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Friction 2024年第6期12卷 1119-1132页

作者： Song YUAN Xiaoguang GUO Hao WANG Renke KANG shang gao State Key Laboratory of High-performance Precision Manufacturing Dalian University of TechnologyDalian 116024China

The roughness of the contact surface exerts a vital role in rubbing.It is still a significant challenge to understand the microscopic contact of the rough surface at the atomic level.Herein,the rough surface with a special root mean square(RMS)value is constructed by multivariate Weierstrass–Mandelbrot(W–M)function and the rubbing process during that the chemical mechanical polishing(CMP)process of diamond is mimicked utilizing the reactive force field molecular dynamics(ReaxFF MD)simulation.It is found that the contact area A/A0 is positively related with the load,and the friction force F depends on the number of interfacial bridge bonds.Increasing the surface roughness will increase the friction force and friction coefficient.The model with low roughness and high lubrication has less friction force,and the presence of polishing liquid molecules can decrease the friction force and friction coefficient.The RMS value and the degree of damage show a functional relationship with the applied load and lubrication,i.e.,the RMS value decreases more under larger load and higher lubrication,and the diamond substrate occurs severer damage under larger load and lower lubrication.This work will generate fresh insight into the understanding of the microscopic contact of the rough surface at the atomic level.

关键词： diamond random roughness reactive force field molecular dynamics(ReaxFF MD) friction Weierstrass-Mandelbrot(W-M)function chemical mechanical polishing(CMP)

Application of artificial intelligence in diagnosis of pulmonary tuberculosis

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Chinese Medical Journal 2024年第5期137卷 559-561页

作者： Jingli Du Yue Su Juan Qiao shang gao Enjun Dong Ruilan Wang Yanhui Nie Jing Ji Zhendong Wang Jianqin Liang Wenping Gong Tuberculosis Control Team Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Department of Computer Science and Technology College of Management Science and EngineeringCapital University of Economics and BusinessBeijing 100071China The Second Ward of Tuberculosis Department Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China

Tuberculosis(TB)is an infectious disease caused by Mycobacterium tuberculosis.Although the diagnostic technology of pulmonary tuberculosis(PTB)has advanced,accurate and differential diagnoses of PTB are still challenging.In recent years,the rapid development of artificial intelligence(AI)and its wide application in the medical field have provided new opportunities for diagnosing and treating TB and PTB.The machine learning model of AI has not only helped physicians improve diagnostic accuracy,but also enabled them to make early preventive diagnoses for individuals at increased risk of infection.Furthermore,AI can guide physicians to formulate targeted treatment strategies for PTB patients with different conditions.

关键词： tuberculosis diagnosis artificial

Smart Breeding Platform:A web-based tool for high-throughput population genetics,phenomics,and genomic selection

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Molecular Plant 2024年第5期17卷 677-681页

作者： Huihui Li Xin Li Peng Zhang Yingwei Feng Junri Mi shang gao Lele Sheng Mohsin Ali Zikun Yang Liang Li Wei Fang Wensheng Wang Qian Qian Fei Gu Wenbin Zhou State Key Laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Nanfan Research Institute CAASSanyaHainan 572024China DAMO Academy Alibaba GroupHangzhou 310023China Hupan Lab Hangzhou 310023China Yazhouwan National Laboratory No.8 Huanjin RoadYazhou DistrictSanya CityHainan Province 572024China State Key Laboratory of Rice Biology China National Rice Research InstituteHangzhouZhejiang 310006China

Dear Editor,In the era of big data and artificial intelligence,"smart breeding"has become a broad conceptual framework encompassing the paradigm shift of crop breeding to relying on analysis of high-throughput population genetics and phenomics data to conduct genomic selection,allowing identification and optimal use of the genetic potential in crop species(Xiao et al.,2022;Xu et al.,2022;Wang et al.,2023).Most existing tools for analyzing high-throughput breeding data require extensive computational power,complex installation processes,and command-line expertise and are therefore challenging and inconvenient for the majority of researchers and breeders(Brandies and Hogg,2021).

关键词： breeding artificial breed

3D face recognition:A comprehensive survey in 2022

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Computational Visual Media 2023年第4期9卷 657-685页

作者： Yaping Jing Xuequan Lu shang gao The School of Information Technology Deakin UniversityWaurn PondsVICAustrali

In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.

关键词： 3D face recognition 3D face databases deep learning local features global feature

PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform

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Intelligent Automation & Soft Computing 2023年第7期37卷 921-938页

作者： Wenbo Li Qi Wang shang gao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.To address the above challenges,we propose a modified You Only Look Once(YOLO)algorithm PF-YOLOv4-Tiny.The algorithm incorpo-rates spatial pyramidal pooling(SPP)and squeeze-and-excitation(SE)visual attention modules to enhance the target localization capability.The PANet-based-feature pyramid networks(P-FPN)are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy.To lighten the network,the standard convolutions other than the backbone network are replaced with depthwise separable convolutions.In post-processing the images,the soft-non-maximum suppression(soft-NMS)algorithm is employed to subside the missed and false detection problems caused by the occlusion between targets.The accuracy of our model can finally reach 61.75%,while the total Params is only 9.3 M and GFLOPs is 11.At the same time,the inference speed reaches 87 FPS on NVIDIA GeForce GTX 1650 Ti,which can meet the requirements of the infrared target detection algorithm for the embedded deployments.

