• Proceedings of the Korea Society for Industrial Systems Conference
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• 2004.06a
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• pp.29-34
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• 2004

본 연구에서는 Mobile Commerce(M-커머스) 사이트의 신뢰도에 영향을 미치는 요인들을 거래안전성, 기업이미지, 검색기능성 그리고 결제편의성으로 분류하여 관련 연구들을 통해 검증된 요인들을 추출하였다. 그리고 M-커머스 사이트의 현재 또는 잠재 고객들을 대상으로 직접 설문조사를 통해 M-커머스 사이트의 신뢰도에 영향을 미치는 요인을 식별해냄으로써 고객과의 신뢰형성을 위해 최우선적으로 고려해야 할 방향을 제시하고 M-커머스의 활성화에 기여하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.177-187
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• 2023

This study aims to validate a tool evaluating MOOCs for higher education from the perspective of education service. Based on the results of related researches, a potential model for evaluating MOOCs (4 factors and 8 sub-factors) was made. An evaluation tool consisting of 18 survey items was delivered to 138 college students. After data cleaning, 136 surveys were used for exploratory factor analysis (principal component analysis. varimax rotation) and reliability analysis that confirmed the fitness of the potential model. Four exploratory constructs and seven sub-factors were extracted: Factor I was labeled as 'Systemic Learning Experience,' Factor II, 'Value Experience,' Factor III, 'Co-creation of Value Experience,' and Factor IV, 'High Order Learning Experience.' Reliability estimates using Cronbach's alpha indicated that the evaluation tool had good internal consistency. In conclusion, the evaluation tool for MOOCs in higher education was proven to be valid and reliable.

• Journal of the Korean association of regional geographers
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• v.15 no.3
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• pp.409-420
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• 2009

This study aims at analyzing location and policy factors influencing the move-in decision of apartment factories on the case study of middle and small sized companies in Daegu City. The main results of this study are as follows. Firstly, analyzing the questionnaire survey results, 58.2% of response companies have the intentions to move-in apartment factories. Secondly, through the factor analysis to the location factors, 5 factors such as network, move-in cost, accessibility, factory space and labor are derived. And through the factor analysis to the policy factors, 3 factors such as management support policy, pecuniary support policy and administrative support policy are also derived. Finally, analyzing logistic regression analysis, we can find pecuniary support policies influence the move-in decision of apartment factories.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.47-56
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• 2020

Decline of pine forests happens in Korea due to various disturbances such as insect pests, forest fires and extreme climate, which may further continue with ongoing climate change. For conserving and reestablishing pine forests, understanding climate-induced future shifts of pine tree distribution is a critical concern. This study predicts future geographical distribution of Pinus densiflora, using Maximum Entropy Model (MaxEnt). Input data of the model are locations of pine tree stands and their environmental variables such as climate were prepared for the model inputs. Alternative future projections for P. densiflora distribution were conducted with RCP 4.5 and RCP 8.5 climate change scenarios. As results, the future distribution of P. densiflora steadily decreased under both scenarios. In the case of RCP 8.5, the areal reductions amounted to 11.1% and 18.7% in 2050s and 2070s, respectively. In 2070s, P. densiflora mainly remained in Kangwon and Gyeongsang Provinces. Changes in temperature seasonality and warming winter temperature contributed primarily for the decline of P. densiflora., in which altitude also exerted a critical role in determining its future distribution geographic vulnerability. The results of this study highlighted the temporal and spatial contexts of P. densiflora decline in Korea that provides useful ecological information for developing sound management practices of pine forests.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• Korea Real Estate Review
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• v.27 no.4
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• pp.7-20
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• 2017

