fuzzy weighting exponent m is an important parameter of fuzzyc-means (FcM), closely related to the performance of the algorithm. First, an improved fuzzycorrelation degree was put forward to measure the relevance be...
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fuzzy weighting exponent m is an important parameter of fuzzyc-means (FcM), closely related to the performance of the algorithm. First, an improved fuzzycorrelation 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 geneticalgorithm 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.
In this paper, an efficient fault detection approach which employs the Support Vector Data Description (SVDD) and fuzzy c-means algorithm (FcM) is proposed for ground-based electronic equipment. Firstly, the FcM metho...
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ISBN:
(纸本)9781728117096
In this paper, an efficient fault detection approach which employs the Support Vector Data Description (SVDD) and fuzzy c-means algorithm (FcM) is proposed for ground-based electronic equipment. Firstly, the FcM method is applied to fault pattern mining in which the prior knowledge of equipment faults is difficult to be known. Then SVDD model is trained with different faults data independently for multi-classification. This fault diagnosis strategy can be used in health condition monitoring for ground-based electronic equipment. The experimental results verify its effectiveness in fault diagnosis with high accuracy and real-time performance.
Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can a...
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Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMc) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs);moreover, it is the most important feature in designing these networks. clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a cluster Head (cH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, fuzzyc-means (FcM) and fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FcM algorithm and fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. comparing the performance of the algorithms implies the 1.5 percent improvement in fuzzy Subtractive algorithm in comparison.
Military equipment project interim evaluation has great significance to project management and control. Firstly, the index system is established from five aspects: development, organization and management, performance...
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ISBN:
(纸本)9781450365383
Military equipment project interim evaluation has great significance to project management and control. Firstly, the index system is established from five aspects: development, organization and management, performance, prospective benefit and resources, which includes qualitative and quantitative indexes. Then an interim evaluation model is built to solve the hybrid multi-index evaluation problem: the values of qualitative and quantitative indexes are pretreated by fuzzy membership function;the indexes weight vectors are determined by AHP;the evaluation results are calculated by multilevel fuzzycomprehensive evaluation;the evaluation conclusions are analysed by fuzzy c-means algorithm, which avoids the disadvantage of information loss by the maximum membership degree method and information annihilation by weighted average method. Finally, the effectiveness and feasibility are verified by a case study.
Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. fuzzyc-mean (FcM) is one of the most popular clustering based segmentation methods. However FcM does not robust against noise...
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Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. fuzzyc-mean (FcM) is one of the most popular clustering based segmentation methods. However FcM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(cSF) tissues using hybrid clustering process which based on: (1) FcM algorithm to get the initial center partition. (2) Geneticalgorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzycorresponding partition matrix. (3) Possibilisticc-means (PcM) algorithm for volumetric measurements of WM, GM, and cSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer's disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FcM and PcM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.
In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the incre...
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ISBN:
(数字)9781728126586
ISBN:
(纸本)9781728126593
In the big data era, the planning, operation and maintenance of power systems are increasingly dependent on the support of various power data. Wind turbine operating data is an important data set considering the increasing installation capacity worldwide. However, due to factors such as unscheduled shutdown, load shedding and communication noise, a great variety and amount of abnormal data may be present in wind power data, which harms the economic and safe operation of wind turbines. In some cases, abnormal operating status even cannot be detected in time due to the disruption of abnormal data, causing serious accidents. Therefore, it is necessary to identify the abnormal wind power data from massive measurements and ensure the availability of accurate and effective data. In this paper, an abnormal wind power data identification strategy is proposed by the improved fuzzyc-means (FcM) algorithm and considering the influence of wind speed. Specifically, the feasible domain matrix is employed to identify abnormal data while the mean comparison method is utilized for data correction. The feasibility and effectiveness of the proposed abnormal wind power data identification and correction strategies in this study is tested by detailed simulations on the East-china Sea offshore wind power ScADA database.
Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the im...
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ISBN:
(纸本)9781538652145
Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of traditional image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy c-means algorithm and mean-shift algorithm. The fuzzy c-means algorithm does not consider the spatial information of the image. Put forward an fuzzy c-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithmconverges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.
In this paper a new technique has been proposed for cotton bale management using fuzzy logic. The fuzzyc-meansclustering algorithm has been applied for clustering cotton bales into 5 categories from 1200 randomly ch...
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In this paper a new technique has been proposed for cotton bale management using fuzzy logic. The fuzzyc-meansclustering algorithm has been applied for clustering cotton bales into 5 categories from 1200 randomly chosen bales of the J-34 variety. In order to cluster bales of different categories, eight fibre properties, viz., the strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. The fuzzyc-meansclustering method is able to handle the haziness that may be present in the boundaries between adjacent classes of cotton bales as compared to the K-meansclustering method. This method may be used as a convenient tool for the consistent picking of different bale mixes from any number of bales in a warehouse.
The effect of a stain repellent treatment on the water-oil repellency characteristics of plush knitted fabrics is investigated. We compared the efficiency of two methods of modeling;a Multicriteria analysis was employ...
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The effect of a stain repellent treatment on the water-oil repellency characteristics of plush knitted fabrics is investigated. We compared the efficiency of two methods of modeling;a Multicriteria analysis was employed by means of surface response method and an artificial intelligence-based system approach is presented by fuzzy logic modeling in which the effects of the operating parameters and intrinsic features of fabrics are studied. These parameters were pre-selected according to their possible influence on the outputs which were the contact angle and the air permeability. An original fuzzy logic-based method was proposed to select the most relevant parameters. The results show that air permeability was influenced essentially by knitted structure's parameters but the variation of treatment parameters has a great effect on the contact angle. Thus, it is believed that artificial intelligence system could efficiently be applied to the knit industry to understand, evaluate, and predict water-oil repellency parameters of plush knitted fabrics more than Multicriteria analysis.
Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the m...
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Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data. And the predictive modeling method based on the ELM integrated fuzzyc-means integrating analytic hierarchy process (FAHP) (FAHP-ELM) is proposed. The fuzzy c-means algorithm is used to cluster the input attributes of the high-dimensional data. The Analytic Hierarchy Process (AHP) based on the entropy weights is proposed to filter the redundant information and extracts characteristiccomponents. Then, the fusion data is used as the input of the ELM. compared with the back-propagation (BP) neural network and the ELM, the proposed model has better performance in terms of the speed of convergence, generalization and modeling accuracy based on University of california Irvine (UcI) benchmark datasets. Finally, the proposed method was applied to build the energy saving and predictive model of the purified terephthalic acid (PTA) solvent system and the ethylene production system. The experimental results demonstrated the validity of the proposed method. Meanwhile, it could enhance the efficiency of energy utilization and achieve energy conservation and emission reduction. (c) 2017 Elsevier Ltd. All rights reserved.
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