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9th International Conference on Electromechanical Control technology and Transportation, ICECTT 2024

作者： Wu, YiFan School of Mechanical Engineering Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781510682399

The development of smart transportation and the popularization of artificial intelligence had to the widespread use of autonomous driving technology. Among the various algorithms used in this field, the deep deterministic policy gradient algorithm (DDPG) has gained popularity among researchers., The uniform sampling approach used in the DDPG algorithm can lead to several issues, including a high proportion of inefficient strategies, easy discarding of high reward samples, low training efficiency, and slow convergence speed. To overcome these problems, this study proposes a deep deterministic policy gradient (E-DDPG) algorithm that incorporates a fusion experience pool. By establishing a successful sample experience pool, the algorithm improves the utilization rate of sampling, enhances the exploration strategy, and increases the efficiency of neural network training. The performance of the E-DDPG algorithm was evaluated on the TORCS racing simulation platform, demonstrating its ability to enhance the learning efficiency of driving strategies in autonomous driving tasks compared to existing algorithms. © 2024 SPIE.

关键词： Autonomous vehicles

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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Clarification of the dispersion mechanism of three typical chemical dispersants in lithium-ion battery (LIB) slurry

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Particuology 2023年第9期80卷 90-102页

作者： Zhilong Wang Zehua Wang xiaodong Liu xiayi Liu Tong Zhao Masahiro Takei Faculty of Mechanical and Precision Instrument Engineering Xi'an University of TechnologyXi'an710048China Division of Artificial Systems Science Graduate School of EngineeringChiba UniversityChiba2638522Japan

This paper mainly clarified the dispersion mechanism of three typical chemical dispersants which are polyethylene glycol octylphenyl ether(Triton X-100,T-100),polyethylene pyrrolidone(PVP)and carboxymethyl cellulose(CMC)within lithium-ion battery(LIB)***,the optimum amounts of T-100,PVP and CMC are selected from 0%,0.5%,1.5%and 2.5%by evaluating the impedance of LIB slurry in the case of adding each typical chemical dispersant with EIS ***,the impedance spectrum of three different slurry samples which are PVDF-NMP solution,LiCoO_(2) slurry and Carbon Black(CB)slurry with the optimum amount of each dispersant are also *** using SEM and C element distribution images of LIB slurry to verify the correctness of the dispersion mechanism of each dispersant,it is concluded that the dispersion CMC with its optimum amount 1.5%is the best one to promote the formation of conductive paths and CB-coated LiCoO_(2) network structure within LIB slurry,which has the considerably potential to improve the performance of LIB.

关键词： Lithium-ion battery(LIB)slurry Chemical dispersants Electrochemical characterizations Electrical impedance spectroscopy(EIS)

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A multi-granularity voiceprint feature adaptive extraction method for rotating machinery based on multi-spectrogram fusion 15

A multi-granularity voiceprint feature adaptive extraction m...

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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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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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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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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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