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Utilization of Fuzzy C-means algorithm as a novel predictive tool for estimation of interfacial tension of hydrocarbon and brine

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第15期36卷 1107-1112页

作者： Rouhibakhsh, Karim Darvish, Houman Shiraz Univ Sch Chem Petr & Gas Engn Dept Petr Engn Shiraz Iran Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran

The interfacial tension that exists between brine and hydrocarbon is known as one of major properties in petroleum industries because it extremely affects oil trapping in reservoirs and consequently oil recovery. Due to aforementioned reasons the importance of investigation of this parameter has been highlighted. In the present study, Fuzzy C-means (fcm) algorithm was developed to predict interfacial tension between hydrocarbon and brine as function of different parameters such as pressure, temperature, carbon number of hydrocarbon and ionic strength of brine. The obtained results of predicting algorithm expressed its low relative error and deviation from the experimental data which gathered from the literature. Also the coefficients of determination (R-2) for training and testing data were calculated 0.9508 and 0.9309 respectively. This predictive tool is simple and user friend to utilize and can be helpful for petroleum engineers to estimate interfacial tension between hydrocarbons and brine.

关键词： EOR fcm algorithm interfacial tension hydrocarbon predicting model

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A Novel Double-Layer Strategy for Three-Phase Imbalance Treatment Using an Improved Genetic algorithm Based on MIT-LXPM

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7404-7416页

作者： Wang, Jing Liu, Kaipei Qin, Liang Fan, Di Wang, Hong Wang, Feng Huang, Fucheng Chen, Jianhua Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China State Grid Jibei Elect Power Co Ltd Elect Power Res Inst Beijing 100045 Peoples R China State Grid Jibei Elect Power Co Ltd Beijing 100032 Peoples R China

Three-phase imbalance in distribution networks poses significant risks to line loss, economic efficiency, and the safe operation of equipment. However, existing approaches often overlook users who cannot adjust their phase sequence in the distribution network and fail to consider the flexible regulation capabilities of household appliances. To address this gap, this article introduces a novel double-layer treatment strategy for mitigating three-phase imbalance. The first layer of the strategy focuses on coarse adjustments to the user phase sequence, while the second layer refines the activation time of electrical equipment. Given the complexity of the proposed strategy, traditional algorithms are prone to local optimization. To overcome this limitation, an improved genetic algorithm based on the MIT-LXPM framework is proposed to implement the double-layer treatment. Furthermore, we construct a typical electricity consumption behavior database based on the weighted fuzzy C-means (Wfcm) algorithm, which facilitates more accurate clustering and minimizes optimization errors. Experimental results demonstrate the effectiveness of the proposed method, achieving a significant reduction in the average three-phase imbalance degree from 36.25% to 2.76%. Moreover, the line loss reduction rate reaches 80.26%. These findings highlight the practical value and application potential of the proposed method in real-world scenarios.

关键词： fcm algorithm genetic algorithm smart household electrical equipment three-phase imbalance user phase sequence

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C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation

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SIGNAL IMAGE AND VIDEO PROCESSING 2017年第3期11卷 541-548页

作者： Gharieb, R. R. Gendy, G. Abdelfattah, A. Assiut Univ Fac Engn Assiut 71516 Egypt Assiut Univ Al Rajhy Liver Hosp Assiut Egypt

In this paper, C-means algorithm is fuzzified and regularized by incorporating both local data and membership information. The local membership information is incorporated via two membership relative entropy (MRE) functions. These MRE functions measure the information proximity of the membership function of each pixel to the membership average in the immediate spatial neighborhood. Then minimizing these MRE functions pushes the membership function of a pixel toward its average in the pixel vicinity. The resulting algorithm is called the Local Membership Relative Entropy based fcm (LMREfcm). The local data information is incorporated into the LMREfcm algorithm by adding to the standard distance a weighted distance computed from the locally smoothed data. The final resulting algorithm, called the Local Data and Membership Relative Entropy based fcm (LDMREfcm), assigns a pixel to the cluster more likely existing in its immediate neighborhoods. This provides noise immunity and results in clustered images with piecewise homogeneous regions. Simulation results of segmentation of synthetic and real-world noisy images are presented to compare the performance of the proposed LMREfcm and LDMREfcm algorithms with several fcm-related algorithms.

关键词： Noisy image segmentation fcm algorithm Local spatial information Membership relative entropy

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Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2019年第9期40卷 3344-3367页

作者： Xie, Fuding Lei, Cunkuan Li, Fangfei Huang, Dan Yang, Jun Liaoning Normal Univ Coll Urban & Environm 850 Huanghe Rd Dalian Liaoning Peoples R China Liaoning Normal Univ Coll Comp Sci Dalian Liaoning Peoples R China

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (fcm) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (GWO) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (kappa).

