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A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester

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STRUcTURAL AND MULTIDIScIPLINARY OPTIMIZATION 2022年第9期65卷 260-260页

作者： Shi, Maolin Liang, Zhenwei Zhang, Jian Xu, Lizhang Song, Xueguan Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Jiangsu Peoples R China Zhonghui Rubber Technol Co Ltd Wuxi 214183 Jiangsu Peoples R China Jiangsu Univ Fac Civil Engn & Mech Inst Struct Hlth Monitoring Zhenjiang 212013 Jiangsu Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

In real-world problems, engineering data often suffer from outliers due to deficiencies in measurement techniques, recording errors, or other reasons. In this work, a robust prediction method is proposed based on the Kriging method and fuzzy c-means algorithm. An outlier detection strategy is designed based on the fuzzy c-means algorithm, in which Kriging method is used to evaluate the relationship between the input and output. The membership of each training sample is calculated based on the prediction error of the Kriging model of each cluster and is used to judge whether the training sample is an outlier. The detected outliers are removed from the training samples, and then the remaining training samples are used to construct the final prediction model. The effect of the parameters of the proposed method on its performance is studied through an one-dimensional and a four-dimensional numerical problem, and ten benchmark functions are used to test its performance thoroughly. The results indicate that the proposed method produces much better performance in terms of outlier detection accuracy and prediction accuracy than the conventional outlier detection method and the Kriging method. Similar results can be found in the experiments on engineering problems. The proposed method is applied to model and analyze the operation data of the cleaning device of a combine harvester. The effect of the operation parameters of the cleaning device on the grain impurity ratio is studied, and the operation parameters of the cleaning device are optimized based on the prediction model of the proposed method. The analysis and optimization results provide a reference for the operation and control of the cleaning device of a combine harvester.

关键词： Robust prediction Outlier detection Kriging method fuzzy c-means algorithm combine harvester

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A cOMPARATIVE STUDY OF fuzzy c-means algorithm AND ENTROPY-BASED fuzzy cLUSTERING algorithmS

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cOMPUTING AND INFORMATIcS 2011年第4期30卷 701-720页

作者： chattopadhyay, Subhagata Pratihar, Dilip Kumar De Sarkar, Sanjib chandra Natl Inst Sci & Technol Dept Comp Sci & Engn Sch Comp Studies Berhampur 761008 Orissa India Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 751013 Orissa India

fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-c-means (FcM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Moreover, in FcM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. The cluster centers and membership values of the data points with them are updated through some iterations. On the other hand, entropy-based fuzzy clustering (EFc) algorithm works based on a similarity-threshold value. contrary to FcM, in EFc, the cluster centers are real, that is, they are chosen from the data points. In the present paper, the performances of these algorithms have been compared on four data sets, such as IRIS, WINES, OLITOS and psychosis (collected with the help of forty doctors), in terms of the quality of the clusters (that is, discrepancy factor, compactness, distinctness) obtained and their computational time. Moreover, the best set of clusters has been mapped into 2-D for visualization using a self-organizing map (SOM).

关键词： fuzzy clustering fuzzy c-means algorithm entropy-based algorithms self-organizing maps

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Debris Flow Susceptibility Assessment in the Wudongde Dam Area, china Based on Rock Engineering System and fuzzy c-means algorithm

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WATER 2017年第9期9卷 669页

作者： Li, Yanyan Wang, Honggang chen, Jianping Shang, Yanjun Chinese Acad Sci Inst Geol & Geophys Key Lab Shale Gas & Geoengn Beijing 100029 Peoples R China Cent Southern China Elect Power Design Inst Co Lt CPECC Wuhan 430071 Hubei Peoples R China Jilin Univ Coll Construct Engn Changchun 130026 Jilin Peoples R China

