Objective For low-voltage current transformer surface crack detection,traditional methods can not effectively distinguish cracks and scratches problem,crack detection method is proposed based on geometrical features a...
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ISBN:
(纸本)9781509046584
Objective For low-voltage current transformer surface crack detection,traditional methods can not effectively distinguish cracks and scratches problem,crack detection method is proposed based on geometrical features and Moment *** Extraction algorithm by osmosis from the gray image of the target area,according to the crack and scratches different texture features,the use of geometric features and invariant moments,determine the characteristic parameters threshold,and finally using clustering algorithm to determine the threshold determination cracks and scratches to be *** After tests proved that the method can effectively distinguish cracks and scratches,and to solve the noise problem on low-voltage current transformer crack *** Compared with the traditional object of cracks and scratches detection methods,the method proposed in this paper has the mathematical property of invariant to rotation,translation and size of image,and it is also used to detect the crack image in the moving state.
The cooperative relay network exploits the space diversity gain by allowing cooperation among users to improve transmission quality. It is an important issue to identify the cluster-head (or relay node) and its member...
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The cooperative relay network exploits the space diversity gain by allowing cooperation among users to improve transmission quality. It is an important issue to identify the cluster-head (or relay node) and its members who are to cooperate. The cluster-head consumes more battery power than an ordinary node since it has extra responsibilities, i.e., ensuring the cooperation of its members' transmissions;thereby the cluster-head has a lower throughput than the average. Since users are joining or departing the clusters from time to time, the network topology is changing and the network may not be stable. Flow to balance the fairness among users and the network stability is a very interesting topic. This paper proposes an adaptive weighted clustering algorithm (AWCA), in which the weight factors are introduced to adaptively control both the stability and fairness according to the number of arrival users. It is shown that when the number of arrival users is large, AWCA has the life time longer than FWCA and similar to SWCA and that when the number of arrival users is small, AWCA provides fairness higher than SWCA and close to FWCA.
A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is prese...
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A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is presented. The hybrid FMM-CT model is explained. We first use several benchmark problems to illustrate the cluster evolution patterns from the proposed modifications in FMM. Then, we employ a case study with real data related to power quality monitoring to assess the usefulness of FMM-CT. The results are compared with those from other clustering models. More importantly, we extract explanatory rules from FMM-CT to justify its predictions. The empirical findings indicate the usefulness of the proposed model in tackling data clustering and power quality monitoring problems under different environments.
clustering plays an important role in discovering underlying patterns of data points according to their similarities. Many advanced algorithms have difficulty when dealing with variable clusters. In this paper, we pro...
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clustering plays an important role in discovering underlying patterns of data points according to their similarities. Many advanced algorithms have difficulty when dealing with variable clusters. In this paper, we propose a simple but effective clustering algorithm, CLUB. First, CLUB finds initial clusters based on mutual k nearest neighbours. Next, taking the initial clusters as input, it identifies the density backbones of clusters based on k nearest neighbours. Then, it yields final clusters by assigning each unlabelled point to the cluster which the unlabelled point's nearest higher-density-neighbour belongs to. To comprehensively demonstrate the performance of CLUB, we benchmark CLUB with six baselines including three classical and three state-of-the-art methods, on nine two-dimensional various-sized datasets containing clusters with various shapes and densities, as well as seven widely-used multi-dimensional datasets. In addition, we also use Olivetti Face dataset to illustrate the effectiveness of our method on face recognition. Experimental results indicate that CLUB outperforms the six compared algorithms in most cases. (C) 2016 Elsevier Ltd. All rights reserved.
The K-mean clustering algorithm was employed for processing signal waveforms from TIBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The c...
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The K-mean clustering algorithm was employed for processing signal waveforms from TIBr detectors. The signal waveforms were classified based on its shape reflecting the charge collection process in the detector. The classified signal waveforms were processed individually to suppress the pulse height variation of signals due to the charge collection loss. The obtained energy resolution of a Cs-137 spectrum measured with a 0.5 mm thick TIBr detector was 1.3% FWHM by employing 500 clusters. (C) 2011 Elsevier B.V. All rights reserved.
As a classical partitional clustering algorithm, k-means algorithm is sensitive to initial centroids and may malfunction when dealing with datasets which contain clusters with different scales and densities. To improv...
