• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.218-218
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• 2017

Adzuki bean (Vigna angularis) has been extensively investigated due to their biological activities. In this study, total polyphenol content (TPC), total phenolic acid content (TPA), and total flavonoid content (TFC) in 209 Korean adzuki bean landraces were determined by colorimetric methods. Antioxidant effects were evaluated with the DPPH, ABTS, ferric reducing antioxidant power (FRAP), reducing power (RP), and SOD assays. TPC, TPA, and TFC in the 209 Korean adzuki bean landraces ranged from 1.1 to 11.7 mg gallic acid equivalents/g, 0.37 to 5.03 mg caffeic acid equivalents/g, and 0.17 to 0.91 mg quercetin equivalents/g, respectively. Antioxidant activities as assessed by the DPPH, ABTS, FRAP, PR, and SOD assays showed wide variation, ranging from 12.2 to 86.3 (IC50), 0.85 to 5.25 mg ascorbic acid equivalents (ASC)/g, 0.41 to 5.44 mg ASC/g, 0.54 to 1.83 mg ASC/g, and 60.4 to 142.8 (IC50), respectively. Using the relative antioxidant capacity index (RACI), we found that the IT189394 sample had the highest antioxidant activity. In clustering analysis, 209 Korean adzuki bean landraces were classified into three clusters. Among them, cluster I contained 22 accessions with higher antioxidant activities, TPC, TFC, and TPA and smaller seed sizes than the other clusters. We anticipate that these results will provide useful information for the development of adzuki bean-based functional foods.

• Journal of Preventive Medicine and Public Health
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• v.41 no.1
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• pp.1-9
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• 2008

Objectives : This study evaluated knowledge structure and its effect factor by analysis of co-author and keyword networks in Korea's preventive medicine sector. Methods : The data was extracted from 873 papers listed in the Journal of Preventive Medicine and Public Health, and was transformed into a co-author and keyword matrix where the existence of a 'link' was judged by impact factors calculated by the weight value of the role and rate of author participation. Research achievement was dependent upon the author's status and networking index, as analyzed by neighborhood degree, multidimensional scaling, correspondence analysis, and multiple regression. Results : Co-author networks developed as randomness network in the center of a few high-productivity researchers. In particular, closeness centrality was more developed than degree centrality. Also, power law distribution was discovered in impact factor and research productivity by college affiliation. In multiple regression, the effect of the author's role was significant in both the impact factor calculated by the participatory rate and the number of listed articles. However, the number of listed articles varied by sex. Conclusions : This study shows that the small world phenomenon exists in co-author and keyword networks in a journal, as in citation networks. However, the differentiation of knowledge structure in the field of preventive medicine was relatively restricted by specialization.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.1-8
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• 2012

This study is a precedent study for deriving transfer function model between growing stock and forest management policies. Its goal is to solve the multicollinearity between forest works inducing growing stock changes through principal component analysis using annual time series data from 1997 to 2008. As the results, the total explanatory power showed 91.4% on the summarized 3 principal components. They were renamed 'good forest management' 'pest & insets management' 'forest fires' for conceptualization on the derived each component.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Genomics & Informatics
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• v.6 no.1
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• pp.36-43
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• 2008

Radiotherapy would be the choice of treatment for human cancers, because of high cost-effectiveness. However, a certain population of patients shows a resistance to radiotherapy and recurrence. In an effort to increase the efficacy of radiotherapy, many efforts were driven to find the genes causing the unresponsiveness to ionizing radiation. In this paper, we compared the gene expression profiles of two lung cancer cell lines, H460 and H1299, which showed differential responses to ionizing radiations. Each cell were irradiated at 2 Gy, and harvested after 0, 2, 4, 8, 12 and 24 hours to examine the expressions. Two-way ANOVA analysis on time-series experiments of two cells could select 2863 genes differentially expressed upon ionizing radiation among 32,321 genes in microarray (p<0.05). We classified these genes into 21 clusters by SOM clustering according to the interaction between cell types and time. Two SOM clusters were enriched with apoptosis-related genes in pathway analysis. One cluster contained higher levels of phosphatidyl inositol 3-phosphate kinase (PI3K) subunits in H1299, radio-resistant cells than H460, radiosensitive cells. TRAIL receptors were expressed in H460 cells while the decoy receptor for TRAIL was expressed in H1299 cells. From these results, we could characterize the differential responsiveness to ionizing radiation according to their differential expressions of apoptosis-related genes, which might be the candidates to increase the power of radiotherapy.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.331-348
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• 2019

Understanding the tumor heterogeneity due to differences in the growth pattern of metastatic tumors and rate of change is important for understanding the sensitivity of tumor cells to drugs and finding appropriate therapies. It is often possible to test for differences in population means using t-test or ANOVA when the group of N samples is distinct. However, these statistical methods can not be used unless the groups are distinguished as the data covered in this paper. Statistical methods have been studied to test heterogeneity between samples. The minimum combination t-test method is one of them. In this paper, we propose a maximum combinatorial t-test method that takes into account combinations that bisect data at different ratios. Also we propose a method based on the idea that examining the heterogeneity of a sample is equivalent to testing whether the number of optimal clusters is one in the cluster analysis. We verified that the proposed methods, maximum combination t-test method and gap statistic, have better type-I error and power than the previously proposed method based on simulation study and obtained the results through real data analysis.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.377-397
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• 2022

The number of public libraries in Korea has been increasing. However, the focus was on quantitative growth, while it did not have much interests in whether its growth trend are have deviations by region, and if that is a fact, what factors caused such a disparity. For this reason, this study analyzes spatial distribution of public libraries in Korea and its affecting factors of regional gap. As a result, public libraries are constantly distributing in the metropolitan area and the distribution of public libraries showed deviations by region. The results of analysis regarding the determinants of public libraries distribution, rate of population growth, the number of businesses and financial independence rate are found to have a positive effect but local taxes per capita are not. Especially economic power of region and financial ability of a local government are key factors of regional disparity. It shows empirically that the supply of public libraries has been determined by the convenience of suppliers.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.161-168
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• 2009

In this paper, we develop an Intelligent Wheelchair(IW) control system for the people with various disabilities. The aim of the proposed system is to increase the mobility of severely handicapped people by providing an adaptable and effective interface for a power wheelchair. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an Intelligent Wheelchair(IW) is determined by the inclination of the user's face, while proceeding and stopping are determined by the shape of the user's mouth. To analyze these gestures, our system consists of facial feature detector, facial feature recognizer, and converter. In the stage of facial feature detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region detected based on edge information. The extracted features are sent to the facial feature recognizer, which recognize the face inclination and mouth shape using statistical analysis and K-means clustering, respectively. These recognition results are then delivered to a converter to control the wheelchair. When assessing the effectiveness of the proposed system with 34 users unable to utilize a standard joystick, the results showed that the proposed system provided a friendly and convenient interface.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.