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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Su, Linzhi Yang, Mengna Liu, Jie Hao, Xingxing Zhang, Chenyang Cao, Xin Northwest University School of Information Science and Technology Shaanxi China Henan Normal University College of Computer and Information Engineering Henan China

ISBN: (纸本)9798350387384

Self-supervised tasks, which extract supervisory signals from the data without human labeling, have garnered significant attention. Mask modeling, in particular, has piqued interest. To leverage unlabeled point data in self-supervised learning, we introduce a self-distilling architecture that integrates mask modeling to enhance the model's ability to extract representations and strengthen its high-level semantic feature learning on two versions of a point cloud. Recognizing that point coordinate embeddings can lead to information disclosure distinct from images and text, we incorporate mask embedding into the decoder of an encoder-decoder structure to mitigate this issue and ensure the model's learning is not overly simplistic. These backbones are typically based on CNNs or Transformers, which have limitations in local receptive field and global modeling, respectively. Drawing inspiration from Mamba, which originates from a state space model with global modeling capabilities and linear complexity, we propose a novel approach called Jointly Mamba Self-Supervised Self-Distilling Point Cloud Representation Learning (Point-MSD). Additionally, we introduce a global semantic consistency loss to enrich the overall shape understanding of 3D objects. Extensive experiments demonstrate that our method can learn superior point cloud representations. Notably, Point-MSD achieves a 93.52% accuracy on ModelNet40, outperforming PointMamba by 0.93%. © 2024 IEEE.

关键词： Self-supervised learning

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A review of the application of deep learning in the detection of Alzheimer's disease

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International Journal of Cognitive Computing in Engineering 2022年 3卷 1-8页

作者： Gao, Shuangshuang Lima, Dimas School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Department of Electrical Engineering Federal University of Santa Catarina Florianópolis88040-900 Brazil

Alzheimer's disease (AD) is the most common chronic disease in the elderly, with a high incidence rate. In recent years, deep learning has become popular in the field of medical image and has achieved great success. It has become the preferred method of analyzing medical images, and it has also attracted a high degree of attention in AD detection. Compared with general machine learning technology, the deep model is more accurate and efficient for AD detection. This paper This paper introduces ad related biomarkers and feature extraction methods, reviews the application of deep learning methods in AD detection, analyzes and summarizes AD detection methods and models. The results show that deep learning technology shows good performance in AD detection. © 2021

关键词： Diagnosis

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Low-light Image Enhancement via Unsupervised Progressive Fusion Network 11

Low-light Image Enhancement via Unsupervised Progressive Fus...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Li, Hengxuan Huo, Zhanqiang Qiao, Yingxu School of Software Henan Polytechnic University Jiaozuo454000 China School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China

ISBN: (纸本)9781450398039

To address the issue that unsupervised low-light image enhancement methods rely on finely selected training sets or contrast exposure stacks, this paper proposes a low-light image enhancement network with unsupervised progressive fusion driven jointly by knowledge and data. The network consists of low-light image pre-processing stage and progressive fusion stage. In the pre-processing stage, the input low-light image is first inverted. Next, we perform three methods of fixed-value gamma correction, adaptive gamma correction, and color-preserving adaptive histgram equalization (color-preserving AHE) to generate three images with different degrees of contrast enhancement. The progressive fusion stage performs a progressive fusion of the three images generated in the preprocessing stage and the inverted input image. The contrast and brightness of the fused result are improved by fusion while suppressing overexposure in the bright regions. It is shown experimentally that the proposed method outperforms the current state-of-the-art unsupervised low-light image enhancement networks in terms of both visual quality and technical specifications. In some low-light scenes, our method even outperforms the supervised learning-based low-light image enhancement network. © 2022 ACM.

