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arXiv 2025年

作者： Zhang, Juan Luo, Junyue Zhang, Fangfang Yue, Xiaoqiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper explores the application of kernel learning methods for parameter prediction and evaluation in the Algebraic Multigrid Method (AMG), focusing on several Partial Differential Equation (PDE) problems. AMG is an efficient iterative solver for large-scale sparse linear systems, particularly those derived from elliptic and parabolic PDE discretizations. However, its performance heavily relies on numerous parameters, which are often set empirically and are highly sensitive to AMG’s effectiveness. Traditional parameter optimization methods are either computationally expensive or lack theoretical support. To address this, we propose a Gaussian Process Regression (GPR)-based strategy to optimize AMG parameters and introduce evaluation metrics to assess their effectiveness. Trained on small-scale datasets, GPR predicts nearly optimal parameters, bypassing the time-consuming parameter sweeping process. We also use kernel learning techniques to build a kernel function library and determine the optimal kernel function through linear combination, enhancing prediction accuracy. In numerical experiments, we tested typical PDEs such as the constant-coefficient Poisson equation, variable-coefficient Poisson equation, diffusion equation, and Helmholtz equation. Results show that GPR-predicted parameters match grid search results in iteration counts while significantly reducing computational time. A comprehensive analysis using metrics like mean squared error, prediction interval coverage, and Bayesian information criterion confirms GPR’s efficiency and reliability. These findings validate GPR’s effectiveness in AMG parameter optimization and provide theoretical support for AMG’s practical application. © 2025, CC BY.

关键词： Poisson equation

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Data gravitation-based three-way sampling method for imbalanced data classification

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Information Sciences 2025年 718卷

作者： Yuanting Yan Weiqing Wang Yingao Ma Yang Tian Peng Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui Province 230601 PR China School of Computer Science and Technology Anhui University Hefei Anhui Province 230601 PR China

Data resampling is widely used for addressing the class imbalance problem, with numerous methods developed over recent decades. Among these, the K-nearest neighbor (KNN) technique is widely adopted for leveraging local neighborhood information in rebalancing imbalanced data. However, KNN struggles to accurately characterize local data distribution, as samples with identical KNNs may also exhibit divergent local structures, which may further hamper imbalanced learning performance. We propose a hybrid sampling method called data gravitation-based three-way sampling (DGS) to improve classification performance on imbalanced data. DGS introduces the concept of data gravitation into KNN to evaluate the local importance of samples, and combines it with three-way decision theory to selectively oversample minority-class samples and remove risky majority-class samples. DGS introduces a cost-sensitive sampling mechanism and adaptive threshold estimation using a Gaussian-based model, reducing reliance on manually tuned parameters. Extensive experiments on 38 datasets and seven classifiers demonstrate the superiority of DGS over 18 resampling methods across three evaluation metrics. Our code is available at https://***/ytyancp/DGS .

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Static Gesture Recognition and Distance Estimation Using a Capacitive Proximity Sensor Array

SSRN

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SSRN 2023年

作者： Ye, Yong Liu, Yuting Du, Chunhui Deng, Jiahao Qiu, Chenguang School of Electronics and Information Engineering The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University No. 111 Jiulong Road Anhui Province Hefei230601 China Institute of high energy physics Chinese academy of sciences The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Tai Yuan institute Co. Ltd. of China coal technology and engineering group Shanxi Province Taiyuan030006 China East China Institute of Photo-Electronic IC Jiangsu Province Suzhou215163 China

In this paper, we present a capacitive proximity sensor (CPS) array for static gesture recognition and distance estimation. A 4×4 electrode matrix is employed as the sensing element that is configured to 14 electrode operation patterns to obtain 14 capacitive sensors to percept objects within their sensing region. These 14 capacitances are fused into one feature vector which is used as the input of machine learning (ML) classifier for gesture recognition and neural network or regression model for distance estimation. In our experiments, we selected 10 static gestures to verify the proposed method. We compared the performance of 8 ML classifiers and their variations with training data size. The results show that the Bagging achieves the highest average accuracy of 93.2% and the KNN shows a good performance even with a small training data size. The results in distance estimation experiment demonstrate that the RBF network performs a high accuracy. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Several constructions of optimal LCD codes over small finite fields

arXiv

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arXiv 2022年

作者： Li, Shitao Shi, Minjia Liu, Huizhou School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Grid Anhui Electric Power Co. Ltd. Hefei China

Linear complementary dual (LCD) codes are linear codes which intersect their dual codes trivially, which have been of interest and extensively studied due to their practical applications in computational complexity and information protection. In this paper, we give some methods for constructing LCD codes over small finite fields by modifying some typical methods for constructing linear codes. We show that all odd-like binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes can be constructed using the modified methods. Our results improve the known lower bounds on the largest minimum distances of LCD codes. Furthermore, we give two counterexamples to disprove the conjecture proposed by Bouyuklieva (Des. Codes Cryptogr. 89(11): 2445-2461, 2021). Copyright © 2022, The Authors. All rights reserved.