Debris flows in theWudongde dam area, china could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock engineering system and fuzzy c-means algorithm (RES_FcM) for debris flow susceptibility assessment. The watershed of the Jinsha River close to the Wudongde dam site in southwest china was taken as the study area, where a total of 22 channelized debris flow gullies were mapped by field investigations. Eight environmental parameters were selected for debris flow susceptibility assessment, namely, lithology, watershed area, slope angle, stream density, length of the main stream, curvature of the main stream, distance from fault and vegetation cover ratio. The interactions among these parameters and their weightings were determined using the RES method. A debris flow susceptibility map was produced by dividing the gullies into three categories of debris flow susceptibility based on the susceptibility index (SI) using the FcM algorithm. The results show that the susceptibility levels for nine of the debris flow gullies are high, nine are moderate and four are low, respectively. The RES based K-means algorithm (RES_KM) was used for comparison. The results suggest that the RES_FcM method and the RES_KM method provide very close evaluation results for most of the debris flow gullies, which also agree well with field investigations. The prediction accuracy of the new method is 90.9%, larger than that obtained by the RES_KM method (86.4%). Therefore, the RES_FcM method performs better than the RES_KM method for assessing the susceptibility of debris flows.

关键词： debris flow susceptibility rock engineering system fuzzy c-means algorithm interaction

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Intuitionistic fuzzy c-means algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

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SENSORS 2021年第3期21卷 696-696页

作者： chen, Haipeng Xie, Zeyu Huang, Yongping Gai, Di Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

The fuzzy c-means clustering (FcM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy c-means algorithm based on membership information transferring and similarity measurements (IFcM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FcM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFcM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FcM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFcM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFcM-MS. Experiments performed on real brain tumor images demonstrate that our IFcM-MS has low noise sensitivity and high segmentation accuracy.

关键词： image segmentation information transferring fuzzy c-means algorithm similarity

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fuzzy c-means clustering algorithm for directional data (FcM4DD)

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EXPERT SYSTEMS WITH APPLIcATIONS 2016年 58卷 76-82页

作者： Kesemen, Orhan Tezel, Ozge Ozkul, Eda Karadeniz Tech Univ Dept Stat & Comp Sci TR-61080 Trabzon Turkey

cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FcM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FcM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FcM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FcM4DD algorithm in terms of consistency, accuracy and computational time. fuzzy clustering algorithms for directional data (FcM4DD and FcD) were compared according to membership values and the FcM4DD algorithm obtained more acceptable results than the FcD algorithm. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： Angular difference clustering algorithm Directional data fuzzy c-means algorithm

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3-D Gravity Anomaly Inversion Based on Improved Guided fuzzy c-means clustering algorithm

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PURE AND APPLIED GEOPHYSIcS 2020年第2期177卷 1005-1027页

作者： Liu, Sheng Jin, Shuanggen Chinese Acad Sci Xinjiang Astron Observ Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Astron Observ Shanghai 200030 Peoples R China Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China

The geophysical inversion with combining prior information is very important for resource exploration and studies of the Earth's internal structure. Guided fuzzy c-means clustering inversion (FcM) is normally applied for the Tikhonov regularized inversion, but has the shortcoming of uniform model parameter shrinkage, leading to inaccuracy. In this paper, an improved guided fuzzy clustering algorithm is proposed by adding a fuzzy entropy term to the original guided FcM. This method not only enforces the discrete values to a high degree of approximation by guiding the recovered model to cluster tightly around the known petrophysical property values, but also calculates the distributed characteristics of the model parameter set. Based on this method, the shortcoming of uniform shrinkage of the original guided FcM clustering algorithm is improved, and more accurate inversion results are obtained, making the FcM method more efficient and broadly applicable. Furthermore, a new parameter search algorithm is proposed to accelerate the search speed. The results recovered by using this method with three kinds of theoretical gravity anomaly data show more accurate density anomalies compared with the results generated from the original guided FcM clustering inversion and greater efficiency in the parametric search process when using the new parameter search algorithm. The improved FcM clustering algorithm could enable more extensive and efficient use of gravity inversion.

关键词： fuzzy entropy discrete-valued inversion gravity inversion parameter search fuzzy c-means algorithm

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Research on Modified fuzzy c-means algorithm in Lung Nodules computer-aided Diagnosis（cAD） System

Research on Modified Fuzzy C-means Algorithm in Lung Nodules...

