• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.1
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• pp.23-31
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• 2012

The number of school crime has grown continuously for last ten years and its intensity also has reached to serious condition. The concept of CPTED(Crime Prevention through Environmental Design) needs to be focused for improving school environment regarding this context. The exterior space of school environment is variously exposed to school crimes committed by colleague students and also intruders. From the perspective of school CPTED, Natural surveillance as one of the practical strategies requires the micro-scale analysis which clarifies local visibility at each different school exterior space. Thus, the purpose of this research is to develop the analysis method clarifying visibility condition at exterior space of school environment, which supports finding the condition of natural surveillance. The programmed analysis algorithm generated quantitative results clarifying Degree for static visibility and Clustering Coefficient for user tracking visibility. The result of this study produced the analysis method feasible to clarify weak natural surveillance conditions at school exterior spaces. Also, it is expected that the developed analysis method will be used to improve the layout of school exterior space from the perspective of CPTED.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.583-586
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• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.22-22
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• 2017

Genetic diversity is one of fundamental parameters for rice cultivar improvement. Rice mutants are also a new source for rice breeding innovation. In this study, ninety-three SSR markers were applied to evaluate the genetic variation among nineteen rice mutant lines. The results showed that a total of 169 alleles from 56 polymorphism markers was recorded with an average of 3.02 alleles per locus. The values of polymorphism information content (PIC) varied from 0.09 to 0.79. The maximum number of alleles was 7, whereas the minimum number of alleles was 2. The heterozygosity values ranged from 0.10 to 0.81. Four clusters were generated using the unweighted pair group method with arithmetic mean (UPGMA) clustering. Fourteen phenotype characteristics were also evaluated. The correlation coefficient values among these phenotye characteristics were obtained in this study. Genetic diversity information of rice mutant lines can support rice breeders in releasing new rice varieties with elite characterisitics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.2
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• pp.142-146
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• 2005

Thirty-two germplasms of Korean adlay landraces were examined to analyse the genetic relationship through the amplified fragment length polymorphism (AFLP) approach. Total number of AFLP products generated by 12 selective primer combinations was 882. The number of polymorphic fragments by each primer combination greatly varied from 4 to 51 with a mean of 20.3, bands visible on the polyacrylamide gel. A genetic similarity coefficient was used for cluster analysis following UPGMA (unweighted pair grouping method of averages) method. The resulting clusters were represented in the form of a dendrogram. The clustering was not tight in the dendrogram. There was generally no clear grouping of the adlay according to the geographic regions in which germplasms were collected. The present AFLP analysis imply that although Korean adlay displayed a larger amount of AFLP variation within germplasms, the variation was shown independently without reflecting a clinal variation. This study demonstrated that AFLP method can be used to examine the genetic relationships among different germplasms of adlay.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.40-45
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• 2015

Online social networks(OSN) are very popular nowadays. As OSNs grows, the commercial markets are expanding their social commerce by applying Influence Maximization. However, in reality, there exist more than two players(e.g., commercial companies or service providers) in this same market sector. To address the Influence Maximization problem between adversaries, we first introduced Influence Maximization against the social adversaries' problem. Then, we proposed an algorithm that could efficiently solve the problem efficiently by utilizing social network properties such as Betweenness Centrality, Clustering Coefficient, Local Bridge and Ties and Triadic Closure. Moreover, our algorithm performed orders of magnitudes better than the existing Greedy hill climbing algorithm.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.231-238
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• 2008

With the development of methodologies, there are also the researches for the concrete item selection for selecting the future emerging researches and technologies. In this paper, we use scientometrics for that purpose in the sectors of construction and transportation. In our scientometric analysis, we use Scopus database, top 1% cited papers, bibliographic coupling, cosine coefficient, and hierarchical clustering and then carry additional experts verification on our results. We try to show the detailed process of scientometric analysis and its possibility as objective methodologies to select the future emerging researches and technologies.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.105-116
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• 2021

In order to preserve the seeds of excellent Hanwoo(Korean traditional cattle) and secure quality competitiveness in the infinite competition with foreign imported beef, production of high-quality Hanwoo beef is absolutely necessary. %IMF (Intramuscular Fat Percentage) is one of the most important factors in evaluating the value of high-quality meat, although standards vary according to food culture and industrial conditions by country. Therefore, it is required to develop a %IMF estimation algorithm suitable for Hanwoo. In this study, we proposed a method of estimating %IMF of Hanwoo using CNN in ultrasound images. First, the proposed method classified the chemically measured %IMF into 10 classes using k-means clustering method to apply CNN. Next, ROI images were obtained at regular intervals from each ultrasound image and used for CNN training and estimation. The proposed CNN model is composed of three stages of convolution layer and fully connected layer. As a result of the experiment, it was confirmed that the %IMF of Hanwoo was estimated with an accuracy of 98.2%. The correlation coefficient between the estimated %IMF and the real %IMF by the proposed method is 0.97, which is about 10% better than the 0.88 of the previous method.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.