• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.222-230
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• 2022

Recently, Korea's cultural content industry is developing, and behind the growing recognition around the world is the real-time sharing service of global network users due to the development of science and technology. In particular, in the case of YouTube, its propagation power is fast and powerful in that everyone, not limited users, can become potential video providers. As more than 80% of mobile phone users are using YouTube in Korea, YouTube's information means that psychological factors of users are reflected. For example, information such as the number of video views, likes, and comments of a channel with a specific personality shows a measure of the channel's personality interest. This is highly related to the fact that information such as the frequency of keyword search on portal sites is closely related to the stock market economically and psychologically. Therefore, in this study, YouTube information from a representative entertainment company is collected through a crawling algorithm and analyzed for the causal relationship with major variables related to stock prices. This study is considered meaningful in that it conducted research by combining cultural content, IT, and financial fields in accordance with the era of the fourth industry.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.21-21
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• 2004

본 논문은 수동 힘반영 기구의 안정성을 제어하기 위한 방법으로 수동성 제어기를 제안한다. 수동 힘반영 기구는 브레이크와 같은 수동 엑츄에이터를 사용함으로써 힘을 반영하게 되는데, 여기에 사용되는 수동 엑츄에이터는 사용자가 움직이고자 하는 방향의 반대방향으로만 힘을 생성할 수 있기 때문에, 힘을 생성할 수 있는 방향에 제한이 있다. 따라서 가상의 벽면을 나타내는 데에도 정확히 원하는 방향의 힘을 제시하지 못하고, 힘의 근사화를 통하여 가장 근접한 방향의 힘을 생성해낸다. 이는 이상적인 수동 힘반영 기구의 연구에서도 나타나며, FME(Force Manipulability Ellipsoid)에 의해 명확하게 설명이 되는 현상이다.(중략)

• Proceedings of the Korean Biophysical Society Conference
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• 2001.06a
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• pp.29-29
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• 2001

Leukocytes are important elements in the host defense against microbial infections. A variety of antimicrobial peptides named as the cathelicidin family have been identified from leukocytes. PMAP-23 derived from porcine myeloid cells is an antimicrobial peptide belong to the cathelicidin family. PMAP-23 was reported to have potent growth inhibition activity against bacterial and tumor cells with no hemolytic activity.(omitted)

• Journal of Acupuncture Research
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• v.17 no.4
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• pp.18-27
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• 2000

There is a acupuncture method which make a difference according to the four seasons, according to body region or depth in skin. We call it Acupuncture follow the four seasons(四時刺法). In several chapters of Huangdineijing(黃帝內經) introduced Acupuncture follow the four seasons. Acupuncture follow the four seasons has two kinds of acupuncture method that is to acupuncture at body region and to acupuncture at five Su points(五兪穴). To use five Su points(五兪穴) according to Yongchu(靈樞) disagree with Nanjing(難經). In Yongchu(靈樞), the five phases property disagree with five Su points(五兪穴), but in Nanjing(難經) the five phases property agree with five Su points(五兪穴). Even if we can acupuncture the same point, there will be the different effect according as what is the purpose of doing acupuncture, and when we do acupuncture. That is to say, we can use apucupuncture for the purpose of prevention in Yongchu(靈樞), and for the purpose of healing the disease in Nanjing(難經). Therefore, because we select the point on the base of meridian Kis origin which spring out, we have to acupuncture Chong point(井穴) in winter according to Yongchu(靈樞). Because we select the point on the base of meridian Kis origin which flowing, we have to acupuncture Chong point(井穴) in spring according to Nanjing(難經). And in the base of five phases' property, the purpose of selecting five Su points(五兪穴) is the prevention according to Yongchu(靈樞), and the healing according to Nanjing(難經). So even though we acupuncture the exactly same Chong point(井穴), we can expect the effect that acupuncture method supply Ki for liver in winter. and the effect that it extract pathogenic Ki(邪氣) from the liver in spring.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.1-13
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• 2014

This study examines the relationship between firms' external search strategy and their innovation performance. In addition to revisiting the relationship between open search strategy and product innovation, we further extend the impact of use of external knowledge sources to process and organizational innovation. Using the 2010 Korean Innovation Survey (KIS) of manufacturing firms, we report that on average, venture firms search more widely (external search breadth) and deeply (external search depth) across a variety of external search channels than non-venture firms. We then further explore the impact of venture and non-venture firms' use of external search strategies on innovation performance. We find that both searching widely and deeply increase the likelihood of non-venture firm's successes in product, process and organizational innovation. Similar results can be found for the venture firm's success in organizational innovation. However, only searching deeply increases the likelihood of venture firms' success in product and process innovation.

• Journal of Korea Technology Innovation Society
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• v.15 no.3
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• pp.586-603
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• 2012

In order to relieve overcrowding, the Korean government has regulated firm's locations in the capital region of Korea. However, the standard of regulation mainly depends on the place of province. Using KIS-Value data of firms that are located Pyeong-taek(the Capital area) or Cheon-an(Non Capital area), in close proximity to each other, we utilize OLS and negative binomial regression models for identifying the difference of firms' management and innovation performance in terms of firms' location difference(regulation difference). Our analysis shows that innovation performance of firms in Cheon-an does better than Pyeong-taek's, but management performance has no gaps between them. This result indicates that the regulation of firm's location has influence on firm's innovation performance. Thus, regulation policy regarding firms' location need to be minutely amended.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6878-6887
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• 2015

This study applies regression analysis and propensity score matching to analyze the agglomeration economies which the characteristics of tenancy affect the performance of the manufacturing industry based on industrial parks. The estimation main data are from Kis-Value, KIPRIS and FEMIS. The results show that the industrial park tenancy tends to work positively on the management performances. But there is no evidence that on-Park firms in the metropolitan areas(Gyeonggi-do) have higher management and innovative performances than the comparable firms. The firms that have lived for a long time in the industrial parks, are good in total sales, however, they have no significant efficiency in net profit, operating profit, and patents. The firms, having several branches of the industrial parks, have lower operating profit than others. Long-term and multiple tenant firms do not learn over time nor do they establish better linkages and networks.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.818-837
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• 2018

Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.