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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Zhang, xinyuan Cao, xiangang Zhao, Jiangbin Duan, Yong Yang, xin Zhao, Fuyuan Li, xiangyu Fan, Hongwei School of Mechanical Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350354010

Aiming at the difficulty of extracting low-frequency features and incomplete voiceprint features in the strong noise environment in rotating machinery fault recognition, this paper proposes a multi-granularity voiceprint feature adaptive extraction (MG-FAE) method for rotating machinery based on multi-spectrogram fusion. Firstly, the space-spectrogram joint feature set based on the nonlinear mapping advantages of Mel spectrum and mel frequency cepstrum coefficients (MFCCs) is constructed to improves the voiceprint feature representation capability of low-frequency signals. Then, the multi-granularity representation method of multi-spectrogram based on Gaussian Difference Pyramid (GDP) is proposed to fully characterize the geometric and semantic information in spectrogram, the Multi-Head Attention Mechanism (MHA) is introduced to adaptively extract multiple voiceprint features to achieve comprehensive representation. Finally, a MG-FAE method is proposed to representat the voiceprint features of rotating machinery. The effectiveness of the method is verified by iFLYTEK rotating machinery challenge dataset. The recognition rate is 97.64% in multi noise state, which is 37.36% higher than the original audio signal. Moreover, the t-SNE feature visualization technology verifies that the MG-FAE method proposed in this paper has clear boundary of voiceprint feature classification and good verification effect. © 2024 IEEE.

关键词： Spectrographs

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AN ITERATIVE TWO-GRID METHOD FOR STRONGLY NONLINEAR ELLIPTIC BOUNDARY VALUE PROBLEMS

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Journal of Computational Mathematics 2025年第3期43卷 673-689页

作者： Jiajun Zhan Lei Yang xiaoqing xing Liuqiang Zhong School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SAR 999078China School of Mathematical Sciences South China Normal UniversityGuangzhou 510631China

We design and analyze an iterative two-grid algorithm for the finite element discretizations of strongly nonlinear elliptic boundary value problems in this *** propose an iterative two-grid algorithm,in which a nonlinear problem is first solved on the coarse space,and then a symmetric positive definite problem is solved on the fine *** main contribution in this paper is to establish a first convergence analysis,which requires dealing with four coupled error estimates,for the iterative two-grid *** also present some numerical experiments to confirm the efficiency of the proposed algorithm.

关键词： Iterative two-grid method Convergence Strongly nonlinear elliptic problems

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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Asymmetric multilayered MXene-Ag NWs/cellulose nanofiber composite films with antibacterial properties for high-efficiency electromagnetic interference shielding

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Journal of Materials science & technology 2022年第34期129卷 181-189页

作者： Fudong Zhang Penggang Ren Zhengzheng Guo Jin Wang Zhengyan Chen Ze Zong Jie Hu Yanling Jin Fang Ren The Faculty of Printing Packaging Engineering and Digital Media TechnologyXi’an University of TechnologyXi’an 710048China School of Materials Science and Engineering Xi’an University of TechnologyXi’an 710048China

Flexible, lightweight, robust and versatile properties are essential for the next generation of wearable as well as intelligent electromagnetic interference(EMI) shielding materials. In this work, multilayered films containing cellulose nanofiber(CNF) layers, CNF/MXene layers, and CNF/silver nanowires(CNF/Ag NWs)layers were fabricated by an efficient and easy-to-use vacuum filtration method. Compared with a uniformly mixed film, the resultant layered composite films that loaded with a low MXene and AgNWs content exhibit superior mechanical properties with a tensile strength of 137 MPa, a strain at break of 5.7%, excellent EMI shielding effectiveness(EMI SE) of 61.9 d B, and higher EMI SE/t of 20,653 d B cm^(-1).This is attributed to the high-performance CNF substrate, the highly efficient layered structures, and extensive hydrogen-bonding interactions. In particular, a high degree of ohmic loss of multiple interfaces and polarization relaxation of local defects, as well as an abundance of terminal groups, favor the loss of electromagnetic waves(EMW) within the material. In addition, the prepared multifunctional layered composite films also show good antibacterial properties. As a result, the obtained new kind of flexible layered structure EMI shielding composite films with excellent EMI shielding performance, and mechanical properties present promising application prospects in the fields of EMI shielding and protection for aerospace, portable, and wearable flexible electronic devices.

关键词： MXene AgNWs Laminar structure Electromagnetic interference shielding Flexibility

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination 20

On-Line System of Garbage Image-Orientated Intelligent Class...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Tian, Jiayin Wang, Yaozhi Liu, Jiaxin Chen, Yan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350365856

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described. © 2024 IEEE.

