Energy limited has an important effect on underwater acoustic sensor network (UASN). It has been a key and primary issue to develop an effective algorithm for reducing energy consumption of UASN. In this paper, a new ...
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ISBN:
(纸本)9781467344999;9780769548814
Energy limited has an important effect on underwater acoustic sensor network (UASN). It has been a key and primary issue to develop an effective algorithm for reducing energy consumption of UASN. In this paper, a new clustering algorithm based on low-energy adaptive clustering hierarchy (LEACH) protocol is proposed. The algorithm combines the factor of the cluster head's position, meanwhile, it involves not only the energy consumption between member nodes and cluster heads but also the energy consumption between cluster heads and base station when the member nodes choose which cluster to join in. Simulation results show that the new algorithm can balance the size of the cluster and the node's energy consumption, and reduce the total energy consumption of the network effectively.
In real world, datasets have large number of attributes but few are important to describe them properly. The paper proposes a novel dimension reduction algorithm for real valued dataset using the concept of Rough Set ...
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ISBN:
(纸本)9783642353796;9783642353802
In real world, datasets have large number of attributes but few are important to describe them properly. The paper proposes a novel dimension reduction algorithm for real valued dataset using the concept of Rough Set Theory and clustering algorithm to generate the reduct. Here, projection of dataset based on two conditional attributes C-i and C-j is taken and K-means clustering algorithm is applied on it with K = number of distinct values of decision attribute D of the dataset to obtain K clusters. Also the dataset is clustered into K-groups using Indiscernibility relation applied on the decision attribute D. Then the connecting factor k of combined conditional attributes (C-i C-j) with respect to D is calculated using two cluster sets and attribute connecting set ACS = {(CiCj ->(K) is an element of D) for all C-i, C-j is an element of C, Conditional attribute set, and D (Decision attribute)} is formed. Each element (CiCj ->(k) D). ACS implies that Ci and Cj connecting together partition the objects that yields ( k* 100) % similar partitions as made on D. Now an undirected weighted graph with weights as the connecting factor k is constructed using attribute connecting set ACS. Finally based on the weight associated with edges, the important attributes, called reduct are generated. Experimental result shows the efficiency of the proposed method.
Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out t...
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Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out the neglected information and hidden learning rules in the MOOCs learning process. The model can provide personalized guidance for each user and improve learning efficiency. The model can provide personalized service to help learners form personalized learning strategies, and it also can alert learners with low grades and risk of dropping out.
To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was esta...
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To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.
For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic...
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ISBN:
(纸本)9780791845028
For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic, and to be certain a very good clustering solution has been obtained, it may be necessary to run the algorithm thousands of times. To make this feasible in practice, the algorithm must be computationally efficient. Two algorithmic improvements are presented. Together they improve the quality of the results obtained and increase speed significantly for normal clustering problems. The proposed new algorithm is applied to a cordless handheld vacuum cleaner.
This paper presents a new methodology of wear state recognition by using fractal parameters, nuillifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonli...
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This paper presents a new methodology of wear state recognition by using fractal parameters, nuillifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonlinear state point of worn surface is established by fractal dimension, average diagonal length and spectrum width. Further, a steady state sphere is obtained by the nonlinear state point and K-means clustering algorithm. Results show that fractal, multifractal and recurrence parameters characterize the worn surface from different perspectives. They should be used simultaneously to comprehensively characterize the integral structures, partial structures and internal structures of worn surface. The proposed nonlinear state point shows a variation process of concentration-stabilization-separation during the wear process. The wear states can be identified effectively by the relationship between nonlinear state points and steady state sphere.
This paper proposes a clustering method, group-driven process in the sociology of the clustering process simulation, whose clustering process simulates the driven process of colony formation in sociology. clustering e...
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ISBN:
(纸本)9781467348836;9781467348812
This paper proposes a clustering method, group-driven process in the sociology of the clustering process simulation, whose clustering process simulates the driven process of colony formation in sociology. clustering experiments show that the clustering accuracy and clustering speed of the algorithm in complex network are superior to the classic optimization Fast-Newman clustering algorithm.
The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural ...
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ISBN:
(纸本)9783037853801
The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural network, a revised clustering method is used to train the hidden node centers of the neural network, in which the subtractive clustering(SC) algorithm was used to determine the initial centers and the fuzzy C - Means(FCM) clustering algorithm to further determined the centers data set. The spread factors and the weights of the neural network are calculated by the modified recursive least squares (MRLS) algorithm for relieving computational burden. The proposed algorithm is verified by its application to the modeling of an electro-hydraulic system, simulation and experiment results clearly indicate the obtained RBF network can model the electro-hydraulic system satisfactorily and comparison results also show that the proposed algorithm performs better than the other methods.
Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-...
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ISBN:
(纸本)9781467311595
Set Pair Analysis (SPA) is a new methodology to describe and process uncertainty system, which has been applied in many fields recently. In this paper, a new approach to remote sensing information extraction, the SPA-based k-means clustering algorithm (SPAKM), has been proposed based on the principle of SPA. The basic ideals and steps of SPAKM are discussed. The proposed algorithm can overcome the limitation of K-means clustering algorithm to certain extent. Finally, cluster analysis experiments of LANDSAT TM image have been made. The results show that the improved K-means clustering algorithm is superior to K-means in classification accuracy of land cover classes of mixed pixels.
BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large mo...
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BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression *** propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of *** novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.
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