Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good perfo...
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Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.
The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is...
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The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
This paper deals with the method of simulating sea wave for decreasing its disturbance in the watercraft navigating. Firstly the ITTC two-parameter spectrum is selected as the power spectrum of sea wave to be simulate...
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Due to utilization of the relativity of every pixel of an image, the Markov random field (MRF) model is effective in solving the problem of detecting moving objects under a complex background. In this paper, the bits-...
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Labriform Mode is an important maneuvering locomotion mode which is largely applied to teleost fish. For the exploration of new maneuvering surface of underwater vehicles, study on bionic pectoral fin of Labriform Mod...
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According to the image reconstruction accuracy influenced by the "soft field" nature and the limited projection data in electrical capacitance tomography, based on the working principle of the electrical cap...
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According to the image reconstruction accuracy influenced by the "soft field" nature and the limited projection data in electrical capacitance tomography, based on the working principle of the electrical capacitance tomography system, a Novel image reconstruction algorithm based on compressed sensing is proposed in the paper. The method based on ART (algebra reconstruction technique) organically combines the gradient sparse of image and ART, and reduces the norm of image gradient with full-variational method, and improves the accuracy and speed of image reconstruction. Experimental results and simulation data indicate that the imaging accuracy is markedly improved, and the image is closed to the *** new algorithm presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.
Power dissipation during testing has been found to be much more than during normal mode due to increased switching activity. Test vector reordering technique helps mitigate this problem as it enables the reduction of ...
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Pedestrian detection is an active research in recent years. Most of the researchers have proposed many methods for detecting pedestrians in static images, especially on how to encode the character of pedestrian images...
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ISBN:
(纸本)9781849196413
Pedestrian detection is an active research in recent years. Most of the researchers have proposed many methods for detecting pedestrians in static images, especially on how to encode the character of pedestrian images. But fewer attentions are paid on the ensemble of multiple kernels of support vector machines (SVM) on the same pedestrian feature. In this paper, the classification performance of the detector and a multiple kernels combination method, which combines the histogram intersection kernel and linear kernel on histogram of oriented gradient (HOG), is proposed. Experimental results on INRIA human dataset show its efficiency.
To motivate more people to participate in vaccination campaigns, various subsidy policies are often supplied by government and the health sectors. However, these external incentives may also alter the vaccination deci...
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To motivate more people to participate in vaccination campaigns, various subsidy policies are often supplied by government and the health sectors. However, these external incentives may also alter the vaccination decisions of the broader public, and hence the choice of incentive needs to be carefully considered. Since human behavior and the networking-constrained interactions among individuals significantly impact the evolution of an epidemic, here we consider the voluntary vaccination on human contact networks. To this end, two categories of typical subsidy policies are considered: (1) under the free subsidy policy, the total amount of subsidy is distributed to a certain fraction of individual and who are vaccinated without personal cost, and (2) under the partial-offset subsidy policy, each vaccinated person is offset by a certain amount of subsidy. A vaccination decision model based on evolutionary game theory is established to study the effects of these different subsidy policies on disease control. Simulations suggest that, because the partial-offset subsidy policy encourages more people to take vaccination, its performance is significantly better than that of the free subsidy policy. However, an interesting phenomenon emerges in the partial-offset scenario: with limited amount of total subsidy, a moderate subsidy rate for each vaccinated individual can guarantee the group-optimal vaccination, leading to the maximal social benefits, while such an optimal phenomenon is not evident for the free subsidy scenario.
Aim at search precocity of particle swarm algorithm and slow convergence speed problem for ant colony algorithm, in the automatic guided vehicle path optimization a path optimization algorithm is proposed, which is fu...
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