• 원자력산업
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• 제24권2호통권252호
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• pp.4-12
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• 2004

• 원자력산업
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• 제28권2호통권300호
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• pp.43-49
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• 2008

• 원자력산업
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• 제25권2호통권264호
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• pp.12-22
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• 2005

• 원자력산업
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• 제29권2호
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• pp.5-11
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• 2009

• 유통과학연구
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• 제12권6호
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.

• 자원ㆍ환경경제연구
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• 제31권2호
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• pp.233-260
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• 2022

본 연구에서는 비모수적 분위수 인과관계 검정 방법을 이용하여 국제유가의 변동이 산업별 주가의 움직임에 어떤 영향을 주는지 분석하였다. 본 연구의 분석 대상은 1998년 1월부터 2021년 4월까지 WTI 가격, KOSPI 지수 및 18개 산업별 주가지수의 주별 데이터이다. 비모수 분위수 인과 검정 결과에서 유가 변화가 KOSPI 지수에 미치는 영향은 유의하지 않은 것으로 나타났다. 이는 KOSPI 지수에 포함된 여러 산업의 주가 반응이 서로 다르기 때문이라고 생각된다. 유가 변화에 대한 산업별 주가지수의 반응을 살펴보면, 주식 수익률을 0.1분위부터 0.9분위까지 0.05단위로 나눈 인과관계 결과에서 전체 18개 산업 중 섬유의복, 종이목재, 의약품 등을 포함한 9개 산업에서 인과성이 유의하게 나타났으며, 나머지 9개 산업에서는 유가와의 인과성을 보이지 않았다. 이 산업들 중 세 분위 이상에 걸쳐 연속적으로 인과성이 나타난 산업은 의약품과 통신업(0.45분위 ~ 0.85분위), 섬유의복(0.15분위 ~ 0.45분위), 건설업(0.5분위 ~ 0.6분위) 등 4개 산업으로 인과성이 나타난 구간은 업종마다 차이가 있다. 검정결과를 통해 유가의 변화에 대한 주가 반응에는 산업별로 큰 차이가 있었고, 한 산업에서도 시장 상황에 따라 다르게 나타남을 알 수 있다. 이것은 유가 변화 시기에 투자자들은 포트폴리오를 산업별로 재조정할 필요가 있음을 보여준다. 또한, 정부의 산업정책과 고용정책 등 거시경제정책도 유가 변화 영향이 산업별, 시장 상황별로 차이나는 점을 충분히 고려하여 운영되어야 할 것이다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2003년도 춘계학술대회
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• pp.211-222
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• 2003

The health industry is highly value-added, compared to other industries. The reason is that increase of income growth and the expanded human life expectancy bring about positively needs of products at health industry. This is related with increase in expenses of health care and R&D investment of health industry. After 1995, the share of GDP at drug & biomedical industry is increased. Especially, the share of GDP at biomedical is 0.12% in 1995 1$^{st}$ quarter, but 0.17% in 2002 3$^{rd}$ quarter, 0.24% in 2008. Biomedical's contribution about GDP growth is to jump into 6.01% in 2008. The share of GDP at drug will continuously expand, compared to other manufacture industries. Also, drug's contribution about GDP growth will increase, compared with before. Conclusionally, total shares of GDP at drug St biomedical industry are to increase, compared with before. Also, this health industry's contribution is to expand as value-added industry and increase of sales.

• 한국식품영양과학회지
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• 제35권9호
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• pp.1280-1285
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• 2006

다양한 조건으로 대두를 침지하여 대두 발아시 미생물에 의한 오염을 최대한 방지하고 발아율을 높이는 조건을 검토하기 위해 대두를 멸균수, 0.1%의 자몽씨 추출물과 0.1%의 비타민 $B_1$ 유도체(AS5, vitagen)용액 등에 $25^{\circ}C,\;35^{\circ}C,\;45^{\circ}C,\;55^{\circ}C$에서 $3\sim12$시간 동안 침지시킨 후 $25^{\circ}C$, 상대습도 95%의 조건에서 3일 동안 발아시키며 발아율을 관찰하였다. 침지전 대두의 일반 미생물수는 $6.0\times10^5CFU/g$, 대장균군수는 $3.0\times10^5CFU/g$이었고, 대부분의 침지조건에서 미생물 오염 정도는 감소하였다. 침지 대두의 발아율은 침지시간이 길어질수록 미침지 대두보다 발아율이 떨어졌고 $45^{\circ}C$이상에서는 급격히 떨어져 $55^{\circ}C$에서는 발아가 거의 일어나지 않음이 관찰되었다. 대두의 발아율은 침지용액의 종류와 침지시간 및 온도에 의해 영향을 받았으며, 대두의 미생물 오염 감소와 초기발아를 유도를 위한 최적 조건은 자몽씨 추출물을 사용하여 $35^{\circ}C$에서 6시간 침지시켰을 때였다.

• 수산해양기술연구
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• 제46권1호
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• pp.56-69
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• 2010

This study was intended to determine the maneuverability of the vessel CHARMBADA. When the rudder angle was at $10^{\circ}$, $20^{\circ}$ and $30^{\circ}$, the maximum advance by slow, half and full ahead were varied in the range of 523.6-131.3m, 528.8-177.2m and 530.6-219.7m, respectively. The maximum transfer was 799.9-181.3m, 792.1-232.8m and 807.7-316.9m, respectively. The turning circle ability was better during starboard turning. When the rudder angle was $10^{\circ}$, $20^{\circ}$ and $30^{\circ}$, variation in the maximum advances was 392.0m, 245.0m and 153.0m. The maximum transfer was 528.0m, 339.0m and 218.0m, respectively based on the regression equations. As the rudder angle became bigger, the maximum advance or maximum transfer became smaller by the exponential function. The advance inertia took 127sec, 145sec, 181sec each until the vessel speed was 7.0konts, 12.0konts, 17.0konts. The static inertia took 245sec, 269sec, 300sec each until the vessel speed was under 2.0konts and the advance distance was 114.4m, 181.2m, 197.0m each. Accordingly, the static inertia was inclined to increase to scale according to the increase in vessel speed. For the CHARMBADA, the smaller the rudder angle was, the much bigger the turning circle became due to adhesion to the skeg, thereby lowering the vessel's turning ability.

• 융합경영연구
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• 제11권6호
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• pp.9-22
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• 2023

Purpose: This paper aims to suggest ways to apply the leading technologies of Industry 5.0 to the housing welfare field, tasks for this, and policy implications. Research design, data, and methodology: The analysis method of this study is a literature study. The analysis steps are as follows. Technology trends and characteristics of Industry 5.0 were investigated and analyzed. The following is a method of applying technology 5.0 in the industrial field. Finally, the application areas of each technology and the challenges to be solved in the process were presented. Results: The results of the analysis are 1) the accessibility and diffusion of technology. This means that all citizens have equal access to and use of the latest technology. To this end, the appropriate use of technology and the development of a user-centered interface are needed. 2) Data protection and privacy. Residential welfare-related technologies may face risks such as personal information leakage and hacking in the process of collecting and analyzing residents' data. 3) Stability, economic feasibility, and sustainability of the technology. Conclusions: The policy implications include: 1) Enhancing technology education and promotion to improve tech accessibility for groups like the low-income, rural areas, and the elderly, 2) Strengthening security policies and regulations to safeguard resident data and mitigate hacking risks, 3) Standardization of technology, 4) Investment and support in R&D.