• Management & Information Systems Review
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• v.38 no.1
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• pp.1-21
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• 2019

This study focuses on (1) the relationship between the network intensity of top managers and the R&D investment of Korean firms, and (2) the moderating effects of the type (related-division vs. unrelated-division) and system (physical division vs. spin-offs) of corporate division on this relationship. The sample of this study was all type and/or system of corporate division implemented by Korean firms during 18-years (1999-2016) study periods. The results of multiple regression analyses as follow. First, as was expected in hypothesis 1 the network intensity of top managers has a strong positive linear relation with the R&D investment of Korean firms. Second, regarding the moderating effect of division type the results show that related-divisions significantly intensify the positive relationship of the network intensity of top managers with the R&D of Korean firms although unrelated-divisions did not. Third, in the analysis of moderating effect of corporate division system the results present the stronger positive moderating effect of spin-offs rather than physical divisions. The findings of the study implies that strong network intensity of top managers can be beneficial to long-term decision such as R&D investment of Korean firms. They accords to network theory that emphasize the importance of strong network effect among top managers based on their trust. The findings also implies that researchers and practitioners should consider organizational-level factors such as organizational structure, culture, corporate governance, etc as well as individual-level factors such as the characteristics and relationships of organizational members when making the decision for firm.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.256-256
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• 2023

집중호우는 전 세계적으로 큰 기후변화 문제 중 하나다. 극심한 집중호우의 빈도수는 지구 온난화로 인해 지난 세기 중반 이후부터 점차 증가하고 있으며 그로인한 인적 및 물적인 피해 또한 증가하고 있다. 이러한 손상을 방지하기 위해서는 적절한 설계 홍수량을 계산하는 것이 중요하다. 최근에는 미계측지역의 유출량 추정 시 분포형 강우-유출 모델을 이용한다. 분포형 모델의 가장 큰 장점은 소유역의 분할 과정을 거칠 필요 없이 유역에서 무작위 점의 유출을 시뮬레이션 할 수 있다는 것이다. 본 연구에서는 2000년 11월 니스에 발생했던 강우를 기반으로 Var 유역의 소유역이자 미계측 지역인 프랑스 니스의 Ubac Vallone의 유출량 및 유출계수를 지형 데이터 등의 물리적 인자와 분포형 강우-유출모델인 MIKE SHE를 이용하여 추정하였다. 또한, 입력되는 인자의 상대적 중요성을 파악하기 위해 민감도 분석을 수행하였다. 본 연구에서는 각 인자에 대한 상대민감도 분석을 바탕으로, 유출량에 상대적으로 큰 영향을 미치는 인자를 제안하였다. 연구 결과, 50년, 100년 및 162년 빈도별 확률강우량에 따른 유출량을 추정하였으며, 162년의경우 총 유출량은 124,384.8m³, 최대 유출량 1.512m³/s, 유출계수 0.53으로 나타났다. 총 유출량과 첨두유량에 대한 상대 민감감도 분석 결과, 수리전도도가 1.5로 첨두유량과의 민감도가 높게 나타났으며, 대수층의 수평방향 수리전도도는 0.48로 총 유출량과의 민감도가 높게 나타났다

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.