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6th International Conference on Physics, Mathematics and Statistics, ICPMS 2023

作者： Zhan, L.L. Chen, N.B. Liu, Q.H. School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541002 China Guilin541002 China

In this paper, we study a class of Kirchhoff-type problems involving strong singular nonlinearities on compact Riemannian manifolds. With the help of variational method and Nehari method, we prove that this problem ha...

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A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption 4

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

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4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cui, Xu Liu, Hao Tang, Min Ma, Yihong School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin54004 China Guilin541002 China

ISBN: (纸本)9798350314496

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads. © 2023 IEEE.

关键词： Cloud computing

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Modified fuzzy clustering algorithm based on non-negative matrix factorization locally constrained

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11373-11383页

作者： Li, Xiangli Fan, Xuezhen Lu, Xiyan School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China Guangxi Applied Mathematics Center Guilin University of Electronic Technology Guilin541004 China

The fuzzy C-means (FCM) algorithm is a classical clustering algorithm which is widely used. However, especially for high-dimensional data sets with complex structures, the large-scale calculation of FCM suffers from decreasing clustering effect. In order to improve the clustering performance, we propose two new modified fuzzy clustering algorithms—modified fuzzy clustering algorithm based on non-negative matrix factorization (MFCM-NMF) and modified fuzzy clustering algorithm based on non-negative matrix factorization with local constraint (MFCM-LCNMF). Since MFCM-NMF combines NMF with modified FCM, the algorithm can use the dimensionality reduction technology of NMF, which greatly improves the computational efficiency. MFCM-LCNMF introduces NMF with local linear constraints into modified FCM, and it has a new objective function and adopts a new algorithm for alternate iteration. In the iterative process, the new membership update formula is utilized for the samples selected by the triangle inequality, which not only reduces the amount of calculation, but also obtains a higher clustering quality. Finally, a number of experiments on many data sets verify that MFCM-NMF and MFCM-LCNMF are more effective compared with other state-of-the-art methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Non-negative matrix factorization

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Numerical Solution of Backward Problem of Distributed-order Time-space Fractional Diffusion Equation

Numerical Solution of Backward Problem of Distributed-order ...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Wang, H.M. Li, Y.S. School of Mathematics and Computing Sciences Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guangxi Guilin541004 China Guilin University of Electronic Technology Guangxi Guilin541004 China

This paper aims to determine the initial value problem in the distributed-order time-space fractional diffusion equation (DTSFDE) with Neumann boundary conditions using final data. Initially, the direct problem of the DTSFDE is computed using the Finite Difference Method (FDM), followed by applying the Tikhonov regularization method to minimize a function and regularize the inverse problem. By employing the novel Optimal Perturbation Algorithm (OPA) for the inversion of initial values, the efficiency and accuracy of solving the inverse problem are enhanced. Ultimately, the effectiveness and practicality of this approach in addressing the initial value problem in the DTSFDE are demonstrated through numerical examples in one and two dimensions. © Published under licence by IOP Publishing Ltd.

关键词： Inverse problems

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ANISOTROPIC(p,q)-EQUATIONS WITH COMPETITION PHENOMENA

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Acta Mathematica Scientia 2022年第1期42卷 299-322页

作者： Zhenhai LIU Nikolaos S.PAPAGEORGIOU Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for NationalitiesNanning 530006China Department of Mathematics National Technical UniversityZografou Campus15780 Athens.Greece

We consider a nonlinear Robin problem driven by the anisotropic(p,q)-Laplacian and with a reaction exhibiting the competing effects of a parametric sublinear(concave) term and of a superlinear(convex) *** prove a bifurcation-type theorem describing the changes in the set of positive solutions as the parameter *** also prove the existence of a minimal positive solution and determine the monotonicity and continuity properties of the minimal solution map.

关键词： concave-convex nonlinearities anisotropic operators regularity theory maximum principle minimal positive solution

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analysis of data patterns of interwoven meltblown nonwoven materials based on polynomial fitting 3

Analysis of data patterns of interwoven meltblown nonwoven m...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Tan, Qiancheng Huang, Jiaoxia Wu, Sixuan Qin, Yonghui School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

The meltblown nonwoven material (MNM) is an important raw material for mask production. With good filtration performance, its production process is simple, low cost, and light quality. The research on the process parameters and structural variables of the MNM is widely concerned by domestic and foreign enterprises, and the relationship between structural variables and product performance is also. To the end, we study the process parameters and product performance of the MNM by using some mathematical methods for the high quality and high efficiency of the industry. We use a multiple nonlinear regression model to build a statistical model based on the data provided by the enterprise to reveal the variation pattern between process parameters and structural variables. The coefficient of decidability was calculated, and the correlation coefficient was above 0.9, which proved the accuracy of the model. Finally, in order to explore the relationship between structural variables and product performance, we used MATLAB to make a correlation four-dimensional diagram, used fitting polynomials to finally fit a quadratic correlation function, and used Monte Carlo multi-objective planning programming to obtain the maximum filtration efficiency of 96.8272% when the acceptance distance was 39.5cm and the hot air speed was 1188r/min. © 2023 SPIE.

