• 한국통신학회논문지
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• 제42권2호
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• pp.453-459
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• 2017

사용자들은 그들의 관심이 관심지점 (POI: Point-of-Interest)과 관련이 있다는 사실을 언급하기 위해 위치 기반 소셜 네트워크에 체크인하거나 그들의 상태를 올리는 경향이 있다. 관심지역 (AOI: Area-of-Interest)을 찾는 기존 연구는 대부분 위치 기반 소셜 네트워크로부터 수집된 공간 태그된 사진과 함께 밀도 기반 군집화 기법을 사용하여 수행되었다. 반면, 본 연구에서는 POI 중심을 포함한 하나의 군집에 해당하는 POI 경계선을 추정하는 데에 초점을 맞춘다. 트위터 사용자들로부터의 공간 태그된 트윗을 사용하여 POI 중심으로부터 도달할 수 있는 적절한 반경을 찾음으로써 POI 경계선을 추정하는 밀도 기반 저복잡도 두 단계 방법을 소개한다. 두 단계 밀도 기반 추정을 통해 선택된 공간 태그의 convex hull로써 POI 경계선을 추정하는데, 각 단계에서 다른 크기의 반경 증가를 가정하여 진행한다. 제안한 방법은 기본 밀도 기반 군집화 방법보다 계산 복잡도 측면에서 우수한 성능을 가짐을 보인다.

• 제어로봇시스템학회논문지
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• 제15권6호
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• pp.619-625
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• 2009

FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• 품질경영학회지
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• 제49권2호
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Communications for Statistical Applications and Methods
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• 제21권5호
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• pp.435-444
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• 2014

The most widely used optimal bandwidth is known to minimize the mean integrated squared error(MISE) of a kernel density estimator from a true density. In this article proposes, we propose a bandwidth which asymptotically minimizes the mean integrated density power divergence(MIDPD) between a true density and a corresponding kernel density estimator. An approximated form of the mean integrated density power divergence is derived and a bandwidth is obtained as a product of minimization based on the approximated form. The resulting bandwidth resembles the optimal bandwidth by Parzen (1962), but it reflects the nature of a model density more than the existing optimal bandwidths. We have one more choice of an optimal bandwidth with a firm theoretical background; in addition, an empirical study we show that the bandwidth from the mean integrated density power divergence can produce a density estimator fitting a sample better than the bandwidth from the mean integrated squared error.

• Journal of the Korean Data and Information Science Society
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• 제25권4호
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• pp.951-959
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• 2014

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.261-276
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• 2002

Probability density or its derivatives may have a discontinuity/change point at an unknown location. We propose a method of estimating the location and the jump size of the discontinuity point based on kernel type density or density derivatives estimators with one-sided equivalent kernels. The rates of convergence of the proposed estimators are derived, and the finite-sample performances of the methods are illustrated by simulated examples.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제2권6호
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• pp.253-256
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• 2013

VANET에서는 차량 안전 관련 정보가 차량들 간에 안정적으로 전달될 수 있도록 해주는 라우팅 프로토콜이 요구된다. 본 논문에서는 도시기반의 VANET 환경에서 차량 간의 빠르고 안정적인 통신을 위하여 차량 밀도 정보를 이용한 라우팅 기법을 제안한다. 제안 기법에서 각 차량은 비콘 메시지와 도로 정보을 이용해서 도로의 차량 밀도를 계산한다. 차량들은 이 정보를 이용해서 패킷을 전송할 안정적인 경로를 설정한다. NS-2 시뮬레이션을 통해 제안 기법과 GPSR의 성능을 비교했으며, 그 결과 제안 기법이 패킷 전송 성공률 측면에서 우수한 성능을 보임을 알 수 있었다.

• 한국전기전자재료학회논문지
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• 제27권2호
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• pp.132-135
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• 2014

The Sr based ceramic thin films were deposited on Si substrate by RF magnetron sputtering method. And Sr based thin films were annealed at $500{\sim}700^{\circ}C$ using RTA. The surface roughness showed about 2.4 nm in annealed thin film at $600^{\circ}C$. The capacitance density of Sr based thin films were increased with the increase of annealing temperature. The maximum capacitance density of $0.6{\mu}F/cm^2$ was obtained by annealing temperature at $700^{\circ}C$. The voltage dependence of dielectric loss showed about 0.02 in voltage ranges of -10~+10 V. The leakage current density of annealing temperature of $600^{\circ}C$ was the $4.0{\times}10^{-6}\;A/cm^2$ at applied voltage of -5~+5 V.

• 한국재료학회지
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• 제33권7호
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.