It is unrealistic to establish the model for analyzing dynamic characteristic of each induction motor. In order to ensure that the equivalent induction motor models impose similar impact on the electric system compare...
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ISBN:
(纸本)9781538674345
It is unrealistic to establish the model for analyzing dynamic characteristic of each induction motor. In order to ensure that the equivalent induction motor models impose similar impact on the electric system compared with the original induction motors group, the critical slip scr and the load rate kL of the induction motor were taken as the clustering indicators in this paper. Induction motors group was clustered based on the k-meansalgorithm to get different cluster numbers. Then the optimal clustering result was obtained by the F statistics. After the clustering, induction motors in different classes were equalized and the accurate dynamic equivalent models were established. The simulation curves were obtained after comparing the established equivalent models with the original models and the classic models in the short circuit fault condition through the example system. The simulation results proved that the method proposed in this paper were reliable.
Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance ...
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ISBN:
(纸本)9781728113227
Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance efficiency of organizations. It is a non-parametric method, which uses linear programming to calculate efficiency in a given set of decision-making units (DMUs). It has widespread application in identifying efficiency and discovering benchmark. In the study, it utilized DEA in identifying School-Based Management (SBM) performance efficiency of one (1) division comprising of elementary and secondary schools under Department of Education (DepEd) in the Philippines. Efficient schools were used as benchmark for improvement of inefficient schools. The schools had also undergone clustering, which is the process of grouping in accordance to similar characteristics. k-means clustering algorithm was used to group the schools according to their respective profile. k-meansclustering is a simple unsupervised learning algorithm that follows a simple procedure of classifying a given data set into a number of clusters. The study also encompasses the development of an application system that utilizes data from DEA and k-means clustering algorithm. The application system also provided recommendations to help inefficient schools improve.
$k$ -meansclusteringalgorithm is one of the most popular technique for clustering in machine learning, however, in the existing $k$ -meansclusteringalgorithm, the ability of the different features and the importan...
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$k$ -meansclusteringalgorithm is one of the most popular technique for clustering in machine learning, however, in the existing $k$ -meansclusteringalgorithm, the ability of the different features and the importance of the different data objects are treated equally;the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively. In the light of this limitation, this paper put forward an enhanced regularized $k$ -means type clusteringalgorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the $k$ -meansclusteringalgorithm and we developed a new objective function with $l<^>clustering$ -norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically. In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted. Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex.
The traditional k-means clustering algorithm occupies a large quantity of memory resources and computing costs when dealing with massive data. It is easy to be restricted by something such as the initial center point ...
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The traditional k-means clustering algorithm occupies a large quantity of memory resources and computing costs when dealing with massive data. It is easy to be restricted by something such as the initial center point as well abnormal data, and usually can not achieve effective clustering of large-scale data. In order to effectively solve the limitations of the algorithm, we propose a MapReduce parallel optimization method based on improved k-means clustering algorithm. Firstly, differential evolution theory is introduced to determine the optimal initial clustering center, after that, on the basis of the influence of samples on clustering results, the corresponding weighted Euclidean distance is designed to achieve effective data differentiation, so as to effectively reduce the impact of samples on clustering *** negative effect of abnormal data on clustering analysis can improve the accuracy of clustering. Finally, MapReduce programming model is used to realize parallel clustering. We use UCI datasets to verify the parallel optimization method. From the experimental results we can clearly know that the method we proposed has relatively stable parallel clustering results, faster operation speed, and effectively saves the operation time.
A method of recognizing 16QAM signal based on k-means clustering algorithm is proposed to mitigate the impact of transmitter finite extinction ratio. There are pilot symbols with 0.39% overhead assigned to be regarded...
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A method of recognizing 16QAM signal based on k-means clustering algorithm is proposed to mitigate the impact of transmitter finite extinction ratio. There are pilot symbols with 0.39% overhead assigned to be regarded as initial centroids of k-means clustering algorithm. Simulation result in 10 GBaud 16QAM system shows that the proposed method obtains higher precision of identification compared with traditional decision method for finite ER and IQ mismatch. Specially, the proposed method improves the required OSNR by 5.5 dB, 4.5 dB, 4 dB and 3 dB at FEC limit with ER= 12 dB, 16 dB, 20 dB and 24 dB, respectively, and the acceptable bias error and IQ mismatch range is widened by 767% and 360% with ER = 16 dB, respectively. (C) 2017 Elsevier B.V. All rights reserved.
