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

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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data analysis Based on Knowledge Payment Behavior of College Students

Data Analysis Based on Knowledge Payment Behavior of College...

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作者： Qimiao Ma Bo Tang Ou Lan Zihao Wang Xinbo Li Jingke Yin School of Mathematics and Computing Sciences Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic Technology

In view of the existing willingness to pay for knowledge, we carried out multiple Logistic regression analysis on the influencing factors of college students' ability to pay for Internet knowledge, and obtained the correlation between college students' willingness to pay for Internet knowledge and various factors. The results show that college students tend to choose content with better quality, more flexible service, high public evaluation and prominent brand effect when paying for online knowledge.

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BOUNDEDNESS OF A CLASS OF MULTILINEAR OPERATORS AND THEIR ITERATED COMMUTATORS ON MORREY-BANACH FUNCTION SPACES

arXiv

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arXiv 2025年

作者： Tan, Jiawei Wang, Jiahui Xue, Qingying Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China

This paper investigates the boundedness of a broad class of operators within the framework of generalized Morrey-Banach function spaces. This class includes multilinear operators such as multilinear ω-Calderón-Zygmund operators, multilinear maximal singular integral operators, multilinear pseudo-differential operators, and multilinear square functions, as well as linear operators such as rough singular integral operators, nonintegral operators, and Stein’s square functions. The boundedness of this class of operators and their commutators on Morrey-Banach spaces is established provided they satisfy either a pointwise sparse domination assumption or the Wr property (Lerner, Lorist and Ombrosi, Math. Z. 2024), which significantly generalize the classical theory of Morrey spaces. As an application, the boundedness of iterated commutators of this class of multilinear operators is extended to Morrey-Lorentz spaces and Morrey spaces with variable *** Codes 42B35 © 2025, CC BY.

关键词： Banach spaces

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TWO INEQUALITIES FOR COMMUTATORS OF SINGULAR INTEGRAL OPERATORS SATISFYING HÖRMANDER CONDITIONS OF YOUNG TYPE

arXiv

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arXiv 2025年

作者： Li, Yuru Tan, Jiawei Xue, Qingying School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

In this paper, we systematically study the Fefferman-Stein inequality and Coifman-Fefferman inequality for the general commutators of singular integral operators that satisfy Hörmander conditions of Young type. Specifically, we first establish the pointwise sparse domination for these operators. Then, relying on the dyadic analysis, the Fefferman-Stein inequality with respect to arbitrary weights and the quantitative weighted Coifman-Fefferman inequality are demonstrated. We decouple the relationship between the number of commutators and the index Ε, which essentially improved the results of Pérez and Rivera-Ríos (Israel J. Math., 2017). As applications, it is shown that all the aforementioned results can be applied to a wide range of operators, such as singular integral operators satisfying the Lr-Hörmander operators, ω-Calderón-Zygmund operators with ω satisfying a Dini condition, Calderón commutators, homogeneous singular integral operators and Fourier *** Codes 42B20, 42B25 © 2025, CC BY.

关键词： Choquet integral

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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AgsNet: An Attention-Guided Lightweight Segmentation Network 1

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5th International Conference on Artificial Intelligence in China, AIC 2023

作者： Li, Minghui Xu, Zengmin Zhang, Yichuan Wei, Lingli Zhou, Ningjie Cui, Yanan Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541002 China School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9789819975457

ISBN: (纸本)9789819975440

Urinalysis test strips are commonly used for urine routine examination. However, due to possible defects in the liquid path, such as blockages, droplets may leak during the process of dropping urine samples onto the test strips, which severely affects the results of medical tests. Therefore, we propose the Attention-guided segmentation Network (AgsNet) to address errors in medical test results caused by defects in the liquid path. AgsNet adapts its focus to different areas of the test strip image, effectively extracting a richer and more diverse set of features. The best segmentation result is obtained with the AgsNet achieving a mean Intersection over Union (mIoU) score of 71.8 and mean Average Precision (mAP) scores of 84.49, respectively. These results underscore AgsNet’s potential in significantly reducing the impact of liquid pathway defects on the reliability of urinalysis test outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Defects

