• Journal of Korean Orthopaedic Sports Medicine
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• v.1 no.1
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• pp.31-36
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• 2002

Purpose : ACL (anterior cruciate ligament) reconstruction using achilles allograft was done for whom ACL injured person in recreational sports activity. The purpose of this study was to evaluate the clinical results and return to their sports activity in these patients. Materials and Methods : ACL injured 56 amateur athletes who had experienced sports 3 times a week more than 5 years, reconstructed with Achilles allograft, and it was analyzed subjective and objective parameter, Tegner scoring, Telos stress arthrometer, Lysholm Knee Scoring System and modified Feagin scoring system. The average age was 25 years old (range: 18$\~$49), the average follow up period was 15 months (range: 12$\~$19). Morbid sports were football (29 cases), basket ball (14 cases), badminton (5 cases), tennis (3 cases), squash (2 cases) and otherwise (3 cases). Result : The mean Lysholm Knee Scoring System was improved to 88.2 from 60. Telos arthrometer in anterior stress test revealed 2.3 mm improved from 7.1 mm. The modified Feagin scoring system showed 50 cases (89$\%$) with excellent and good results. We had obtained 12 cases (21$\%$) of Tegner score VI, 32 cases (57$\%$) of score V, 20 cases (35%$\%$ of score IV, 3 (5.3$\%$) cases of score III. Conclusions : Reconstruction of anterior cruciate ligaments can restore stability sufficient to allow sports activity in ACL injured patients, but it’s difficult to achieve 'normal' sports activity. So we will have to solve the reasons of this dissatisfaction at furthermore.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• 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 radiological science and technology
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• v.29 no.1
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• pp.1-6
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• 2006

Purpose: Renal stones are common and typically arise within the collecting system. The renal sinus are contains the collection system, the renal vessels, lymphatcs, fat, and fibrous tissue. Because of the compression of all the large echoes in signal processing, the echo from the renal stone generally cannot be distinguished from large echoes emanating from normal structures of the renal sinus. Use of ultrasonography has been difficult for detecting small renal stone without posterior shadowing and chemical composition of stone. The aim of study was measuring for posterior acoustic shadowing to a stone for various scan parameter and it examines a help in renal stone diagnosis. Material & Methods: The stone was place on sponge examined in a water bath with a 3.5MHz or 7.5MHz transducer(LOGIQ 400, USA). First, tested a variety of gain. Second, tested a variety of dynamic range. Third, tested a variety of focal zone. Fourth, measuring of the echo level for low and high frequency for depth. Results: 1) Average echo level was 98 for low total gain(10 dB) and was 142 for high total gain(40 dB). Posterior acoustic shadowing of renal stone was clear for low gain. 2) Average echo level was 129 for low dynamic range(42 dB) and was 101 for high dynamic range(72 dB). Posterior acoustic shadowing of renal stone was clear for high dynamic range. 3) When stone is in focal zone of transducer, definite posterior acoustic shadow is identified. 4) Stone was clear appeared for high frequency(7.5 MHz) than low frequency(3.5 MHz) and it is not distorted. Conclusion: The demonstration of an posterior acoustic shadow of renal stone dependents on several technical factors such as gain, dynamic range, focus, and frequency. This various factors are a help in renal stone diagnosis.

• Korean Security Journal
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• no.42
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• pp.7-36
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• 2015

This study aims at analyzing difference of social Security network, Community unity and local government trust according to socio-demographical features, exploring the relation of social Security network, Community unity and local government trust according to socio-demographical features, presenting results between each variable as a model and verifying the property of mutual ones. This study sampled general citizens in Gwangju for about 15 days Aug. 15 through Aug. 30, 2014, distributed total 450 copies using cluster random sampling, gathered 438 persons, 412 persons of whom were used for analysis. This study verified the validity and credibility of the questionnaire through an experts' meeting, preliminary test, factor analysis and credibility analysis. The credibility of questionnaire was ${\alpha}=.809{\sim}{\alpha}=.890$. The inout data were analyzed by study purpose using SPSSWIN 18.0, as statistical techniques, factor analysis, credibility analysis, correlation analysis, independent sample t verification, ANOVA, multi-regression analysis, path analysis etc. were used. the findings obtained through the above study methods are as follows. First, building a social Security network has an effect on Community institution. That is, the more activated a, the higher awareness on institution. the more activated street CCTV facilities, anti-crime design, local government Security education, the higher the stability. Second, building a social Security network has an effect on trust of local government. That is, the activated local autonomous anti-crime activity, anti-crime design. local government's Security education, police public oder service, the more increased trust of policy, service management, busines performance. Third, Community unity has an effect on trust of local government. That is, the better Community institution is achieved, the higher trust of policy. Also the stabler Community institution, the higher trust of business performance. Fourth, building a social Security network has a direct or indirect effect on Community unity and local government trust. That is, social Security network has a direct effect on trust of local government, but it has a higher effect through Community unity of parameter. Such results showed that Community unity in Gwangju Region is an important factor, which means it is an important variable mediating building a social Security network and trust of local government. To win trust of local residents, we need to prepare for various cultural events and active communication space and build a social Security network for uniting them.