关键词： Infrared target detection visual attention module spatial pyramid pooling dual-path feature fusion depthwise separable convolution soft-NMS

Quantum algorithm for kernelized correlation filter

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Science China(Information Sciences) 2023年第2期66卷 293-294页

作者： shang gao Shijie PAN Yuguang YANG Faculty of Information Technology Beijing University of Technology State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Dear editor,Kernelized correlation filter(KCF) is a key component of target tracking that is aimed to distinguish the target from the surrounding environment and is a fundamental problem in computer vision. Motivated by the progress in quantum computing, we present a quantum algorithm for KCF based on the core algorithm proposed by Henriques et al. [1, 2].This algorithm comprises two stages: training and detection. In the training stage, two quantum sub-algorithms,

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Subsurface damage pattern and formation mechanism of monocrystalline β-Ga_(2)O_(3) in grinding process

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Frontiers of Mechanical Engineering 2022年第2期17卷 117-127页

作者： Xin YANG Renke KANG shang gao Zihe WU Xianglong ZHU Key Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education Dalian University of TechnologyDalian 116024China

Monocrystalline beta-phase gallium oxide (β-Ga_(2)O_(3)) is a promising ultrawide bandgap semiconductor material. However, the deformation mechanism in ultraprecision machining has not yet been revealed. The aim of this study is to investigate the damage pattern and formation mechanism of monocrystalline β-Ga_(2)O_(3)in different grinding processes. Transmission electron microscopy was used to observe the subsurface damage in rough, fine, and ultrafine grinding processes. Nanocrystals and stacking faults existed in all three processes, dislocations and twins were observed in the rough and fine grinding processes, cracks were also observed in the rough grinding process, and amorphous phase were only present in the ultrafine grinding process. The subsurface damage thickness of the samples decreased with the reduction in the grit radius and the grit depth of cut. Subsurface damage models for grinding process were established on the basis of the grinding principle, revealing the mechanism of the mechanical effect of grits on the damage pattern. The formation of nanocrystals and amorphous phase was related to the grinding conditions and material characteristics. It is important to investigate the ultraprecision grinding process of monocrystalline β-Ga_(2)O_(3). The results in this work are supposed to provide guidance for the damage control of monocrystalline β-Ga_(2)O_(3)grinding process.

关键词： monocrystalline beta-phase gallium oxide grinding process subsurface damage nanocrystals amorphous phase

Towards uniform point distribution in feature-preserving point cloud filtering

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Computational Visual Media 2023年第2期9卷 249-263页

作者： Shuaijun Chen Jinxi Wang Wei Pan shang gao Meili Wang Xuequan Lu School of Information Technology Deakin UniversityGeelongVIC 3220Australia College of Information Engineering Northwest A&F UniversityYangling 712100China School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou 510641China

While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.

关键词： point cloud filtering point distribution feature preservation

Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling

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aBIOTECH 2023年第4期4卷 291-302页

作者： shang gao Shoukun Chen Maogeng Yang Jinran Wu Shihua Chen Huihui Li State Key Laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural SciencesBeijing 100081China Nanfan Research Institute Chinese Academy of Agricultural SciencesSanya 572024China Hainan Yazhou Bay Seed Laboratory Sanya 572024China Key Laboratory of Plant Molecular&Developmental Biology College of Life SciencesYantai UniversityYantai 264005China The Institute for Learning Sciences and Teacher Education Australian Catholic UniversityBrisbaneQLD 4001Australia

With the increasing number of sequenced species,phylogenetic profiling(PP)has become a powerful method to predict functional genes based on co-evolutionary information.However,its potential in plant genomics has not yet been fully explored.In this context,we combined the power of machine learning and PP to identify salt stress-related genes in a halophytic grass,Spartina alterniflora,using evolutionary information generated from 365 plant species.Our results showed that the genes highly co-evolved with known salt stress-related genes are enriched in biological processes of ion transport,detoxification and metabolic pathways.For ion transport,five identified genes coding two sodium and three potassium transporters were validated to be able to uptake Na?.In addition,we identified two orthologs of trichome-related AtR3-MYB genes,SaCPC1 and SaCPC2,which may be involved in salinity responses.Genes co-evolved with SaCPCs were enriched in functions related to the circadian rhythm and abiotic stress responses.Overall,this work demonstrates the feasibility of mining salt stress-related genes using evolutionary information,highlighting the potential of PP as a valuable tool for plant functional genomics.

关键词： Phylogenetic profiling Spartina alterniflora Salt stress-related gene Machine learning Evolutionary information

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2405-2424页

作者： Feisha Hu Qi Wang Haijian Shao shang gao Hualong Yu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

关键词： UAV safety kernel extreme learning machine triangular global alignment kernel fast independent component analysis