• The Journal of Korean Orthopaedic Ultrasound Society
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• v.7 no.1
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• pp.20-27
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• 2014

Purpose: The purpose of this study was to evaluate coracohumeral distance (CHD) in patients with or without subcoracoid impingement with hypothesis that patients with subcoracoid impingement would have narrower CHD. Materials and Methods: One hundred twenty-four patients with subacromial impingement were evaluated. The subjects with subcoracoid impingement which was affirmed clinically and confirmed by ultrasound guided subcoracoid injection (n=28) was compared with patients with subacromial impingement only (n=96). Patients with stiffness and rotator cuff tear were excluded. Absolute CHD was measured on magnetic resonance imaging (MRI) axial images and on ultrasound with the humerus in neutral position and internal rotation. Also relative ratio of distance difference (RRDD) defined as the difference of CHD in neutral position and internal rotation compared with absolute CHD in neutral on ultrasound was also measured. Results: The distance measured in neutral position was similar between US imaging and MRI (p>0.05) and both measurements did not have significant difference between the two groups (p>0.05). On ultrasound, the difference in CHD in internal rotation between the two groups nearly met the level of significance (p=0.07). No significant difference of CHD difference in two humeral positions was seen between the two groups. However, RRDD value was significantly greater in subcoracoid impingement group (p<0.05). Conclusion: No significant difference of CHD was seen between the subcoracoid impingement group and the control group. RRDD value was greater in subcoracoid impingement group suggesting that individualized coracohumeral distance in internal rotation should be taken into account when assessing patients with subcoracoid impingement.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.543-559
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• 2003

The information drive of the businesses requires new alternatives in that the promotion of business efficiency through information process technologies ends up conflicting with the protection of information human rights on laborers’side. Nevertheless, apathy on information protection has a tendency to be distorted by the efficiency of the businesses. Should the capital and mass media warn economic red lights, political circles with uneasiness would ignore the significance of information protection on the behalf of business efficiency. Therefore, the importance of information protection is considered a smaller interest than that of business efficiency with the infringements of human rights on laborers’side arising. Informatization of the businesses along with the developments of information process technologies has enabled the management to monitor and control the behaviors of laborers. This new problem needs to establish both information protection mechanism and institutional devices to regulate those labor controls. The security of business activity without human rights infringement warrants both basic rights of the public and spirit of the Constitution. The study suggests the establishment and revision of laws suitable to the period of information human rights. On top of that, the establishment of the basic law for information protection of individuals’with the common principle that integrates the related laws and rules on-off line is needed. This will warrant the active participation of labor unions and create specific alternatives for information protection.

• 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.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.18 no.2
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• pp.177-192
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• 2012

This is a study of match-making systems that considers the mutual satisfaction of matching partners. Recently, recommendation systems have been applied to people recommendation, such as recommending new friends, employees, or dating partners. One of the prominent domain areas is match-making systems that recommend suitable dating partners to customers. A match-making system, however, is different from a product recommender system. First, a match-making system needs to satisfy the recommended partners as well as the customer, whereas a product recommender system only needs to satisfy the customer. Second, match-making systems need to include as many participants in a matching pool as possible for their recommendation results, even with unpopular customers. In other words, recommendations should not be focused only on a limited number of popular people; unpopular people should also be listed on someone else's matching results. In product recommender systems, it is acceptable to recommend the same popular items to many customers, since these items can easily be additionally supplied. However, in match-making systems, there are only a few popular people, and they may become overburdened with too many recommendations. Also, a successful match could cause a customer to drop out of the matching pool. Thus, match-making systems should provide recommendation services equally to all customers without favoring popular customers. The suggested match-making system, called Mutually Beneficial Matching (MBM), considers the reciprocal satisfaction of both the customer and the matched partner and also considers the number of customers who are excluded in the matching. A brief outline of the MBM method is as follows: First, it collects a customer's profile information, his/her preferable dating partner's profile information and the weights that he/she considers important when selecting dating partners. Then, it calculates the preference score of a customer to certain potential dating partners on the basis of the difference between them. The preference score of a certain partner to a customer is also calculated in this way. After that, the mutual preference score is produced by the two preference values calculated in the previous step using the proposed formula in this study. The proposed formula reflects the symmetry of preferences as well as their quantities. Finally, the MBM method recommends the top N partners having high mutual preference scores to a customer. The prototype of the suggested MBM system is implemented by JAVA and applied to an artificial dataset that is based on real survey results from major match-making companies in Korea. The results of the MBM method are compared with those of the other two conventional methods: Preference-Based Matching (PBM), which only considers a customer's preferences, and Arithmetic Mean-Based Matching (AMM), which considers the preferences of both the customer and the partner (although it does not reflect their symmetry in the matching results). We perform the comparisons in terms of criteria such as average preference of the matching partners, average symmetry, and the number of people who are excluded from the matching results by changing the number of recommendations to 5, 10, 15, 20, and 25. The results show that in many cases, the suggested MBM method produces average preferences and symmetries that are significantly higher than those of the PBM and AMM methods. Moreover, in every case, MBM produces a smaller pool of excluded people than those of the PBM method.