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MVMS-RCN: A Dual-Domain Unified CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction

arXiv

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arXiv 2024年

作者： Fan, Xiaohong Chen, Ke Yi, Huaming Yang, Yin Zhang, Jianping The School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China The Centre for Mathematical Imaging Techniques Department of Mathematical Sciences The University of Liverpool LiverpoolL6972L United Kingdom The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan411105 China

X-ray Computed Tomography (CT) is one of the most important diagnostic imaging techniques in clinical applications. Sparse-view CT imaging reduces the number of projection views to a lower radiation dose and alleviates the potential risk of radiation exposure. Most existing deep learning (DL) and deep unfolding sparse-view CT reconstruction methods: 1) do not fully use the projection data;2) do not always link their architecture designs to a mathematical theory;3) do not flexibly deal with multi-sparse-view reconstruction assignments. This paper aims to use mathematical ideas and design optimal DL imaging algorithms for sparse-view CT reconstructions. We propose a novel dual-domain unified framework that offers a great deal of flexibility for multi-sparse-view CT reconstruction through a single model. This framework combines the theoretical advantages of model-based methods with the superior reconstruction performance of DL-based methods, resulting in the expected generalizability of DL. We propose a refinement module that utilizes unfolding projection domain to refine full-sparse-view projection errors, as well as an image domain correction module that distills multi-scale geometric error corrections to reconstruct sparse-view CT. This provides us with a new way to explore the potential of projection information and a new perspective on designing network architectures. The multi-scale geometric correction module is end-to-end learnable, and our method could function as a plug-and-play reconstruction technique, adaptable to various applications. Extensive experiments demonstrate that our framework is superior to other existing state-of-the-art methods. Our source codes are available at https://***/fanxiaohong/MVMS-RCN. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation

arXiv

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arXiv 2024年

作者： Wang, Shanshan Zhou, Hao Yang, Xun He, Zhenwei Wang, Mengzhu Zhang, Xingyi Wang, Meng The Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei230601 China The Department of Electronic Engineering and Information Science School of Information Science and Technology University of Science and Technology of China Hefei230026 China The College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Artificial Intelligence Hebei University of Technology Tianjin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The School of Computer Science and Technology Anhui University Hefei230601 China

Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target domain. Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain. However, the generalization boundary of UDA models remains unclear. When the domain discrepancy is too large, the model can not preserve the distribution structure, leading to distribution collapse during the alignment. To address this challenge, we propose an efficient UDA framework named Gradually Vanishing Gap in Prototypical Network (GVG-PN), which achieves transfer learning from both global and local perspectives. From the global alignment standpoint, our model generates a domain-biased intermediate domain that helps preserve the distribution structures. By entangling cross-domain features, our model progressively reduces the risk of distribution collapse. However, only relying on global alignment is insufficient to preserve the distribution structure. To further enhance the inner relationships of features, we introduce the local perspective. We utilize the graph convolutional network (GCN) as an intuitive method to explore the internal relationships between features, ensuring the preservation of manifold structures and generating domain-biased prototypes. Additionally, we consider the discriminability of the inner relationships between features. We propose a pro-contrastive loss to enhance the discriminability at the prototype level by separating hard negative pairs. By incorporating both GCN and the pro-contrastive loss, our model fully explores fine-grained semantic relationships. Experiments on several UDA benchmarks validated that the proposed GVG-PN can clearly outperform the SOTA models. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Complete b-symbol weight distribution of some irreducible cyclic codes

arXiv

引用

arXiv 2021年

作者： Zhu, Hongwei Shi, Minjia Özbudak, Ferruh Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China Department of Mathematics Institute of Applied Mathematics Middle East Technical University Ankara Turkey

Recently, b-symbol codes are proposed to protect against b-symbol errors in b-symbol read channels. It is an interesting subject of study to consider the complete b-symbol weight distribution of cyclic codes since b-symbol metric is a generalization for Hamming metric. The complete b-symbol Hamming weight distribution of irreducible codes is known in only a few cases. In this paper, we give a complete b-symbol Hamming weight distribution of a class of irreducible codes with two nonzero b-symbol Hamming weights. © 2021, CC0.

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Deep Learning-based Multi-task Network for intelligent Management of Garbage Deposit Points

Deep Learning-based Multi-task Network for Intelligent Manag...

