• Journal of Periodontal and Implant Science
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• v.23 no.3
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• pp.535-563
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• 1993

New techniques for regenerating the destructed periodontal tissue have been studied for many years. Current acceptable methods of promoting periodontal regeneration alre basis of removal of diseased soft tissue, root treatment, guided tissue regeneration, graft materials, biological mediators. Platelet-derived growth factor (PDGF) is one of polypeptide growth factor. PDGF have been reported as a biological mediator which regulate activities of wound healing progress including cell proliferation, migration, and metabolism. The purposes of this study is to evaluate the possibility of using the PDGF as a regeneration promoting agent for furcation involvement defect. Eight adult mongrel dogs were used in this experiment. The dogs were anesthetized with Pentobarbital Sodium (25-30 mg/kg of body weight, Tokyo chemical Co., Japan) and conventional periodontal prophylaxis were performed with ultrasonic scaler. With intrasulcular and crestal incision, mucoperiosteal flap was elevated. Following decortication with 1/2 high speed round bur, degree III furcation defect was made on mandibular second(P2) and fourth(P4) premolar. For the basic treatment of root surface, fully saturated citric acid was applied on the exposed root surface for 3 minutes. On the right P4 20ug of human recombinant PDGF-BB dissolved in acetic acid was applied with polypropylene autopipette. On the left P2 and right P2 PDGF-BB was applied after insertion of ${\beta}-Tricalcium$ phosphate(TCP) and collagen (Collatape) respectively. Left mandibular P4 was used as control. Systemic antibiotics (Penicillin-G benzathine and penicillin-G procaine, 1 ml per 10-25 1bs body weight) were administrated intramuscular for 2 weeks after surgery. Irrigation with 0.1% Chlorhexidine Gluconate around operated sites was performed during the whole experimental period except one day immediate after surgery. Soft diets were fed through the whole experiment period. After 2, 4, 8, 12 weeks, the animals were sacrificed by perfusion technique. Tissue block was excised including the tooth and prepared for light microscope with H-E staining. At 2 weeks after surgery, therer were rapid osteogenesis phenomenon on the defected area of the PDGF only treated group and early trabeculation pattern was made with new osteoid tissue produced by activated osteoblast. Bone formation was almost completed to the fornix of furcation by 8 weeks after surgery. New cementum fromation was observed from 2 weeks after surgery, and the thickness was increased until 8 weeks with typical Sharpey’s fibers reembedded into new bone and cementum. In both PDGF-BB with TCP group and PDGF-BB with Collagen group, regeneration process including new bone and new cementum formation and the group especially in the early weeks. It might be thought that the migration of actively proliferating cells was prohibited by the graft materials. In conclusion, platelet-derived growth factor can promote rapid osteogenesis during early stage of periodontal tissue regeneration.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

• Clinical and Experimental Reproductive Medicine
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• v.2 no.2
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• pp.27-36
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• 1975