关键词： Image enhancement

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Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

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Digital Communications and Networks 2024年第1期10卷 38-44页

作者： Mianjie Li Senfeng Lai Jiao Wang Zhihong Tian Nadra Guizani Xiaojiang Du Chun Shan School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhou510665China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Schoolof Electrical Engineering and Computer Science Washington State UniversityPullmanWA99164USA Department of Computer and Information Science Temple UniversityPhiladelphiaPA19122USA

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication *** that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of *** multiwatermarking process employs spread transform dither *** the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading ***,multiple watermarks can be simultaneously embedded into the same position of a multimedia ***,the multiple watermarks can be extracted without affecting one another during the extraction *** analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

关键词： B5G Multimedia information security Spread transform dither modulation Spreading vector measurement Unit maximum capacity

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Full friendly index sets of mCn

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Frontiers of computer science 2022年第3期16卷 95-99页

作者： Yurong JI Jinmeng LIU Yujie BAI Shufei WU School of Mathematics and Information Science Henan Polytechnic UniversityJiaozuo 454003China Basic Department Henan College of Industry and Information TechnologyJiaozuo 454003China

Let G be a connected simple graph with vertex set V(G)and edge set E(G).A binary vertex labeling f:V(G)→Z2,is said to be friendly if the number of vertices with different labels differs by at most *** vertex friendly labeling/induces an edge labeling f*E(G)→Z2,defined by f*(xy)=f(x)+f(y)for each xy∈E(G).Let er(i)=\{e∈E(G):f*(e)=i}|.The full friendly index set of G,denoted by FFI(G),is the set{ef*(1)-ep(0):f is friendly}.In this paper,we determine the full friendly index set of a family of cycle union graphs which are edge subdivisions of P2×Pn.

关键词： vertex labeling friendly labeling full friendly index set partition bisection

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Toward Effective 3D Object Detection via Multimodal Fusion to Automatic Driving for Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 281-291页

作者： Gao, Honghao Sun, Yan Xiao, Junsheng Fang, Danqing Xu, Yueshen Wei, Wei Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do461-701 Korea Republic of Henan University of Chinese Medicine School of Information Technology Zhengzhou450046 China Xidian University School of Computer Science and Technology Xi'an710126 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China

AI-empowered automatic driving has experienced rapid development in industrial cyber-physical systems (CPSs), especially in safety vehicles and driverless technologies. 3D object detection is an important task for perceiving the surrounding environment and supporting decision-making when vehicles are on the road, and is also a focus in CPSs. Light detection and ranging (LiDAR)-based detection methods usually lack semantic information, resulting in high uncertainty with incorrect outputs. Thus, handling complex road scenes is difficult. Some data fusion-based methods have been developed to solve these issues. However, the spatiotemporal data misalignment between different sensors is prone to losing information during data fusion. This paper proposes exploiting multimodal information to learn more high-level features to address these issues thus reducing the uncertainty of 3D object detection. First, the VxMLA (voxel and multilevel attention) framework is employed to improve point cloud identification and modeling during 3D object detection. Second, the MF-CAMRL (modal fusion-based channel attention and multidimensional regression loss) model is proposed with two subnetworks. Our model encompasses two strategies, i.e., a multimodal fusion and a deep learning model based on CAMRL. One focuses on the semantic complementarity and geometric proximity for decisionlevel fusion. The other focuses on weighted ensemble bounding boxes to fully utilize the highlevel decision information derived from both modalities and reduce the information loss incurred during modal fusion. Finally, sufficient experiments are performed on the KITTI dataset and presented. The results show that our method is superior to baseline methods. © 2023 IEEE.

关键词： Feature extraction

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Multi-granularity transformer fusion for temporal action localization

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Soft Computing 2024年第20期28卷 12377-12388页

