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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Yang, Lu He, Hualin Liu, Tao Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin300384 China

ISBN: (纸本)9781665480451

Road damage detection is particularly essential for improving road traffic safety. Existing methods do not perform well due to complex road conditions and sparse features contained in road damage. Therefore, we propose a new road damage detection network - YPLNet, which could obtain more refined multi-scale contextual features. Specifically, the network proposed in this paper is based on YOLOv5, by adding the PSA module to the backbone network, the spatial information of the feature map at multi-scale is obtained to enrich the feature space, and the long-term information dependence between channels is further constructed. In addition, in the feature fusion stage, the LCFI module is utilized to obtain contextual features under the multi-scale receptive fields, and these contextual features include rich low-level and high-level information. Our proposed network achieves a Mean-F1 Score of 0.635 in the competition. The source code and model are publicly available at https://***/hehualin-tut/YPLNet © 2022 IEEE.

关键词： Damage detection

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Direct Visual Odometry by Fusing Luminosity and Depth Information

Direct Visual Odometry by Fusing Luminosity and Depth Inform...

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2021 International Conference on Robotics Automation and intelligent Control, ICRAIC 2021

作者： Yin, Jinming Zhou, Haibo Zhang, Haoxin Li, Chenming Sun, Guoqing Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin 300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin 300384 China

In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. The algorithm proposed in this paper uses Kinect 2 to collect RGB images with the corresponding depth information, and selects points with large changes of gray gradient to construct a luminosity error function and uses the corresponding depth information to construct a depth error function. The two error functions are merged into one function and converted into the least squares function of the pose of camera, the Levenberg-Marquardt algorithm is used to solve the camera pose. Finally, the Graph optimization theory and the g2o library are used to optimize the initial pose. Experiments show that the algorithm can reduce the error to a certain extent and reduce the drift caused by illumination changes. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Vision

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Hydrogel-elastomer-based stretchable strain sensor fabricated by a simple projection lithography method

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International Journal of Smart and Nano Materials 2021年第3期12卷 256-268页

作者： Zhenqing Li Xiangnan He Jianxiang Cheng Honggeng Li Yuan-Fang Zhang Xiaojuan Shi Kai Yu Hui Ying Yang Qi Ge Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhenChina Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and TechnologyShenzhenChina Digital Manufacturing and Design Centre Singapore University of Technology and DesignSingapore Department of Mechanical Engineering University of Colorado DenverDenverCOUSA National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbinP.R.China

Stretchable strain sensor detects a wide range of strain variation and is therefore a key component in various *** traditional ones made of elastomers doped with conductive components or fabricated with liquid conductors,ionically conductive hydrogel-based strain sensors remain conductive under large deformations and are ***,dehydration is a challenging issue for the *** have developed hydrogel-elastomer-based strain sensors where an elastomer matrix encapsulates a hydrogel circuit to prevent its ***,the reported multistep approaches are generally *** group recently reported a multimaterial 3D printing approach that enables fast fabrication of such sensors,yet requires a self-built digital-light-processing-based multimaterial 3D ***,we report a simple projection lithography method to fabricate hydrogel-elastomer-based stretchable strain sensors within 5 *** method only requires a UV projector/lamp with photomasks;the chemicals are commercially available;the protocols for preparing the polymer precursors are friendly to users without chemistry ***,the manufacturing flexibility allows users to readily pattern the sensor circuit and attach the sensor to a 3D printed soft pneumatic actuator to enable strain sensing on the *** proposed approach paves a simple and versatile way to fabricate hydrogel-elastomer-based stretchable strain sensors and flexible electronic devices.

