• Journal of Radiation Protection and Research
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• v.18 no.2
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• pp.1-16
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• 1993

As guides to decision-making in the management of the victims in case of acute whole body or partial body radiation exposure, we studied the relationship between radiation dose and the frequency of chromosomal aberrations observed in peripheral lymphocytes that were irradiated in vitro with $^{60}Co\;{\gamma}-rays$ at doses ranging from 2Gy to 12Gy. The yields of cells with unstable chromosomal aberrations (dicentric chromosomes, ring chromosomes, and acentric fragment pairs) were 32% at 2Gy, 47% at 4Gy, 80% at 6Gy, 94% at 8Gy, and 100% at 10Gy and over. Ydr, which reflect average dose to the whole body in case of acute whole body exposure, were 1.373 at 2Gy, 0.669 at 4Gy, 1.734 at 6Gy, 2.773 at 8Gy, 3.746 at 10Gy and 5.454 at 12Gy. The relationship between radiation dose (D) and the frequency of dicentric plus ring chromosomes per cell(Ydr) could be expressed as $Ydr=9.322{\times}10^{-2}/Gy {\times}D+2.975{\times}10^{-2}/Gy^2{\times}D^2$. Qdr, which are used in estimating dose of partial body exposure and dose of past exposure, were 1.166 at 2Gy, 1.436 at 4Gy, 2.173 at 6Gy, 2.945 at 8Gy, 3.746 at 10Gy and 5.454 at 12Gy. To see how confidently this dosimetry system may be used, we obtained Qdr values from those who received one fraction of homogenous partial body irradiation of 1.BGy, 2.5Gy, and 7.OGy therapeutically; in vivo Qdr values were 1.109, 1.222 and 2.222 respectively. The estimated doses calculated from these in vivo Qdr values using the equation $Qdr=Ydr/(1- e^{-Ydr})$ were 1.52Gy, 2.48Gy, and 6.54Gy respectively, which were very close to the doses actually given.

• Korean Journal of Food Science and Technology
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• v.41 no.4
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• pp.392-398
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• 2009

This study identified risk factors of cross-contamination of foodborne pathogens and established a good agricultural practice (GAP) system for an agricultural products processing center (APC) for perilla leaves. All samples were collected before and after a standard work shift at the APC, while perilla leaves were also collected after each step in the APC. In addition, the workers and their surroundings were sampled by swabbing. The total plate count (TPC) and coliform count in the water samples increased significantly (p<0.05) to 3.36 and 1.73 log CFU/mL after work, respectively. However, no Escherichia coli and Listeria monocytogenes were detected. The bacterial populations of the workers and their surroundings did not differ significantly (p${\geq}$0.05) before and after work. However, Staphylococcus aureus (<1.66 log CFU) was detected at a high rate (13-50%) in the basket, packing table, gloves and cloth. Although perilla leaves passed through the washing steps, the TPC and coliform bacterial populations on the final products were higher (p${\geq}$0.05) than those of unwashed perilla leaves, which indicates that the washing system was not functioning properly. Accordingly, a GAP system with a better washing system should be employed at this facility.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.1-28
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• 2013

