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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Zheng, Yiyang School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798331506582

This paper introduces HybSR, a novel image super- resolution method that leverages the strengths of both Convolutional Neural Networks (CNNs) and State Space Models (SSMs) within a hybrid architecture. CNNs effectively capture local image details, while SSMs offer linear scalability and excel at modeling long-range dependencies. By combining these two paradigms, HybSR achieves a compelling balance between efficiency and performance. Trained with a hybrid loss function encompassing pixel-wise and perceptual components, the model ensures both accurate reconstruction and high visual fidelity. Furthermore, knowledge distillation from a larger teacher network to a smaller student network enhances the model's efficiency. Ex- tensive evaluations on benchmark datasets demonstrate HybSR's superior performance in terms of reconstruction quality, model size, and computational efficiency compared to existing efficient super-resolution methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved YOLOv8-based Vision Inspection Method for Weld Defect Classification 6

Improved YOLOv8-based Vision Inspection Method for Weld Defe...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Wu, Juncheng Zhao, Jiangbin Liang, Mengtao Cao, xiangang Xi'an University of Science and Technology School of Mechanical Engineering Xi'an China

ISBN: (纸本)9798350356083

With the rapid development of industrial manufacturing technology, there has been an increased emphasis on defect detection in steel weld seams to ensure the quality and safety of steel products. However, in the welding seam defect detection process, the foreground and background of the welding seam being inspected may have overlap, blending, blurring, and other phenomena, which give challenges to the detection of welding seam defects. To enhance the efficiency and accuracy of the algorithm for weld seam defect detection, this paper proposes the YOLOv8-SS algorithm by introducing the SE-Attention module and the SIoU loss function based on the YOLOv8 algorithm. SE-Attention module can suppress the influence of background noise on target detection and improve the accuracy of small targets. SIoU loss function is used to optimize the output predicted box boundaries, which can enhance the algorithm's accuracy and generalization ability. The experimental results on the same weld seam defect dataset show that compared to the YOLOv8 baseline model (YOLOv8n), the improved YOLOv8-SS model has no difference in floating-point calculations and parameter quantity while achieving an improvement of 0.7 % in mAP @ 0.5 and 1.3 % in mAP@0.5:0.95. Compared with YOLOv6, the improved YOLOv8-SS model has 68.6 % less of the floating-point calculations and 34.5 % less of the parameters of the YOLOv6 model, with respective improvements of 17.0 % in mAP @ 0.5 and 17.6 % in mAP@0.5:0.95. This indicates that the YOLOv8-SS model enhances the accuracy of weld seam defect recognition and maintains high efficiency. Furthermore, the results of ablation experiments verify the effectiveness of adding modules for enhancing performance. © 2024 IEEE.

关键词： Inspection

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Bridging Fourier and Spatial-Spectral Domains for Hyperspectral Image Denoising 24

Bridging Fourier and Spatial-Spectral Domains for Hyperspect...

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32nd ACM International Conference on Multimedia, MM 2024

作者： xiao, Jiahua Liu, Yang Zhang, Shizhou Wei, xing School of Software Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering Northwest Polytechnical University Xi'an China

ISBN: (纸本)9798400706868

Remarkable progresses have been made in hyperspectral image (HSI) denoising. However, the majority of existing methods are predominantly confined to the spatial-spectral domain, overlooking the untapped potential inherent in the Fourier domain. This paper presents a novel approach to address HSI denoising by bridging the information from the Fourier and spatial-spectral domains. Our method highlights key insights into the Fourier properties within spatial and spectral domains through the Fourier transform. Specifically, we note that the amplitude inherently embody noise and photon reflection characteristics, while the phase holds structural information. These insights unveil new perspectives on the physical properties of HSIs, motivating us to leverage complementary information exchange between Fourier and spatial-spectral domains. To this end, we introduce the Fourier-prior Integration Denoising Network (FIDNet), a potent yet straightforward approach that utilizes Fourier insights to synergistically interact with spatial-spectral domains for superior HSI denoising. In FIDNet, we independently extract spatial and Fourier features through dual branches and merge these representations to enhance spectral evolution modeling through the inherent structure consistency constraints and continuing reflection variation revealed in Fourier prior. Our proposed method demonstrates robust generalization across synthetic and real-world benchmark datasets, achieves comparable results with state-of-the-art methods in both quantitative quality and visual results. The code is available at https://***/MIV-XJTU/FIDNet. © 2024 ACM.