关键词： Ionically conductive hydrogel stretchable strain sensor projection lithography

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Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

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Food Science and Human Wellness 2024年第1期13卷 166-172页

作者： Changge Guan Zhenglin Tan Shucheng Li Yi Wang Naoyuki Yamamoto Chong Zhang Songjun Wang Junjie Chen Xinhui Xing Key Laboratory for Industrial Biocatalysis Ministry of Education of ChinaDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China School of Life Science and Technology Tokyo Institute of TechnologyYokohama 226-0026Japan Sensing System Research Center National Institute of Advanced Industrial Science and TechnologyTsukuba 305-8565Japan Center for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Henan Giant Salamander Protection and Development Association Luoyang 471000China Intelligent Computing Research Center Harbin Institute of TechnologyShenzhen 518071China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolShenzhen 518055China Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhen 518067China

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in ***,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence *** this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA *** functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to *** have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide ***-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is *** study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

关键词： Chinese giant salamander Transcriptomic analysis Bioactive components Functional peptides mining

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Nonlinear vibration mechanism of elastic beam in large overall rotation with rotor eccentricity effect

Nonlinear vibration mechanism of elastic beam in large overa...

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2021 International Conference on Mechanical Engineering, intelligent Manufacturing and Automation Technology, MEMAT 2021

作者： Zhao, Lei Yan, Zhao-Fang Zhao, Xin-Hua Li, Bin Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin China Tianjin China School of Mechanic Engineering Tianjin University of Technology Tianjin China

The eccentric effect of rotor has seriously affected on the stability and precision of the mechanism at high speed for light and flexible mechanism in industrial application. The analysis of nonlinear vibration mechanism must be completed to realize precision and stable control of elastic beam in a large overall rotation. Firstly, the slender flexible rod is assumed as Euler-Bernoulli beam. We apply the deformation theory of elastic beam to establish its kinematic constraint relationship. Its dynamic model is built with Galerkin method and Hamilton principle. Then the Lagrange method is used to establish the rotor dynamics model based on the Jeffcott rotor. The coupling part between the motor and the elastic beam is assumed to be rigidly connected, and the dynamic model of the motor-elastic beam system can be obtained. Lastly, we analyze the nonlinear vibration mechanism of the elastic beam with rotor eccentricity effect through applying the fourth-order Runge-Kutta method and the superposition principle of traveling wave. It provides an important theoretical basis for making an effective vibration suppression strategy for elastic beam. © Published under licence by IOP Publishing Ltd.

关键词： Dynamic models

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA 21

Implementation of Multi-compartment Neuron Model with Synapt...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： DIng, Yiwen Fu, Wenlong Cao, Lihong Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432061

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Firmware Vulnerability Detection Method Based on Feature Filtering

A Firmware Vulnerability Detection Method Based on Feature F...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Bing Xia Chongjun Tang Wenbo Liu Shihao Chu Yu Dong dept Key Laboratory of Mathematical Engineering and Advanced Computing ZhongYuan University of Technology Zhengzhou China dept Henan Key Laboratory on Public Opinion Intelligent Analysis ZhongYuan University of Technology Zhengzhou China dept ZhongYuan University of Technology Zhengzhou China

Open source code reuse and code cross-platform deployment accelerate the spread of software vulnerabilities, and pose challenges for accurate detection of cross-platform vulnerabilities. The binary vulnerability similarity detection method based on machine learning lacks a fine-grained vulnerability similarity feature filtering mechanism, and cannot solve cross-function vulnerabilities triggered by data dependencies, resulting in a high vulnerability false positive rate in existing solutions, which limits practical applications. Software code vulnerabilities are usually caused by function key statements. Therefore, based on binary function decompilation, we identify the key statement that causes the vulnerability, analyze the cross-function data dependency of the key statements, extract the code similarity features and code vulnerability features of the key statements, and propose a feature filtering-based Binary vulnerability detection method SimFilter. SimFilter calculates the similarity of key statements by comparing the characteristics of function key statements, and infers the similarity of binary function vulnerabilities based on the similarity of key statements. The experimental results show that the vulnerability semantic features extracted by SimFilter are highly interpretable, and the accuracy rate is increased by 10.8% compared with other models. At the same time, we analyzed and evaluated the impact of different similarity features on the accuracy of SimFilter, and gave the best empirical value of the SimFilter model in firmware vulnerability detection.

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing 41

How Universal Polynomial Bases Enhance Spectral Graph Neural...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. Copyright 2024 by the author(s)

关键词： Wiener filtering

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