Given that smartphone-based social network services (SNS), such as KakaoStory is now being widely used as a way for people to connect and communicate with each other, this study examines key factors leading to the continued use of SNS. People have been using PC-based SNS, such as Cyworld, for years are now using smartphone-based SNS, such as KakaoStory. KakaoStory developed by KakaoTalk has rapidly grown up as the largest smartphone-based SNS in Korea as smartphone penetration increases. It is more difficult for firms to maintain their current users over time in that alternative SNSs satisfying people's new needs are constantly emerging and evolving. In this sense, one of the most challenging issues for SNS firms is how to retain their current users in the long run. However, there are few empirical studies on this issue. Applying the IS continuance model proposed by Bhattacherjee [2001], this study explores key determinants of users' smartphone-based SNS continuance intention. The model suggests that perceived usefulness and user satisfaction are the key determinants of IS continuance intention. However, the model includes only the utilitarian value that users can obtain through the use of smartphone-based SNS, by considering perceived usefulness. Therefore, the study attempts to extend the IS continuance model by considering hedonic and social values simultaneously. More specifically, we consider subjective norms as social value that are proposed by the theory of reasoned action and the theory of planned behavior. We also consider perceived enjoyment as hedonic value that is emphasized as a key factor influencing users' behavior intention and actual behavior, particularly in the context of hedonic IS use. By considering the three values in our model simultaneously, we could offer a deeper understanding of smartphone-based SNS continuance. That is, this study could offer an explanation of how each value is associated with user satisfaction and SNS continuance intention. The customer value-satisfaction-loyalty model can strengthen the assertion that smartphone-based SNS continuance intention is determined by various different types of customer values, such as utilitarian, hedonic, and social ones. Moreover, the model provides a theoretical basis for the assertion that customer values lead to increased loyalty via customer satisfaction. In this regard, we theorize that SNS continuance intention is influenced by users' various values, namely perceived usefulness, perceived enjoyment, and subjective norms, via user satisfaction. To test the proposed research model and hypotheses, we conducted a partial least squares analysis using a total of 253 data collected on the users of smartphone-based SNS (i.e., KakaoStory). The key findings are as follows: First, it has been found that SNS continuance intention considerably depends on user satisfaction. Second, user satisfaction is determined by confirmation, perceived usefulness, and perceived enjoyment. Third, concerning the effects of the three values on SNS continuance intention, only perceived enjoyment regarded as hedonic value was statistically significant. That is, perceived usefulness considered as utilitarian value and subjective norms considered as social value had no effect on SNS continuance intention. Finally, our results indicate that confirmation increases perceived usefulness, perceived enjoyment, and user satisfaction. The results reconfirm the effectiveness of IS continuance model in predicting smartphone-based SNS continuance intention. Moreover, the results demonstrate that perceived enjoyment reflecting hedonic value is the most important predictor of SNS continuance intention. Therefore, it is imperative for firms to meet SNS users' hedonic value to retain them in the long run. Meanwhile, we could not find any empirical evidence to support the assertion that subjective norms are associated with user satisfaction and SNS continuance intention. The results lead us to conclude that when users have enough direct experience in SNS use, subjective norms have no effect on SNS continuance intention. Discussions and implications of the results are provided.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.141-153
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• 2012

Samples collected from six tomato farms(A, B, C : soil culture, D, E, F : Nutriculture) located in Gyeongsangnam-do were tested for the analyses of biological(sanitary indications, major foodborne pathogens, fungi), chemical(heavy metals, pesticides) and physical hazards. The highest levels of total bacteria(7.5 log CFU/g) and coliforms(5.0 log CFU/g) in soil culture farms were higher than those of nutriculture farms(total bacteria: 2.5 log CFU/mL, coliforms: 0.6 log CFU/mL). In crops and personal hygiene soil culture farms showed a slightly higher contamination levels. From all farms, the levels of fungi in soil farms were higher than those of nutrient solution. In case of major pathogens, Bacillus cereus and Staphylococcus aureus were detected in all sample with the exception of nutrient solution. Meantime, Escherichia coli, Listeria monocytogenes, E.coli O157 and Salmonella spp. were not detected. For airborne bacteria, soilculture farms showed less contamination than nutriculture farms. A piece of glass and can was confirmed asphysical hazards. Heavy metal(Cd, Pb, Cu, Cr, Hg, Zn, Ni and As) and pesticide residues as chemical hazards were detected, but their levels were lower than the regulation limit. These results demonstrate that potential hazards on harvesting stage of tomato fam were exposed. Therefore, proper management is needed to prevent biological hazards due to cross-contamination, while physical and chemical hazards were in appropriate levels based on GAP criteria.

• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.400-406
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• 2021

The purpose of this study was to evaluate the microbial contamination level of Korean traditional rice cakes (Garaetteok, Injeolmi, Gyeongdan), as well as manufacturing environment of small-sized businesses in Korea. The contamination levels of total aerobic bacteria, coliforms, and Bacillus cereus in raw materials were 3.76-4.48, 2.21-4.14, and 1.02-1.15 log CFU/g respectively. On the other hand, Escherichia coli was not found. It has been found that the contamination level of total aerobic bacteria, coliforms, and B. cereus in the raw material decreased after the washing process, but it increased again during the soaking and grinding process. However, after the steaming stage, the contamination level increased again during the molding and cooling process, suggesting the need to take cautions in managing cooling water and molded rice cakes in the process. These results suggest that the safe management of cooling water and taking cautions in the drying process after steaming of rice cakes are necessary for controlling cross-contamination. No E. coli was detected during the manufacturing process involving all tested rice cakes. The microbial contamination level of manufacturing environment such as rice grinder and rice cake forming machine was high. Therefore, in terms of food safety strategy, it is necessary to consider introducing systematic cleansing and disinfection procedure to processing equipment and environment for the sake of reducing microbiological risks.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.2
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• pp.1-18
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• 2011

