• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4685-4694
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• 2023

The ability to calculate the material density sensitivity coefficients of power with respect to the material density has broad application prospects for accelerating Monte Carlo-Thermal Hydraulics iterations. The second-order material density sensitivity coefficients for the general Monte Carlo score have been derived based on the differential operator sampling method in this paper, and the calculation of the sensitivity coefficients of cell power scores with respect to the material density has been realized in continuous-energy Monte Carlo code RMC. Based on the power-density sensitivity coefficients, the sensitivity coefficients of power scores to some other physical quantities, such as power-boron concentration coefficients and power-temperature coefficients considering only the thermal expansion, were subsequently calculated. The effectiveness of the proposed method is demonstrated in the power-density coefficients problems of the pressurized water reactor (PWR) moderator and the heat pipe reactor (HPR) reflectors. The calculations were carried out using RMC and the ENDF/B-VII.1 neutron nuclear data. It is shown that the calculated sensitivity coefficients can be used to predict the power scores accurately over a wide range of boron concentration of the PWR moderator and a wide range of temperature of HPR reflectors.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• ETRI Journal
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• v.35 no.5
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• pp.900-910
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• 2013

Small-screen mobile terminals have difficulty accessing existing Web resources designed for large-screen devices. This paper presents an adaptive transformation method based on webpage semantic features to solve this problem. According to the text density and link density features of the webpages, the webpages are divided into two types: index and content. Our method uses an index-based webpage transformation algorithm and a content-based webpage transformation algorithm. Experiment results demonstrate that our adaptive transformation method is not dependent on specific software and webpage templates, and it is capable of enhancing Web content adaptation on small-screen terminals.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.375-383
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• 2003

Clustering in spatial mining is to group similar objects based on their distance, connectivity or their relative density in space. In the real world. there exist many physical objects such as rivers, lakes and highways, and their presence may affect the result of clustering. In this paper, we define distance to handle obstacles, and using that we propose the density based clustering algorithm called DBSCAN-O to handle obstacles. We show that DBSCAN-O produce different clustering results from previous density based clustering algorithm DBSCAN by our experiment result.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Applied Microscopy
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• v.44 no.2
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• pp.74-78
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• 2014

Dislocation density and distribution in epitaxial GaAs layer on Si are evaluated quantitatively and effectively using image processing of transmission electron microscopy image. In order to evaluate dislocation density and distribution, three methods are introduced based on line-intercept, line-length measurement and our coding with line-scanning method. Our coding method based on line-scanning is used to detect the dislocations line-by-line effectively by sweeping a thin line with the width of one pixel. The proposed method has advances in the evaluation of dislocation density and distribution. Dislocations can be detected automatically and continuously by a sweeping line in the code. Variation of dislocation density in epitaxial GaAs films can be precisely analyzed along the growth direction on the film.

• Korean Management Science Review
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• v.32 no.3
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• pp.29-36
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• 2015

In this research, we analyze the sensitivity of the network density to the estimates for the Bass model parameters with both theoretical model and a simulation. Bass model describes the process that the non-adopters in the market potential adopt a new product or an innovation by the innovation effect and imitation effect. The imitation effect shows the word of mouth effect from the previous adopters to non-adopters. But it does not divide the underlying network structure from the strength of the influence over the network. With a network based Bass model, we found that the estimate for the imitation coefficient is highly sensitive to the network density and it is decreasing while the network density is decreasing. This finding implies that the interpersonal influence can be under-looked when the network density is low. It also implies that both of the network density and the interpersonal influence are important to facilitate the diffusion of an innovation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.4
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• pp.391-395
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• 2012

We present a continuous cell separator that achieves density-dependent and size-independent cell separation based on the net force of gravity and buoyancy forces on the cells in dissimilar density fluids. Previous cell separators are, based on the size or dielectrophoretic property of the cells and, are suitable for size-dependent and density-independent cell separation. However, these properties can make it difficult to collect the same types of cells with the same density but with size variations. The present separator, however, is capable of collecting the same types of cells based on the cell density in the fluid. Regardless of cell size, the proposed chip isolates low density cells, (white blood cells, or WBCs) at the upper outlet while obtaining high-density cells (red blood cells, or RBCs) from the lower outlet based on density. Efficiency levels for separation of WBCs and RBCs were $90.9{\pm}9.1%$ and $86.4{\pm}1.99%$, respectively. The present separator therefore has the potential for use in the pretreatment of whole blood.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.241-248
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• 2002

The problem of wavelet density estimation based on Shannon's wavelets is studied when the sample observations are contaminated with random noise. In this paper we will discuss the asymptotic normality for deconvolving wavelet density estimator of the unknown density f(x) when courier transform of random noise has polynomial descent.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.453-459
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• 2017

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). While previous studies on discovering area-of-interests (AOIs) were conducted mostly on the basis of density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on estimating a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a density-based low-complexity two-phase method to estimate a POI boundary by finding a suitable radius reachable from the POI center. We estimate a boundary of the POI as the convex hull of selected geo-tags through our two-phase density-based estimation, where each phase proceeds with different sizes of radius increment. It is shown that our method outperforms the conventional density-based clustering method in terms of computational complexity.