• The Journal of the Petrological Society of Korea
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• v.20 no.3
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• pp.161-172
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• 2011

Open beaker digestion method is routinely used as the sample preparation technique for trace element determination of rock samples by inductively coupled plasma mass spectrometry, With this method, however, dissolution of Zr and Hf is not always guaranteed especially when the samples contain refractory minerals. In this study, glass bead digestion technique was compared with conventional open beaker digestion technique for the sample preparation of three USGS rock standards such as AGV-2, BHVO-2, and G-3. Thirty trace elements including rare earth elements were analysed by ICP-MS and ICP-AES. There were no clear differences in analytical results for the AGV-2 and BHVO-2 standards between the two techniques, but Zr, Hf, Y, and middle- to heavy- rare earth element concentrations of the G-3 standard prepared by open beaker digestion technique were significantly lower than the recommended values. This can be attributed to the presence of refractory mineral zircon. On the contrary, all the analytical results of the G-3 standard prepared by glass bead digestion technique were in good agreement with the recommended values, indicating complete dissolution of zircon. The analytical results show that the volatile elements such as Pb and Zn were not lost during the preparation of glass bead. Low dilution glass bead digestion technique described here will be very helpful to enhance precision and accuracy of trace element analysis for geological samples containing refractory minerals.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.827-837
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• 2018

The stabilities of sliding and overturning of caisson and bearing capacity of mound against eccentric and inclined loads, which possibly happen to a composite caisson breakwaters, have been analyzed by using the technique of multiple failure modes. In deterministic approach, mathematical functions have been first derived from the ultimate limit state equations. Using those functions, the minimum cross section of caisson can straightforwardly be evaluated. By taking a look into some various deterministic analyses, it has been found that the conflict between failure modes can be occurred, such that the stability of bearing capacity of mound decreased as the stability of sliding increased. Therefore, the multiple failure modes for the composite caisson breakwaters should be taken into account simultaneously even in the process of deterministically evaluating the design cross section of caisson. Meanwhile, the reliability analyses on multiple failure modes have been implemented to the cross section determined by the sliding failure mode. It has been shown that the system failure probabilities of the composite breakwater are very behaved differently according to the variation of incident waves. The failure probabilities of system tend also to increase as the crest freeboards of caisson are heightening. The similar behaviors are taken place in cases that the water depths above mound are deepening. Finally, the results of the first-order modal are quite coincided with those of the second-order modal in all conditions of numerical tests performed in this paper. However, the second-order modal have had higher accuracy than the first-order modal. This is mainly due to that some correlations between failure modes can be properly incorporated in the second-order modal. Nevertheless, the first-order modal can also be easily used only when one of failure probabilities among multiple failure modes is extremely larger than others.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.11
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• pp.1610-1616
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• 2016

This study aimed to establish an optimal extraction process and high performance liquid chromatography-ultraviolet (HPLC-UV) analytical method for determination of 3,5-dicaffeoylquinic acid (3,5-DCQA) as a part of materials standardization for the development of a xanthine oxidase inhibitor as a health functional food. The quantitative determination method of 3,5-DCQA as a marker compound was optimized by HPLC analysis using a Luna RP-18 column, and the correlation coefficient for the calibration curve showed good linearity of more than 0.9999 using a gradient eluent of water (1% acetic acid) and methanol as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 320 nm. The HPLC-UV method was applied successfully to quantification of the marker compound (3,5-DCQA) in Aster glehni extracts after validation of the method with linearity, accuracy, and precision. Ethanolic extracts of A. glehni (AGEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 30, 50, 70, and 80% ethanol for 3, 4, 5, and 6 h, respectively. Among AGEs, 70% AGE at $70^{\circ}C$ showed the highest content of 3,5-DCQA of $52.59{\pm}3.45mg/100g$ A. glehni. Furthermore, AGEs were analyzed for their inhibitory activities on uric acid production by the xanthine/xanthine oxidase system. The 70% AGE at $70^{\circ}C$ showed the most potent inhibitory activity with $IC_{50}$ values of $77.01{\pm}3.13{\sim}89.96{\pm}3.08{\mu}g/mL$. The results suggest that standardization of 3,5-DCQA in AGEs using HPLC-UV analysis would be an acceptable method for the development of health functional foods.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.128-136
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• 2018

This study was centered on the protested data of the issue of "History of Landscape Architecture" in the handwritten course of landscaping articles of National Qualifications Test. The purpose of this study is to examine the types of social problems in the process of correcting erroneous historical facts. The purpose of this study was to find alternatives for the development of the field of landscape and culture history that can assist in the verification of the historical facts of the landscape sciences examination questions. The main results are as follows. First, as a result of analyzing the contents of the landscape architects' subject matter, the establishment of concept of landscape style and form and the confirmation of historical facts were investigated as important types to be established for development of landscape landscape history department. It seems that the social consensus of the expert group is needed to supplement the lack of data to refer to landscape architectural theory. Second, the analysis of the problematic narrative contents resulted in a total of five types of questionnaires. The appeared in the Undefined style and form(52.94%), Unproven historical facts(25.13%), Obscurity Era classification(11.77%), Lack of specificity(6.95%), Content scope of obscurity events(3.21%) Third, it is not only the lack of information to learn the theory by comparing and analyzing the contents of the statements in the landscape architect 's question items, but also the difference of contents between books was analyzed as the main cause of the problem. As a result of examining the characteristics and examples of the issues raised in landscape architectural problems, it was related to the social phenomenon, and it was classified into cultural factors and political factors. Fourth, the resolution of problematic issues in landscape architects' landscaping articles, which are national technical qualification tests, shows positive results. The information determined in the process of solving the perceived content can be used directly in landscaping field, and it helps the accuracy of the verification process by identifying the types and characteristics of the issues.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of the Korean Society of Radiology
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• v.8 no.4
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• pp.171-180
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• 2014

In this study, the authors attempted to measure the skin dose by irradiating the actual dose on to the TLD(Thermo-Luminescence Dosimeter) and EBT3 Film used as the In-vivo dosimetry after planning the same treatment as the actual patient on a Phantom, because the erythema or dermatitis is frequently occurred on the patients' skin at the time of the proton therapy of medulloblastoma patient receiving the proton therapy. They intended to know whether there is the usefulness for the dosimetry of skin by the comparative analysis of the measured dose values with the treatment planned skin dose. The CT scan from the Brain to the Pelvis was done by placing a phantom on the CSI(Cranio-spinal irradiation) Set-up position of Medulloblastoma, and the treatment Isocenter point was aligned by using DIPS(Digital Image Positioning System) in the treatment room after planning a proton therapy. The treatment Isocenter point of 5 areas that the proton beam was entered into them, and Markers of 2 areas shown in the Phantom during CT scans, that is, in all 7 points, TLD and EBT3 Film pre-calibrated are alternatively attached, and the proton beam that the treatment was planned, was irradiated by 10 times, respectively. As a result of the comparative analysis of the average value calculated from the result values obtained by the repeated measurement of 10 times with the Skin Dose measured in the treatment planning system, the measured dose values of 6 points, except for one point that the accurate measurement was lacked due to the measurement position with a difficulty showed the distribution of the absolute dose value ${\pm}2%$ in both TLD and EBT Film. In conclusion, in this study, the clinical usefulness of the TLD and EBT3 Film for the Enterance skin dose measurement in the first proton therapy in Korea was confirmed.