• Journal of the Korean Arthroscopy Society
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• v.3 no.2
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• pp.85-90
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• 1999

Purpose : The purpose of this study is to evaluate the clinical and histological results of the osteoarthritic patients who had second look arthroscopy after microfracture surgery. Materials and Methods : From Oct. 1997 to Dec. 1998, 46 patients, 48 knees were treated by microfracture technique. In the 22 patients, 24 knees, 'second-look' arthroscopies and biopsies were performed at 6 months following microfracture. Three patients were men and 19 patients were women. Average age of the patients were 58 years (range, 40-75 years). The average follow up period was 12 months(7-20 months). We analysed clinical results according to the nine-point scale. Also we observed type II collagen formation with immunohistochemical staining. Results : Clinical results were excellent in 83% and good in 17%. Among the 24 knees, more than 80% areas of chondral defect were covered with regenerated cartilage in 21 knees. Histologically, the regenerated tissue appears to be a hybrid of hyaline cartilage and fibrocartilage. Regenerated cartilage contains variable amount of type II collagen with immunohistochemical staining. Conclusion : Most of the patients had significant improvement clinically. 'Second-look' showed that the chondral defect areas were covered with newly grown grayish white tissue. Microfracture in the full thickness chondral defect provides and enriched environment for cartilaginous tissue regeneration.

• Journal of Korean Orthopaedic Sports Medicine
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• v.4 no.1
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• pp.18-28
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• 2005

Purpose: The purpose of this study is to evaluate the clinical, radiological and histopathological results after microfracture surgery for degenerative arthritis of the knee. Materials and Methods: From Oct. 1997 to Dec. 1998, 48 knees in 46 patients were treated by microfracture technique. Their mean age at the time of operation was 56 years(range, 40-75 years) and mean period of follow-up study was one year(range, 7-20 months). For 24 knees in 22 patients, 'second-look' arthroscopies and biopsies were performed at 6 months following microfracture. At the last follow up clinical results were evaluated with Baumgaertner's scale. The specimens of 24 cases were stained with H-E, Safranin-O, and Masson's trichrome. Eighteen of 24 cases were stained immunohistochemically and the Western blotting test was performed on 12 cases for type II collagen. We analyzed the relationship of the Western blotting for type II collagen with clinical score, preoperative varus deformity, joint space widening in radiological result, extent of repaired articular cartilage in '2nd-look' arthroscopic findings, patient's age and weight. Results: Clinical results were excellent in 90% and good in 10%. Among the 24 knees, more than 80% of areas of chondral defect were covered with regenerated cartilage in 21 knees Histologically, the repaired tissue appears to be a hybrid of hyaline cartilage and fibrocartilage. Repaired cartilage contains variable amounts of type II collagen with immunohistochemical staining. The results of the Western blotting test were similar. The amounts of type II collagen formation had positive correlation with the extent of repaired cartilage and preoperative varus deformity. Conclusion: 'Second-look' showed that the chondral defect areas were covered with newly grown grayish white tissue. Articular cartilage repair was confirmed with histological and immunohisto-chemical study qualitatively, and the amount of type II collagen was calculated with the Western blotting test quantitatively. The exact nature and fate of repaired cartilagenous tissues need further long term follow-up study. The results of this study provide the rationale to select osteoarthritic patients indicated for microfracture surgery.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.3
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• pp.527-533
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• 2004

One clinical technique recommended for improving marginal integrity is "rebonding" or application of unfilled resins to the surface of composite restoration. But continuously the restorations are affected with occlusal load. There is room for doubt that the rebonding agent has the positive effect on microleakage in spite of the stress generated by the occlusal load. This study determined the effect of rebonding on microleakage of Class V resin composite restorations under load cycling. Class V cavities were prepared on the buccal surface of 40 sound extracted premolars and restored with a hybrid light-cured resin composite according to manufacturers' directions. They were randomly divided into two groups consisting of 20 samples: a control(group I), without surface sealing, and the other group(group II) in which margins were etched and rebonded. After thermocycling, each of groups was divided into subgroups(group A, B), and load cycling(total 100,000 cycles with 4-100N load at a rate of 1 Hz) were applied on the group B. Assessment of microleakage utilized methylene blue dye penetration. The following results were obtained: 1. In the occlusal region, no significant difference was noted in the scores regardless of whether or not the rebonding agent was used(group TA-IIA, IB-IIB)(p>0.05). 2. In the cervical region, the control group with rebonding(group IIA) showed the better result than the group without rebonding(group IA)(p<0.05). 3. In the cervical region, the rebonded group with load cycling(group IIB) showed similar results to the group without rebonding(group IB) and no significant difference was noted(p>0.05).