关键词： hyperspectral image feature selection fcm algorithm GWO algorithm

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Histogram-based colour image fuzzy clustering algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第18期75卷 11417-11432页

作者： Chen, Hai-peng Shen, Xuan-Jing Long, Jian-Wu Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400045 Peoples R China

In this work, a histogram-based colour image fuzzy clustering algorithm is proposed for addressing the problem of low efficiency due to computational complexity and poor clustering performance. Firstly, the presented scheme constructs the red, green and blue (short for RGB) component histograms of a given colour image, each of which is pre-processed to preserve their smoothness. Secondly, the proposed algorithm multi-thresholds each component histogram, using some dominant valleys identified from a fast peak-valley location scheme in each global histogram. Thirdly, a new histogram is reconstructed by applying a histogram merging scheme to the RGB three-component histograms, and multi-thresholding this new histogram again using some dominant valleys obtained from the fast peak-valley location scheme. Thus, the proposed approach can easily identify the initialisation condition of cluster centroids and centroid number. Finally, we construct a new dataset composed of some pre-segmented small regions using the WaterShed algorithm, and the fcm (Fuzzy C-Means) algorithm is executed on this dataset, instead of on pixels, in combination with the initial cluster centroids. Experimental results have demonstrated that the proposed algorithm is more efficient than the DSRPCL (Distance Sensitive Rival Penalised Competitive Learning) algorithm and the HTfcm (Histogram Thresholding Fuzzy C-Means) algorithm with respect to run times and PRI (Probability Rand Index) values.

关键词： Colour image segmentation Histogram Clustering fcm algorithm

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Designing reward and penalty scheme in performance-based regulation for electric distribution companies

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第9期6卷 893-901页

作者： Simab, M. Alvehag, K. Soder, L. Haghifam, M. R. Islamic Azad Univ Sci & Res Branch Dept Elect Engn Fars Iran Royal Inst Technol KTH Div Elect Power Syst Stockholm Sweden Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

A reward and penalty scheme (RPS) in performance-based regulation (PBR) penalises companies for providing poor reliability and rewards them for providing good reliability. In this study, an algorithm is presented to obtain the parameters of RPS for each electric company by using data envelopment analysis (DEA) and fuzzy c-means clustering (fcm). This algorithm is based on system average interruption duration index (SAIDI) and customers' value of interruptions. fcm algorithm is applied to find similar distribution companies and cluster companies into different clusters. The best performer in each cluster is utilised as a benchmark for other companies and DEA is used to set a quality target for each electric distribution company. The performance of the proposed algorithm is demonstrated in a case study to design RPS for Iranian electricity distribution companies. The results of the algorithm include DEA efficiency score, parameters of RPS and financial risk assessment.

关键词： power distribution reliability reliability fcm algorithm PBR fuzzy set theory Reliability electricity supply industry DEA efficiency score data envelopment analysis Iranian electricity distribution companies performance-based regulation Power system management, operation and economics Combinatorial mathematics reward and penalty scheme SAIDI system average interruption duration index RPS fuzzy c-means clustering

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Consistency evaluation of hole series surface quality using vibration signal

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期92卷 1069-1079页

作者： Zhou, Youhang Yang, Wenjia Xu, Zhiyuan Shi, Xianwei Xiangtan Univ Sch Mech Engn Xiangtan 411105 Peoples R China Xiangtan Univ Minist Educ Engn Res Ctr Complex Tracks Proc Technol & Equipm Xiangtan 411105 Peoples R China Hunan Univ Sci & Engn Sch Elect & Informat Engn Yongzhou 425199 Peoples R China

The hole series parts is widely used in the aerospace, automotive electronics, instrumentation, and hydraulic components. There are many of holes with the same machining parameter in this kind of parts, the consistency evaluation of these hole surface quality is one of the most important considerations. Based on wavelet packet energy spectrum, higher-order statistics, radar chart, and fuzzy c-means (fcm) algorithm, a method is presented to evaluate hole surface quality quickly and effectively. Vibration signal is used as the main source of information about the drilling process. In this paper, wavelet packet energy spectrum (WPES) and higher-order statistics, which can image the information on both time and frequency domain, are applied as preparatory work for cluster analysis. Then, a ten-dimensional feature vector for cluster analysis is constructed from the value of higher-order statistics and wavelet packet energy ratio of the frequency bands where the energy is abnormal. Finally, cluster analysis based on radar chart and fcm algorithm is conducted on the feature vector for classification. By comparison, it is found that the cluster analysis result is basically consistent with the artificial test result. The proposed method can be further extended to the quality evaluation of other machining processes.