This study has been examined characteristics of Yeoju Lee family, rich group at Miryang in the middle of the Joseon Dynasty, around Geumsidang(今是堂) Lee Gwang-jin remains as a cultural landscape appeared in pictures, poetry, and a strange story. Geumsidang Lee Gwang-jin returned to his old home abandoned the middle government post after the death of Moonjeong queen in socially confused stage and tried to manage an annex to a Geumsidang located in Baekgok of Eungchun riverside, and Geumsidang he managed was affected by his teacher and uncle Wolyoun Lee Tae of a view of nature, filial behavior, and nature management etc. Also, 'Painting of 12 landscapes to Geumsidang' is landscape painting with the actual view not like the '8 landscapes of So-Sang' or '8 landscapes of Sa-Si' which is abstract landscape and Lee Gyeong-hong drew 12 landscapes of Geumsidang that includes Angbong(鶯峰: nightingale peak), Yongdu mountain(龍頭山), Mubong Buddhist temple(舞鳳寺), Maam mountain(馬巖山), Wolyeon-dae(月淵臺), Saindang village(舍人堂村), Youngnam-ru(嶺南樓), Miryang eubseong(密陽邑城), Eyeonso(梨淵沼: pear tree deep water), Yullim(栗林: chestnut tree forest), Miryang river(密陽江), Sammundong fields(沙門野), land and government office owned by Yeoju Lee family as landscape objects. 'Poems of 12 landscapes to Geumsidang' by Lee Yong-gu 11th sons of Lee Gwang-jin was written based on 'Painting of 12 landscapes to Geumsidang', and sang for time, season, and changes of the weather. All 12 poems are all a quatrain with seven Chinese characters in each line consisted of all 28 words, but does not match completely with shown elements in pictures because it is not a simple description of pictures but it is recreated by writer's personality. Therefore these painting shows not only th meaning of filial behavior but also village owned by Yeoju Lee family rich group in Miryang, and these poem recreated the pictures by changing as certain scenic spot with the object of enforcing territory of Yeoju Lee family.

• The Journal of Korean Philosophical History
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• no.58
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• pp.161-199
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• 2018

The contents and method of education of all Korean scholars are similar to the contents and method of education provided by Zhu Xi(朱熹), but they operated in a somewhat different way according to schools. Those who served as the first directors of Donam Seowon were Kim Gip(金集, 1574~1656), Song Joon-gil(宋浚吉, 1606~1672) and Song Si-yeol(宋時烈, 1607~1689), who were the writers of Kim Jang-saeng(金長生, 1548~1631). Donam Seowon is supposed to have weakened the status of scholarship and the activities of lectures as HwaYang Seowon and Seoksil Seowon, which principals were all the Noron(老論) scholars, grew to be the center of education institution of the Noron. Donam Seowon have not preserved the school regulations. But the way of operating system of Donam Seowon can be guessed through the letter of Song Joon-gil, who was the headmaster of the late 17th century on the whole operation of Donam Seowon. From this letter, it is assumed that the school of Donam Seowon is similar to the 'Unbyoung-Jungsa regulations' written by Lee Yi(李珥). The headmasters of Donam Seowon was the Noron scholars. And scholars of the Kim Chang-hyeop(金昌協, 1651~1708) school became headmasters more than the scholars of Kwon Sang-ha(權尙夏, 1641~1721) school. Headmasters of the Donam Seowon had served as the headmasters of HwaYang Seowon and Seoksil Seowon also. In the early days of the establishment of the Donam Seowon, the lecture activities conducted in Donam Seowon were preceded by the textbooks of Kim Jang-saeng/Song Si-yeol's teaching curriculum and neo-confucian books[i.e Sohak (小學)${\rightarrow}$Family Ritual(家禮)${\rightarrow}$Simkyong(心經)${\rightarrow}$Keunsarok(近思錄). It is assumed that the scholars of Seoksil Seowon, who was a Noron Nak-ron(洛論) scholars, gradually adopted Lee Yi's teaching curriculum[i.e, Sohak(小學)${\rightarrow}$Sasoe(四書)${\rightarrow}$Okyoung(五經)]. This lecture contents and procedure was contents and procedure of the Seoksil Seowon, established and operated by the scholars of the Kim Chang-hyeop school. Entrance qualification of Donam Seowon's did not place importance on the social status, but on scholarship and personality. The examination for a high-ranking government official was not allowed. Although the principle, students had to participate in the lecture and study(講學), they were living in Seowon, while the financial and operating of the Seowon became increasingly difficult, the students were changed to participate in the conference(講會) held twice a month while studying at their homes.