• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.3
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• pp.78-84
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• 2011

This study investigated the user's attitude to the supdari(a traditional wooden bridge) itself and the supdari project, and evaluated the quality of user's satisfaction of the bridge in Jeonju-River by importance-performance analysis(IPA). User's evaluation was achieved through questionnaire survey, and total 267 pieces of subjects were used for the analysis. The supdari users didn't realize the supdari construction was performed as a governance project, and didn't consider the supdari as one of landmarks of Jeonju-River. But local people thought that the supdari is one of the traditional and cultural facilities to reminds their hometown's landscapes and the supdari project can make the community network vitalize. Through the IPA, attributes with relative dissatisfaction were 'landscape facilities like small squares and rest area', 'safety facilities like handrails and guardrails' and width of the supdari. To make the supdari as a traditional landmark in Jeonju-River, an open space based on the tradition and ecological education has to be constructed near the bridge. And in the supdari design, especially handrails system and the bridge width has to be improved to enhance the user's safety.

• Food Engineering Progress
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• v.23 no.1
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• pp.62-68
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• 2019

In this study, the physical and sensorial properties of the meat analog were studied for the purpose of improving sensory preference and mimicking animal meat. The meat analog was made with different types of liquid materials such as oil, water, lecithin, polysorbate 80, or the emulsion of these components. At the aspect of density, the sample mixed with oil was higher than the sample mixed with water. Cooking loss value was higher at the sample with water than the sample with oil and this was the result opposite to the liquid holding capacity analysis. Also, texture profile analysis result showed that the samples with medium chain triglycerides (MCT) oil only showed the highest values in all attributes except for adhesiveness. Principal component analysis was carried out to analyze sensorial properties and it showed that the overall acceptance was high when the juiciness and softness increased. This result was highly related with the addition of emulsion. Therefore, emulsion technology can be a good candidate for improving the quality of meat analog and for mimicking the taste of animal meat.

• Clean Technology
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• v.26 no.4
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• pp.239-250
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• 2020

Biomass is an abundant renewable energy resource that can replace fossil fuels for the reduction of greenhouse gas (GHG). Indonesia has a large number of cheap biomass feedstocks, such as reforestation (waste wood) and palm residues (empty fruit bunch or EFB). In general, raw biomass contains more than 20% moisture and lacks calorific value, energy density, grindability, and combustion efficiency. Those properties are not acceptable fuel attributes as the conditions currently stand. Recently, torrefaction facilities, especially in European countries, have been built to upgrade raw biomass to solid fuel with high quality. In Korea, there is no significant market for torrefied solid fuel (co-firing) made of biomass residues, and only the wood pellet market presently thrives (~ 2 million ton yr-1). However, increasing demand for an upgraded solid fuel exists. In Indonesia, torrefied woody residues as co-firing fuel are economically feasible under the governmental promotion of renewable energy such as in feed-in-tariff (FIT). EFB, one of the chief palm residues, could replace coal in cement kiln when the emission trading system (ETS) and clean development mechanism (CDM) system are implemented. However, technical issues such as slagging (alkali metal) and corrosion (chlorine) should be addressed to utilize torrefied EFB at a pulverized coal boiler.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.367-373
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• 2022

The purpose of this study is to derive the factors for purchasing a meal kit in their 20s and 30s and analyze the purchasing behavior from which factors they want to buy a meal kit in each lifestyle type. The first methodology of this study is inducing 7 factors derived from previous research on purchasing a meal kit. The second is the in-depth interview on 3 male and 3 female participants with clear purchasing criteria. As a result of the study, meal kit buyers in their 20s-30s evaluated the importance of purchasing factors in the order of quality, convenience, and taste on average in the survey. In in-depth interviews, more than half answered that they could be satisfied with the experience of using the meal kit at least freshness met. In conclusion, MZ generation meal kit consumers have a high rate of pursuing rational consumption. This study is valuable in understanding the priorities of the MZ generation's meal kit purchasing attributes and examining lifestyle type's purchasing behaviors.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.23-31
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• 2023

Predicting forest productivity is essential to evaluate sustainable forest management or to enhance forest ecosystem services. Ordinary least squares (OLS) and partial least squares (PLS) regression models were used to develop predictive models for forest productivity (site index) from the site characteristics and soil profile, along with soil physical and chemical properties, of 112 Quercus mongolica stands. The adjusted coefficients of determination (adjusted R²) in the regression models were higher for the site characteristics and soil profile of B horizon (R²=0.32) and of A horizon (R²=0.29) than for the soil physical and chemical properties of B horizon (R²=0.21) and A horizon (R²=0.09). The PLS models (R²=0.20-0.32) were better predictors of site index than the OLS models (R²=0.09-0.31). These results suggest that the regression models for Q. mongolica can be applied to predict the forest productivity, but new variables may need to be developed to enhance the explanatory power of regression models.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.69-90
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• 2023

The purpose of this study is to evaluate the service attributes and consumer reactions of telemedicine apps in South Korea and visualize their differentiation by constructing positioning maps. We crawled 23,219 user reviews of 6 major telemedicine apps in Korea from the Google Play store. Topics were derived by BERTopic modeling, and sentiment scores for each topic were calculated through KoBERT sentiment analysis. As a result, five service characteristics in the application attribute category and three in the medical service category were derived. Based on this, a two-dimensional positioning map was constructed through principal component analysis. This study proposes an objective service evaluation method based on text mining, which has implications. In sum, this study combines empirical statistical methods and text mining techniques based on user review texts of telemedicine apps. It presents a system of service attribute elicitation, sentiment analysis, and product positioning. This can serve as an effective way to objectively diagnose the service quality and consumer responses of telemedicine applications.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.1
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• pp.169-192
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• 2024

This study was conducted with the aim of providing a foundation for high-quality national place name authority data by developing Korean-specific guidelines for place name authority data in response to the need for systematic construction and standardization of authority databases. To this end, a survey of domestic and international trends and cases related to place name authority data was conducted, and the rules and guidelines of each country for establishing place name authority data were analyzed. Based on these surveys and rule analyses, the scope of concepts and terminology required to build a place name authority database were defined and the direction for the development of place name authority data guidelines was set. The analysis also determined the scope and framework of the guidelines, and how they should be referenced to existing rules. The structure of the guidelines proposed in this study is based on the original RDA and NCR. Based on the implications derived from the analysis process, the guidelines were organized and presented in terms of scope of construction, selection and recording of preferred place names, recording of variant place names, and attributes of place names to propose a technical guideline for place name authority data that fits the Korean situation. Future discussions were revealed accordingly.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.43-57
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• 2024

As coastal areas have a distinct nature with the interaction of the lands and waters, they attract people to enjoy and experience the natural environments physically and intellectually; this generates cultural ecosystem services. Coastal beaches are one of the most common coastal areas for cultural ecosystem services. The purpose of this study was to 1) estimate the economic values of cultural ecosystem services derived from coastal beaches, and 2) expand the estimated values into other beaches across the country using a benefit transfer method. We divided the values of cultural ecosystem services into five different categories based on an extensive literature review: recreation and tourism, landscape and aesthetic, educational, heritage, and inspirational values. The values of tourism and recreation, landscape and aesthetic, and educational services were estimated using the choice experiments. The attributes of the choice experiments consisted of conservation funds, litter, water quality, seascape, landscape, and biodiversity, and the data were collected through online surveys with visitors of 11 representative beaches in Korea. Heritage and inspiration services were estimated using a market goods method based on their expenditures. These values were transferred to 257 beaches across the country. Study results can be used for policy decisions on various restoration and conservation projects caused by coastal erosion and development and on the need and extent of public investments.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.