关键词： MATLAB

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Hybrid genetic algorithm with rank sorting for obtaining bijective S-boxes with good cryptographic properties 4

Hybrid genetic algorithm with rank sorting for obtaining bij...

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4th International Conference on Network Communication and Information Security, ICNCIS 2024

作者： Li, Xin Liu, Zhenghong Zhao, Haixia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

An S-box is an essential component in block ciphers, and its cryptography properties play a significant role in the security of the cipher. A good S-box must have high nonlinearity (NLF ) and low differential uniformity (χF ). In this paper, a rank-sorting hybrid genetic algorithm is designed to find S-boxes that possess high nonlinearity and low difference uniformity. Compared with the traditional genetic algorithm, the rank-sorting hybrid genetic algorithm designed in this work was improved in the following aspects. First, the crossover protection strategy was designed and used in the crossover phase, which can assure the bijectivity of offspring. Second, an elite selection strategy based on the value of the fitness function was utilized in the selection step to prevent the loss of excellent individuals. In particular, a sorting method named the rank-sorting algorithm was proposed and implemented on the selected offspring before the next iteration began. The result of using the rank-sorting algorithm is that the differences between individuals for the next crossover increase, the possibility of generating excellent offspring increases, and the risk of the whole algorithm becoming stuck in local optimal solutions decreases. The simulation results show that when the number of variables is even (8 to 12), the bijective S-boxes with almost high nonlinearity and low differential uniformity can be searched by using the rank-sorting hybrid genetic algorithm. © 2025 SPIE.

关键词： Genetic algorithms

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A Symmetric Nonconvex Method for MRI 4

A Symmetric Nonconvex Method for MRI

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Luo, Zhijun Zhu, Zhibin School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350337181

Magnetic resonance imaging is an essential tool for clinical diagnosis, but acquisition time and reconstruction errors have always been bottleneck issues limiting the development of this field. In this paper, we use SCAD nonconvex function to construct a new MRI reconstruction nonconvex model, which can overcome the shortcomings of the traditional convex model. In model solving, considering the symmetry ADMM method improves the image reconstruction performance while increasing the reconstruction speed. The experiment shows that the method proposed in this paper has significant improvements over traditional TV methods. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Cross-View Nearest Neighbor Contrastive Learning of Human Skeleton Representation 3rd

Cross-View Nearest Neighbor Contrastive Learning of Human S...

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3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Zhang, Xuelian Xu, Zengmin Wang, Lulu Chen, Jiakun School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China Anview.AI Guilin Anview Technology Co. Ltd Guilin China

ISBN: (纸本)9789819903009

Traditional self-supervised contrastive learning approaches regard different views of the same skeleton sequence as a positive pair for the contrastive loss. While existing methods exploit cross-modal retrieval algorithm of the same skeleton sequence to select positives. The common idea in these work is the following: ignore using other views after data augmentation to obtain more positives. Therefore, we propose a novel and generic Cross-View Nearest Neighbor Contrastive Learning framework for self-supervised action Representation (CrosNNCLR) at the view-level, which can be flexibly integrated into contrastive learning networks in a plug-and-play manner. CrosNNCLR utilizes different views of skeleton augmentation to obtain the nearest neighbors from features in latent space and consider them as positives embeddings. Extensive experiments on NTU RGB+D 60/120 and PKU-MMD datasets have shown that our CrosNNCLR can outperform previous state-of-the-art methods. Specifically, when equipped with CrosNNCLR, the performance of SkeletonCLR and AimCLR is improved by 0.4% ∼ 12.3% and 0.3% ∼ 1.9%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musculoskeletal system

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Fabric4show: real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Lin, Huaizhou Cai, Dan Xu, Zengmin Wu, Jinsong Sun, Lixian Jia, Haibin School of Materials Science and Engineering Guangxi Key Laboratory of Information Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi (GUET) Guilin China Anview.ai Guilin China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Department of Electrical Engineering University of Chile Santiago Chile

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry. © The Author(s) 2024.

关键词： Automation Computer vision Defect detection Fabric manufacturing Faster R-CNN

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