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As a classical partitional clustering algorithm, k-means algorithm is sensitive to initial centroids and may malfunction when dealing with datasets which contain clusters with different scales and densities. To improve the effectiveness of k-means algorithm, an outlier factor based partitional clustering analysis method is presented in this paper. Outlier factor is usually used to indicate the degree of an object to be abnormal in the dataset For the proposed method, it is used to find the core objects. And then the Must-link constraints is generated to put the neighboring core objects into the same cluster. First, a similar-density-array-based outlier factor is proposed to find the core objects in the dataset. Then the neighboring core objects are distributed into the same sub-cluster. The sub-clusters are treated as the representative objects and these representative objects are then clustered following the process of the traditional k-means algorithm. Finally, the non-core objects are assigned to their nearest clusters, respectively. The experiments are performed on four datasets from UCI Machine Learning Repository and a field dataset from a ball mill pulverizing system. The experimental results verify that the effectiveness of our algorithm is high. (C) 2015 Elsevier B.V. All rights reserved.
clustering is among the most popular data mining algorithm families. Before applying clustering algorithms to datasets, it is usually necessary to preprocess the data properly. Data preprocessing is a crucial, still n...
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clustering is among the most popular data mining algorithm families. Before applying clustering algorithms to datasets, it is usually necessary to preprocess the data properly. Data preprocessing is a crucial, still neglected step in data mining. Although preprocessing techniques and algorithms are well-known, the preprocessing process is very complex and takes usually a lot of time. Instead of handling preprocessing more systematically, it is usually undervalued, i.e. more emphasis is put on choosing the appropriate clustering algorithm and setting its parameters. In our opinion, this is not because preprocessing is less important, but because it is difficult to choose the best sequence of preprocessing algorithms. We argue that it is important to better standardize this process so it is performed efficiently. Therefore, this paper proposes a generic framework for data preprocessing. It is based on a survey with data mining experts, as well as a literature and software review. The framework enables pipelining preprocessing algorithms and methods which facilitate further automated preprocessing design and the selection of a suitable preprocessing stream. The proposed framework is easily extendible, so it can be applied to other data mining algorithm families that have their own idiosyncrasies.
Power large users are the key users of power supply enterprises, and their potential value and development trend in power market environment are closely related to the profit of power supply enterprises. In order to i...
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ISBN:
(纸本)9781538614273
Power large users are the key users of power supply enterprises, and their potential value and development trend in power market environment are closely related to the profit of power supply enterprises. In order to identify valuable user behavior and value characteristics, a large user segmentation method based on Affinity Propagation(AP) and K-means algorithm is proposed. First of all, from the existing indicators to extract the key sub-indicators, and consider the recent and long-term power consumption rate of electricity, put forward to assess the development potential of large users of the breakdown of indicators;Secondly, the AP and K-means are combined to solve the problem and finding the initial clustering center and the number of clusters, at the same time, it avoids the problem that the K-means clustering is easy to fall into the local optimum;finally, the user data of a region in Zhejiang Province is analyzed and verified, and the proposed method is feasible.
The deviation of motor speed directly affects the accuracy of the project control system, for the current questions about the accuracy of motor speed measurement, combined with traditional optical enc
ISBN:
(纸本)9781467389808
The deviation of motor speed directly affects the accuracy of the project control system, for the current questions about the accuracy of motor speed measurement, combined with traditional optical enc
Ant colony algorithm can resolve dynamic optimization problems due to its robustness and adaptation. The aim of such algorithms in dynamic environments is no longer to find an optimal solution but to trail it over tim...
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Ant colony algorithm can resolve dynamic optimization problems due to its robustness and adaptation. The aim of such algorithms in dynamic environments is no longer to find an optimal solution but to trail it over time. In this paper, a clustering ant colony algorithm (KACO) with three immigrant schemes is proposed to address the dynamic location routing problem (DLRP). The DLRP is divided into two parts constituted by a location allocation problem (LAP) and a vehicles routing problem (VRP) in dynamic environments. To deal with the LAP, a K-means clustering algorithm is used to tackle the location of depots and surrounding cities in each class. Then the ant colony algorithm is utilized to handle the VRP in dynamic environments consisting of random and cyclic traffic factors. Experimental results based on different scales of DLRP instances demonstrate that the clustering algorithm can significantly improve the performance of KACO in terms of the qualities and robustness of solutions. The ultimate analyses of time complexity of all the heuristic algorithms illustrate the efficiency of KACO with immigrants, suggesting that the proposed algorithm may lead to a new technique for tracking the environmental changes by utilizing its clustering and evolutionary characteristics. (C) 2016 Elsevier Inc. All rights reserved.
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