• Korean Journal of Poultry Science
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• v.29 no.2
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• pp.95-100
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• 2002

Two experiments were conducted to investigate the effect of taurine supplementation on lipid metabolism in laying hens. In experiment 1, 19-wk-old laying hens were given one of four taurine supplemented diets (0 (control), 0.4, 0.8, and 1.2% taurine) fur 10 weeks. Abdominal fat weight was lower in the 1.2% diet by 29.2% compared to the control. Serum concentrations of triacylglycerol and HDL-cholesterol were not different among the treatments. However, seam concentration of total cholesterol was higher by 22.4% in the 1.2% diet compared to the control. Concentration of triacylglycerol or total cholesterol in the liver were decreased by 26.1% or 26.4% and 28.2% or 26.4%, respectively in the 0.8% and 1.2% diets compared to the control. The concentration of HDL-cholesterol in liver was also lower by 33.9% in the 1.2% diet compared to the control. In experiment 2, 81-wk-old laying hens were allocated to one of three taurine supplemented diets (0 (control), 1 and 2% taurine) fur 6 weeks. Abdominal fat weight was lower by 25% in 1% taurine supplementation compared to the control. Serum concentrations of triacylglycerol, total cholesterol and HDL-cholesterol of hens fed with 1% diet were not different from those of control group. However, sew concentrations of triacylglycerol and total cholesterol were lower by 44.0% and 19.8%, respectively in the 2% diet compared to the control. Furthermore, serum concentration of HDL -cholesterol in the 2% diet was higher by 75% compared to the control. Concentrations of triacylglycerol and total cholesterol in the liver in the 2% diet were decreased in the 1% diet by 36.8 and 23%, respectively, but increased by 78.4% and 70%, respectively, compared to the control. The concentration of HDL-cholesterol in the liver was not different between the 1% diet and the control, but higher by 62.8% in the 2% diet compared to the control. These results indicated that taurine supplementation decreased the fat storage in abdominal cavity, which was accompanied by the changes in triacylglycerol and cholesterol metabolisms of laying hens.

• Membrane Journal
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• v.25 no.5
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• pp.422-430
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• 2015

The present study announces that the pH value of seed coating solution makes a significant effect on the microstructure of silicalite-1 zeolite layer formed on ${\alpha}$-alumina support. Seed with an average diameter of 75 nm was dispersed in ethanol to prepare three kinds of seed coating solutions with different pH values, and dip-coated on the support. The pH value was controlled to be 2.2, 7.0, and 9.3, respectively. In the secondary growth process, pH 7 seed solution resulted an uniform, 3 to $4{\mu}m$ thick, completely covered, and 100 nm grained silicalite-1 zeolite separation layer. The uniformity and completeness were explained by a uniform, closely packed, multi-layered, and completely covered seed coating in the pH 7 condition. In the condition, ${\alpha}$-alumina support and seed are oppositely charged: support is positively charged (8.4 mV) and seed, negatively (-1.7 mV). The opposite charging induced a strong electrostatic attraction between seed and support, which made the good seed coating state. On the other hand, pH 2.2 and pH 9.3 seed solutions resulted non-uniform, partially covered, and around $1{\mu}m$ grained zeolite separation layer, since seed and support are the same sign charged in the conditions. The same sign charging induced a strong electrostatic repulsion between seed and support which caused a low coverage of seed. It could be concluded that the pH value of seed coating solution is a key parameter to determine the microstructure of silicalite-1 zeolite separation layer.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.2
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• pp.92-99
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• 2000

To compare the effect of food waste compost(FWC) application on the sodicity of paddy and upland soil, laboratory experiment was conducted. Six kinds of FWC made of various mixing ratio of food waste and pig slurry as raw material were applied to paddy soil under submerged condition and to upland soil in field water capacity, and were kept at $25^{\circ}C$ under laboratory incubation. The higher the mixing ratio of food waste on making FWC, the higher the FWC showed Na content and EC. Mineralized ratio of cations in FWC during incubation showed no difference between paddy and upland soil. It was high in the order of Na>K>Mg>Ca as 99, 94, 71, and 71%, respectively. NaCl contents of FWC applied to soils against SAR and ESP were fitted well to first linear regression with extremely high significance($R^2=0.99$). Increasing rate of SAR and ESP was higher in upland soil than paddy soil by 2.3 times. The difference was considered to be caused by dilution effect which was exerted by the application of more soil to water ratio to paddy soil than to upland soil on SAR analysis in consideration of cultivating condition. The calculated values of $([Ca^{2+}+Mg^{2+}]/2)^{1/2}$ used as a denominator on SAR calculation showed a little difference among FWC treatments by 2.1~2.4, while [$Na^+$] used as a numerator showed much variance by 3.1~9.5. Therefore, as a parameter for the assessment of FWC quality affecting soil sodicity, the use of only Na content in FWC was proposed without regarding Ca and Mg contents. Soil Ex. Na contents showed extremely high correlation($R^2=0.99$) with ESP. Moreover, because the former can be more easily determined than the latter, soil Ex. Na content was proposed as a new sodicity index.