• Journal of Applied Biological Chemistry
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• v.66
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• pp.467-476
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• 2023

Schisandra chinensis (S. chinensis) is a deciduous broad-leaved cave plant belonging to the Schisandraceae family and is widely distributed in East Asia including Korea, Japan, China, and Taiwan. It has been reported that the main components contained in S. chinensis include lignan compounds and triterpenoid compounds. To distinguish the characteristics of S. chinensis by production region of Korea, a discriminant was established by performing metabolite profiling and principal component analysis, a multivariate statistical analysis technique. As a result, 16 types of triterpenoids, 9 types of lignan, and 1 type each of flavonoid, phenylpropanoid, and fatty acid were identified. In addition, through multivariate statistical analysis, it was confirmed that the four groups in Danyang, Moongyeong, Geochang, and Pyeongchang were divided, by applying the s-plot model of orthogonal partial least squares discriminant analysis. Biomarkers were identified: lanostane, cycloartane, schiartane triterpenoid, and dibenzocyclo-octadiene lignan were identified as chemical markers, respectively.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.285-293
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• 2017

Globally, 1.3 million people develop colon cancer every year, and 600,000 people die each year it. In Korea, colorectal carcinoma was associated with the highest death rate, accounting for 8,380 people, among solid cancers in 2015. Among the various methods for the diagnosis and study of colorectal carcinoma, the results obtained by cytogenetic and molecular genetic methods were compared. Detection rate was 47% in 18q, 40% in 17p, 27% in 22q, and 17% in 10q via CGH; detection rate was 57% in D18S59, 50% in D18S68, 50% in TP53CA, 47% in D18S6940% in D22S274, 37% in D22S283, 27% in D10S187, and 23% in D10S541 with LOH. Microsatellite marker matching rates were 100% in D22S274, 100% in D22S283, 100% in D10S186, 100% in D10S187, 100% in D10S541, 93% in D18S69, 93% in D18S68, 92% in TP53CA, and 89% in D18S59. The agreement rate between the two methods was 94.4% based on positive results using CGH. Based on the advantages of CGH, which was the ability to obtain information regarding the entire tumor genome at once, this experiment could identify the region with significant deletion using CGH and the more limited region LOH, with a completely different approach. LOH in the recurrent high-risk group, 18q21, was helpful in the selection of treatment modalities and in prognostic estimation as well as making the most appropriate decision for treatment. Therefore, it is suggested that LOH with surgical site tissues could be one of the treatment methods for recurrent high-risk group among patients with colorectal carcinoma.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Journal of the Korean Arthroscopy Society
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• v.6 no.1
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• pp.1-6
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• 2002

Purpose : Evaluation and analysis of the incidence and causes of intraoperative and postoperative complications after arthoscopic anterior cruciate ligament(ACL) reconstruction using bone-patella. tendon-bone (BPTB) auto graft. Materials and Methods : We reviewed 85 cases of arthroscopic ACL reconstruction using BPTB autograft which had been followed up for more than 1 year. Intraoperative complications had been recorded and corrected during the operation. Postoperative complications were analyzed and compared between 3 groups which were classified by the last follow-up period after the operation. Results : There were a few intraoperative complications including patellar fracture(1 case), contamination of harvested graft(1 case), impingement of graft(2 cases), blow-out of tile posterior wall of the femoral tunnel(1 case) and intraarticular retraction of the screw(1 case). The result of the comparison of postoperative complications among the groups shows that the incidence of anterior knee pain, donor site pain and patellofemoral crepitation were significantly decreased after 2 years. But there was no significant decrease in these complications after 3 years. Conclusion : Intraoperative complications after arthroscopic ACL reconstruction using BPTB autograft were due to incorrect technique and carelessness of the operation team, and can be prevented by improvement of surgical technique and accumulation of experience. Postoperative complication were somewhat unavoidable but gradually improved with time; hence, we should consider it for the selection of graft donor.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Wetlands Research
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• v.23 no.4
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• pp.327-341
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• 2021

The riparian eco-belt is an efficient technique that can reduce non-point pollution sources in the basin and improve ecological connectivity and health. In Korea, a legal system for the construction and management of riparian eco-belts is in operation. However, it is currently excluded that rivers and floodplains in dam reservoir that are advantageous for buffer functions such as control of non-point pollutants and ecological habitats. Accordingly, this study presented and analyzed a plan to select a site for an integrated riparian ecol-belt that comprehensively evaluates the water quality and ecosystem characteristics of each dam floodplain and riparian zone for the Daecheong Dam basin in Geum River watershed. First, the Daecheong Dam basin was divided into 138 sub-basin with GIS, and the riparian zone adjacent to the dam floodplain was analyzed. Sixteen evaluation factors related to the ecosystem and water quality impact that affect the selection of integrated riparian eco-belt were decided, and weights for the importance of each factor were set through AHP analysis. The priority of site suitability was derived by conducting an integrated evaluation by applying weights to sub-basin by floodplains and riparian zone factors. In order to determine whether the sites derived through GIS site analysis are sutiable for actual implementation, five sites were inspected according to three factors: land use, pollution sources, and ecological connectivity. As a result, it was confirmed that all sites were appropriate to apply integrated riparian ecol-belt. It is judged that the riparian eco-belt site analysis technique proposed through this study can be applied as a useful tool when establishing an integrated riparian zone management policy in the future. However, it might be necessary to experiment various evaluation factors and weights for each item according to the characteristics and issues of each dam. Additional research need to be conducted on elaborated conservation and restoration strategies considering the Green-Blue Network aspect, evaluation of ecosystem services, and interconnection between related laws and policy and its improvements.

