• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.307-317
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• 2013

The value and functionality of boundary barriers in rural villages have been neglected in the aspects as the buffer zone(boundary barrier) that links between the inside space(housing site) and the outside space(road). On this understanding, this study evaluated conservation value, economical efficiency and durability by the types and materials of the boundary barriers in rural village through Multi-attribute Decision Making. By applying to the current situations of boundary barriers on total 21 case study villages, each factor value was measured. And using Matrix Analysis Technique, the boundary barriers are classified into 4 types and the improvement ways for each type were proposed. As a result, the durability of boundary barriers in rural villages showed similarity value(more than 0.85 out of 1). But economical efficiency of those was low(less than 0.5 out of 1) and those functionalities were very lacking(about 0.3 out of 1). In the conclusion, the maintenance of boundary barriers in rural villages requires the policy that is able to complement conservation value and economical efficiency and is proper to the characteristic of each village. These renewable policies would contribute to the increase of the value of rural amenity as well as creation of economical and social value.

• Journal of the Korean association of regional geographers
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• v.14 no.1
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• pp.54-67
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• 2008

This paper is to analyze the relationships between the factors of choice attributes and the related satisfaction focused on Domestic tourists in Jeju region. First, this paper used the structural equation model with the questionnaires to investigate the attributes affecting the visitors' satisfaction and service value. In conclusion, the attribute factors influencing service value and the visitors' satisfaction are 'equipment and convenience' and 'culture and leisure'. Also, this paper presents that the 'foods' factor is affecting re-visit and others' recommendation through the mediation of satisfaction factor.

• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.207-213
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• 2004

The layered depth image (LDI) is an efficient approach to represent three-dimensional objects with complex geometry for image-based rendering (IBR). LDI contains several attribute values together with multiple layers at each pixel location. In this paper, we propose an efficient preprocessing algorithm to compress depth information of LDI. Considering each depth value as a point in the two-dimensional space, we compute the minimum distance between a straight line passing through the previous two values and the current depth value. Finally, the minimum distance replaces the current attribute value. The proposed algorithm reduces the variance of the depth information , therefore, It Improves the transform and coding efficiency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.5
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• pp.93-103
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• 2015

Residential environment is not only the physical aspects affected by the unique characteristics of the residents, social, economic, cultural, etc. are closely related to many environmental factors. This research is based on choice attribute theory that substantially explains how housing choice attributes affect loyalty via image, perceived value in Secho. This paper investigate empirically relationship between selection properties of residential environment and loyalty, moderating effect of image and perceived value. Results were computed using SPSS 20.0 statistical analysis programs. The results are summarized as follows. First, The elements of choice attribute are divided into six factor, such as regional reputation, green environment, convenience, property value, safety, housing status. In the results of the analyses, housing choice properties gives a positive influence to the loyalty. Second, testing its mediating role, I use the three regression equation models by Baron and Kenny. When the mediator effect of image, perceived value was represented, the effect of image and perceived value was statistically significant. Thus, the mediating role of image and perceived value was supported. It means the higher image and perceived value can enhance loyalty of Secho.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.11-19
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• 2006

Sewerage is the system that improves the quality of human life and prevents many disasters such as floods. However the investigators in Korea only have been concerned about the sewer system, so the sewage treatment plant stays in the basic level like mapping. For example, only one attribute can be recognized in the linear object. Because of this limitation, it makes difficult to manage the linear attribute regarding to the sewage pipe plan. And it is impossible to control a partial (point type, line type) attribute changes of the linear object. We will therefore present the applicable method for the attribute changes of the linear object like the sewage pipe plans. For this reason, this paper is designed on the basis of Dynamic Segmentation(DS). DS has the advantage of giving the attribute value to the exact place in the linear object. As a result of using DS, the variety environment changes around the sewage pipes are applied to the building sewerage data. This also makes it possible to get a precise estimation for the maximum dirty water amount.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.4
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• pp.34-43
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• 2001

Unlike ordinary recreational facilities, an amusement park consists of various entertainment facilities, attractions, food services, souvenir shops and other attribute. The purpose of this study is to survey users´ responses to such attributes and analyze the importance and performance of each attribute, and thereby, ultimately help improve the efficiency of management and operation of the amusement parks. For this purpose, a survey was conducted of Everland users in August, 1999. 420 users were chosen by means of he systematic sampling. All the suers were asked to rate the importance of 14 attributes of Everland at the entrance and all of them were asked to do the same at the exit. As a result, it was found that the attribute rated highest by the users was ´attraction´, followed by ´service´, ´accessibility´ and ´cost´ in that order. On the other hand, it was found that the total average of importance rated for 14 attributes was 3.31, while that of performance was 3.10. As a consequence of analyzing the action grids, it was found that ´appropriateness of the circulation system´ should be improved most urgently. 7 attributes were categorized as ´keeping up good work´, and 6 ones were rated ´low priority´ in terms of improvement. There was no attribute considered to be ´possible overkill´. Meanwhile, as a result of analyzing the difference among groups in order to determine users´ response depending on their demographic and socio-economic variables, it was found that only the ´age´ variable was significant. It is expected that the results that the results of this study would be useful in determining priorities when improving amusement park facilities or their programs.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.1-6
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• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.

• The Korean Journal of Food And Nutrition
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• v.35 no.6
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• pp.481-490
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• 2022

The influence of the food value consumption type of MZ generation on food choice attribute and sustainable food consumption behavior was studied using structural equation modeling. A survey was conducted on April 11~17, 2022, among panels aged 20 to 39. A total of 350 valid replicates (100%) were analyzed using statistical program SPSS The validity of the measurement instrument was verified through exploratory factor analysis and confirmatory factor analysis. The data reliability was confirmed using Cronbach's alpha coefficient. The hypothesis was verified by performing path analysis through structural equation modeling using AMOS. Regarding the influence of food choice characteristics on sustainable food consumption behavior, health has a significant positive (+) effect on the selection consumption behavior of certified food and local food. Among food value consumption categories social value consumption has a significant negative (-) influence on the consumption behavior of certified food and the choice of local food. Ethical value consumption has a significant positive (+) influence on the selection consumption behavior of certified food and local food. This study is significant because it has identified sustainable food consumption behaviors that domestic consumers can adopt daily. It can use as baseline data for preparing political and institutional measures.

• Journal of Radiation Protection and Research
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• v.29 no.1
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• pp.65-71
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• 2004

A multi-attribute utility analysis was investigated as a tool for the decision support of countermeasures in early phase of a nuclear accident. The utility function of attributes was assumed to be the second order polynomial expressions, and the weighting constant of attributes was determined using a swing weighting method. Because the main objective of this study focuses on the applicability of a multi-attribute utility analysis as a tool for the decision support of countermeasures in early phase of a nuclear accident, less quantifiable attributes were not included due to lack of information. In postulated accidental scenarios for the application of the designed methodology, the variation of the numerical values of total utility for the considered actions, e.g. sheltering, evacuation and no action, was investigated according to the variation of attributes. As a result, it was shown that the numerical values of total utility for the actions are distinctly different depending on the exposure dose and monetary value of dose. As increasing in both attributes, the rank of the numerical values of total utility increased for evacuation, which is more extreme action than for sheltering, while that of no action decreased. As expected probability of high dose is higher, the break-even values for the monetary value of dose, which are the monetary value of dose when the ranking of actions is changed, were lower. In audition, as aversion psychology for dose is higher, the break-even values for dose were lower.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).