关键词： Hole series surface quality Vibration signal Wavelet packet energy spectrum High-order statistics fcm algorithm

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Regional Frequency Analysis of Droughts in China: A Multivariate Perspective

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WATER RESOURCES MANAGEMENT 2015年第6期29卷 1767-1787页

作者： Zhang, Qiang Qi, Tianyao Singh, Vijay P. Chen, Yongqin David Xiao, Mingzhong Sun Yat Sen Univ Dept Water Resources & Environm Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong High Educ Inst Key Lab Water Cycle & Water Secur South China Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Guangdong Peoples R China Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Inst Environm Energy & Sustainabil Shatin Hong Kong Peoples R China

Joint probability behavior of droughts is important for China due to the fact that China is the agricultural country with the largest population in the world and it is particularly the case in the backdrop of intensifying weather extremes in a warming climate. In this case, regionalization of droughts is done using Fuzzy C- Means (fcm) clustering technique and also multivariate L-moment method. Besides, copula is used to estimate regional joint probability in terms of drought duration and severity. Evaluation of uncertainty in the joint probability curves is done using the Bootstrap resampling technique. The results indicate that: (1) five homogenous regions of droughts are subdivided. Regionalization in this study clarified the changing properties or nature of droughts, i.e., the blurred or ambiguous boundaries of the drought-impacted regions;(2) droughts in the northwest China are characterized by longer drought duration and larger drought severity, and the occurrence of the droughts in the northwest China is subject to be higher due to longer waiting time between drought events. Adverse is found for changes of droughts in the southeast China. The droughts in the north China are moderate in terms of drought duration and severity and also waiting time between drought events when compared to those in the northwest and southeast China;(3) the regional joint frequency curves are obtained with respect to drought duration and severity using the bivariate copula functions. Then the joint probabilities of droughts can be calculated using the regional probability curves and also results of mean drought duration, drought severity and waiting time between drought events. Furthermore, droughts in the regions without meteorological data can also be estimated in terms of joint probability using index-drought method proposed in this study. This study will provides theoretical and practical grounds for development and enhancement of human mitigation to drought hazards in China, a

关键词： Meteorological droughts Regional frequency analysis Multivariate L-moment Copula functions fcm algorithm China

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Improving probabilities in a fuzzy clustering partition

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FUZZY SETS AND SYSTEMS 2008年第4期159卷 406-421页

作者： Soto, J. Flores-Sintas, A. Palarea-Albaladejo, J. Univ Catolica San Antonio Dept Comp Sci Murcia 30107 Spain

Fuzzy clustering procedures based on the fcm algorithm calculate group membership probabilities or degrees taking into account the distance between objects and group prototypes. This paper seeks to improve the computation of such membership probabilities by a new membership function which also reflects the relative position of an object with respect to each group. By this way, some illogical results are avoided and a convex partition is provided. Finally, numerical examples illustrate the performance of the proposed algorithm. (c) 2007 Published by Elsevier B.V.

关键词： fuzzy clustering fcm algorithm membership probabilities convex partitions

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A fuzzy clustering approach for cloud-based personalized distance music education and resource management

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SOFT COMPUTING 2024年第2期28卷 1803-1818页

作者： Jiang, Lei Jiangxi Normal Univ Sch Mus Nanchang 330027 Jiangxi Peoples R China

Advancements in information technology (IT), the Internet of Things (IoT), and other emerging technologies have led to an overflow of information and big data. The emergence of these technologies and fast internet connectivity has led to the growing popularity of distance learning in academic circles and society. The goal of this paper is to improve online music learning through the use of cloud computing and fuzzy clustering. A significant amount of data regarding students and their activities is collected. Following data cleaning and organization, students are grouped according to their preferred style of music learning using a technique called fuzzy C-mean (fcm) clustering. Each group is then given a customized learning plan that matches them with appropriate music resources. To put this into practice, a simple online music learning platform is created, enabling teachers and students to sign in, give instructions, view results, and log out with ease. To ensure that our approach remains effective even under high user loads, we designed the system to function properly for up to 400 concurrent users. An educational resource database is used to find the best learning materials for students, and fcm helps them pick the right resources. To make everything more organized, a resource library is designed using a simple language called XML. This library has different categories for learning materials and follows international standards to make sure everything works together smoothly. The performance of the system is measured in terms of scalability (8/10 response time, 7/10 throughput, 9/10 resource utilization), usability (8/10 user satisfaction, 7/10 task completion time, 6/10 error rate), effectiveness (9/10 learning outcomes, 8/10 student engagement, 9/10 retention rates). It has a 99% uptime and no security incidents have been reported. This study shows that our method can improve online music education, making it more personalized and effective for each student. The

关键词： Fuzzy clustering algorithm Cloud computing technology Distance music education Resource library fcm algorithm

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