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International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Zhang, Chun-cheng Chen, Xiang-guang Xu, Yuan-qing Beijing Inst Technol Sch Chem Engn & Environm Beijing 100081 Peoples R China Beijing Inst Technol Sch Life Sci Beijing Peoples R China

ISBN: (纸本)9783037852958

In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (fcm) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on fcm data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (fcm+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (fcm+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the fcm+SVM method has better forecasting accuracy compared with fcm+BPNN method.

关键词： fcm algorithm Support Vector Machines PMV Index BP

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Segmentation of MRI Brain Images by Incorporating Intensity Inhomogeneity and Spatial Information Using Probabilistic Fuzzy C-Means Clustering algorithm

Segmentation of MRI Brain Images by Incorporating Intensity ...

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International Conference on Communications, Devices and Intelligent Systems (COOlS)

作者： Adhikari, Sudip Kumar Sing, J. K. Basu, D. K. Nasipuri, M. Saha, P. K. Neotia Inst Technol Management & Sci Dept Comp Sci & Engn Kolkata India Univ Jadavpur Dept Comp Sci & Engn Kolkata India Univ Iowa Dept Elect & Comp Engn Iowa City IA USA

ISBN: (纸本)9781467346986;9781467346993

Segmentation of magnetic resonance imaging (MRI) brain images is an important task to analyze tissue structures of a human brain. Due to improper image acquisition systems, MRI images are generally corrupted by intensity inhomogeneity (IIH) or intensity nonuniformity (INU). Conventional methods try to segment MRI images using only spatial information about the distribution of pixel intensities and are highly sensitive to noise and the IIH or INU. This paper presents a method to segment MRI brain images by considering the INU and spatial information using fuzzy C-means (fcm) clustering algorithm. Firstly, the INU of MRI brain image is corrected using fusion of Gaussian surfaces. The individual Gaussian surface is estimated independently over the different homogeneous regions by considering its center as the center of mass of the respective homogeneous region. Secondly, the IIH corrected image is segmented using probabilistic fcm algorithm, which considers spatial features of image pixels. The experiments using 3D synthetic phantoms and real-patient MRI brain images reveal that the proposed method performs satisfactorily.

关键词： MRI images INU or bias field fcm algorithm

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A Research on the Model of University Apartment Occupancy Distribution Based on the Student Preferences

A Research on the Model of University Apartment Occupancy Di...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Yu, Qilin Sun, Xi-an Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

ISBN: (纸本)9781509011025

Generally, Almost university apartment ware allocated by different colleges and classes in random. And without considering between students of different living habits and difference in character. And this might cause contradiction and conflict between students in the same dorm, which can bring some bad effects to colleges and society. This paper considers that when we allocated the students' apartment we should considering between students of different living habits and difference in character and other accommodation preferences. By established the Fuzzy clustering model based on Fuzzy, the students will be divided into Fuzzy cluster according to the accommodation preference, and the optimization strategy of the dormitory allocation considering the students' preference is also given by this allocation model. This kind of university apartment assignment method that considering the student's preferences, which put forward a kind of effective solution to alleviate the problem of frequent problems in the same dormitory in Colleges and universities. On this basis, combined with Internet technology will this distribution method is embedded into the student apartment management system, the traditional student apartment management system process reengineering, build a new model of the "Smart Apartment" management in Colleges and universities.

关键词： student apartment students' preference fcm algorithm management system

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Analysis for Customer Lifetime Value Categorization with RFM Model 5

Analysis for Customer Lifetime Value Categorization with RFM...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Monalisa, Siti Nadya, Putri Novita, Rice UIN Suska Riau HR Soebrantas St Pekanbaru 28293 Indonesia

The same treatment of all customers will cause customers who are not so valuable will be value destroyer rather than a value creator. Giving discounts and promos to all customers has not provided benefits to a company in retaining customers. These two things are currently being experienced by LWC Company so that changes are needed in evaluating the strategy taken to keep the relationship with customers, especially potential customers, by forming customer portfolios. Customers portfolios can be analyzed from customer transactions. With customer transactions data, the company can find out which potential customers and the customers who do give less value for the company. The way to analyze customer transaction data is by clustering customers with Fuzzy C-means algorithm by using RFM model. Forming the number of clusters is validated by PCI model and ranking is done by multiplying AHP weight to find the life value of the customer so that it can be known which customer group gives high value to the company. The result of this research is that the customers of LWC Company categorized as superstar customer, typical customer and dormant customer. Based on the portfolio, LWC Company can carry out strategy in managing their customer according to the type of the portfolio. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： fcm algorithm PCI AHP CLV Categorization

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Document Image Binarization Based on Nfcm

Document Image Binarization Based on NFCM

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2nd International Congress on Image and Signal Processing

作者： Tong Li-Jing Chen Kan Zhang Yan Fu Xiao-Ling Duan Jian-Yong N China Univ Technol Multimedia Technol Lab Beijing Peoples R China

ISBN: (纸本)9781424441303

Document image binarization plays an important role in image segmentation and its effect directly impacts on the quality of the OCR recognition system. However, binarization is difficult for camera-based document images with poor contrast or illumination. In this paper, we propose a binarization algorithm, called Nfcm, for camera-based document image. Nfcm, a local threshold method, is a combination of Niblack algorithm and fcm (Fuzzy C-Means) algorithm. It is good at not only preserving the character stokes, but also alleviating the ghost artifacts. Comparative experiments show that Nfcm can obtain favorable results with respect to the OCR performance.