关键词： Image denoising

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Superior lithium storage performance in MoO_(3) by synergistic effects:Oxygen vacancies and nanostructures

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Journal of Energy Chemistry 2023年第3期78卷 91-101,I0003页

作者： Xueyang Hou Miao Ruan Lijiao Zhou Jianchun Wu Bicheng Meng Wenlong Huang Kenan Zhong Kai Yang Zhao Fang Keyu xie School of Metallurgical Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina State Key Laboratory of Solidiﬁcation Processing Center for Nano Energy MaterialsSchool of Materials Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072ShaanxiChina School of Nuclear Science and Technology Sichuan UniversityChengdu 610064SichuanChina

Molybdenum trioxide(MoO_(3))has recently attracted wide attention as a typical conversion-type anode of Li-ion batteries(LIBs).Nevertheless,the inferior intrinsic conductivity and rapid capacity fading during charge/discharge process seriously limit large-scale commercial application of MoO_(3).Herein,the density function theory(DFT)calculations show that electron-proton co-doping preferentially bonds symmetric oxygen to form unstable HxMoO_(3).When the-OH-group in HxMoO_(3) is released into the solution in the form of H_(2)O,it is going to form MoO_(3-x)with lower binding *** the means of both electron-proton co-doping and high-energy nanosizing,oxygen vacancies and nanoflower structure are introduced into MoO_(3) to accelerate the ion and electronic diffusion/transport *** from the promotion of ion diffusion kinetics related to nanostructures,as well as both the augmentation of active sites and the improvement of electrical conductivity induced by oxygen vacancies,the MoO_(3-x)/nanoflower structures show excellent lithium-ion storage *** prepared specimen has a high lithium-ion storage capacity of 1261 mA h g^(-1)at 0.1 A g^(-1)and cyclic stability(450 cycle),remarkably higher than those of previously reported MoO_(3)-based anode materials.

关键词： 2D materials MoO_(3-x) Electron proton co-doping Lithium-ion anode Molybdenum oxide ore

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Parsing Objects at a Finer Granularity: A Survey

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Machine Intelligence Research 2024年第3期21卷 431-451页

作者： Yifan Zhao Jia Li Yonghong Tian School of Computer Science Peking UniversityBeijing100871China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing100191China

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

关键词： Finer granularity visual parsing part segmentation fine-grained object recognition part relationship

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Corrosion behavior of a Mg-Zn-Ca-La alloy in 3.5 wt%NaCl solution

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Journal of Magnesium and Alloys 2022年第2期10卷 567-580页

作者： Yuzhou Du xin Wang Dongjie Liu Wanting Sun Bailing Jiang School of Materials Science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Province Engineering Research Center for Magnesium Alloys Xi’an University of TechnologyXi’an 710048China School of Materials Science and Engineering Dongguan University of TechnologyDongguan 523808China

The present study investigates the corrosion behavior of a Mg–Zn–Ca–La *** indicate that the corrosion resistance is enhanced by rolling deformation and subsequent annealing treatment *** microstructure,distribution of second phase and grain orientation are the main factors affecting corrosion *** second phase with semi-continuous network in the as-cast alloy gives rise to the penetration of corrosion into the ***,the dispersed second phase after deformation results in uniform *** formation of dense corrosion layer at the initial stage of corrosion is favorable for the improvement of corrosion *** sample with<0001>plane oriented towards surface exhibits good corrosion resistance,which is ascribed to the homogeneous microstructure and strong basal texture.

