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Identification of the Dental Root canal from Micro-cT Records Using 3D curve Skeleton Extraction

IFAc Proceedings Volumes 2011年第1期44卷 6184-6189页

作者： Balázs Benyó László Szilágyi csaba Dobó-Nagy Department of Control Engineering and Information Technology Budapest University of Technology and Economics (Tel: +36-1-463-4027) Independent Section of Radiology Semmelweis University of Budapest Hungary

Abstract This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro cT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and an enhanced fuzzy c-means based partitioning, center point detection in the segmented slice, three dimensional inner surface reconstruction and curve skeleton extraction in three dimensions. The central line of the root canal can finally be approximated as a 3D spline curve. The proposed procedure can help prepare several kinds of endodontic interventions.

关键词： image processing skeleton extraction micro computed tomography fuzzy c-means algorithm

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covariance matrix approach to feature-weight selection in FcM with application to color image segmentation

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智慧科技与应用统计学报 2017年第2期15卷 13-30页

作者：蔡政霖

fuzzy c-means (FcM) algorithm is a general method for clustering analysis. When there exitsts noise variables in the data, the error rate of the FcM algorithm relatively increases. Thus, how to choose the weight of the variable to reduce the error rate is an important issue. To solve this problem, this paper presents a new method of variable weight selection, called covariance Matrix (cM) method. The simulation results show that the proposed variable selection method can effectively reduce the error rate. Finally, the proposed cM method is applied to color image segmentation.

关键词： fuzzy c-means algorithm feature-weight covariance matrix color image segmentation

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A Soft-Hard combination Decision Fusion Scheme for a clustered Distributed Detection System with Multiple Sensors

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SENSORS 2018年第12期18卷 4370-4370页

作者： Luo, Junhai He, Xiaoting Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (Fc): a one-bit hard decision and a multiple-bit soft decision. compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft-hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (cH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads' one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system.

关键词： distributed detection system soft decision hard decision clustering fuzzy logic system fuzzy c-means algorithm

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Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

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ScIENTIFIc REPORTS 2024年第1期14卷 1-16页

作者： Santhi, G. B. Jacob, Suma Sira Sheela, D. Kumaran, P. New Prince Shri Bhavani Coll Engn & Technol Dept Comp Sci & Engn Chennai India Sri Krishna Coll Technol Dept Artificial Intelligence & Data Sci Coimbatore India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai India Wolaita Sodo Univ Coll Engn Dept Mech Engn Wolaita Sodo Ethiopia

In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the fuzzy c-means algorithm (FcM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the fuzzy c-means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

关键词： Vehicular adhoc network fuzzy c-means algorithm Modified fisheye state routing algorithm Modified Manhattan grid topology Particle swarm optimization

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Methods of Resource Scheduling Based on Optimized fuzzy clustering in Fog computing

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SENSORS 2019年第9期19卷 2122.-2122.页

作者： Li, Guangshun Liu, Yuncui Wu, Junhua Lin, Dandan Zhao, Shuaishuai Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276800 Peoples R China

cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.

关键词： fog computing fuzzy c-means algorithm particle swarm optimization resource clustering resource scheduling

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A Self-Adaptive FcM for the Optimal fuzzy Weighting Exponent

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INTERNATIONAL JOURNAL OF cOMPUTATIONAL INTELLIGENcE AND APPLIcATIONS 2019年第2期18卷

作者： Ren, Min Wang, Zhihao Jiang, Jirong Shandong Univ Finance & Econ Sch Math & Quantitat Econ Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Management & Engn Jinan Shandong Peoples R China

fuzzy weighting exponent m is an important parameter of fuzzy c-means (FcM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FcM for the optimal value of m was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of m and meanwhile achieved better clustering results.

