• Information Systems Review
• /
• v.5 no.2
• /
• pp.241-256
• /
• 2003

This empirical study is to explain critical success factors for building effective regional IT cluster from the literature reviews which have some limitations, and is to suggest new key factors from the views of Regional Innovation System and Sectoral Systems of Innovation. For building successful cluster, the new key factors not only stress on regional networks, the spill-over of tacit knowledge through learning by interacting, institutions which contain regional custom, norms, established practices, culture, and characteristics from the Regional Innovation System, but also emphasize on heterogeneous agents who are interacting by each others from Sectoral Systems of Innovation. From these factors we suggest some strategies for building effective "Daegu IT Cluster" as following; making characterized IT brands which are selected and concentrated based on regional and IT sectoral characteristics, strengthening learning competence of tacit knowledge built in multiple heterogeneous agents network, establishing strong agent networks which are composed of universities, companies, institutes and government, and sharing the institution of mind-opening culture in order to correspond with environmental changes and link to other industrial clusters. By putting above strategies in force, the compatabilities of Daegu region are reinforced. Tacit knowledges spill over and the regional innovation competence are accumulated. Also IT cluster plays core role of employment in Daegu for long term. Especially, "Daegu IT Cluster" changes city's image from medium and small manufacturing city to new industrial city based on high technologies.

• Journal of the Korea Society for Simulation
• /
• v.17 no.1
• /
• pp.9-16
• /
• 2008

The sink mobility can request frequent routing change in sensor networks. By active mobility a sink can gather needed information easily so that the network availability will be higher. However, instead static sink system, the connection between sensor nodes and a mobile sink can be changed continuously. That is, the rerouting should be implemented caused by the routing alteration. It is two connection setups for the mobile sink system: the connection from sink to sensor nodes and the connection from sensor nodes to sink. However, sensor nodes actually have many functional limitations. Hence, the low cost scheme will be needed for the connection setup from sensor nodes to the mobile sink. In this paper, we propose an agent scheme to the connection setup from sensor nodes to the mobile sink. The agent scheme provides the reliable setup scheme to the connection by using an agent sector.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.697-703
• /
• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.