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2017 5th International conference on Machinery,Materials and computing Technology(IcMMcT 2017)

作者： Qing Li Hui Liu Department of Mechanical Engineering Shandong Labor Vocational and Technical College Shandong Provincial Key Laboratory of Digital Media Technology School of Computer Science and Technology Shandong University of Finance and Economics

It is important for the early diagnosis and treatment of lung cancer in the computer-aided Diagnosis/Detection（cAD） system, and accurate segmentation of pulmonary nodules from tomographic images is the basic and active research problem for the benign or malign diagnosis. For this reason, this work seeks to develop automatic detection and classification method of lung nodules. First, the algorithm separates lung parenchyma from the anatomical structures based on maximum between-cluster variance, image dilation and erosion. Secondly, a modified robust fuzzy c-means clustering（r FcM） segmentation algorithm is proposed, this method improves the objective function by adding a punishment factor, for eliminating the influence from noise and non-uniform gray problem. Experimental results have shown that the proposed method can achieve more accurate segmentation and perform better than other traditional algorithms in classification and recognition, Furthermore, the segmentation results on brain images also get a satisfied performance.

关键词： cAD fuzzy c-means algorithm Punishment Factor Neighborhood Space Window Gray Scale

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Improved fuzzy c-means algorithm and Its Application to classification of Remote Sensing Image in chengdu city,china

Improved Fuzzy C-Means Algorithm and Its Application to Clas...

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2016 IEEE International conference on Progress in Informatics and computing

作者： Dongmei Han Jiayu Ji Yonghui Dai Guowei Li Weiguo Fan Huagen chen School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai Financial Information Technology Key Research Laboratory Management School Shanghai University of International Business and Economics Pamplin Coll Business Virginia Polytechnic Institute & State University School of Ocean and Earth Science Tongji University

The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering automatically and removed the noise of image after classification. Meanwhile, as an alternative to expert knowledge, data fusion method was used, which included the fusion of aeromagnetic data, gravity data and elevation data. The empirical results showed that this method can avoid the highly dependent on domain knowledge experts in image recognition and got a better classification effect in remote sensing image.

关键词： fuzzy c-means algorithm remote sensing image image classification data fusion

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Detection of Suspicious Lesions in Mammogram using fuzzy c-means algorithm

Detection of Suspicious Lesions in Mammogram using Fuzzy C-M...

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International conference on Advances in computing, communications and Informatics

作者： Mukesh Kumar V. M. Thakkar Upendra Bhatt Neema Soliyal CSED GBPEC

ISBN: (纸本)9781509020300

Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is used, which is an X-ray inspection of the breast, which can be used to detect the breast tumor which may lead to breast cancer. Using Mammography, a small lump that may lead to breast cancer can be detected at the initial stage. Sometimes it is not possible to recognize very small tumors because of noisy, blurred, and fuzzy images. Therefore, they need to be enhanced to increase the contrast for better visual perception and reduce the noise from it for better diagnosis. In this work, FcM algorithm is used to detect the suspicious lesions in a mammogram. To achieve the objective of this work, MIAS (Mammographic Image Analysis Society) and INbreast databases are used, which contain 322 and 412 images of the breast (both left and right breast) respectively. In these databases, every image is examined by the expert radiologists. The effectiveness of the algorithm is measured in terms of MSE and PSNR.

关键词： Mammograms Breast cancer Wavelet Denoising Filter fuzzy c-means algorithm Morphological Operation Suspicious Lesions

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Optimum Sensor Nodes Deployment using fuzzy c-means algorithm

Optimum Sensor Nodes Deployment using Fuzzy C-means Algorith...

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2011 International Symposium on computer Science and Society

作者： Yong Kuk Park Min Goo Lee Kyung Kwon Jung Jun Jae Yoo Sang Han Lee Hyeong-Seok Kim U-embedded Convergence Research Center Korea Electronics Technology Institute

In this paper, a fuzzy c-means clustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simul... 详细信息

关键词： sensor deployment fuzzy c-means algorithm square grid coverage

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