• 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.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.1
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• pp.85-88
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• 2015

This study attempted to establish an HPLC analysis method for determination of marker compounds as a part of material standardization for the development of health functional food materials from Perilla frutescens Britton var. acuta Kudo. The quantitative determination method of rosmarinic acid as a marker compound of P. frutescens Britton var. acuta Kudo extract (PFE) was optimized by HPLC analysis using a C18 column ($4.6{\times}150mm$, $5{\mu}m$) with 0.1% acetic acid as the elution gradient and methanol as the mobile phase at a flow rate of 1 mL/min and detection wavelength of 280 nm. The HPLC/UV method was applied successfully to quantification of the marker compound in PFE after validation of the method with linearity, accuracy, and precision. The method showed high linearity in the calibration curve at a coefficient of correlation ($R^2$) of 0.9995, and the limit of detection and limit of quantitation were $0.36{\mu}g/mL$ and $1.2{\mu}g/mL$, respectively. Relative standard deviation (RSD) values of data from intra- and inter-day precision were less than 3.21% and 1.43%, respectively. Recovery rate test at rosmarinic acid concentrations of 12.5, 25 and $50{\mu}g/mL$ scored between 97.04~98.98% with RSD values from 0.25~1.97%. These results indicate that the established HPLC method is very useful for the determination of marker compound in PFE to develop a health functional material.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.1 s.6
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• pp.43-56
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• 2005

This research has examined a time series analysis(TSA) of an every hour traffic information such as occupancy, a traffic flow, and a speed, a statistical model of a surveyed data on the traffic fundamental diagram and an expand aspect of a traffic jam by many Parts of the traffic flow. Based on the detected data from traffic accidents on the Cheonan-Nonsan high way and events when the road volume decreases dramatically like traffic accidents it can be estimated from the change of occupancy right after accidents. When it comes to a traffic jam like events the changing gap of the occupancy and the mean speed is gentle, in addition to a quickness and an accuracy of a detection by the time series analyse of simple traffic index is weak. When it is a stable flow a relationship between the occupancy and a flow is a linear, which explain a very high reliability. In contrast, a platoon form presented by a wide deviation about an ideal speed of drivers is difficult to express by a statical model in a relationship between the speed and occupancy, In this case the speed drops shifty at 6$\~$8$\%$ occupancy. In case of an unstable flow, it is difficult to adopt a statistical model because the formation-clearance Process of a traffic jam is analyzed in each parts. Taken the formation-clearance process of a traffic jam by 2 parts division into consideration the flow having an accident is transferred to a stopped flow and the occupancy increases dramatically. When the flow recovers from a sloped flow to a free flow the occupancy which has increased dramatically decrease gradually and then traffic flow increases according as the result analyzed traffic flow by the multi regime as time series. When it is on the traffic jam the traffic flow transfers from an impeded free flow to a congested flow and then a jammed flow which is complicated more than on the accidents and the gap of traffic volume in each traffic conditions about a same occupancy is generated huge. This research presents a need of a multi-regime division when analyzing a traffic flow and for the future it needs a fixed quantity division and model about each traffic regimes.

• Journal of agriculture & life science
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• v.46 no.5
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• pp.73-82
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• 2012

Allele information from the analysis of the 13 microsatellite (MS) markers, were classified into the $F_0$, $F_1$ and $F_2$ generations, and probabilities of the same individual emergency in each generation was calculated. As a result, the 13 MS markers showed an estimate of $3.84{\times}10^{-23}$ on the premise of the randomly mated group of $F_2$, which implies that the same individuals may emerge by the use of 37 kinds of SNP markers. In this study, the experimental pigs were intercross between only 2 breeds (Korean native pig and Landrace). In addition, the success rate of paternity tests was analyzed on the whole group, by the use of the 13 MS markers and 37 SNP markers. As regards the exclusionary power of the second parent ($PE_{pu}$), MS markers and SNP markers showed 0.97897 and 0.99149, respectively. In relation to the parent exclusion power of both parent (PE), MS markers and SNP markers showed 0.99916 and 0.99949, respectively. In the case of the estimate to identify parental candidates that had the highest probability ($PNE_{pp}$), the two showed 1.00000 all. The Korean pig industry tends to mass produce hogs with limited numbers of alleles in limited parents. Such being the case, there is a need to organize a marker, for which it is imperative to find markers with high efficiency and high economic feasibility of the characteristics of DNA markers, sample size, the accuracy and expenses of genotyping cost, the manageability of data and the compatibility among analysis systems.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.31 no.4 s.54
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• pp.289-293
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• 2005