The success of the family planning program in Korea is reflected in the decrease in the growth rate from 3.0% in 1962 to 2.0% in 1971, and in the decrease in the fertility rate from 43/1,000 in 1960 to 29/1,000 in 1970. However, it would be erroneous to attribute these reductions entirely to the family planning program. Other socio-economic factors, such as the increasing age at marriage and the increasing use of induced abortions, definitely had an impact on the lowered growth and fertility rate. Despite the relative success of the program to data in meeting its goals, there is no room for complacency. Meeting the goal of a further reduction in the population growth rate to 1.3% by 1981 is a much more difficult task than any one faced in the past. Not only must fertility be lowered further, but the size of the target population itself will expand tremendously in the late seventies; due to the post-war baby boom of the 1950's reaching reproductive ages. Furthermore, it is doubtful that the age at marriage will continue to rise as in the past or that the incidence of induced abortion will continue to increase. Consequently, future reductions in fertility will be more dependent on the performance of the national family planning program, with less assistance from these non-program factors. This paper will describe various approaches to help to the solution of these current problems. 1. PRACTICE RATE IN FAMILY PLANNING In 1973, the attitude (approval) and knowledge rates were quite high; 94% and 98% respectively. But a large gap exists between that and the actual practice rate, which is only 3695. Two factors must be considered in attempting to close the KAP-gap. The first is to change social norms, which still favor a larger family, increasing the practice rate cannot be done very quickly. The second point to consider is that the family planning program has not yet reached all the eligible women. A 1973 study determineded that a large portion, 3096 in fact, of all eligible women do not want more children, but are not practicing family planning. Thus, future efforts to help close the KAP-gap must focus attention and services on this important large group of potential acceptors. 2. CONTINUATION RATES Dissatisfaction with the loop and pill has resulted in high discontinuation rates. For example, a 1973 survey revealed that within the first six months initial loop acceptance. nearly 50% were dropouts, and that within the first four months of inital pill acceptance. nearly 50% were dropouts. These discontinuation rates have risen over the past few years. The high rate of discontinuance obviously decreases the contraceptive effectiveness. and has resulted in many unwanted births which is directly related to the increase of induced abortions. In the future, the family planning program must emphasize the improved quality of initial and follow-up services. rather than more quantity, in order to insure higher continuation rates and thus more effective contraceptive protection. 3. INDUCED ABORTION As noted earlier. the use of induced abortions has been increase yearly. For example, in 1960, the average number of abortions was 0.6 abortions per women in the 15-44 age range. By 1970. that had increased to 2 abortions per women. In 1966. 13% of all women between 15-44 had experienced at least one abortion. By 1971, that figure jumped to 28%. In 1973 alone, the total number of abortions was 400,000. Besides the ever incre.sing number of induced abortions, another change has that those who use abortions have shifted since 1965 to include- not. only the middle class, but also rural and low-income women. In the future. in response to the demand for abortion services among rural and low-income w~men, the government must provide and support abortion services for these women as a part of the national family planning program. 4. TARGET SYSTIi:M Since 1962, the nationwide target system has been used to set a target for each method, and the target number of acceptors is then apportioned out to various sub-areas according to the number of eligible couples in each area. Because these targets are set without consideration for demographic factors, particular tastes, prejudices, and previous patterns of acceptance in the area, a high discontinuation rate for all methods and a high wastage rate for the oral pill and condom results. In the future. to alleviate these problems of the methodbased target system. an alternative. such as the weighted-credit system, should be adopted on a nation wide basis. In this system. each contraceptive method is. assigned a specific number of points based upon the couple-years of protection (CYP) provided by the method. and no specific targets for each method are given. 5. INCREASE OF STERILIZA.TION TARGET Two special projects. the hospital-based family planning program and the armed forces program, has greatly contributed to the increasing acceptance in female and male sterilization respectively. From January-September 1974, 28,773 sterilizations were performed. During the same time in 1975, 46,894 were performed; a 63% increase. If this trend continues, by the end of 1975. approximately 70,000 sterilizations will have been performed. Sterilization is a much better method than both the loop and pill, in terms of more effective contraceptive protection and the almost zero dropout rate. In the future, the. family planning program should continue to stress the special programs which make more sterilizations possible. In particular, it should seek to add the laparoscope techniques to facilitate female sterilization acceptance rates. 6. INCREASE NUMBER OF PRIVATE ACCEPTORS Among the current family planning users, approximately 1/3 are in the private sector and thus do not- require government subsidy. The number of private acceptors increases with increasing urbanization and economic growth. To speed this process, the government initiated the special hospital based family planning program which is utilized mostly by the private sector. However, in the future, to further hasten the increase of private acceptors, the government should encourage doctors in private practice to provide family planning services, and provide the contraceptive supplies. This way, those do utilize the private medical system will also be able to receive family planning services and pay for it. Another means of increasing the number of private acceptors, IS to greatly expand the commercial outlets for pills and condoms beyond the existing service points of drugstores, hospitals, and health centers. 7. IE&C PROGRAM The current preferred family size is nearly twice as high as needed to achieve a stable poplation. Also, a strong boy preference hinders a small family size as nearly all couples fuel they must have at least one or more sons. The IE&C program must, in the future, strive to emphasize the values of the small family and equality of the sexes. A second problem for the IE&C program to work. with in the: future is the large group of people who approves family planning, want no more children, but do not practice. The IE&C program must work to motivate these people to accept family planning And finally, for those who already practice, an IE&C program in the future must stress continuation of use. The IE&C campaign, to insure highest effectiveness, should be based on a detailed factor analysis of contraceptive discontinuance. In conclusion, Korea faces a serious unfavorable sociodemographic situation- in the future unless the population growth rate can be curtailed. And in the future, the decrease in fertility will depend solely on the family planning program, as the effect of other socio-economic factors has already been maximumally felt. A second serious factor to consider is the increasing number of eligible women due to the 1950's baby boom. Thus, to meet these challenges, the program target must be increased and the program must improve the effectiveness of its current activities and develop new programs.

• 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.