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

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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coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

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INTERNATIONAL JOURNAL OF GEOGRAPHIcAL INFORMATION ScIENcE 2020年第10期34卷 1930-1952页

作者： Liang, Xun Liu, Xiaoping chen, Guangliang Leng, Jiye Wen, Youyue chen, Guangzhao Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan Hubei Peoples R China Guangzhou Landtu Geog Informat Technol Co Ltd Guangzhou Peoples R China Univ Toronto Dept Geog & Planning Toronto ON Canada South China Inst Environm Sci MEE Guangzhou Peoples R China

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (cA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified cA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, china from 2013 to 2019. The proposed model is applied to future simulation in 2019-2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.

关键词： Economic development zone urban emergence simulation fuzzy c-means algorithm cellular automata

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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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DYNAMIc HAND GESTURE SEGMENTATION METHOD BASED ON UNEQUAL-PROBABILITIES BAcKGROUND DIFFERENcE AND IMPROVED FcM algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE cOMPUTING INFORMATION AND cONTROL 2015年第5期11卷 1823-1834页

作者： Zhang, Qiuyu Lu, Junchi Wei, Huiyi Zhang, Moyi Duan, Hongxiang Lanzhou Univ Technol Sch Comp & Commun 287 Langongping Rd Lanzhou 730050 Gansu Peoples R China

To extract complete hand gesture region under complex dynamic background and to effectively solve problems of skin-color interference and varying illumination, we present a novel dynamic hand gesture segmentation method which combines unequal probabilities and improved fuzzy c-means (FcM) algorithm. Firstly, this method utilizes unequal-probabilities to build background model of complex dynamic background and detects motion region of hand gesture with background difference. Secondly, FcM algorithm is improved to accelerate the rate of convergence, cluster hand gesture image and distinguish skin-color region and non-skin-color region. Finally, we process motion region and skin-color region with logic operation and morphological operation, and complete dynamic gesture segmentation under complex background. The experimental results illustrate that the proposed method has high accuracy and is robust to skin-color interference and varying illumination.

关键词： Human-computer interaction Dynamic hand gesture segmentation Unequal-probabilities background difference fuzzy c-means algorithm Morphological processing

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Performance analysis for clustering algorithms

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INTERNATIONAL JOURNAL OF cOMPUTING ScIENcE AND MATHEMATIcS 2016年第5期7卷 485-493页

作者： Xue, Yu Zhao, Binping Ma, Tinghuai Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FcM) and evolution algorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FcM to fall into a local minimum, and the hybrid algorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.

关键词： optimal clustering K-means fuzzy c-means algorithm hybrid differential evolution algorithm performance analysis high dimension

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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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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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