• Korean Journal of Heritage: History & Science
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• v.47 no.3
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• pp.42-59
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• 2014

Han Pilgyo (1807~1878) was a scholar-official in the later period of the Joseon Dynasty. The research results on spatial structure and landscape techniques of the central government office reviewed through the Sukcheonjeado(宿踐諸衙圖) album collection edited by Han Pilgyo are as follows. First, Sukcheonjeado(宿踐諸衙圖) using Sabangjeondomyobeob(四方顚倒描法, a Korean traditional drawing type) is uniquely proven historical data which helps to understand the spatial structure of the center and local government offices and the characteristics of cultural landscape. Secondly, the central government office located in Yookcho(六曹) Street which is the outside Gwanghwamun(光化門) of the Main Palace(Gyeongbokgung, 景福宮) of the Joseon Dynasty has a center facility(Dangsangdaecheong, 堂上大廳) and attached buildings which are distributed from high to low or from left to right, according to its order of presidency in square-shaped portion of land. The main building was located facing south and by considering the administrative convenience, the environmental effect and the practice of Confucian norms this structure reflects a hierarchical landuse system. Thirdly, the main buildings such as Dangsangdaecheong and Hyangcheong(鄕廳), which are the working place for government officials had large square front yards for constructing a practical patio garden. The back garden was tended to reflect the meaning landscape, with such as pond and pavilion. A particular point was the repeated crossing of active space and passive space(movement and stillness, building and yard, yard and garden), which implements the Yin-Yang principle. Fourth, the characteristics that can be extracted from the central government office landscapes are (1) expandability of outdoor space, connects of front gardens, emphasizes the characteristic of serviceable gardens and back gardens, which in turn emphasizes scenic sides, (2) introduction of water features(square-shaped ponds) that can be used as fire-water and considers environmental-amenity and landscape characteristics, (3) introduction of pavilions for relaxation, mental and physical discipline, and the development of back gardens, (4) significance of Jeongsimsoo(庭心樹) in such things as selection of concise landscape plants like lotus, willow, pine, zelkova and so on, and limited plant introduction, (5) environmental design techniques which set importance on not only aesthetics and ideality but also practical value. Thus, these aspects of the government office landscape can be said to be the universality and particularity of Korean traditional landscape technique and can be extracted similarly in the palaces, temples, lecture halls, and houses of the upper class of the Joseon Dynasty.

• Korean Journal of Heritage: History & Science
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• v.46 no.1
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• pp.208-227
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• 2013

Twenty-seven bronze bowls excavated from the Goryo burial complex at Deobu-gol were examined for their microstructure and chemical composition to characterize the bronze technology practiced by commoners at the time. Results showed that the objects examined can be classified into four groups: 1) objects forged out of Cu-near 22%Sn alloys and then quenched; 2) objects cast from Cu-below 10% Sn alloys containing lead; 3) objects cast from Cu-10%~20% Sn alloys containing lead and then quenched; 4) objects forged out of Cu-10~20% Sn alloys containing lead and then quenched. This study revealed that the fabrication technique as determined by alloy compositions plays an important role in bronze technology. The use of lead was clearly associated with the selection of quenching temperatures, the character of inclusions and the color characteristics of bronze surfaces. It was found that the objects containing lead were quenched at temperatures of $520^{\circ}{\sim}586^{\circ}C$ while those without lead were quenched at the range of $586^{\circ}{\sim}799^{\circ}C$. The presence of selenium in impurity inclusions was detected only in alloys containing lead, suggesting that the raw materials, Cu and Sn, used in making the lead-free alloys for the first group were carefully selected from those smelted using ores without lead contamination. Furthermore, the addition of lead was found to have significant effects on the color characteristics of the surface of bronze alloys when they are subjected to corrosion during interment. In leaded alloys, corrosion turns the surface light green or dark green while in unleaded alloys, corrosion turns the surface dark brown or black. It was found that in fabrication, the wall thickness of the bronze bowls varies depending on the application of quenching; most of the quenched objects have walls 1mm thick or below while those without quenching have walls 1mm thick or above. Fabrication techniques in bronze making usually reflect social environments of a community. It is likely that in the late Goryo period, experiencing lack of skilled bronze workers, the increased demand for bronze was met in two ways; by the use of chief lead instead of expensive tin and by the use of casting suitable for mass production. The above results show that the Goryo bronze workers tried to overcome such a resource-limited environment through technological innovations as apparent in the use of varying fabrication techniques for different alloys. Recently, numerous bronze objects are excavated and available for investigation. This study shows that with the use of proper analytical techniques they can serve as a valuable source of information required for the characterization of the associated technology as well as the social environment leading to the establishment of such technology.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• 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.