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Indentation behavior of a semi-infinite piezoelectric semiconductor under a rigid flat-ended cylindrical indenter

Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 649-662页

作者： Shijing GAO Lele ZHANG Jinxi LIU Guoquan NIE Weiqiu CHEN state key laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei key laboratory for Mechanics of Intelligent Materials and Structures Department of Engineering MechanicsShijiazhuang Tiedao UniversityShijiazhuang 050043China state key laboratory of computer aided design and computer graphics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou 310027China

This paper theoretically studies the axisymmetric frictionless indentation of a transversely isotropic piezoelectric semiconductor(PSC)half-space subject to a rigid flatended cylindrical indenter.The contact area and other surface of the PSC half-space are assumed to be electrically insulating.By the Hankel integral transformation,the problem is reduced to the Fredholm integral equation of the second kind.This equation is solved numerically to obtain the indentation behaviors of the PSC half-space,mainly including the indentation force-depth relation and the electric potential-depth relation.The results show that the effect of the semiconductor property on the indentation responses is limited within a certain range of variation of the steady carrier concentration.The dependence of indentation behavior on material properties is also analyzed by two different kinds of PSCs.Finite element simulations are conducted to verify the results calculated by the integral equation technique,and good agreement is demonstrated.

关键词： piezoelectric semiconductor(PSC) insulating indenter electromechanical response singular integral equation finite element simulation

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China state key Lab of computer-aided design&computer graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National key laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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A General Simulation Method for Complex Deformation of Irregular-Shaped Origami Configurations

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Acta Mechanica Solida Sinica 2024年第1期37卷 90-98页

作者： Zhaochen Dong Yichao Zhu Xu Guo state key laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116023China

Most existing treatments for origami-folding simulations have focused on regular-shaped configurations.This article aims to introduce a general strategy for simulating and analyzing the deformation process of irregular shapes by means of computational capabilities nowadays.To better simulate origami deformation with folding orders,the concept of plane follow-up is introduced to achieve automated computer simulation of complex folding patterns,thereby avoiding intersection and penetration between planes.Based on the evaluation criteria such as the lowest storage energy with tightening and the fastest pace from tightening to unfolding,the optimal crease distribution patterns for four irregular(‘N’-,‘T’-,‘O’-,and‘P’-shaped)origami configurations are then presented under five candidates.When the dimensions of the origami are fixed,it is discovered that simpler folding patterns lead to faster deformation of the origami configuration.When the folding complexity is fixed,higher strain energy results in more rapid origami expansion.

关键词： Irregular-shaped origami Complex deformation computer-aided origami design General simulation

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of computer Science and Engineering North Minzu UniversityYinchuan750021China key laboratory of Image and graphics Intelligent Processing of state Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China key laboratory of Image and graphics Intelligent Processing of state Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia computer-aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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computer-aided molecular design and optimization of potent inhibitors disrupting APC-Asef interaction

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Acta Pharmaceutica Sinica B 2024年第6期14卷 2631-2645页

作者： Xuefei Wang Zeqian Du Yuegui Guo Jie Zhong Kun Song Junyuan Wang Jianqiang Yu Xiuyan Yang Chen-Ying Liu Ting Shi Jian Zhang Department of Pathophysiology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of EducationShanghai Jiao Tong UniversitySchool of MedicineShanghai 200025China key laboratory of Protection Development and Utilization of Medicinal Resources in Liupanshan AreaMinistry of EducationPeptide&Protein Drug Research CenterSchool of PharmacyNingxia Medical UniversityYinchuan 750004China state key laboratory of Microbial Metabolism Joint International Research Laboratory of Metabolic and Developmental SciencesSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Department of Colorectal and Anal Surgery Xinhua HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Basic Science Research Center Base(Pharmaceutical Science) Yantai UniversityYantai 264005China Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong UniversitySchool of MedicineShanghai 200025China state key laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Colorectal cancer(CRC)is the second leading cause of cancer mortality worldwide.At initial diagnosis,approximately 20%of patients are diagnosed with metastatic CRC(mCRC).Although the APC-Asef interaction is a well-established target for mCRC therapy,the discovery and development of effective and safe drugs for mCRC patients remains an urgent and challenging endeavor.In this study,we identified a novel structural scaffold based on MAI inhibitors,the first-in-class APC-Asef inhibitors we reported previously.ONIOM model-driven optimizations of the N-terminal cap and experimental evaluations of inhibitory activity were performed,and 24-fold greater potency was obtained with the best inhibitor compared to the parental compound.In addition,the cocrystal structure validated that the two-layerπ-πstacking interactions were essential for inhibitor stabilization in the bound state.Furthermore,in vitro and in vivo studies have demonstrated that novel inhibitors suppressed lung metastasis in CRC by disrupting the APC-Asef interaction.These results provide an intrinsic structural basis to further explore drug-like molecules for APC-Asef-mediated CRC therapy.