关键词： Zero-shot learning

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Design and implementation of instruction-driven and data-driven self-reconfigurable cell array

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High technology Letters 2023年第1期29卷 31-40页

作者：山蕊 xiA xinyuan YANG Kun CUI xinyue LIAO Wang GAO Xu School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121P.R.China School of Safety Science and Engineering Xi’an University of Science and TechnologyXi’an 710054P.R.China

The reconfigurable chip,which integrates the advantages of high performance,high flexibility,high parallelism,low power consumption,and low cost,has achieved rapid development and wide ***,the control part and the computing part of algorithm is accelerated based on different reconfigurable architectures,but it is difficult to obtain overall performance *** improving efficiency of reconfigurable structure both for the control part and the computing part,a hybrid of instruction-driven and data-driven self-reconfigurable cell array is *** instruction-driven mode,processing element(PE)works like a reduced instruction set computer(RSIC)machine,which is mainly for the control part of *** data-driven mode,data is calculated by flowing between the preconfigured PEs,which is mainly for the computing of *** verifying the efficiency of architecture,some high-efficiency video coding(HEVC)video compression algorithms are implemented on the proposed *** proposed architecture has been implemented on xilinx FPGA Virtex UltraScale VU440 develop *** same circuitry is able to run at75 *** with the architecture that only supports instruction-driven,the proposed architecture has better calculation efficiency.

关键词： cell array configurable computing data-driven instruction-driven

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics

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Communications in Transportation Research 2024年第1期4卷 382-392页

作者： Fizza Hussain Yuefeng Li Md Mazharul Haque School of Civil&Environment Engineering Queensland University of TechnologyBrisbane4000Australia School of Computer ScienceQueensland University of TechnologyBrisbane4000Australia

Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over threshold,form the core of these *** studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical ***,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity *** address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity *** particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity ***-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of ***-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were *** non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters *** results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant ***,the modeling f

关键词： Bivariate models Traffic conflicts Crash severity Crash frequency Generalized extreme value Anomaly detection techniques

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Mechanical properties and damage evolution characteristics of waste tire steel fiber-modified cemented paste backfill

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International Journal of Mining science and technology 2024年第7期34卷 909-924页

作者： Shenggen Cao Chiyuan Che Yun Zhang Changhao Shan Yang Liu Changzheng Zhao Shuyu Du State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources School of MinesChina University of Mining and TechnologyXuzhou 221116China College of Energy Engineering Xi’an University of Science&TechnologyXi’an 710054China

During the process of constructional backfill mining,the cemented paste backfill(CPB)typically exhibits a high degree of brittleness and limited resistance to *** this study,the mechanical and damage evolution characteristics of waste tire steel fiber(WTSF)-modified CPB were studied through uniaxial compression tests,acoustic emission(AE)tests,and scanning electron microscopy(SEM).The results showed that the uniaxial compressive strength(UCS)decreased when the WTSF content was 0.5%,1%,and 1.5%.When the WTSF content reached 1%,the UCS of the modified CPB exhibited a minimal decrease(0.37 MPa)compared to that without *** the WTSF content was 0.5%,1%,and 1.5%,peak strain of the WTSF-modified CPB increased by 18%,31.33%,and 81.33%,while the elastic modulus decreased by 21.31%,26.21%,and 45.42%,*** addition of WTSF enhances the activity of AE events in the modified CPB,resulting in a slower progression of the entire failure *** the failure,the modified CPB retained a certain level of load-bearing ***,the failure of the CPB was dominated by tensile *** the addition of WTSF,a gradual increase in the proportion of tensile cracks was observed upon loading the modified CPB sample to the pore compaction *** three-dimensional localization of AE events showed that the WTSF-modified CPB underwent progressive damage during the loading,and the samples still showed good integrity after ***,the response relationship between energy evolution and damage development of WTSF-modified CPB during uniaxial compression was analyzed,and the damage constitutive model of CPB samples with different WTSF contents was *** study provides a theoretical basis for the enhancement of CPB modified by adding WTSF,serving as a valuable reference for the design of CPB constructional backfill.

关键词： Constructional backfill Cemented paste backfill Waste tire steel fiber Acoustic emission Damage characteristics

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Studies on Vehicle Object Detection and Tracking in UAV Aerial Data 19th

Studies on Vehicle Object Detection and Tracking in UAV Aeri...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Cao, Ting Zhang, xinrong Wang, Penghui Wang, Chenle School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Key Laboratory of Road Construction Technology and Equipment Ministry of Education Chang’an University Xi’an710064 China School of International Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9783031651229

Studies on multi-object detection and tracking for vehicles are important research topics and have been widely used in various fields such as autonomous driving, anomaly detection, traffic monitoring, and intelligent transportation system. Because of the interference factors such as long-time occlusion and heavy traffic flow, there are two major limitations to accurate vehicle tracking: missed detections and false detections. In this paper, for the demand of accuracy and real-time vehicle object detection and tracking, the YOLO v5 and Deepsort are combined to realize object detection and tracking in unmanned aerial vehicle (UAV) data. The object detector is trained and validated on its own dataset of traffic collected from highways;the open-source vehicle deep feature training dataset is used to train for tracker weights. Finally, the experiments verify the vehicle multi-object detection and tracking function in the case of heavy traffic flow with different vehicle types. The proposed method in this paper has certain theoretical significance and practical application value in the field related to multi-object detection and tracking of moving vehicles. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Unmanned aerial vehicles (UAV)

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