• 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.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• Restorative Dentistry and Endodontics
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• v.18 no.1
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• pp.73-83
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• 1993

The purpose of this study was to evaluate the effect of various acid treatments on dentin bonding. Freshly extracted human teeth were uprightly embedded in self curing acrylic resin, and their occlusal surfaces were grinded to expose flat dentin surfaces. The specimens were divided into 4 groups. Specimens of one group were not treated so as to be a control and those of the other three groups were threated with 10% polyacrylic acid, 10% phosphoric acid, and 10-3 solution(10% citric acid/3% ferric chloride) respectively. Primer, bonding resin and composite resin were applied over the treated dentin surfaces sequentially. All specimens were stored in $37^{\circ}C$ distilled water for 24 hours, then the tensile bond strength was measured and the treated dentin surfaces and fracured dentin surfaces were examined under a scanning electron microscope. The results were as follows: Bond strengths of acid-treated groups were higher than those of the untreated group. In the acid-treated groups, bond strength was found to be the highest in the 10-3 solution group followed by the 10% phosphoric acid group and the 10% polyacrylic acid group(P<0.01). On SEM examination of dentin surfaces, the untreated dentin surface showed a remaining smear layer and closed dentinal tubules. Dentin surfaces treated with 10 % polyacrylic acid showed a clean dentin surface without the smear layer, but showed remaining smear plugs in dentinal tubules. A dentin surface treated with 10% phosphoric acid or 10-3 solution showed open dentinal tubules without the smear layer or smear plugs. On SEM observation of the fractured dentin-resin interface, the untreated group showed that failure occurred in the smear layer. The group treated with 10% polyacrylic acid showed no resin tag remained in the dentinal tubules, but resin tags in the dentinal tubules were observed in the group treated with the 10% phosphoric acid or the 10-3 solution. On the failure mode examination, the higher the bond strength of the group, the higher the frequency of cohesive failure. The coefficient between bond strength and cohesive failure rate was 0.71.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.169-176
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• 2009

Diabetes Mellitus (DM) is a syndrome accompanied with the abnormal secretion or function of insulin, a hormone that plays a vital role in controlling the blood glucose level (BGL). Type land 2 DM are most common form and the prevalence of the latter is recently increasing, The aim of this article was to assess whet her Type 2 DM could act as a predisposing risk factor on the pulpo-periapical pathogenesis. Previous literature on the pathologic changes of blood vessels in DM was thoroughly reviewed. Furthermore, a histopathologic analysis of artificially-induced periapical specimens obtained from Type 2 diabetic and DM-resistant rats was compared. Histopathologic results demonstrate that the size of periapical bone destruction w as larger and the degree of pulpal inflammation was more severe in diabetic rats, indicating that Type 2 D M itself can be a predisposing risk factor that makes the host more susceptible to pulpal infection. The possible reasons may be that in diabetic state the lumen of pulpal blood vessels are thickened by atheromatous deposits, and microcirculation is hindered, The function of polymorphonuclear leukocyte is also impair ed and the migration of immune cells is blocked, leading to increased chance of pulpal infection. Also, lack of collateral circulation of pulpal blood vessels makes the pulp more susceptible to infection. These decrease the regeneration capacity of pulpal cells or tissues, delaying the healing process, Therefore, when restorative treatment is needed in Type 2 DM patients, dentists should minimize irritation to the pulpal tissue un der control of BGL.

• Restorative Dentistry and Endodontics
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• v.35 no.2
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• pp.88-95
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• 2010

The purpose of this study was to compare the root canal shaping ability of 4 rotary NiTi instruments in simulated root canals. For the preparation of thirty two curved root canals, Mtwo instruments using "single length"technique, and Profile, ProTaper Universal, and K3 using crown-down technique (N = 8) were used. All canal samples were prepared by reaching an apical canal size of #30. Pre- and post-instrumentation digital images were recorded and an assessment of canal shape was determined using a computer image analysis program SigmaScan Pro (Systat Software Inc., San Jose, CA, USA). The changes of the dimension of inner walls of canals, (2) the changes of the dimension of outer walls of canals, and (3) the centering ratio were measured at 7 measuring points, and then data were statistically analyzed using one-way ANOVA and Duncan's test. The results were as below; 1. The root canal shaping ability of Profile was significantly faster than that of other rotary NiTi instruments (p < 0.05). 2. The deformation and fracture of all instruments used for this study were not experienced. 3. In the degree of changes of the dimension of inner walls of canals, Profile demonstrated the lowest changes of the dimension of inner walls of canals except at the measuring points of the 1 and 2 mm (p < 0.05). However, the ProTaper Universal showed the highest changes of the dimension of inner walls of canals at all measuring points (p < 0.05). 4. In the degree of changes of the dimension of outer walls of canals, Mtwo demonstrated the lowest changse of the dimension of outer walls of canals except at the measuring point of the 1 mm (p < 0.05). However, Profile exhibited the highest changes of the dimension of outer walls of canals at the measuring points of 3 and 4 mm and ProTaper Universal and K3 showed the largest changes of the dimension of outer walls of canals at the measuring points of 1, 2, 6, and 7 mm (p < 0.05). 5. In degree of centering ratio, Profile demonstrated the least centering ratio comparing with the centering ratio shown by other NiTi instruments at the measuring points of 1, 4, 5, and 6 mm. Results suggest that in the coronal part of canal preparation, active cutting files such as ProTaper Universal may efficiently flare the canal orifice and form a better taper, and in the apical part of the canal, files which have a better centering ability such as Profile may maintain the original canal curvature and reduce the shaping time.