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Study on comprehensive evaluation of environmental pollution in tourist attractions based on fcm algorithm

INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2025年第1-3期28卷 174-189页

作者： Pan, Yurong Jia, Chaoyong Bengbu Univ Sch Math & Phys Bengbu 233000 Peoples R China

To address the shortcomings of traditional evaluation approaches for environmental pollution in tourist destinations, such as their limited precision, accuracy, and reliability, we must seek innovative strategies, a comprehensive evaluation method of environmental pollution in tourist attractions based on fcm algorithm is proposed. The comprehensive evaluation index system of environmental pollution in tourist attractions is established, and the evaluation index data are clustered by fcm algorithm. The improved principal component analysis is improved by logarithmic processing, so that the improved principal component analysis can process the evaluation index data with high quality, and environmental pollution evaluation results are obtained by combining the factor load matrix. The empirical findings indicate that the average precision of the evaluation index stands at an impressive 97.36%, the evaluation accuracy is between 96.5% and 98.3%, and the average reliability of the evaluation result is 0.97, which can realise the accurate evaluation of environmental pollution.

关键词： fcm algorithm tourist attractions environmental pollution comprehensive evaluation logarithmic processing improved principal component analysis

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Real-time Segmentation of Yarn Images Based on an fcm algorithm and Intensity Gradient Analysis

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FIBRES & TEXTILES IN EASTERN EUROPE 2016年第4期24卷 45-50页

作者： Li, Zhongjian Pan, Ruru Wang, Jing'an Wang, Ziyu Li, Bianbian Gao, Weidong Jiangnan Univ Sch Text & Clothing Wuxi 214122 Peoples R China

This paper presents a new method for real-time segmentation of yarn images which are captured by a real-time image acquisition device. The first frame of the images is clustered by the local average intensity and entropy of the image based on the fcm (Fuzzy C-means) algorithm to obtain a segmentation threshold value. The pixels with an intensity below the threshold value in each column of the image are convolved with a convolve template to construct an intensity gradient curve. The points of maximum value and minimum value in the curve are considered as the upper and lower edge points of yarn. A robust real-time segmentation algorithm of yarn images is obtained for evaluating yarn diameter more precisely. Finally two indices of SE (Segmentation Error) in % and ADE (Average Diameter Error) in % are proposed to evaluate the segmentation method, which is then compared with the manual method.

关键词： real-time segmentation image processing yarn evenness fcm algorithm gradient analysis

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Measuring Linear Density of Threads in Single-System-Melange Color Fabrics with fcm algorithm

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COLOR RESEARCH AND APPLICATION 2013年第6期38卷 456-462页

作者： Pan, Ruru Liu, Jihong Gao, Weidong Jiangnan Univ Sch Text & Clothing Key Lab Ecotext Wuxi Jiangsu Peoples R China

According to the color yarns in the fabric, the fabrics can be divided into three categories: solid color fabrics, single-system-melange color fabrics, and double-system-melange color fabrics. The density of solid fabrics can be inspected with gray-projection method or Fourier analysis method. But the methods cannot be applied to yarn-dyed fabrics directly. A method for detecting the density of single-system-melange color fabrics will be discussed in this article. By analyzing the pattern and color characters of single-system-melange color fabrics, fuzzy C-means algorithm is proposed to classify the colors in the fabric image based on CIELAB color space first. With the color segmentation results, the fabric can be divided into different blocks. The yarns can be located in different blocks with different average gray-levels, and then the number of yarns can be counted in each block. The linear density of threads can be obtained by counting the yarns in a unit length finally. The experiment proved that the algorithm proposed in this study can inspect the density of single-system-melange color fabric successfully. (c) 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 456-462, 2013

关键词： linear density of threads single-system-melange color fabric fcm algorithm CIELAB color space color classification

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A study on semi-supervised fcm algorithm

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KNOWLEDGE AND INFORMATION SYSTEMS 2013年第3期35卷 585-612页

作者： Zeng, Shan Tong, Xiaojun Sang, Nong Huang, Rui Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Hubei Peoples R China Wuhan Text Univ Coll Math & Comp Sci Wuhan 430077 Hubei Peoples R China

Most variants of fuzzy c-means (fcm) clustering algorithms involving prior knowledge are generally based on the modification of the objective function or the clustering process. This paper proposes a new weighted semi-supervised fcm algorithm (SSfcm-HPR) that transforms the prior knowledge in the labeled samples into constraint conditions in terms of fuzzy membership degrees, assigns different weights according to the representativeness of the samples, and then uses the HPR multiplier to solve the clustering problem. The "representativeness" of the labeled samples is decided by their distances to the cluster centers they belong to. In this paper, we take the ratio of the largest to the second largest fuzzy membership degree from a labeled sample as its weight. This algorithm not only retains the fuzzy partition of the labeled samples, which guarantees the effective guidance on the clustering process, but also can detect whether a sample is an outlier or not. Moreover, when part of the supervised information of the labeled samples is wrong, this algorithm can reduce the influence of the incorrectly labeled samples on the final clustering results. The experimental evaluation on synthetic and real data sets demonstrates the efficiency and effectiveness of our approach.

