• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.2
• /
• pp.530-547
• /
• 2018

This study examines the importance of location and transportation in the operation of small- and medium-sized hospitals in the rapidly changing clinical environment. Approximately 350 persons were surveyed for approximately 40 days from July-August 2017, and responses were analyzed using SPSS and AMOS Statistical Package for 335 parts excluding 8 parts of 343.The main research results were as follows: First, a hypothesis was adopted that the attractiveness of building materials will positively affect customer satisfaction. Second, the hypothesis that transport infrastructure has a significant impact on customer satisfaction was rejected. Third, the hypothesis that the building appeal will positively affect the site was supported. Fourth, the hypothesis that transport infrastructure will have a significant impact on location marketability was supported. Fifth, the hypothesis that location marketability will significantly impact customer satisfaction was supported. Sixth, the hypothesis that the relationship between the site and customer satisfaction with the relationship between the client and the client is explained is partly attributable to the fact that there is a partial function. The assumption that the relationship between traffic infrastructure and customer satisfaction was confirmed. The hypothesis that the relationship between traffic infrastructure and customer satisfaction was supported. This research focuses on small- and medium-sized hospitals located in Seoul, which does not include the recognition of patients' awareness of the location and transportation of small- and medium-sized hospitals. Moreover, this study has the limitation in analyzing patient satisfaction, rather than the objective data of the financial management performance of the hospital. The results of this study could provide the basis for selecting the future locations of health care facilities.

• Journal of KIISE:Software and Applications
• /
• v.30 no.3_4
• /
• pp.316-325
• /
• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Wetlands Research
• /
• v.7 no.4
• /
• pp.97-107
• /
• 2005

As the distributed model is developed and widely used, the accuracy of a rainfall measurement and more dense rainfall observation network are required for the reflection of various spatial properties. However, in reality, it is not easy to get the accurate data from dense network. Generally, we could not have the proper rainfall gages in space and even we have proper network for rainfall gages it is not easy to reflect the variations of rainfall in space and time. Often, we do also have missing rainfall data at the rainfall gage stations due to various reasons. We estimate the distribution of mean areal rainfall data from the point rainfalls. So, in the aspect of continuous rainfall property in time, we should fill the missing rainfall data then we can represent the spatial distribution of rainfall data. This study uses the Fuzzy-Genetic algorithm as a interpolation method for filling the missing rainfall data. We compare the Fuzzy-Genetic algorithm with arithmetic average method, inverse distance method, normal ratio method, and ratio of distance and elevation method which are widely used previously. As the results, the previous methods showed the accuracy of 70 to 80 % but the Fuzzy-Genetic algorithm showed that of 90 %. Especially, from the sensitivity analysis, we suggest the values of power in the equation for filling the missing data according to the distance and elevation.

• Journal of KIISE:Software and Applications
• /
• v.32 no.11
• /
• pp.1111-1120
• /
• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Journal of Korea Water Resources Association
• /
• v.48 no.4
• /
• pp.245-255
• /
• 2015

This study used high density network of integrated meteorological sensor, which are operated by SK Planet, with KMA weather stations to estimate the quantitative precipitation field in Seoul area. We introduced SK Planet network and analyzed quality of the observed data for 3 months data from 1 July to 30 September 2013. As the quality analysis result, we checked most SK Planet stations observed similar with previous KMA stations. We developed the real-time quality check and adjustment method to reduce the error effect for hydrological application by missing and outlier value and we confirmed the developed method can be corrected the missing and outlier value. Through this method, we used the 190 stations(KMA 34 stations, SK Planet 156 stations) that missing ratio is less than 20% and the effect of the outlier was the smallest for quantitative precipitation estimation. Moreover, we evaluated reproducibility of rainfall field high density rain gauge network has $3km^2$/gauge. As the result, the spatial relative frequency of rainfall field using SK Planet and KMA stations is similar with radar rainfall field. And, it supplement the blank of KMA observation network. Especially, through this research we will take advantage of the density of the network to estimate rainfall field which can be considered as a very good approximation of the true value.

