• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.267-278
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• 2022

Recently Flood damage volume has increased as heavy rain has frequently occurred. Especially urban areas are a vulnerability to flooding damage because of densely concentrated population and property. A local government is preparing to mitigate flood damage through the heavy rain warning issued by Korea Meteorological Administration. This warning classification is identical for a national scale. However, Seoul has 25 administrative districts with different regional characteristics such as climate, topography, disaster prevention state, and flood damage severity. This study considered the regional characteristics of 25 administrative districts to analyze the flood vulnerability using entropy weight and Euclidean distance. The rainfall classification was derived based on probability rainfall and flood damage rainfall that occurred in the past. The result shows the step 2 and step 4 of rainfall classification was not significantly different from the heavy rain classification of the Korea Meteorological Administration. The flood vulnerability is high with high climate exposure and low adaptability to climate change, and the rainfall classification is low in the northern region of Seoul. It is possible to preemptively respond to floods in the northern region of Seoul based on relatively low rainfall classification. In the future, we plan to review the applicability of rainfall forecast data using the rainfall classification of results from this study. These results will contribute to research for preemptive flood response measures.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.635-652
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• 2023

This study conducts a comprehensive analysis of the curricular progression of the concepts and learning sequences of 'lines', specifically, 'line segments', 'straight lines', and 'rays', at the elementary school level. By examining mathematics curricula and textbooks, spanning from 2nd to 7th and 2007, 2009, 2015, and up to 2022 revised version, the study investigates the timing and methods of introducing these essential geometric concepts. It also explores the sequential delivery of instruction and the key focal points of pedagogy. Through the analysis of shifts in the timing and definitions, it becomes evident that these concepts of lines have predominantly been integrated as integral components of two-dimensional plane figures. This includes their role in defining the sides of polygons and the angles formed by lines. This perspective underscores the importance of providing ample opportunities for students to explore these basic geometric entities. Furthermore, the definitions of line segments, straight lines, and rays, their interrelations with points, and the relationships established between different types of lines significantly influence the development of these core concepts. Lastly, the study emphasizes the significance of introducing fundamental mathematical concepts, such as the notion of straight lines as the shortest distance in line segments and the concept of lines extending infinitely (infiniteness) in straight lines and rays. These ideas serve as foundational elements of mathematical thinking, emphasizing the necessity for students to grasp concretely these concepts through visualization and experiences in their daily surroundings. This progression aligns with a shift towards the comprehension of Euclidean geometry. This research suggests a comprehensive reassessment of how line concepts are introduced and taught, with a particular focus on connecting real-life exploratory experiences to the foundational principles of geometry, thereby enhancing the quality of mathematics education.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of the Korean Regional Science Association
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• v.40 no.1
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• pp.89-110
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• 2024

REITs are very closely related to real estate management, but there have been no prior studies analyzing the location of REITs assets. Therefore, this study analyzed the location characteristics of REITs assets in two aspects to clarify the location characteristics by using spatial information of REITs assets. First, the characteristics of the type of city where REITs assets are distributed were analyzed, and second, the characteristics of the zoning where REITs assets are distributed were analyzed. As a result of analyzing the characteristics of the city where REITs assets are distributed by type, it was analyzed that in the case of the capital area, both the ratio of cities with REITs assets location and the intensity of REITs assets location (number of REITs assets per city) have location characteristics by city hierarchy in the order of metropolitan city > big city > small and medium-sized city. In the case of non-capital area's metropolitan and large cities, the ratio of REITs assets location cities is similar to that of the capital area, but the location intensity of REITs assets was analyzed to be significantly lower than that of the capital area. As a result of the analysis of REITs assets by type, housing REITs assets tend to be located in the old downtown commercial zoning and the new downtown residential zoning, office REITs assets are characterized by concentration of location in specific commercial zoning of Seoul, and retail REITs assets are located mainly in the old downtown station area. In addition, it was found that logistics REITs assets tend to be located in management zoning, centering on key logistics hub cities in the region.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.161-178
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• 2017