关键词： fuzzy weighting exponent fuzzy c-means algorithm cluster validity function particle swarm optimization

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AF-DHNN: fuzzy clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

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SENSORS 2015年第7期15卷 17366-17396页

作者： Jin, Shan cui, Wen Jin, Zhigang Wang, Ying Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Fire Brigade Hexi Dist Tianjin 300222 Peoples R China Tianjin Polytech Univ Sch Management Tianjin 300387 Peoples R China Nankai Hosp Tradit Chinese Med Tianjin 300102 Peoples R China Guangxi Expt Ctr Informat Sci Guilin 541004 Peoples R China

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a fuzzy c-means algorithm (FcMA) and Sorting and classification algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

关键词： wireless sensor networks node fault diagnosis method fire detection adaptive Discrete Hopfield Neural Network fuzzy c-means algorithm fuzzy inference

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An Evolutionary clustering algorithm Based on Adaptive fuzzy Weighted Sum Validity Function

An Evolutionary Clustering Algorithm Based on Adaptive Fuzzy...

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The Third International Joint conference on computational Science and Optimization(第三届计算科学与优化国际大会 cSO 2010)

作者： Hongbin Dong Wei Hou Guisheng Yin School of Computer Science and Technology Harbin Engineering University Harbin 150001 China

In this paper, we propose a novel objective function called the adaptive fuzzy Weighted Sum Validity Function (FWSVF), which is a merged weight of the several fuzzy cluster validity functions, including XB, PE, Pc and PBMF. The improved validity function is more efficient than others. Furthermore, we present a Mixed Strategy Evolutionary clustering algorithm based adaptive validity function (AMSEcA), which is merged from Evolutionary algorithm along with Mixed Strategy and fuzzy c-means algorithm. Moreover, in the experiments, we show the effectiveness of AMSEcA, AMSEcA could find the proper number of clusters automatically as well as appropriate partitions of the data set and avoid local optima.

关键词： fuzzy c-means algorithm evolutionary algorithm the adaptive fuzzy weighted sum validity function evolutionary clustering algorithm

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The fuzzy clustering combined an improved artificial bee colony algorithm with new rank fitness selection

The fuzzy clustering combined an improved artificial bee col...

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International conference on Software Intelligence Technologies and Applications & International conference on Frontiers of Internet of Things 2014

作者： He Yunbin Xiao Yupeng Wan Jing Li Song School of Computer Science and Technology Harbin University of Science and Technology China

The traditional fuzzy c-means clustering algorithm is easy to trap in local optimums as its sensitive selection of the initial cluster centers. For overcoming this disadvantage, this paper presents a fuzzy c-means algorithm combined an improved artificial bee colony algorithm with the strategy of rank fitness selection. The strategy is aimed to increase the selection probability of the individual with better fitness. The proposed algorithm combines the advantages of the high efficiency of fuzzy c-means algorithm and the global search ability of the artificial bee colony algorithm. The experiment and analysis results demonstrate that the algorithm can solve the optimization problem of the initial cluster centers with high robustness and better quality of clustering.

关键词： artificial bee colony algorithm clustering analysis fuzzy c-means algorithm artificial bee colony algorithm fuzzy health cluster Analysis algorithms Rank of data

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A new feature-weighted relative entropy clustering algorithm 23

A new feature-weighted relative entropy clustering algorithm

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Yifan Hu Jing Li Bin Jia School of Communication and Information Engineering Xi 'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The fuzzy c-means (FcM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition learning. However, real-world data is more complex and there may be some irrelevant features in the data that affect the final clustering results of FcM. The weighted clustering algorithm increases the importance of relevant features in the data by assigning different weights to features of different dimensions, and at the same time weakens the influence of irrelevant features on the clustering results. However, both the weighted clustering algorithm and the FcM algorithm will have classification errors as the observation noise increases. As a distance measure between two distributions, relative entropy is added to the objective function as a regularization function, which can minimize the distance within the cluster and maximize the difference between clusters. Therefore, this paper proposes a new feature-weighted relative entropy clustering algorithm (REFcM_EW). The REFcM_EW algorithm combines feature weight and relative entropy, which not only enhances the importance of relevant features in the data but also has better noise detection capability. Experimental results show that REFcM_EW has a good effect on the strip data.

关键词： fuzzy c-means algorithm feature weight relative entropy

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