An imbalance of the battery pack in the voltage and state of charge(SOC) leads to problems in the lifetime, performance, and safety. To efficiently operate the battery pack in the power-driven systems, each cell in th...
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ISBN:
(纸本)9788986510201
An imbalance of the battery pack in the voltage and state of charge(SOC) leads to problems in the lifetime, performance, and safety. To efficiently operate the battery pack in the power-driven systems, each cell in the battery pack has similar electrochemical characteristics. This paper proposes the optimal cell screening method using k-means clustering algorithm, which is one of the unsupervised learning methods. To investigate the proposed scheme, 2000 units of 18650 Li-ion cell are used in this paper. The results of the verification demonstrate that the proposed screening method minimizes the cell imbalance.
We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyz...
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We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyzing large data sets is with the k-meansalgorithm. However, that algorithm has yet to be widely applied to analyzing card sorting data sets to measure the similarity between cards and result displays using multidimensional scaling. The multidimensional scaling, which employs particle dynamics to the error function minimization, is a good candidate to be a computational engine for interactive card sorting data. In this paper, we apply the combination of a similarity matrix, a k-meansalgorithm, and multidimensional scaling to cluster and calculate an information architecture from card sorting data sets. We chose card sorting to improve an information architecture. The proposed algorithm handled the overlaps between cards in the card sorting data quite well and displayed the results in a basic layout showing all clusters and card coordinates. For outliers, the algorithm allows grouping of single cards to their closest core clusters. The algorithm handled outliers well choosing cards with the strongest similarities from the similarity matrix. We tested the clusteringalgorithm on real-world data sets and compared to other techniques. The results generated clear knowledge on relevant usability issues in visualizing information architecture. The identified usability issues point to a need for a more in-depth search of design solutions that are tailored for the targeted group of people who are struggling with complicated visualizing techniques. This study is for people who need support to easily visualize information architecture from data sets.
In order to solve the problem of manual input of k value in traditional k-means clustering algorithm, a method to obtain k value automatically is proposed. Firstly, the data needed to be clustered are sampled, and the...
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In order to solve the problem of manual input of k value in traditional k-means clustering algorithm, a method to obtain k value automatically is proposed. Firstly, the data needed to be clustered are sampled, and the distance between the data in the sample group is calculated, Then the distance matrix is formed and the de-noising process is done, and the clustering number k is selected based on the principle of distance maximization. Finally, we take two-dimensional data as an example to simulate the k-means clustering algorithm of the number of clusters k based on the principle of distance maximization, then the simulation results are verified by MATLAB. The experimental results show that the algorithm can obtain k value automatically and improve the accuracy of clustering.
Sensor placement is a combinatorial optimization problem. Considering the number of factors, the selection of measuring points is easy to cause information redundancy and low signal-to-noise ratio. In order to solve t...
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Sensor placement is a combinatorial optimization problem. Considering the number of factors, the selection of measuring points is easy to cause information redundancy and low signal-to-noise ratio. In order to solve this problem, according to the matrix of structure’s frequency response, bisect k-means clustering algorithm is designed to classify the degrees of freedom according to the similarity of response. This method is applied to the steel truss arch bridge with the background of Nanjing Dashengguan Yangtze River Bridge. The results show that the proposed method in this paper can better classify the degrees of freedom with similar vibration characteristics, make the sensor more balanced in the overall structure, overcome the redundant information among the sensors, and improve the signal-to-noise ratio at the measurement point.
Diseases like black spot, anthracnose and Cylindrocarpon destructans always appear on ginseng leaf. The traditional way to distinguish the disease in farmland is checking by planter in person. This way takes more time...
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ISBN:
(纸本)9781538619377
Diseases like black spot, anthracnose and Cylindrocarpon destructans always appear on ginseng leaf. The traditional way to distinguish the disease in farmland is checking by planter in person. This way takes more time and energy. In this paper, according to cicatrices are different with normal leaf in color, we use k-means clustering algorithm and combine the difference between a and b components in Lab color space to discern the cicatrices in leaf image. Firstly, we can distinguish cicatrices and normal leaves. Secondly, we take out useless part like petiole. Lastly, we calculate the ratio of area of cicatrices and normal leaf. The ratio can be used as reference of the ginseng disease and its degree. The result of experiment shows that k-means clustering algorithm can segment the leaf image, and the segmentation is based on the difference of color, then we can calculate the ratio of pixel in each part, and judge the degree of disease.
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