• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.43-50
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• 2014

In case of all patients who receive radiation therapy, a treatment plan is established and all steps of treatment are planned in the same geometrical condition. In case of head and neck cancer patients who undergo simulated treatment through computed tomography (CT), patients are fixed onto a table for planning, but laid on the top of the treatment table in the radiation therapy room. This study excogitated and fabricated an adjustable holder for head and neck cancer patients to fix patient's position and geometrical discrepancies when performing radiation therapy on head and neck cancer patients, and compared the error before and after adjusting the position of patients due to difference in weight to evaluate the correlation between patients' weight and range of error. Computed tomography system(High Advantage, GE, USA) is used for phantom to maintain the supine position to acquire the images of the therapy site for IMRT. IMRT 4MV X-rays was used by applying the LINAC(21EX, Varian, U.S.A). Treatment planning system (Pinnacle, ver. 9.1h, Philips, Madison, USA) was used. The setup accuracy was compared with each measurement was repeated five times for each weight (0, 15, and 30Kg) and CBCT was performed 30 times to find the mean and standard deviation of errors before and after the adjustment of each weight. SPSS ver.19.0(SPSS Inc., Chicago, IL,USA) statistics program was used to perform the Wilcoxon Rank test for significance evaluation and the Spearman analysis was used as the tool to analyze the significance evaluation of the correlation of weight. As a result of measuring the error values from CBCT before and after adjusting the position due to the weight difference, X,Y,Z axis was $0.4{\pm}0.8mm$, $0.8{\pm}0.4mm$, 0 for 0Kg before the adjustment. In 15Kg CBCT before and after adjusting the position due to the weight difference, X,Y,Z axis was $0.2{\pm}0.8mm$, $1.2{\pm}0.4mm$, $2.0{\pm}0.4mm$. After adjusting position was X,Y,Z axis was $0.2{\pm}0.4mm$, $0.4{\pm}0.5mm$, $0.4{\pm}0.5mm$. In 30Kg CBCT before and after adjusting the position due to the weight difference, X,Y,Z axis was $0.8{\pm}0.4mm$, $2.4{\pm}0.5mm$, $4.4{\pm}0.8mm$. After adjusting position was X,Y,Z axis was $0.6{\pm}0.5mm$, $1.0{\pm}0mm$, $0.6{\pm}0.5mm$. When the holder for the head and neck cancer was used to adjust the ab.0ove error value, the error values from CBCT were $0.2{\pm}0.8mm$ for the X axis, $0.40{\pm}0.54mm$ for Y axis, and 0 for Z axis. As a result of statistically analyzing each value before and after the adjustment the value was significant with p<0.034 at the Z axis with 15Kg of weight and with p<0.038 and p<0.041 at the Y and Z axes respectively with 30Kg of weight. There was a significant difference with p<0.008 when the analysis was performed through Kruscal-Wallis in terms of the difference in the adjusted values of the three weight groups. As it could reduce the errors, patients' reproduction could be improved for more precise and accurate radiation therapy. Development of an adjustable device for head and neck cancer patients is significant because it improves the reproduction of existing equipment by reducing the errors in patients' position.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.3
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• pp.201-207
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• 2007

To reduce both the floatation of the seedling of rice and the failure in standing in the paddy field when the barley straw was applied to paddy field before planting the rice, we tested the effect of rice rooting with plowing methods and irrigation rates for 2 years from 2003 to 2004. This study was carried out in paddy field with Fluvio-Marine deposit in Jeonbug series and the operating accuracy and the change of soil physico-chemical properties depending on plowing methods and irrigation rates following the barley straw applying were examined. There was a less floatation of barley straw in the dry-rotaryI+water-rotaryI(DRI+WRI) plot than in the plowing+water-rotary(PL+WRI) plot. The ratio of miss-planted and floating seedling also decreased by 1.7%, 2.6% in the DRI+WRIplot compared with PL+WRI plot. The soil physical property was improved with the decreasing soil hardness, bulk density and increasing soil porosity after the application of barley straw, especially enhanced greatly in the increase of porosity, gaseous phase and with the decrease of soil hardness, bulk density of subsurface soil in DRI+WRI plot. And the change of soil chemical property were increased the content of total carbon$^{\circ}{\S}$nitrogen$^{\circ}{\S}$organic matter and available phosphate while decreased the content of exchangeable cations and available silicate after the application of barley straw. Also the content of organic matter, available phosphate and cation exchangeable capacity were increased, whereas caron/nitrogen ratio was decreased in DRI+WRI plot compared with PL+WRI plot. The number of panicles, spikelets per square meter were increased and 1,000 grains weight of hulled rice was gained more in DRI+WRI plot at irrigation rate of $500ton\;ha^{-1}$, in DRI+WRII plot at irrigation rate of $700ton\;ha^{-1}$. So the rice yields were increased by 7%, in DRI+WRI and 5% in DRI+WRII plot, respectively compared with PL+WRI plot. The result of this study indicated that the most appropriate plowing method with barley straw application on rice cultivation at double cropping in normal paddy field plain land was DRI+WRI.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.47-55
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• 2016