关键词： Mg–Zn–Ca–La Rolling Second phase Texture Corrosion properties

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Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1007-1021页

作者： Yunjun Zheng Jinchuan Zheng Ke Shao Han Zhao Hao xie Hai Wang the School of Mechanical Engineering Hefei University of TechnologyHefei 230009China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn VIC 3122Australia the School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China the Shanghai Aerospace Control Technology Institute Shanghai 201109 the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the College of Science HealthEngineering and EducationMurdoch UniversityPerth WA6150Australia

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external *** paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for *** with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances *** advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier *** benefit is that the overestimation of control gain can be eliminated,resulting in chattering ***,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the *** stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

关键词： Adaptive sliding mode barrier function nonholonomic wheeled mobile robot(NWMR) trajectory tracking control

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Application of DDPG Algorithm for Residual Structured Networks in Autonomous Driving 6

Application of DDPG Algorithm for Residual Structured Networ...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Wu, Yifan School of Mechanical Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350353174

The development of intelligent mobility and the spread of artificial intelligence have contributed to the widespread use of autonomous driving techniques. Among the various algorithms used in this field, Deep Deterministic Policy Gradient (DDPG) algorithm is favoured by researchers. The DDPG algorithm may ignore some features in the deep learning of the network, slow updating of parameters, and lack of stability and learning efficiency of the algorithm. To overcome these problems, this study proposes the deep deterministic policy gradient (R-DDPG) algorithm with the introduction of residual blocks. By establishing an experience pool of successful samples, the algorithm improves the sampling utilisation, enhances the exploration strategy and improves the neural network training efficiency. The performance of the R-DDPG algorithm is evaluated on the TORCS racing simulation platform, which demonstrates that the algorithm is able to improve the learning efficiency of driving strategies in an autonomous driving task compared to existing algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

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Journal of Bionic engineering 2022年第6期19卷 1850-1885页

作者： Gang Hu Liuxin Chen Xupeng Wang Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China School of Art and Design Xi’an University of TechnologyXi’an710054China Department of Mathematics and Computer Science University of North Carolina at PembrokePembrokeNC28372USA

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in ***,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization *** ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this *** Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local ***,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of ***,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems *** comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

关键词： Golden eagle optimizer Lévy flight Sine cosine algorithm Differential evolution strategy engineering design Bionic model

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FERMixNet: An Occlusion Robust Facial Expression Recognition Model with Facial Mixing Augmentation and Mid-Level Representation Learning

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IEEE Transactions on Affective Computing 2024年第2期16卷 639-654页

作者： Huang, Yansong Peng, Junjie Zhang, Wenqiang Zhao, Tong Chen, Gan Tan, Shuhua Yi, Fen Wang, Lu Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Computer Engineering and Science The Shanghai Institute for Advanced Communication and Data Science Shanghai China Fudan University Academy for Engineering and Technology School of Computer Science and Technology Shanghai China YTO Express Co. Ltd. The National Logistics Engineering Laboratory Shanghai China

Facial expressions can provide a better understanding of people's mental status and attitudes towards specific things. However, facial occlusion in real world is an unfavorable phenomenon that greatly affects the performance of facial expression recognition models. Recent works addressing the occlusion problem have primarily relied on attention mechanisms or occlusion discarding methods that focus on non-occluded regions of the face. However, these methods have not achieved a good balance between occlusion robustness and model efficiency. In this paper, we propose a simple and efficient model, called FERMixNet, for occluded facial expression recognition. The model incorporates a novel facial mixing augmentation strategy (FERMix) that generates new training samples by simulating real-world facial occlusion and preserving high expression-related semantic information. By co-training the original and newly generated samples, the model's occlusion robustness is improved without increasing its complexity during inference. Additionally, to further enhance the model's occlusion robustness, we include mid-level representation learning in the network to learn the discriminative non-occluded local features of the samples with low computational cost. Extensive experiments on four public facial occlusion datasets: Occlusion-RAF-DB, Occlusion-FERPlus and FED-RO show that the proposed model achieves state-of-the-art results which demonstrates the good robustness of our method for occluded facial expression recognition. Meanwhile, the proposed model also achieves state-of-the-art results on the in-the-wild facial expression datasets RAF-DB, AffectNet-8, and AffectNet-7. It proves that the proposed model has good application prospects in real world. © 2010-2012 IEEE.

关键词： Face recognition

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