• Clinical and Experimental Reproductive Medicine
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• v.29 no.1
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• pp.1-12
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• 2002

Objective: In order to acquire the technique for the establishment of human embryonic stem cells (ESe) derived from the human frozen-thawed embryos produced in IVF-ET program, this study was performed to establish mouse ESC derived from the in vitro fertilized embryos. Materials and Methods: After Fl hybrid (C57BL female $\times$ CBA mael) female mice were superovulated with PMSG and hCG treatment, their oocytes were retrieved and inseminated, and the fertilized embryos were cultured for 96-120 hours until the expected stages of blastocysts were obtained. To isolate the inner cell mass (ICM), either the blastocysts were treated with immunosurgery, or the whole embryos were cultured for 4 days. Isolated ICMs were then cultured onto STO feeder cell layer, and the resultant ICM colonies were subcultured with trypsin-EDTA treatment. During the subculture process, ESC-like cell colonies were observed with phase contrast microscopy. To identify ESC in the subcultured ESC-like cell colonies, alkaline phosphatase activity and Oct-4 (octamer-binding transcription factor-4) expression were examined by immunohistochemistry and RT-PCR, respectively. To examine the spontaneous differentiation, ESC-like cell colonies were cultured without STO feeder cell layer and leukemia inhibitory factor (LIF). Results: Seven ESC-like cell lines were established from ICMs isolated from the in vitro fertilized embryos. According to the developmental stage, the growth of ICMs isolated from the expanded blastocysts was significantly better than that of ICMs isolated from the hatched blastocysts (80.3% vs. 58.7%, p<0.05). ESC-like cell colonies were only obtained from ICMs of expanded blastocysts. However, the ICMs isolated from the embryos treated with immunosurgery were poorly grown and frequently differentiated during the culture process. The established ESC-like cell colonies were positively stained with alkaline phosphatase and expressed Oct-4, and their morphology resembled that observed in the previously reported mouse ESC. In addition, following the extended in vitro culture process, they maintained their expression of cell surface markers characteristic of the pluripotent stem cells such as alkaline phosphatase and Oct-4. When cultured without STO feeder cell layer and LIF, they were spontaneously differentiated into the various types of cells. Conclusion: The findings of this study suggest that the establishment of mouse ESC can be successfully derived from the in vitro fertilized embryos. The established ESC-like cells expressed the cell surface markers characteristic of the pluripotent stem cells and spontaneously differentiated into the various types of cells.

• Journal of the korean academy of Pediatric Dentistry
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• v.30 no.2
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• pp.196-203
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• 2003

The application of sealants is a highly technique-sensitive procedure, requiring an extremely dry field prior to placement. Moisture contamination of the etched enamel surface before sealant placement is cited as the main reason for sealant failure. The purpose of this study was to evaluate the effects of different methods of sealant application on the shear bond strength of sealants to enamel. In groups 1, 2, 3, 4 Teethmate(unfilled sealant) was used, while Ultraseal XTplus(filled sealant) was used in groups 5, 6, 7, 8. Groups 1 and 5(control) were acid etched for 15 seconds using 35% phosphoric acid, washed and then dried. In groups 2, 6 drying agents were applied, and in groups 3, 7 bonding agents were applied and light cured. In groups 4 and 8 both drying agent and bonding agent were applied. Then sealant was cured to the specimen using molds 3mm in diameter and 2mm in height. Thermocycling was performed and shear bond strength was finally measured. The following results were obtained : 1. Groups using filled sealant(groups 5, 6, 7, 8) showed higher shear bond strengths compared to groups using unfilled sealant(groups 1, 2, 3, 4). 2. Among groups using unfilled sealant(groups 1, 2, 3, 4), groups 2, 3, 4 showed significantly higher shear bond strength compared to group 1(p<0.05). There were no significant differences among groups 2, 3 and 4. 3. There were no significant differences(p>0.05) among groups using filled sealant(groups 5, 6, 7, 8). 4. When modes of fracture were examined, cohesive failure was observed in groups 2, 3 and 4.

• Korean Journal of Animal Reproduction
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• v.25 no.2
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• pp.163-169
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• 2001

OSBA(oocytes-sperm binding assay) is a tool developed for rapid test of optimal condition of IVF medium and protein source by binding ability of mouse sperm and egg. Mouse oocyte-cumulus complexes were prepared by removing of the cumulus cells with 0.1% hyaluronidase. 10$\pm$2 oocytes per 30 ${mu}ell$ medium drop were inseminated with 3 ${mu}ell$ sperm suspension and were cultured f3r 3 hours and 24 hours, respectively. And the oocytes were recovered gently and the No. of sperm bound on oocytes were counted. In the Exp. 1, the ratio of oocytes bound with one sperm at least were 60.2%(50/83), 2%(2/77) and 100%(79/79) in the medium with no protein, FBS(15%, v/v) and BSA(0.4%. w/v), respectively, Fetal bovine serum(FBS) seriously inhibited sperm binding on oocyte, although bovine serum albumin(BSA) promoted the binding ability. The inhibiting effect of FBS was dependent on the concentration of FBS. The sperm binding ability according to oocyte maturity was tested in the Exp. 2. There was no significant difference between Met. II (mature) and Met. I (intermediate mature) oocytes in the number of oocytes bound with sperm and the number of sperm bound on oocytes. Finally, in Exp. 3, two batches of Ham's F10 medium with good and poor quality by OSBA were tested (The ratios of embryos developed from PN 1-cell stage to hatched blastocyst; 25% vs. 70%). In the medium with good quality, sperm binding ability was significantly increased (P ＜ 0.05). The ratio of oocytes bound with one sperm at least was 66% and 90% in the medium with poor and good quality, respectively. Conclusively, It was possible to test IVF medium condition rapidly and easily by OSBA.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.