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The Exponential - Polynomial Coupling Model Based on Particle Swarm Optimization Algorithm and its Ternary Interval Number Prediction of GDP in Each Region of China

SSRN

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SSRN 2023年

作者： Ning, Lihua Zeng, Xiangyan Cang, Haozeng Gu, Yujuan School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

The prediction of GDP can be used for economic structure analysis, such as industrial structure, demand structure and regional structure. It is an important basis for macroeconomic decision-making. Affected by regional characteristics and national macro-control, the development trend of GDP in various regions of China in the past 15 years has been different. Some central regions grew rapidly in the early stage and fell back in the later stage, showing a saturated growth trend. Some coastal economically developed areas showed exponential growth. While some areas show an unstable upward and downward fluctuation trend. In order to predict the development trend of different GDP, this paper proposes a coupling model based on exponential and polynomial regression. Exponential function model can predict exponential growth series, while polynomial regression model can predict parabolic and saturated growth series. The coupling model is suitable for different growth sequences. Further, the coupled model is matrix, so that it can directly model the ternary interval number. Ternary interval number includes both the total amount of GDP in a certain period and its change range. Comprehensive information is more conducive to management decision-making. The new model can not be linearized, so the particle swarm optimization algorithm and the least square method are combined to estimate the parameters of the new model, which overcomes the weakness that the least square method must be linearized. The new model, quadratic polynomial, GM (1,1) and exponential function are used to predict the GDP of 31 regions in China from 2005 to 2020, except Hong Kong, Macao and Taiwan. The results show that the new model is suitable for predicting different GDP development trends, and the effect of GDP prediction for 20 regions in four years is better than that of other models. © 2023, The Authors. All rights reserved.

关键词： Exponential functions

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Boundedness of Vector Valued Bilinear Calderón-Zygmund Operators on Products of Weighted Herz-Morrey Spaces with Variable Exponents

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Chinese Annals of Mathematics,Series B 2021年第5期42卷 693-720页

作者： Shengrong WANG Jingshi XU School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004GuangxiChina School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyGuilin 541004GuangxiChina

In this paper,the authors obtain the boundedness of vector valued bilinear Calderón-Zygmund operators on products of weighted Herz-Morrey spaces with variable exponents.

关键词： Bilinear Calderón-Zygmund operator Vector valued inequality Muckenhoupt weight Variable exponent Herz-Morrey space

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Confidential Spatio-Temporal Graph Convolutional Networks with Differential Privacy for Traffic Forecasting

Confidential Spatio-Temporal Graph Convolutional Networks wi...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Xiangyang Chen Aiwen Xiao Weiling Pang Xinyi Jiang Zehua Ren Min Tang Key Laboratory of Data Analysis and Computation School of Mathematics and Computing Science Guangxi Colleges and Universities Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

Traffic forecasting is essential for urban traffic management in intelligent transportation systems (ITS). However, it involves personal sensitive information, such as user location data. In this paper, we propose an innovative secure protocol for addressing the privacy challenges of traffic prediction in ITS. Our model integrates Spatio-Temporal Graph Convolutional Network (STGCN) and differential privacy (DP) to effectively protect user privacy on the premise of predictive performance. Our experimental results demonstrate that the model maintains a high level of prediction accuracy while offering privacy protection for the user data. On the PeMSD7 dataset, compared to the STGCN benchmark model, our model shows a slight increase of 0.5% in performance metrics (e.g., mean absolute error) while safeguarding the user data. Additionally, the model's average training time increases by roughly 8.4% in the ciphertext domain.

关键词： Training Differential privacy Privacy Protocols Graph convolutional networks Predictive models data models

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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