关键词： APC-Asef Peptidomimetic computer-aided molecular design ONIOM model π-πstacking Metastatic colorectal cancer

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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-aided Internet of Vehicles

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Engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China state key laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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Deep3DSketch-im:rapid high-fidelity AI 3D model generation by single freehand sketches

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 149-159页

作者： Tianrun CHEN Runlong CAO Zejian LI Ying ZANG Lingyun SUN College of computer Science and Technology Zhejiang UniversityHangzhou 310027China School of Software Technology Zhejiang UniversityHangzhou 310027China School of Information Engineering Huzhou UniversityHuzhou 313000China

The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to their complex nature and lack of training data.The conventional approach of creating 3D content through computer-aided design(CAD)is labor-intensive and requires expertise,making it challenging for novice users.To address this issue,we propose a sketch-based 3D modeling approach,Deep3DSketch-im,which uses a single freehand sketch for modeling.This is a challenging task due to the sparsity and ambiguity.Deep3DSketch-im uses a novel data representation called the signed distance field(SDF)to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points,and a specially designed neural network that can capture point and local features.Extensive experiments are conducted to demonstrate the effectiveness of the approach,achieving state-of-the-art(SOTA)performance on both synthetic and real datasets.Additionally,users show more satisfaction with results generated by Deep3DSketch-im,as reported in a user study.We believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

关键词： Content creation Sketch Three-dimensional(3D)modeling 3D reconstruction Shape from X Artificial intelligence(AI)

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STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 224-238页

作者： Lujing Chen Rui Liu Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei tional and Local Joint Engineering laboratory of computer aided design School of Software EngineeringDalian UniversityDalian 116622China School of computer Science and Technology Dalian University of TechnologyDalian 116024China

In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive frames.However,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in space.Thus,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each frame.To overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous frame.The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods.The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

关键词： Human motion prediction Transformer Gragh convolutional network

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View-Aware Image Object Compositing and Synthesis from Multiple Sources

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Journal of computer Science & Technology 2016年第3期31卷 463-478页

作者： Xiang Chen Wei-Wei Xu Sai-Kit Yeung Kun Zhou state key laboratory of computer aided design and computer graphics Zhejiang University Hangzhou 310058 China Vision Graphics and Computational Design Group Singapore University of Technology and Design Singapore 487372 Singapore

Image compositing is widely used to combine visual elements from separate source images into a single image. Although recent image compositing techniques are capable of achieving smooth blending of the visual elements from different sources, most of them implicitly assume the source images are taken in the same viewpoint. In this paper, we present an approach to compositing novel image objects from multiple source images which have different viewpoints. Our key idea is to construct 3D proxies for meaningful components of the source image objects, and use these 3D component proxies to warp and seamlessly merge components together in the same viewpoint. To realize this idea, we introduce a coordinate- frame based single-view camera calibration algorithm to handle general types of image objects, a structure-aware cuboid optimization algorithm to get the cuboid proxies for image object components with correct structure relationship, and finally a 3D-proxy transformation guided image warping algorithm to stitch object components. We further describe a novel application based on this compositing approach to automatically synthesize a large number of image objects from a set of exemplars. Experimental results show that our compositing approach can be applied to a variety of image objects, such as chairs, cups, lamps, and robots, and the synthesis application can create novel image objects with significant shape and style variations from a small set of exemplars.

关键词： image cloning 3D proxy probabilistic modeling data-driven method

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