• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.14 no.3
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• pp.253-266
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• 2016

In accordance with the classification system of radioactive waste in Korea, all the disused sealed radioactive sources (DSRSs) fall under the category of EW, VLLW or LILW, and should be managed in compliance with the restrictions for the disposal method. In this study, the management and disposal method are drawn in consideration of half-life of radionuclides contained in the source and A/D value (i.e. the activity A of the source dividing by the D value for the relevant radionuclide, which is used to provide an initial ranking of relative risk for sources) in addition to the domestic classification scheme and disposal method, based on the characteristic analysis and review results of the management practices in IAEA and foreign countries. For all the DSRSs that are being stored (as of March 2015) in the centralized temporary disposal facility for radioisotope wastes, applicability of the derivation result is confirmed through performing the characteristic analysis and case studies for assessing quantity and volume of DSRSs to be managed by each method. However, the methodology derived from this study is not applicable to the following sources; i) DSRSs without information on the radioactivity, ii) DSRSs that are not possible to calculate the specific activity and/or the source-specific A/D value. Accordingly, it is essential to identify the inherent characteristics for each of DSRSs prior to implementation of this management and disposal method.

• Journal of the Korean Wood Science and Technology
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• v.45 no.2
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• pp.202-212
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• 2017

This study was to establish the interrelation between chemical compositions and near infrared (NIR) spectra for the classification on distinguishability of domestic gymnosperms. Traditional wet chemistry methods and infrared spectral analyses were performed. In chemical compositions of five softwood species including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cypress (Chamaecyparis obtusa), and cedar (Cryptomeria japonica), their extractives and lignin contents provided the major information for distinction between the wood species. However, depending on the production region and purchasing time of woods, chemical compositions were different even though in same species. Especially, red pine harvested from Naju showed the highest extractive content about 16.3%, whereas that from Donghae showed about 5.0%. These results were expected due to different environmental conditions such as sunshine amount, nutrients and moisture contents, and these phenomena were also observed in other species. As a result of the principal component analysis (PCA) using NIR between five species (total 19 samples), the samples were divided into three groups in the score plot based on principal component (PC) 1 and principal component (PC) 2; group 1) red pine and Korean pine, group 2) larch, and group 3) cypress and cedar. Based on the chemical composition results, it was concluded that extractive content was highly relevant to wood classification by NIR analysis.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• Special Issue of the Society of Naval Architects of Korea
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• 2008.09a
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• pp.1-9
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• 2008

This report contains the results of structural analysis for the verification of the optimum offset range on the top of T-Bar with stiffener and BKT using at DSME Offset range as $6.0{\sim}10.0mm$ based on the 3-D FE analysis and experimental results of angie type stiffener as described in Annex 1 has been used as yard standard over ten (10) years under all Classification approval. Recently, Owner and Class have requested the confirmation for the misalignment based on the Yard's Standard so that a couple of locations for LNGC and LPGC has been investigated the structural strength by FE method using the offset ranges from 0.0 to 18.0 mm.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.705-708
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• 2010

An eigen color co-occurrence approach is proposed that exploits the correlation between color channels to identify the degree of image similarity. This method is based on traditional co-occurrence matrix method and histogram equalization. On the purpose of feature extraction, eigen color co-occurrence matrices are computed for extracting the statistical relationships embedded in color images by applying Principal Component Analysis (PCA) on a set of color co-occurrence matrices, which are computed on the histogram equalized images. That eigen space is created with a set of orthogonal axes to gain the essential structures of color co-occurrence matrices, which is used to identify the degree of similarity to classify an input image to be tested for various purposes. In this paper RGB, Gaussian color space are compared with grayscale image in terms of PCA eigen features embedded in histogram equalized co-occurrence features. The experimental results are presented.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.

• The Journal of Society for e-Business Studies
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• v.13 no.4
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• pp.193-207
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• 2008

The study investigates the co-authorship networks of MIS Quarterly as one of the leading journals in IS field and examines patterns of collaboration networks of the intellectuals. These issues are addressed through a systematic Social Network Analysis (SNA) of 242 articles published from 1996 to 2004 in MIS Quarterly. Results of co-authorship network analysis indicate that the whole incomplete network has a low degree of density. Thus, we analyzed three biggest sub-networks to find out who the key players of each sub-network are. Then, following the keyword classification scheme, relevant data from the articles were collected and coded to analyze three major co-authorship networks of MIS Quarterly community. Some implications are drawn from different research keywords of each sub-network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.551-553
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• 2016

Since air compressor, as an essential equipment used in the factory and plant operations, accounts for around 20% of the total domestic electricity consumption, a real time sensor data monitoring based analysis for electricity consumption reduction is important. In particular, flow rates and pressures of these monitored variables has a direct correlation with the power consumption. This paper proposes a method to identify if the measurement error of the flow rate sensor comes from the sensor measurement limit through bivariate classification analysis of the flow rate and power using the EM (Expectation and Maximization) Algorithm and show how to enable more accurate analysis by the correlation between the flow rate and power on the right-censored data.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.85-93
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• 2019

Despite its matchless position in the global semiconductor industry, Korea has not distinguished itself in the semiconductor equipment sector. Semiconductor cleaning equipment is one of the semiconductor fabrication equipment, and it is expected to be more important along with the advancement of semiconductor fabrication processes. This study attempts to analyze technology competitiveness of major countries in the sector including Korea, and explore specialty sub-areas of the countries. For this purpose, we collected patents of semiconductor cleaning equipment during the last 10 years from the US patent database, and implemented quantitative patent analysis and co-classification network analysis. The result shows that, the US and Japan have been leading the technological progress in this sector, and Korea's competitiveness has lagged behind not only the leading countries but also its competitors and even latecomers. Therefore, intensive R&D and developing technological capabilities are needed for advancing the country's competitiveness in the sector.