This study attempts to diagnose and categorize the characteristics of old industrial parks, and eventually link the results to the regeneration of industrial complexes. For this reason, we performed a factor analysis by utilizing 15 indices of 89 industrial parks, excluding 5 large equipment industry sites. The 15 indices were classified into 5 factors. Factor 1 can be described as a category of 'urbanization possibility' for the indices of building age, plot ratio of less than $1,650m^2$, and urbanization ratio of the surrounding area. Factor 2 can be described as a category of 'productive efficiency' for the indices of land productivity, amount of exports by land, employment productivity, and repair costs of industrial areas. Factor 3 can be described as a category of 'infrastructure amenity' for the indices of road ratio, plot ratio attached to the road, and parks and recreation ratio. Factor 4 can be described as a category of 'location potentiality' for the indices of land price, infrastructure age, and distance to the highway, while factor 5 can be described as a category of 'availability of supporting facilities' for the indices of parking lot ratio and supporting facility land ratio. By using these 5 factor scores, we were able to extract industrial parks included in the lower 25% of the factor score and searched for what kind of factor problem they have for each industrial park. Based on these results, this research will provide sufficient information on the decline of industrial parks with respect to their demerits. The results of this study show significant implications and contribute to the establishment of policies for regional competitiveness, as well as job creation, in the process of industrial regeneration.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.219-233
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• 2013

We employed the ecological niche modeling framework using GARP (Genetic Algorithm for Ruleset Production) to model the current and future geographic distribution of Pinus densiflora based on environmental predictor variable datasets such as climate data including the RCP 8.5 emission climate change scenario, geographic and topographic characteristics, soil and geological properties, and MODIS enhanced vegetation index (EVI) at 4 $km^2$ resolution. National Forest Inventory (NFI) derived occurrence and abundance records from about 4,000 survey sites across the whole country were used for response variables. The current and future potential geographic distribution of Pinus densiflora, one of the tree species dominating the present Korean forest was modeled and mapped. Future models under RCP 8.5 scenarios for Pinus densiflora suggest large areas predicted under current climate conditions may be contracted by 2090 showing range shifts northward and to higher altitudes. Area Under Curve (AUC) values of the modeled result was 0.67. Overall, the results of this study were successful in showing the current distribution of major tree species and projecting their future changes. However, there are still many possible limitations and uncertainties arising from the select of the presence-absence data and the environmental predictor variables for model input. Nevertheless, ecological niche modeling can be a useful tool for exploring and mapping the potential response of the tree species to climate change. The final models in this study may be used to identify potential distribution of the tree species based on the future climate scenarios, which can help forest managers to decide where to allocate effort in the management of forest ecosystem under climate change in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2251-2258
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• 2012

This study compared the unweighted sample design, frequency weighted sample design and complex sample design to using 2009 Korea National Health and Nutrition Examination Survey in an effort to identify whether or not there is any difference in potential risk factors. Pearson chi-square test and Rao-scott chi-square test were applied to the analytic methods. As a result of analyses, all the variables were overestimated as significant risk factors in case of the unweighted sample design to which only the frequency weights were applied. In addition, there were differences in the confidence levels and results from the simple random sampling analysis and complex sample design to which no weight was applied. It is necessary to carry out the complex sample design rather than the analysis to which the frequency weights are applied, in order to ensure the findings to represent the whole population when our national statistics data is used.

• Journal of the Korea Computer Graphics Society
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• v.25 no.4
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• pp.9-16
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• 2019

In this paper, we propose a hydrodynamic scene separation method for wave propagation from video imagery using autoencoder. In the coastal area, image analysis methods such as particle tracking and optical flow with video imagery are usually applied to measure ocean waves owing to some difficulties of direct wave observation using sensors. However, external factors such as ambient light and weather conditions considerably hamper accurate wave analysis in coastal video imagery. The proposed method extracts hydrodynamic scenes by separating only the wave motions through minimizing the effect of ambient light during wave propagation. We have visually confirmed that the separation of hydrodynamic scenes is reasonably well extracted from the ambient light and backgrounds in the two videos datasets acquired from real beach and wave flume experiments. In addition, the latent representation of the original video imagery obtained through the latent representation learning by the variational autoencoder was dominantly determined by ambient light and backgrounds, while the hydrodynamic scenes of wave propagation independently expressed well regardless of the external factors.