Nowadays there are many sun protection cosmetics including organic or inorganic UV filter as an active ingredient. Chemically stable inorganic sunsEreen agents, usually metal oxides, we widely employed in high SPF products. Titanium dioxide is one of the most frequently used inorganic UV filters. It has been used as pigments for a long period of cosmetic history. With the development of micronization techniques, it becomes possible to incorporate titanium dioxide in sunscreen formulations without whitening effect and it becomes an important research topic. However, there are very few works related to quantitations of titanium dioxide in sunscreen products. In this research, we analyzed amounts of titanium dioxide in sunscreen cosmetics by adapting redof titration, reduction of Ti(IV) to Ti(III) and reoxidation to Ti(IV). After calcification of other organic ingredients of cosmetics, titanium dioxide is dissolved by hot sulfuric acid. The dissolved Ti(IV) is reduced to the Ti(III) by adding aluminum metals. The reduced Ti(III) is titrated against a standard oxidizing agent, Fe(III) (ammonium iron(III) sulfate), with potassium thiocyanate as an indicator In order to test accuracy and applicability of the proposed method, we analyzed the amounts of titanium dioxide in four types of sunscreen cosmetics, such as cream, make-up base, foundation and powder, after adding known amounts of titanium dioxide $(1{\sim}25w/w%)$. The percent recoveries of the titanium dioxide in four types of formulations were in the range between 96 and 105%. We also analyzed 7 commercial cosmetic products labeled titanium dioxide as an ingredient and compared the results with those of obtained from ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry), one of the most powerful atomic analysis techniques. The results showed that the titrated amounts were well coincided with the analyzed amounts of titanium dioxide by ICP-AES. Although instrumental analytical methods, ICP-MS (Inductively Coupled Plasma-Mass Spectrometry) and ICP-AES, are the best for the analysis of titanium, it is hard to adopt because of their high prices for small cosmetic companies. It was found that the volumetric method presented here gat e quantitative and reliable results with routine lab-wares and chemicals.

• Journal of Soil and Groundwater Environment
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• v.14 no.3
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• pp.1-13
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• 2009

Alkalinity and total carbon contents were measured by acid neutralizing titration (ANT), back titration (BT), gravitational weighing (GW), non-dispersive infrared-total carbon (NDIR-TC) methods for assessing precision and accuracy of alkalinity and total carbon concentration in $CO_2$-rich water. Artificial $CO_2$-rich water(ACW: pH 6.3, alkalinity 68.8 meq/L, $HCO_3^-$ 2,235 mg/L) was used for comparing the measurements. When alkalinity measured in 0 hr, percent errors of all measurement were 0~12% and coefficient of variation were less than 4%. As the result of post-hoc analysis after repeated measure analysis of variance (RM-AMOVA), the differences between the pair of methods were not significant (within confidence level of 95%), which indicates that the alkalinity measured by any method could be accurate and precise when it measured just in time of sampling. In addition, alkalinity measured by ANT and NDIR-TC were not change after 24 and 48 hours open to atmosphere, which can be explained by conservative nature of alkalinity although $CO_2$ degas from ACW. On the other hand, alkalinity measured by BT and GW increased after 24 and 48 hours open to atmosphere, which was caused by relatively high concentration of measured total carbon and increasing pH. The comparison between geochemical modeling of $CO_2$ degassing and observed data showed that pH of observed ACW was higher than calculated pH. This can be happen when degassed $CO_2$ does not come out from the solution and/or exist in solution as $CO_{2(g)}$ bubble. In that case, $CO_{2(g)}$ bubble doesn't affect the pH and alkalinity. Thus alkalinity measured by ANT and NDIR-TC could not detect the $CO_2$ bubble although measured alkalinity was similar to the calculated alkalinity. Moreover, total carbon measured by ANT and NDIR-TC could be underestimated. Consequently, it is necessary to compare the alkalinity and total carbon data from various kind of methods and interpret very carefully. This study provide technical information of measurement of dissolve $CO_2$ from $CO_2$-rich water which could be natural analogue of geologic sequestration of $CO_2$.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.102-108
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• 2016

Objectives : The deficit of recognition memory has been found as one of the common neurocognitive impairments in patients with schizophrenia. In addition, they were reported to fail to enhance the memory about emotional stimuli. Previous studies have shown that bilateral eye movements enhance the memory retrieval. Therefore, this study was conducted in order to investigate the memory enhancement of bilaterally alternating eye movements in schizophrenic patients. Methods : Twenty one patients with schizophrenia participated in this study. The participants learned faces (angry or neutral faces), and then performed a recognition memory task in relation to the faces after bilateral eye movements and central fixation. Recognition accuracy, response bias, and mean response time to hits were compared and analysed. Two-way repeated measure analysis of variance was performed for statistical analysis. Results : There was a significant effect of bilateral eye movements condition in mean response time(F=5.812, p<0.05) and response bias(F=10.366, p<0.01). Statistically significant interaction effects were not observed between eye movement condition and face emotion type. Conclusions : Irrespective of the emotional difference of facial stimuli, recognition memory processing was more enhanced after bilateral eye movements in patients with schizophrenia. Further study will be needed to investigate the underlying neural mechanism of bilateral eye movements-induced memory enhancement in patients with schizophrenia.