关键词： fcm algorithm Semi-supervised clustering HPR multiplier Incorrectly labeled samples

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Application of data mining in female sports behavior prediction based on fcm algorithm

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SOFT COMPUTING 2023年第14期27卷 10045-10055页

作者： Yuan, Liyun Cao, Jiaxing Tianjin Univ Sport Tianjin Peoples R China Tianjin Sports Dev Co LTD Tianjin Peoples R China

Data mining is the process of continuously optimizing the problem to the goal. It has to meet the needs of the user, which means it has to unearth content that is useful or interesting to the user. This paper analyzes the data mining of female sports behavior prediction based on fcm algorithm. We modified the standard speech frequencies of the data into high-level descriptions, used various data mining methods to determine speech rules, and obtained good experimental results. Data mining technology is used to mine valuable information and knowledge hidden in a large amount of test security data, and make the acquired expertise into understandable rules or patterns, and then use the rules or patterns that have been created to effectively detect Guidelines for any activity suspected of attacking network data. The simulation results show that the data mining system in this paper can effectively predict and analyze the data. The results show that there are many differences among working women in terms of average annual consumption of sports and leisure activities, participation and consumption of sports and leisure activities. In addition, based on the results of the study, we will take more effective and specific references and measures to expand the participation of professional women in sports and leisure activities.

关键词： Data mining Language processing fcm algorithm Sports research

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Extraction of plateau lake water bodies based on an improved fcm algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1727-1740页

作者： Li, Yingxin Li, Shihua Peng, Shuangyun Zhao, Shoulu Yang, Wenxian Qiu, Lidan Yunnan Normal Univ Fac Geog Kunming Yunnan Peoples R China Yunnan Prov Geomat Ctr Kunming Yunnan Peoples R China Yunnan Univ Sch Earth Sci Kunming Yunnan Peoples R China

Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (fcm) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise" of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional fcm algorithm, this paper proposes an improved fcm algorithm. This algorithm replaces the Euclidean distance of the traditional fcm algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional fcm algorithm, improved fcm algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved fcm algorithm has a higher water extraction accuracy than the traditional fcm algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved fcm algorithm can be increased by 5.56%-9.45% and 2.66%-5.32%, respectively, and the omission error and commission error can be reduced by 1.72%-4.55% and 12.14%-22.10%, respectively. When the improved fcm algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other metho

关键词： Remote sensing fuzzy clustering fcm algorithm mahalanobis distance spectral angle matching

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Automatic Detection of the Layout of Color Yarns for Yarn-dyed Fabric via a fcm algorithm

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TEXTILE RESEARCH JOURNAL 2010年第12期80卷 1222-1231页

作者： Pan, Ruru Gao, Weidong Liu, Jihong Wang, Hongbo Jiangnan Univ Sch Textile & Clothing Key Lab Eco Textiles Wuxi 214122 Jiangsu Peoples R China

In the process of analyzing the yarn-dyed fabric, two kinds of color information about color yarns should be detected: (1) the number of yarn colors;(2) the layout of the color yarns. The traditional detection methods are time-consuming and labor-intensive. An automatic method based on image analysis is proposed in this study. The image of yarn-dyed fabric captured with a flat scanner is analyzed by a fuzzy C-means clustering (fcm) algorithm. By the analysis of the image of the yarn-dyed fabric based with the fcm algorithm, we can conclude that the number of yarn colors can be obtained with cluster validity analysis, and the layout of color yarns can be inspected automatically with the help of Hough transform. Experiments on two actual fabrics show that the approach proposed in this study is effective for detecting the number of yarn colors and the layout of color yarns in the yarn-dyed fabric.

关键词： yarn-dyed fabric fcm algorithm layout of color yarns number of yarn colors cluster validity Hough transform

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Biomedical Image Processing Using fcm algorithm Based on the Wavelet Transform

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Journal of Wuhan University of Technology(Materials Science) 2004年第3期19卷 18-20页

作者：闫玉华 Wuhan University of Technology

An effective processing method for biomedical images and the Fuzzy C-mean (fcm) algorithm based on the wavelet transform are *** using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the fcm clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the fcm algorithm, fcm alternation frequency was decreased and the accuracy of segmentation was advanced.

关键词： biomedical image processing fcm algorithm wavelet transform texture feature

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Improved fcm algorithm Based on K-Means and Granular Computing

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JOURNAL OF INTELLIGENT SYSTEMS 2015年第2期24卷 215-222页

作者： Lu, Wei Jia Yan, Zhuang Zhi Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Affiliated Hosp Nantong Univ Dept Informat Nantong 226001 Jiangsu Peoples R China

The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (fcm) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional fcm methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and fcm, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved fcm algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing fcm algorithms.

关键词： Fuzzy cluster K-means fcm algorithm principle of granularity

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Optimization of overprint error detection based on the fcm algorithm

Optimization of overprint error detection based on the FCM a...

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International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

作者： Wang, Yuchuan Qi, Yusheng Zhang, Yongbin Du, Yunfei Beijing Inst Graph Commun Beijing Peoples R China

ISBN: (纸本)9781665464680

Because the printing accuracy of printed matter needs to be higher and higher, it is necessary to strengthen the accuracy of the overprint error detection. However, in the current machine vision detection methods, when separating the four primary colors of CMYK, there is still the phenomenon of four colors overlapping, which decrease the detection accuracy. In this paper, based on the existing machine vision detection methods, The fcm algorithm is used to cluster image colors, so as to improve the accuracy of color segmentation and decrease the impact of four-color overlap. Through this method, the identifier image can be clustered to improve the detection accuracy. The results indicate that this method can reduce the interaction between the four colors to a certain extent and provide convenience for successive color segmentation.

关键词： fcm algorithm Machine vision inspection CMYK color space Quadratic segmentation algorithm

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