关键词： binarization document image threshold fcm algorithm

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Interference-graph and fuzzy C-means based resource allocation scheme in LTE-V2V communication networks

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2021年 142卷 153982-153982页

作者： Liu, Zhixin Wang, Yongkang Xie, Yuan-ai Yuan, Yazhou Chan, Kit Yan Yang, Yi Ma, Kai Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Curtin Univ Dept Elect & Comp Engn Perth WA 6102 Australia

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (fcm) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the fcm algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity.

关键词： Vehicular network Resource allocation fcm algorithm KM algorithm Graph theory

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A Method for Identifying Gross Errors in Dam Monitoring Data

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WATER 2024年第7期16卷 978页

作者： Chen, Liqiu Gu, Chongshi Zheng, Sen Wang, Yanbo Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (fcm) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the fcm algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

关键词： dam monitoring data gross errors environmental change fcm algorithm OPTICS algorithm LOF algorithm

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Modeling of thermal error electric spindle based on KELM ameliorated by snake optimization

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CASE STUDIES IN THERMAL ENGINEERING 2022年 40卷

作者： Dai, Ye Pang, Jian Li, Zhaolong Li, Weiwei Wang, Qinghai Li, Shikun Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automatic Machinery Prod Dev Harbin 150080 Peoples R China

In this article, to heighten the accuracy of thermal error forecast model of motorized spindle, a thermal error modeling method of kernel extreme learning machine based on snake optimization is advanced. ANSYS simulation software is used to simulate the thermal characteristics of the electric spindle, and the temperature field distribution is acquired. Then, according to the experimental requirements, a platform is built to obtain the axial temperature and thermal displacement data of the spindle. The fcm algorithm and grey relation analysis are utilized to optimize the survey points, and ten measuring points are reduced to four. The SO-KELM model is established based on the snake-optimized kernel extreme learning machine. Comparing the established model with the KELM model and PSO-KELM model, it is proved that SO-KELM model has good prediction accuracy and stability.

关键词： Snake optimization algorithm Kernel extreme learning machine fcm algorithm Grey relation analysis Thermal error modeling

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Determination of threshold of asphalt pavement structure crack damage rate to quantify the severity of structural crack damage

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2024年第1期25卷

作者： Zhang, Weiguang Chang, Haolei Lin, Jun Southeast Univ Sch Transportat Nanjing Peoples R China Jiangxi Prov Highway Engn Testing Ctr Nanchang Peoples R China

Cracks in asphalt pavement are typically identified by analyzing electromagnetic or mechanical wave propagation. Validation is done by examining core samples obtained through drilling. This study proposes a threshold based on the structure crack damage rate (SCDR) collected by ground-penetrating radar (GPR) to quantify the severity of structure cracks. The Fuzzy c-means (fcm) algorithm categorises SCDR into different severity levels. Results indicate clear separation among most data points, but some overlap exists, particularly between non-structural and low-structural damage, and low-structural and medium-structural damage. High-structural damage areas are consistently distinct. The most successful outcomes are achieved when dividing structural crack damage levels into three groups. Thresholds for crack damage rates are ultimately determined to be 0.1909% to differentiate between non-structural and low-structural damage, and 0.6632% to differentiate between low-structural and high-structural damage. These findings serve as a foundation for assessing asphalt pavement crack damage and guiding maintenance decisions.

关键词： Ground penetrating radar Asphalt pavement structure crack structural crack damage rate threshold fcm algorithm deflection

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Anti-noise fcm image segmentation method based on quadratic polynomial

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SIGNAL PROCESSING 2021年 178卷 107767-107767页

作者： Zhang, Xijing Ning, Yang Li, Xuemei Zhang, Caiming Shandong Univ Software Coll Jinan 250101 Peoples R China Shandong Coinnovat Ctr Future Intelligent Comp Yantai 264025 Peoples R China

Image segmentation is an important and challenging task in computer vision and image understanding, in which the segmentation model is the key to improve the quality of image segmentation. In this paper, a new fuzzy c-means clustering(fcm) algorithm based on quadratic polynomials is proposed, which can better distinguish the weak edge region in the image and has certain noise resistance. Firstly, the algorithm proposes to define the segmentation center using a quadratic polynomial surface,and divide the set of data points according to the algebraic distance between data points and the segmentation surfaces. The existing model with constant as the segmentation center is a special case with quadratic polynomial surface as the segmentation center, so the new model has higher segmentation accuracy. Secondly, based on the quadratic polynomial surface as the segmentation center, a new fuzzy factor is designed, and the deviation value is used to represent the difference between the mean algebraic distance of neighborhood points and the algebraic distance of center pixel. By calculating the deviation value, the influence of neighborhood points on the center point can be better measured and the segmentation accuracy can be improved. Experimental results show that the new algorithm has better anti-noise ability as well. Thirdly, select a local window on the edge of the global segmentation result for local window segmentation, which is equivalent to using a clustering center that is more in line with local information to do segmentation in a local small window, optimize misclassified pixels, and obtain the final segmentation result. The experimental results show that in the final segmentation results of medical images with 5% noise, segmentation accuracy can reach above 96%, partition coefficient V has been increased by 0.14. So the algorithm can get the membership matrix with less ambiguity, which means more reliable segmentation results, and can effectively eliminate noise e

关键词： fcm algorithm Classification Quadratic polynomial Local window

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