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The Effects of Technological Change on Employment
Jaeuk Ju 한국경제연구학회 2014 Korea and the World Economy Vol.15 No.2
This paper empirically analyzes the effects of technological change, particularly the Information & Communication Technology (ICT) utilization, on employment structure. A relative labor compensation model and an employment model are estimated to find out the characteristics of change in technology and complementarity between capital and labor, which are the critical variables determining the relationship between technological change and employment. In the relative labor compensation model by technological level, empirical results show a significant capital-augmenting effect of technological change and a consequent substitutability between capital and labor at the mid-low level of skill group in the manufacturing industry, which is the only critical evidence of a decrease in employment due to technological advancement. Through the rest of the models, the effects of technological progress on employment can be represented in both positive and negative directions. Therefore, it is not true that technology is always the cause of jobless growth.
Kim, Jaeuk U.,Ku, Boncho,Kim, Young-Min,Do, Jun-Hyeong,Jang, Jun-Su,Jang, Eunsu,Jeon, Young Ju,Kim, Keun Ho,Kim, Jong Yeol Hindawi Publishing Corporation 2013 Evidence-based Complementary and Alternative Medic Vol.2013 No.-
<P>Sasang constitutional medicine (SCM) shares its philosophy with that of personalized medicine: it provides constitution-specific treatment and healthcare individualized for each patient. In this work, we propose the concept of the Sasang Health Index (SHI) as an attempt to assess the individualized health status in the framework of SCM. From the target population of females in their fifties and older, we recruited 298 subjects and collected their physiological data, including complexion, radial pulse, and voice, and their questionnaire responses. The health status of each subject was evaluated by two Korean medical doctors independently, and the SHI model was obtained by combining all the integrative features of the phenotype data using a regression technique. As a result, most subjects belonged to either the healthy, subhealthy, or slightly diseased group, and the intraclass correlation coefficient between the two doctors' health scoring reached 0.95. We obtained an SHI model for each constitution type with adjusted <I>R</I>-squares of 0.50, 0.56, and 0.30, for the TE, SE, and SY constitution types, respectively. In the proposed SHI model, the significant characteristics used in the health assessment consisted of constitution-specific features in accordance with the classic literature and features common to all the constitution types.</P>
김재욱(Jaeuk U. Kim),김성훈(Sung Hun Kim),이유정(Yu Jung Lee),전영주(Young Ju Jeon),강재환(Jaehwan Kang),김종열(Jong Yeol Kim) 대한전기학회 2009 정보 및 제어 심포지엄 논문집 Vol.2009 No.10
Pulse diagnosis is one of the most important diagnosis method in Oriental medicine. To make the pulse diagnosis more objective and standardize, it is essential to reinterpret the pulse images in the literature in terms of the physical quantities such as the strength, width, length, and depth of the pulse waveform, as well as to develop a high fidelity pulse measuring instrument. As a way towards such quantification, in this work, we introduce an effective method of extracting the characteristic features of the measured pulse wave. As an application of so-obtained features of the pulse wave, we outline the procedure of calculating the coefficient of the floating/sinking pulse, which is effective in quantifying the floating pulse and sinking pulse in a unified scale. In addition, we apply the algorithm of calculating the floating/sinking pulse to the database of pulse waves at Chon, Gwan, and Cheok in the left wrist, which were collected from 213 healthy male subjects in their 20s. As a result, we found that statistically the pulse image at Gwan tends to be relatively floating and the pulse image at Cheok tends to be relatively sinking.
김명주 ( Myung Ju Kim ),황재욱 ( Jaeuk Hwang ),우성일 ( Sung-il Woo ) 한국정신병리진단분류학회 2020 精神病理學 Vol.24 No.2
The human brain undergoes aging throughout one’s lifetime after its maturation period. However, the patterns of brain aging vary from person to person. Recently, the brain age prediction brings our attention increasingly since it may be useful as a biological marker of the level of brain aging. Estimation of brain age is of great clinical significances, since it can suggest the severity of neurological diseases as well as psychiatric disorders. It has been reported that regional volumes of cerebral structures and cortical thickness would be decreased with aging. Yet, these changes vary depending on numerous factors such as tissue and area of the brain aging and accelerate after a certain period of time. In order to establish brain age prediction algorithms with high accuracy, it is necessary to select relevant input data and algorithms. Majority of the previous studies trained the models through volume-based data and in particular, the study applying the latest prediction algorithms, such as convolutional neural networks, has shown reasonably high accuracy with a mean absolute error of around 4 years. Growing body of literatures regarding the brain age prediction has been available since 2015. In the future, more accurate model will be developed with more advanced machine learning and larger number of brain imaging data. If the model would be developed for younger population, its clinical significance will be increased.