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- 저자 : 유태경(Tae-Kyung Yoo) (검색결과 18 건)
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CW Operation of $1.3{$mu}$ GaInAsP/p-InP BH Lasers at Room Temperature
유태경,정기웅,권영세,홍창희,Yoo, Tae Kyung,Chung, Gi Oong,Kwon, Young Se,Hong, Tchang Hee The Institute of Electronics and Information Engin 1986 전자공학회논문지 Vol.23 No.6
1.3\ulcorner GaInAsP BH(Buried Heterostructure) lasers were fabricated on the p-InP substrate. Two step chemical etching processes and melt-back etching technique during 2nd epitaxy were used for BH active layer. BH laser had the threshold current, Ith, of 72mA(23\ulcorner), peak wavelength of 1.2937\ulcorner, nd of 10-20%, and To of 85K. They operated in single mode under pulse condition up to 1.4 Ith. CW(DC) operation was successfully performed at room temperature.
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유태경(Tae-Kyung Yoo) 한국엔터테인먼트산업학회 2021 한국엔터테인먼트산업학회논문지 Vol.15 No.1
After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.
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밤나무와 오동나무 향판용재의 옥외 옥내 천연건조 속도의 비교
정희석,유태경 ( Hee Suk Jung,Tae Kyung Yoo ) 한국산림과학회 1998 한국산림과학회지 Vol.87 No.4
Chestnut and paulownia boards for the traditional musical instruments were air dried to compare moisture contents(MC), drying rates and drying times between the air drying for 70 days in a yard and the shed drying for 150 days in a closed shed when piled in early June. An average final MC and the drying rate of chestnut boards were 20.6 percent and 0.78%/day for the air drying, and 16.6 percent and 0.44%/day for the shed drying. An average final MC and the drying rate of paulownia boards were 16.7 percent and 1.53%/day for the air drying, and 13.5 percent and 0.77%/day for the shed drying. Drying rates of air-dried boards were nearly twice as high as those of shed-dried boards for both species. Air drying rates of chestnut and paulownia boards were very high and exhibited falling drying rate above the fiber saturation point(30%), and then decreased irregularly. However, shed drying rates of chestnut and paulownia boards were high and exhibited falling drying rate above 55 percent MC for chestnut boards and above 80 percent MC for paulownia boards, and then decreased irregularly.
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이태희(Lee Tae Hee),유일선(Yoo Il Seon),유태경(Yoo Tae Kyung) 한국인적자원관리학회 2018 인적자원관리연구 Vol.25 No.1
According to the reports released by the ministry of employment & labor and statistical data, wage differentials exist significantly in the whole industry. This paper seeks to analyze what determinants have effects on educational labor demand, segregating the labor market into two markets, high-education labor and low-education labor, based on educational wage differentials explained by human capital theory. The former which includes human capital is considered the different production factor from the latter which does not include human capital. Three factor Cobb-Douglas production function is set up in order to derive the educational labor demand functions. Based on that functions the stochastic model is built up to make it possible to estimate the given parameters. The estimated results are as follows. Firstly, low-education labor is complementary to high-education labor, while high-education is substitutive for capital. Secondly, the change of production affects the demand of low-education labor, almost not affecting that of high-education labor. Thirdly, technical progress contribute to increasing the demand of high-education labor but not increasing that of low-education labor. Several policy implications can be inferred from these results. Firstly, when economic repression is going on, the produced quantity in construction industry is decreased rapidly. It leads to unemployment of low-education labor, compared to high-education labor. Therefore, government should prepare the employment promotion policy for low-education labor in economic slowdown. Secondly, the reduction of construction investment may cut down the price of capital because of lack of capital demand. Then the demand of high-education labor with higher wage is reduced along with that of low-education labor complementary to high-education labor. So government need to implement the policy which can encourage both labor to be hired. Thirdly, Technical progress in capital sector produces higher capital productivity which increase the demand of capital. That leads to higher price of capital, which results in the increase of demand of high-education labor with substitutive relation for capital and low-education labor with complementary relation for high-education labor. Technical progress policy can be beneficial for promotion of employment for both labor.
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