Whereas the demand for development of forested areas covering more than 60% of Korean territory, permission on the forest development has been still given from the perspective of effective land utilization rather than conservation. As the assessment of large forested areas usually focuses more on forest structure, it has its limitation of observing and analyzing the interior change in forest in this way. This study was aimed at computing landscape metrics using a presence vegetation map and FRAGTSTATS 4.2 and analyzing vegetation mosaics. Colonies in native vegetation were classified into a series of major groups and sub-groups based on the native species within the colonies. The colonies were investigated by analyzing a suite of landscape metrics - Core Area, Percentage of Landscape, Number of Patches, Patch Density, Largest Patch Index, Total Edge, Edge Density, Landscape Shape Index, Mean Patch Area, Euclidean Nearest Neighbor. In the Chungnam province major groups and sub-groups of colonies classified based on the proportion of pine and oak species, and pine species was the principal one in terms of distribution area. As for the competition between pines and oaks, while the coverage of pine-centered colonies were three times larger than those of oak-centered ones, pine colonies showed the greater number of patches and therefore higher fragmentation than oaks at the major group level. For the sub-groups, the largest coverage colonies were not only indicated by Pinus densiflora-Quesrcus mongolica colonies among P. densiflora-centered colonies, Q. accutissima colonies among Q. accutissima-centered ones, Q. accutissima-P. densiflora colonies among Q. accutissima-centered ones, Q. mongolica colonies among Q. mongolica-centered ones, P. thumbergii colonies among P. thumbergii-centered ones, and Q. serrata-Q. acutissima colonies among Q. serrata-centered ones, but also revealed more severely mosaicked than other smaller colonies. The overall mosaicking degree estimated by landscape metrics was considered useful for monitoring and investigating vegetation. However, in order to develop management strategy based on analyzing the reason for the mosaicking process and anticipating a trend in vegetation succession, it is essential to further study about ecological characteristics of each colony in the vegetation.

• Journal of Korean Society of Forest Science
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• v.84 no.2
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• pp.151-165
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• 1995

This study was conducted to establish a systematized taxonomic problems of through the leaf morphological characters and leaf venation patterns, and stomatal cell patterns and cell characteristics of abaxial and adaxial surface of the leaflets by SEM, of 6 native species in Korea and 2 foreign species of the Genus Rhus in the Family Anacardiaceae. The results obtained from this study are summarized as followings: 1. Morphological study measured 32 characters of leaves from herbarium specimen and field-collected samples for each species. The results of cluster analysis based on the Euclidean distance showed that the species could be classified into 3 groups: R. sylvestris. R. typhina, R. succedanea: R. trichocarpa. R. chinensis. R. verniciflua: and R. ambigua. R. radicans subsp. orientale, Analysis of principal components showed 5 groups: The major factors in the first principal component group was length of petiole of the terminal leaflets, that in the second group angle of left side in the terminal leaflet bash, that in the third group area ratio between first and terminal leaflets, that in the forth group angle ratio between right and left side in the terminal leaflet base, and that in the fifth group was angle of main and secondary vein at midrib of terminal leaflet. Cumulative contribution by the first, second and third principal component group was explained with 82.6%, a large percent of all information. 2. The leaf venation pattern investigated using soft X-ray photography revealed clado-and reticulo-camptodromous types according to branching angle of the secondary vein. And three groups by the developing degree of secondary vein were R. trichocarpa, R. ambigua. R. chinensis, R. typhina; R. radicans subsp. onentale, R. succedanea, R. verniciflua: and R. sylvestris. Classification key for the Rhus of Korean-native Anacardiaceae was made by the venation pattern and devevoping degree of the secondary vein. 3. The stomatal cell patterns were greatly classified into paracytic and anomocytic types, specific among species according to stomatal and subsidiary cell patterns, and various differences among the species was determined. Microstructure of the adaxial and abaxial surfaces could be divided into synclinal and anticlinal cell wall patterns, and were specific-species. Stomatal cells of R. chinensis were surrounded with characterized villus-like cells.