Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper p...
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In this paper, we first construct a one-round Diffie-Hellman key exchange protocol based on Ring-LWE. The security of our construction is based on the hardness of the Ring-LWE problem. Second, we adaptively extend our...
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With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of di...
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ISBN:
(纸本)9781467372220
With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.
Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatm...
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Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.
This research aims to recognize the defect of concrete materials using an ultrasonic computed tomography imaging technique. Filtered Backprojection method was used to reconstruct concrete images in this paper. Ultraso...
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This research aims to recognize the defect of concrete materials using an ultrasonic computed tomography imaging technique. Filtered Backprojection method was used to reconstruct concrete images in this paper. Ultrasonic time of flight data was measured to reconstruct computer tomography images. 306 data paths were obtained in total by manual scanning for one computer tomography image. We examined the effect of the interpolation data as the density of time of flight data has a considerable effect on image quality. The feasibility of concrete reconstruction system and time of flight data interpolation were examined in detail using numerical and concrete phantoms.
The aim of this study is to use neural network tools as an environmental decision support in assessing environmental quality. A three-layer feedforward neural network using three learning approaches of BP, LM and GA-B...
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The aim of this study is to use neural network tools as an environmental decision support in assessing environmental quality. A three-layer feedforward neural network using three learning approaches of BP, LM and GA-BP has been applied in non-linear modelling for the problem of environmental quality assessment. The case study shows that the well designed and trained neural networks are effective and form a useful tool for the prediction of environmental quality. Furthermore, the LM network has the fastest convergence speed and the GA-BP network outperforms the other two networks in both predictive and final classification accuracies of environmental quality.
keys are very important for data management. Due to the hierarchical and flexible structure of XML, mining keys from XML data is a more complex and difficult task than from relational databases. In this paper, we stud...
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This paper presents a moving vehicle detection and tracking system, which comprising of Horizontal Edges method and Local Auto Correlation. Horizontal Edges characteristic can be strengthened and the influence of weat...
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This research aims to evaluate the internal structure of concrete material configuration using an immersed ultrasonic computed tomography imaging technique. We propose a relative difference method of time of flight da...
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