• 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 Chest Surgery
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• v.40 no.1 s.270
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• pp.8-16
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• 2007

Background: We performed a prospective clinical study to evaluate the ultrastructural integrity of the myocardium after using Histidine-Tryptophan-Ketoglutarate (HTK) solution in comparison with blood cardioplegic solution during congenital heart surgery. Material and Method: Twenty two patients with acyanotic heart disease, who were scheduled for elective open heart surgery, were randomized into two groups. The HTK Group (n=11) received HTK cardioplegic solution; the blood group (n=11) received conventional blood cardioplegic solution during surgery. The preoperative diagnoses included ventricular septal defect (n=9) and atrial septal defect (n=2) in each group. A small biopsy specimen was taken from the right ventricle's myocardium, and this was processed for ultrastructural examination at the end of 30 minutes of reperfusion. Semiquantitative electron microscopy was carried out 'blindly' in 4 areas per specimen and in 5 test fields per area by 'random systematic sampling' and 'point and intersection counting'. The morphology of the mitochondrial membrane and cristae were then scored. The interstitial edema of the myocardium was also graded. Result: The semiquantitative score of the mitochondrial morphology was $19.65{\pm}4.75$ in the blood group and $25.25{\pm}5.85$ in the HTK group (p=0.03). 6 patients (54.5%) in the blood group and 3 patients (27.3%) in the HTK group were grade 3 or more for the interstitial edema of the myocardium. Conclusion: The ultrastructural integrity was preserved even better with HTK solution than with conventional blood cardioplegic solution.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.153-165
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• 2021

With the disappearance of boundaries between online and offline, the O2O(online to offline) platform service is rapidly growing. Unlike general products, freshness is an important decision-making factor for agri-food, and there are many limiting factors for growth as an open market among O2O platforms due to the characteristics of difficult refunds and exchanges compared to other items and new transaction methods. In order to overcome these obstacles, consumer innovation must be considered. The purpose of this study was to investigate the influence of O2O(online to offline) platform characteristics perception on agri-food repurchase intentions. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. For this purpose, Using a convenience sampling technique, an online survey was conducted through Google survey from April 1 to April 15, 2021. A total of final analysis data were collected from a total of 270 purchase experienced of agri-food O2O(online to offline) platform. The SPSS program was used for analysis, and multiple regression analysis was used for hypothesis verification. The results showed that Economic, Interaction, and Playfulness had a significant positive effect on agri-food repurchase intend. Also, Interactivity × innovation, playfulness × innovation were found to have a significant positive (+) effect on repurchase intention. The results of this study show that innovation reduces the burden on consumers for new systems and mobile transactions. The results of this study suggest that convenient interface design is important for activating O2O transactions of agri-food. In addition, education and support are needed to strengthen the IT competency of farmers. The results of this study will be able to contribute to the establishment of infrastructure for agri-food open market shopping malls. In future studies, the influence of the O2O platform type on the purchase intention should be studied continuously.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.49-58
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• 2019

There have been studies on the satisfaction, preference, and post occupancy evaluation of urban parks in order to reflect users' preferences and activities, suggesting directions for future park planning and management. Despite using questionnaires that are proven to be affective to get users' opinions directly, there haven been limitations in understanding the latest changes in park use through questionnaires. This study seeks to address the possibility of utilizing the thirdgeneration SNS data, Instagram and Google, to compare behavior patterns and trends in park activities. Instagram keywords and photos representing user's feelings with a specific park name were collected. We also examined reviews, peak time, and popular time zones regarding selected parks through Google. This study tries to analyze users' behaviors, emerging activities, and satisfaction using SNS data. The findings are as follows. People using park near residential areas tend to enjoy programs being operated in indoor facilities and to like to use picnic places. In an adjacent park of commercial areas, eating in the park and extended areas beyond the park boundaries is found to be one of the popular park activities. Programs using open spaces and indoor facilities were active as well. Han River Park as a detached park type offers a popular venue for excercises and scenery appreciation. We also identified companionship characteristics of different park types from texts and photos, and extracted keywords of feelings and reviews about parks posted in $3^{rd}$ generation SNS. SNS data can provide basis to grasp behavioral patterns and satisfaction factors, and changes of park activities in real time. SNS data also can be used to set future directions in park planning and management in accordance with new technologies and policies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.