To evaluate the position accuracy of the MLC. This study analyzed the variations of the dosimetric leaf gap(DLG) and MLC transmission factor to reflect the location of the MLC leaves according to the dose rate variation for dynamic IMRT. We used the 6 MV and 10 MV X-ray beams from linear accelerator with a Millennium 120 MLC system. We measured the variation of DLG and MLC transmission factor at depth of 10 cm for the water phantom by varying the dose rate to 200, 300, 400, 500 and 600 MU/min using the CC13 and FC-65G chambers. For 6 MV X-ray beam, a result of measuring based on a dose rate 400 MU/min by varying the dose rate to 200, 300, 400, 500 and 600 MU/min of the difference rate was respectively -2.59, -1.89, 0.00, -0.58, -2.89%. For 10 MV X-ray beam, the difference rate was respectively ?2.52, -1.69, 0.00, +1.28, -1.98%. The difference rate of MLC transmission factor was in the range of about ${\pm}1%$ of the measured values at the two types of energy and all of the dose rates. This study evaluated the variation of DLG and MLC transmission factor for the dose rate variation for dynamic IMRT. The difference of the MLC transmission factor according to the dose rate variation is negligible, but, the difference of the DLG was found to be large. Therefore, when randomly changing the dose rate dynamic IMRT, it may significantly affect the dose delivered to the tumor. Unless you change the dose rate during dynamic IMRT, it is thought that is to be the more accurate radiation therapy.

• Tuberculosis and Respiratory Diseases
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• v.62 no.5
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• pp.406-416
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• 2007

Background: Single nucleotide polymorphisms (SNPs), which consist of a substitution of a single nucleotide pair, are the most abundant form of genetic variations occurring with a frequency of approximately 1 per 1000 base pairs. SNPs by themselves do not cause disease but can predispose humans to disease, modify the extent or severity of the disease or influence the drug response and treatment efficacy. Single nucleotide polymorphisms (SNPs), particularly those within the regulatory regions of the genes often influence the expression levels and can modify the disease. Studies examining the associations between SNP and the disease outcome have provided valuable insight into the disease etiology and potential therapeutic intervention. Traditionally, the genotyping of SNPs has been carried out using polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP), which is a low throughput technique not amenable for use in large-scale SNP studies. Recently, TaqMan real-time PCR chemistry was adapted for use in allelic discrimination assays. This study validated the accuracy and utility of real-time PCR technology for SNPs genotyping Methods: The SNPs in promoter sequence (-37 and -524) of lung cancer suppressor gene, RRM1 (ribonucleotide reductase M1 subunit) with the genomic DNA samples of 89 subjects were genotyped using both real-time PCR and PCR-RFLP. Results: The discordance rates were 2.2% (2 mismatches) in -37 and 16.3% (15 mismatches) in -524. Auto-direct sequencing of all the mismatched samples(17 cases) were in accord with the genotypes read by real-time PCR. In addition, 138 genomic DNAs were genotyped using real-time PCR in a duplicate manner (two separated assays). Ninety-eight percent of the samples showed concordance between the two assays. Conclusion: Real-time PCR allelic discrimination assays are amenable to high-throughput genotyping and overcome many of the problematic features associated with PCR-RFLP.

• Journal of Korean Biological Nursing Science
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• v.4 no.1
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• pp.25-39
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• 2002

The purpose of this study is to develop and evaluate a web-based instruction Program(WBI) to help nurses improving their knowledge and skill of cardiopulmonary resuscitation. Using the model of web-based instruction(WBI) program designed by Rhu(1999), this study was carried out during February-April 2002 in five different steps; analysis, design, data collection and reconstruction, programming and publishing, and evaluation. The results of the study were as follows; 1) The goal of this program was focused on improving accuracy of knowledge and skills of cardiopulmonary resuscitation. The program texts consists of the concepts and importances of cardiopulmonary resuscitation(CPR), basic life support(BLS), advanced cardiac life support(ACLS), treatment of CPR, nursing care after CPR treatment. And in the file making step, photographs, drawings and image files were collected and edited by web-editor(Namo), scanner and Adobe photoshop program. Then, the files were modified and posted on the web by file transfer protocol(FTP). Finally, the program was demonstrated and once again revised by the result, and then completed. 2) For the evaluation of the program, 36 nurses who in K university hospital located in D city, and related questionnaire were distributed to them as well. Higher scores were given by the nurses in its learning contents with $4.2{\pm}.67$, and in its structuring and interaction of the program with $4.0{\pm}.79$, and also in its satisfactory of the program with $4.2{\pm}.58$ respectively. In conclusion, if the contents of this WBI educational program upgrade further based upon analysis and applying of the results the program evaluation, it is considered as an effective tool to implement for continuing education as life-long educational system for nurse.

• Journal of Radiation Protection and Research
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• v.39 no.1
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• pp.54-60
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• 2014

IAEA has employed various types of radiation detectors - HPGe, NaI, CZT - for accountancy of nuclear material. Among them, HPGe has been mainly used in verification activities required for high accuracy. Due to its essential cooling component(a liquid-nitrogen cooling or a mechanical cooling system), it is large and heavy and needs long cooling time before use. New hand-held portable HPGe has been developed to address such problems. This paper deals with results of performance evaluation test of the new hand-held portable HPGe prototype which was used during IAEA's inspection activities. Radioactive spectra obtained with the new portable HPGe showed different characteristics depending on types and enrichments of nuclear materials inspected. Also, Gamma-rays from daughter radioisotopes in the decay series of $^{235}U$ and $^{238}U$ and characteristic x-rays from uranium were able to be remarkably separated from other peaks in the spectra. A relative error of enrichment measured by the new portable HPGe was in the range of 9 to 27%. The enrichment measurement results didn't meet partially requirement of IAEA because of a small size of a radiation sensing material. This problem might be solved through a further study. This paper discusses how to determine enrichment of nuclear material as well as how to apply the new hand-held portable HPGe to safeguard inspection. There have been few papers to deal with IAEA inspection activity in Korea to verify accountancy of nuclear material in national nuclear facilities. This paper would contribute to analyzing results of safeguards inspection. Also, it is expected that things discussed about further improvement of a radiation detector would make contribution to development of a radiation detector in the related field.