• Journal of Technology Innovation
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• v.6 no.2
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• pp.32-53
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• 1998

This paper analyzes the characteristics of embodied technological knowledge structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the embodied knowledge network is introduced which is defined as a set of industries and their interactions(embodied knowledge flow) or linkages. The analysis of the inter-industrial embodied knowledge flows is conducted by using such methodologies as input-output technique, network analysis, indicator analysis and correlation analysis for a set of empirical data with reference period of 1983-1990. The main findings are as follow. First, as a whole, the structure of embodied knowledge flow can be classified into knowledge outflow sectors, inflow sectors and intermediary sectors. Second, outflow sectors exhibit a multi-central structure whereas inflow sectors form a dualistic structure. These idiosyncratic characteristics should be addressed in developing industrial policy.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.155-162
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• 2021

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.699-700
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• 2021

This study investigated patterns of co-occuring chronic diseases and disorders in old ages. For this purpose, we utilized data from the Korean National Health and Nutrition Examination Survey for 3,734 old adults aged over 65. Data on 18 conditions were obtained, and analyzed using network analysis, associated rule mining, cluster analysis. The majority of participants has multimorbidity. Association rules analysis reveals unexpected comorbidities with high lift and confidence. Also, some morbidity clusters were present. Diabetes and emotional disorder had the greatest comorbidity and represent complex comorbid conditions. Old age is characterized by a complex pattern of multimorbidity and comorbidity. In conclusion, particular combinations of morbidities were very prevalent and will be needed to policy of health care interventions for old ages.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1442-1453
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• 2016

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.327-332
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• 2005

This paper aims to analysis for evaluation factor and success prediction of port innovative cluster. This paper is divided three factors such ac policy, source and operation. In addition, three factors are divided into the twelve detail factors. the weight of each factor is calculated by Kohonen Network. At the result, this paper places the priority on the source factor.

• Journal of Family Resource Management and Policy Review
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• v.13 no.1
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• pp.61-81
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• 2009

The purpose of this study was to investigate the policy demands of the family sector for work-family balance, with a focus on the child care and household labor provisions. For empirical analysis, data were collected from 197 dual-income families with at least one young child under age 10 in the metropolitan area via self-administered questionnaires. The major findings of this study were as follows. First, child care provisions for working mothers and fathers were insufficient and unfair, and the ratio of child care provisions offered by familial child care network was high. The characteristics of child care provisions for working parents were associated with family structure, working conditions, and demographic variables. Second, household labor provisions for working mothers and fathers were also insufficient and unfair, and the ratio of socialization was high. Third, the level of family satisfaction varied by the level of child care and household labor provisions, respectively, controlling family structure, working conditions, and demographic variables. The empirical results of the study provided policy implications for work-family balance.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.1-8
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• 2003

In this paper. we develop an analytical model to evaluate the virtual topology reconfiguration phase of optical Internet networks. To counter the continual approximation problem brought by traditional heuristic approach, we take the traffic prediction into consideration and propose a new heuristic reconfiguration algorithm called Prediction based Multi-stage Reconfiguration approach. We then use this analytical model to study the different configuration operation policies in response to the changing traffic patterns in the higher layer and the congestion level on the virtual topology. This algorithm persists to decide the optimal instant of reconfiguration easily based on the network state. Simulation results show that our virtual topology management Policy significantly outperforms the conventional one, while the required physical resources are limited.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.213-230
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• 2014

Increasing unemployment rate and creation of new jobs are most important issues around the world recently. Then many developed countries, including Republic of Korea, establish and enforce a variety of start-up activation policies to increase employment rate and boom up the national economy. Establishing linkage of entrepreneurship motivation, entrepreneurship, entrepreneurial intention and firm performance, focusing on potential entrepreneurs and entrepreneur, it could provide personalized and targeted entrepreneurial policy programs to increase entrepreneurship, because entrepreneurship is the most important factor to activate startups. On this study, it established factors of entrepreneurial motivation on potential entrepreneurs and entrepreneurs, and analyzed the linkage of factors of entrepreneurial motivation, entrepreneurship, entrepreneurial intention(potential entrepreneurs) and firm performance(entrepreneurs). For analysis, this study conducted descriptive statistics, reliability analysis, factor analysis to verify validity, correlation analysis, and regression to analyze influence between factors. Potential entrepreneurs group has 202 samples, and findings show self-efficacy, social network, economic status and government policy influence on entrepreneurship positively. And self-efficacy, startup education, economic status and government policy have a positive effect on entrepreneurial intention, too. Entrepreneurs group has 212 samples, and findings show self-efficacy, social network and economic status influence on entrepreneurship. And each linkage has a positive effect, that self-efficacy - financial and non-financial performance, startup education - financial and technological performance, social network - financial performance, economic status - financial and non-financial performance, and government policy - financial and technological performance.