• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.265-276
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• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.393-404
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• 2005

As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.

• Korean Journal of Optics and Photonics
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• v.23 no.6
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• pp.246-250
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• 2012

We derive analytical expressions for the output spectral density and the noise power $P_{\beta}$ in noise loading analysis using Volterra kernels to characterize fiber nonlinearities. The bandwidth of the input noise source has little effect on $P_{\beta}$, but the power of the input noise source and the dispersion parameter value of the fiber have a significant effect on $P_{\beta}$. The Volterra method predicts ${\Delta}P_{\beta}[dB]$ = 30 dB/decade, which agrees very accurately over a wide range of fiber parameters compared with the numerical results by the split-step Fourier method. Therefore the Volterra method could be useful to predict the performance of a dense WDM system when we plan to upgrade fiber or increase signal power.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.271-273
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• 2015

The bulk motion of star clusters can be determined after careful membership analysis using parametric or non-parametric approaches. This study aims to implement non-parametric membership analysis based on Binned Kernel Density Estimators which takes into account measurements errors (simply called BKDE-e) to determine the average proper motion of each cluster. This method is applied to 178 selected star clusters with angular diameters less than 20 arcminutes. Proper motion data from UCAC4 are used for membership determination. Non-parametric analysis using BKDE-e successfully determined the average proper motion of 129 clusters, with good accuracy. Compared to COCD and NCOVOCC, there are 79 clusters with less than $3{\sigma}$ difference. Moreover, we are able to analyse the distribution of the member stars in vector point diagrams which is not always a normal distribution.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.245-255
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• 2011

There are two nonparametric methods that use empirical distribution functions and probability density estimators for the test of the distribution change of data. In this paper we investigate the two methods precisely and summarize the results of previous research. We assume several probability models to make a simulation study of the change point analysis and to examine the finite sample behavior of the two methods. Empirical powers are compared to verify which is better for each model.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.272-284
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• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Journal of Ship and Ocean Technology
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• v.11 no.4
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• pp.29-54
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• 2007

Systematic sensitivity analysis of smoothed particle hydrodynamics method (SPH), a gridless Lagrangian particle method, was carried out in this study. Unlike traditional grid-based numerical schemes, systematic sensitivity study for computational parameters is very limited for SPH. In this study, the effect of computational parameters in SPH simulation is explored through two-dimensional dam-breaking and sloshing problem. The parameters to be considered are the speed of sound, the type of kernel function, the frequency of density re-initialization, particle number, smoothing length and pressure extraction method. Through a series of numerical test, detailed information was obtained about how SPH solution can be more stabilized and improved by adjusting computational parameters.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.4
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• pp.199-203
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• 2022

The spring home range and habitat use of the spot-billed duck in Korea were studied using GPS-mobile phone-based telemetry (WT-300). The study areas were Anseong-si, Seosan-si, Nonsan-si, and Sejong-si. Analysis was performed using minimum convex polygon (MCP) and kernel density estimation (KDE) spot-billed ducks had an average home range of 70.28 km² (standard deviation [SD]=84.50, n=6), and a core habitat (50%) 2.66 km² (SD=2.60, n=6), according to MCP and KDE, respectively. Wetlands (41.5%) and rice fields (35.7%) were highly used as habitats. The rice field use rate was high during the day, and the wetland utilization rate was high at night. Rice fields and wetlands were the primary habitats in spring.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.791-802
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• 2021

In Korea, the cities continue to deteriorate, while the vacant houses in the small local towns emerge as a serious social problem. Despite the vacant houses emerge as a serious social problem in the small local towns as well as in the large cities, the basic researches into them are yet to be conducted on a full scale. Thus, in order to know about the spatial distribution of the vacant houses, this study conducted the square analysis and the kernel density analysis. As a result, it was confirmed that the vacant houses in Okcheon-gun had certain crowding forms and characteristics at the level of statistical significance. Next, in order to examine the distribution of the vacant houses in terms of the accessibility to the living SOC facilities, the GIS network analysis was performed, focusing on the major facilities and road networks. As a result, it was found that the better the accessibility to the living SOC facilities such as medical and well-being was, the ratio of the vacant houses was lower. In contrast, it was found that the accessibility to the obligatory facilities such as public administration and educational facilities did not have any important effects on the distribution of the vacant houses. All in all, through this study, the spatial distribution of the vacant houses in the small local town and their accessibility to the major SOC facilities could be analyzed.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.129-138
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• 2007

The cases of drunk driving accidents have been steadily increasing every year. The number of accidents was quadrupled from 7,303 cases in 1990 to 25,150 cases in 2004. In addition, the proportion of drunk driving accidents to total traffic accidents was 2.9% in 1990 but it increased to 13.0% in 2003. Studies of drunk driving accidents have been focusing on analyzing psychological decisive factors, classifying drivers' individual characters and types of drunk driving accidents by considering the location of drunk driving accidents. This study assumed that drunk driving accidents would have regular characteristics in respect to spatiality and analyzed its relation with spatial factors such as, accident black spot, the location of bars, the distance of drivers' houses, and spatio-temporal distributional characteristics through drawing density distribution and connecting the time of accidents. In order to achieve the goal of this study, the individual location information was organized and drawn as types of GIS data. From the result of density distribution using Kernel Density Mapping and analysis through the coefficient of areal correspondence, it was understood that drunk driving accidents correlates with some spatial factors.