引用

IEEE International Symposium on Information (IT) in Medicine and education, ITME

作者： Yezhen Wang Haobin Zheng Changjiang Mao Jing Zhang Xiao Ke College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou

ISBN: (纸本)9781728194202

With the economic and social development and the substantial improvement of material conditions, the generation of domestic waste has grown rapidly and has become a constraint factor for the development of new urbanization. In the past few years, research on the domestic waste industry has been limited to intelligent waste sorting, neglecting the role of intelligent management of waste storage sites. To relieve it, We propose a deep learning-based multi-task network for intelligent management of garbage deposit points, which combines algorithms such as YoloV5,Deepsort, Insightface, and Openpose to achieve waste bin detection, waste bin status recognition and analysis, face recognition, action recognition, and multiple object tracking based on real-time surveillance video. Besides, we propose a new dataset named Waste Bin Status, which provides a meaningful addition to the existing field of waste bin identification. Experiments on WBS dataset validate that our method is superior to other methods for garbage point status identification. Moreover, our network is trained to work with different scenarios of garbage deposits, demonstrating state-of-the-art performance in real-world tests.

关键词： Industries Face recognition Surveillance education Streaming media Multitasking Real-time systems

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A GENERAL ALTERNATING-DIRECTION IMPLICIT FRAMEWORK WITH GAUSSIAN PROCESS REGRESSION PARAMETER PREDICTION FOR LARGE SPARSE LINEAR SYSTEMS

arXiv

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arXiv 2021年

作者： Jiang, Kai Su, Xuehong Zhang, Juan Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper proposes an efficient general alternating-direction implicit (GADI) framework for solving large sparse linear systems. The convergence property of the GADI framework is discussed. Most of existing ADI methods can be unified in the developed framework. Meanwhile the GADI framework can derive new ADI methods. Moreover, as the algorithm efficiency is sensitive to the splitting parameters, we offer a data-driven approach, the Gaussian process regression (GPR) method based on the Bayesian inference, to predict the GADI framework’s relatively optimal parameters. The GPR method only requires a small training data set to learn the regression prediction mapping, can predict accurate splitting parameters, and has high generalization capability. It allows us to efficiently solve linear systems with a one-shot computation, and does not require any repeated computations. Finally, we use the three-dimensional convection-diffusion equation, two-dimensional parabolic equation, and continuous Sylvester matrix equation to examine the performance of our proposed methods. Numerical results demonstrate that the proposed framework is faster tens to thousands of times than the existing ADI methods, such as (inexact) Hermitian and skew-Hermitian splitting type methods in which the consumption of obtaining relatively optimal splitting parameters is ignored. As a result, our proposed methods can solve much larger linear systems which these existing ADI methods have not reached. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear systems

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An Octagonal Iterative Fractal Antenna with Notch Band and UWB Characteristics

An Octagonal Iterative Fractal Antenna with Notch Band and U...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yong Cai Shuping He Xingang Ren Zhixiang Huang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Key Laboratory of Electromagnetic Environmental Sensing Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

In this paper, based on the rectangular iterative technique, we propose an omnidirectional UWB fractal antenna by using microstrip line as feed. The antenna takes regular octagonal shape as patch element. Meanwhile, the iterative fractal and notch technique has been adopted to optimize the UWB antenna performance. After multiple iterations of the configured octagonal patch element, the optimized UWB performance can be obtained. The simulation results reveal that the operating bandwidth of the antenna meets Federal Communications Commission (FCC) broadband standard. Moreover, in order to avoid the electromagnetic interference in the X-band identified by the IEEE 521-2002 standard, we further adopt notch techniques and add the U-shaped gap to the microstrip line to achieve electromagnetic shielding. The impedance matching has been achieved for the antenna by imposing the slot on the ground. After optimizing the slot structure, the reflection of the antenna has reduced in the whole operating frequency band. The overall size of the ultra-wideband fractal antenna was designed to $25 \times 35 mm^{2}$ and was composed of FR-4 material with the relative dielectric constant $\varepsilon_{r} = 4.4$ and loss tangent angle $\tan \theta = 0.02$. After optimization, the antenna has good impedance matching and meets the UWB antenna omnidirectional standard. In 3.1–15.4GHz (excluding X-band), and the reflection coefficient S 11 is lower than -10 dB, voltage standing wave ratio (VSWR) is less than 2, maximum gain reaches 6dB. The results indicated that our proposed fractal antenna is promising for UWB antenna applications.

关键词： Slot antennas Impedance matching Voltage FCC Ultra wideband antennas Fractals Microstrip

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