词： Graph pattern matching contextual social network multi-fuzzy-constrained strong simulation social group query

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LEGNN: A lab.l Ensemble Perspective for Training a lab.l-Noise-Resistant GNN with Reduced Complexity

arXiv

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arXiv 2024年

作者： Zhao, Rui Shi, Bin Liang, Zhiming Ruan, Jianfei Dong, Bo Lin, Lu School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China School of Distance Education Xi'an Jiaotong University China College of Information Sciences and Technology Penn State University United States

Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by lab.l noise, that is, a small amount of incorrectly lab.led nodes can substantially misguide model training. Mainstream solutions define node classification with lab.l noise (NCLN) as a reliable lab.ling task, often introducing node similarity with quadratic computational complexity to more accurately assess lab.l reliability. To this end, in this paper, we introduce the lab.l Ensemble Graph Neural Network (LEGNN), a lower complexity method for robust GNNs training against lab.l noise. LEGNN reframes NCLN as a lab.l ensemble task, gathering informative multiple lab.ls instead of constructing a single reliable lab.l, avoiding high-complexity computations for reliability assessment. Specifically, LEGNN conducts a two-step process: bootstrapping neighboring contexts and robust learning with gathered multiple lab.ls. In the former step, we apply random neighbor masks for each node and gather the predicted lab.ls as a high-probability lab.l set. This mitigates the impact of inaccurately lab.led neighbors and diversifies the lab.l set. In the latter step, we utilize a partial lab.l learning based strategy to aggregate the high-probability lab.l information for model training. Additionally, we symmetrically gather a low-probability lab.l set to counteract potential noise from the bootstrapped high-probability lab.l set. Extensive experiments on six datasets demonstrate that LEGNN achieves outstanding performance while ensuring efficiency. Moreover, it exhibits good scalab.lity on dataset with over one hundred thousand nodes and one million edges. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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