作者： Zhang, Min Hu, Haiyang Li, Zhongjin Department of Design and Art Zhejiang Industry Polytechnic College Shaoxing312000 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Temporal action localization plays a significant role in video understanding, which aims to recognize action category as well as temporal interval in untrimmed videos. Most of previous transformer-based methods employ a feature space of single-temporal granularity. However, low-level temporal features can not provide enough semantic information for action recognition while high-level temporal features lack rich details for boundary localization. To address the above issue, we propose a multi-granularity transformer fusion framework (MGTF) to localize temporal actions in videos. Specifically, the MGTF builds a multi-granularity feature fusion pipeline based on transformer, and uses a direct set prediction strategy to generate action instances. Through top-down cross-granularity attention interaction, the low-level features of boundary details and high-level semantic information can be combined to improve the feature discrimination. To reduce computation cost, we design temporal shift attention to adaptively focus on a sparse set of key segments. In addition, actionness regression head is utilized to refine the confidence score of different candidate instances. As a self-contained system, MGTF achieves state-of-the-art performance on THUMOS’14 and comparable performance on ActivityNet-1.3. Ablation studies and qualitative visualization also demonstrate the effectiveness of the proposed approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantics

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MeshWGAN: Mesh-to-Mesh Wasserstein GAN with Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation

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IEEE Transactions on Visualization and computer Graphics 2024年第8期30卷 4927-4940页

作者： Zhang, Jie Zhou, Kangneng Luximon, Yan Lee, Tong-Yee Li, Ping The Hong Kong Polytechnic University Department of Computing The School of Design Hong Kong University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China The Hong Kong Polytechnic University School of Design Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications. © 1995-2012 IEEE.

关键词： Face recognition

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Insights into the Origins of Solar-Assisted Electrochemical Water Oxidation in Allotropic Co_(5.47)N/CON Heterojunctions

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Energy & Environmental Materials 2024年第5期7卷 234-243页

作者： Sirui Liu Qiong Gao Bo Geng Lili Wu Zhikun Xu Xinzhi Ma Shijie Liu Boquan Li Mingyi Zhang Lirong Zhang Xitian Zhang Key Laboratory for Photonic and Electronic Bandgap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal UniversityHarbin 150025China School of Physics and Engineering Henan University of Science and Technology Luoyang 471023China School of Science Guangdong University of Petrochemical TechnologyMaoming 525000China Department of Mechanical Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 999077China College of Science Southern University of Science and TechnologyShenzhen 518055China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

Solar irradiation can efficiently promote the kinetics of the oxygen evolution reaction(OER)during water splitting,where heterojunction catalysts exhibit excellent photoresponsive ***,insights into the origins of photoassisted OER catalysis remain unclear,especially the interfaced promotion under convergent solar irradiation(CSI).Herein,novel allotropic Co_(5.47)N/CoN heterojunctions were synthesized,and corresponding OER mechanisms under CSI were comprehensively uncovered from physical and chemical aspects using the in situ Raman technique and electrochemical cyclic voltammetry *** results provide a unique mechanism where high-energy UV light promotes the Co^(3+/4+)conversion process in addition to the ordinary photoelectric effect excitation of the Co^(2+)***,visible light under CSI can produce a photothermal effect for Co^(2+)excitation and Co^(3+/4+)conversion,which promotes the OER significantly more than the usual photoelectric *** a result,Co_(5.47)N/CoN(containing 28%CoN)obtained 317.9%OER enhancement,which provides a pathway for constructing excellent OER catalysts.

关键词： chemical origins in situ Raman OER photothermal effect physical origins

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Multi-frequency formation mechanism and modulation strategy of self-priming enhanced submerged pulsed waterjet

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International Journal of Mining science and technology 2025年第2期35卷 175-189页

作者： Haojie Jia Yanwei Liu Weiqin Zuo Hongkai Han Ping Chang Mohammad Waqar Ali Asad Guozhong Hu Jian Miao Hani SMitri College of Safety Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China Henan International Joint Laboratory of Coal Mine Safety and Occupational Hazards Prevention and Control Jiaozuo 454003China Zhengzhou Institute for Advanced Research of Henan Polytechnic University Zhengzhou 451464China WA School of Mines:Minerals Energy and Chemical EngineeringCurtin UniversityKalgoorlie WA 6430Australia School of Mines China University of Mining and TechnologyXuzhou 221116China School of Intelligent Engineering Shandong Management UniversityJinan 250357China Department of Mining and Materials Engineering McGill UniversityMontreal H3A0E8Canada

Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.

关键词： Multi-frequency Modulation Self-priming Submerged waterjet Cavitation

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