• Korean Journal of Social Welfare
• /
• v.52
• /
• pp.5-32
• /
• 2003

This paper examines the labor entry of conditional welfare recipients. This paper focuses on two questions. First, what is the percentage of conditional welfare recipients who have labor entry? Second, what are the predictors in the labor entry and the duration to the entry? Using Data about 917 welfare recipients who participated in the self-sufficiency programs of the Offices for Secure Employment in Seoul, this paper attempts to answer the above questions. Logistic regression analysis and survival analysis are adopted to identify variables predicting labor entry of conditional welfare recipients. This paper also utilizes a multiple imputation method to deal with the limitation of data by the missing values in some variables. The major findings are as follows: about 43.8% of the conditional welfare recipients have successful labor entry; and in the labor entry and the duration to the entry, gender, household, information and referral services for employment, health and willingness for self-sufficiency are the predictors that are statistically significant. Among these variables, health and willingness for self-sufficiency are more noticeable; it is recognized that programs to care for health of welfare recipients who want to have the labor entry and counseling programs to strengthen welfare recipients' willingness for labor entry are very important for them to be successful in the labor entry. This paper provides a basic knowledge about realities of the conditional welfare recipients' labor entry, identifies research areas for further research, and develops policy implications for their self-sufficiency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.3 no.3
• /
• pp.89-123
• /
• 2008

This study has the purpose to research on the actual conditions of discriminative treatment between regular and irregular work and find what kind of environment factors can effect on the job-satisfaction, organizational commitments. Especially, this study has addressed to improvement of conditions for irregular work and researched the difference factors between regular and irregular workers's job-satisfaction, organizational commitment, and employment - environmental. For the data collection, a total of 400 questionnaires were distributed for survey, 195 responses were available for statistical analysis. This questionnaires were sent to the distribution companies that has a variety type of employment as regular and irregular worker in Seoul province by e-mail and direct way. And SPSS 12.0(ver.) was used to analyze the data. The main results were as the following; First, the human relationship and the normative commitment were statistically different between regular and irregular work in the part of job satisfaction and organizational commitment. Second, income were important to improve the satisfaction of job environment and personnel policy and the normative commitment. Corporation welfare were important for the satisfaction of human relationship, job environment and personnel policy, normative commitment affective commitment and continuous commitment. Stability of employment were important to improve the satisfaction of human relationship and job environment, normative commitment and affective commitment.

• Journal of Korea Water Resources Association
• /
• v.53 no.11
• /
• pp.929-938
• /
• 2020

In this study, we estimated missing evapotranspiration (ET) data at a eddy-covariance flux tower in the Cheongmicheon farmland site using the Artificial Neural Network (ANN). The ANN showed excellent performance in numerical analysis and is expanding in various fields. To evaluate the performance the ANN-based gap-filling, ET was calculated using the existing gap-filling methods of Mean Diagnostic Variation (MDV) and Food and Aggregation Organization Penman-Monteith (FAO-PM). Then ET was evaluated by time series method and statistical analysis (coefficient of determination, index of agreement (IOA), root mean squared error (RMSE) and mean absolute error (MAE). For the validation of each gap-filling model, we used 30 minutes of data in 2015. Of the 121 missing values, the ANN method showed the best performance by supplementing 70, 53 and 84 missing values, respectively, in the order of MDV, FAO-PM, and ANN methods. Analysis of the coefficient of determination (MDV, FAO-PM, and ANN methods followed by 0.673, 0.784, and 0.841, respectively.) and the IOA (The MDV, FAO-PM, and ANN methods followed by 0.899, 0.890, and 0.951 respectively.) indicated that, all three methods were highly correlated and considered to be fully utilized, and among them, ANN models showed the highest performance and suitability. Based on this study, it could be used more appropriately in the study of gap-filling method of flux tower data using machine learning method.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.287-296
• /
• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• Journal of the Korean Geotechnical Society
• /
• v.37 no.5
• /
• pp.47-63
• /
• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.