• Journal of agricultural medicine and community health
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• v.43 no.4
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• pp.234-249
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• 2018

This study was performed to identify the impact of awareness and educational experiences on cardiopulmonary resuscitation in the ability to execute of cardiopulmonary resuscitation among Korean adults. This study used original data of 2014 Community Health Data Survey. 228,712 participants in this survey were resident in South Korea who is aged 19 or older on July 2014. Participants in this survey were sampled an average of 900 residents(target error ${\pm}3percent$) per community health center of Korea. Data were analyzed by using R 3.1.3 employing chi-squared test, fisher's exact analysis, and logistic regression analysis. Ability to execute CPR was significantly higher in males(3.34 time), higher the education level (1.61 times), the white color occupation (1.14 times), the higher the income level (1.07 times), the higher the education level (0.91 times), non-hypertensive patients (1.12 times), non-diabetic patients (1.16 times), non-dyslipidemic patients (0.86 times), non-stroke patients (0.30 times), CPR education experience group (3.25 times), CPR experience group with manikin-based training (4.30 times), higher subjective health status (1.08 times, 1.16 times) respectively. This study identified that awareness, educational experience, and mannequin-based learning experience of CPR impacted on the ability to execute CPR. Responding to education-related factors could contribute to reducing the rate of out-of-hospital acute cardiac arrest by improving the ability to execute CPR of the general public.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.41-63
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• 2021

The purpose of this study was to investigate the nutritional knowledge and eating behavior of middle school students in Gwangju area in order to provide basic data for the development of home economics curriculum that can help the students form healthy eating habits. For this purpose, a self-administered questionnaire was distributed to middle school students in Gwangju area. A total of 330 questionnaires were collected by convenience sampling and analyzed using SPSS(Statistics Package for the Social Science) Version 20.0 for Windows. Results of this study are as follows. First, school curriculum session ranked top(31.82%) on the list of sources for middle school students to acquire nutrition knowledge. Second, the mean score of nutrition knowledge of all respondents was moderately high(14.33 points out of maximum 20 points). In terms of nutrition knowledge by gender, female students had a higher level of nutrition knowledge in the 'Food' domain than their male counterparts(p<0.05). With regard to nutrition knowledge by grade, Grade 9 showed a higher score than Grade 7 and 8(p<0.001). Third, the mean score of eating behavior of all respondents was at an average level(69.75 out of 100 points). In terms of eating behavior by gender, male students showed a higher score than their female counterparts(p<0.05). In particular, male students had higher scores than female students for the following items: "I exercise regularly after school"(p<0.001); "I regularly eat meal three times per day"(p<0.01); "I don't skip breakfast"(p<0.01); and "I don't eat sweet food often"(p<0.01). In terms of eating behavior by grade, Grade 9 showed higher scores than Grades 7 and 8 for the following items: "I eat meal out of thankfulness for those who have prepared food"(p<0.01) and "I eat grains for every meal"(p<0.01). Finally, with regard to eating behavior depending on the level of nutrition knowledge, the 'Upper' and 'Middle' groups had higher scores for eating behavior than the 'Lower' group, indicating that a lower level of nutrition knowledge resulted in a lower score in eating behavior. Based on the above results, home economics teachers responsible for dietary education should have a greater sense of mission and pride and make more efforts to improve nutrition knowledge and eating behavior of middle school students.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.853-860
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• 2022

The purpose of this study is to suggest an improvement plan for the calculation method of the engineer standard wage rate (ESWR) and to compute a reasonable ESWR. To this end, an adequacy review of theESWR calculation criteria was conducted along with an extensive engineering industry survey. The survey results were analyzed using an effective response sample of 748 companies out of 1,000 survey samples extracted by stratifying the 5,879 survey population. The main results were as follows. ①When calculating the engineering service fee, the prime contractor's engineer wage is suitable for the ESWR. The ESWR can be estimated by the formula 'average wage÷[1-proportion of subcontract orders×(1-subcontract rate)].' ② The field survey showed that the number of monthly working days was 20.35-20.54 days at 99 % confidence interval, which was significantly different from the current standard (22 days). In addition, as a result of a legal review of the ESWR criteria, it was found that the number of working days should be calculated in accordance with the Labor Standards Act after 2022. ③ Applying government guidelines, the time difference between the wage survey and the ESWR application can be corrected by the past ESWR increase rate for a specific period. ④ Using modeling based on the analysis above, the current ESWR was 13.5-14.5 % lower than the appropriate level. A lower ESWR was driven by the non-reflection of subcontract structure (4.1 %), overestimation of monthly work days (6.8-7.8 %), and application of past wage (2.6 %). The proposed model is expected to be widely used in policy making, as it can provide a useful framework for calculating the standard wage rate in similar industries as well as calculating appropriate engineering fees.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.