• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1574-1581
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• 2007

DVB-H (Digital Video Broadcasting for Handhold) is a new standard, currently being developed for a portable digital broadcasting, which enhance the multimedia broadcasting service in the Euroapean standard DVB-T (DVB-Terrestrial). Seamless mobility and power saving are essential requirements in the DVB-H system. To support seamless mobility, DVB-H system should provides seamless handover for mobile stations in the MFN (multi frequency network). For seamless handover, the receiver should monitor neighboring cells and it increases the power consumption. And so, power efficient sell searching algorithm for seamless handover is required. In this paper, we propose hypothesis feeling based handover algorithm to enhance the power efficiency by using the fast cell searching, and analyze the performance of handover schemes through the numerical evaluation and simulation.

• Journal of the Korea Knowledge Information Technology Society
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• v.12 no.1
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• pp.69-78
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• 2017

KOIN (Korean medicine 人, http://www.koin.re.kr) is a Korean medicine knowledge portal developed for users who are interested in Korean medicine to share relevant information. The purpose of the present study is to seek methods for advancing the quality of the contents and services of KOIN to secure future users of the portal services before fully initiating the KOIN service. The crowd testing method was applied to test the functionality and usability of the current KOIN service, and domestic and international websites providing similar services were investigated and analyzed. About 150 errors were found in this functional testing procedure, but the identified functional problems were all corrected. An average score of 3.33 was calculated in the usability test, in which the reliability and the playfulness showed the highest and lowest score, respectively. We in this paper surveyed the 15 relevant websites with respect to KOIN in the traditional medicine and the modern medicine fields. The strengths and weaknesses of similar websites were analyzed to improve the KOIN services. In particular, it is shown that the evidence-based Korean medicine knowledge is KOIN's biggest strength. Users' needs and demand for the KOIN services will be continuously gathered to provide the Korean medicine knowledge services that the users require.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.2
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• pp.14-25
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• 1999

The IEEE standard 1149.1, which was proposed to increase the observability and the controllability in I/O pins, makes it possible the board level testing. In the boundary-scan environments, many shift operations are required due to their serial nature. This increases the test application time and the test application costs. To reduce the test application time, the method based on the parallel opereational multiple scan paths was proposed, but this requires the additional I/O pins and the internal wires. Moreover, it is difficult to make the designs in conformity to the IEEE standard 1149.1 since the standard does not support the parallel operation of data shifts on the scan paths. In this paper, the multiple scan path access algorithm which controls two scan paths simultaneously with one test bus is proposed. Based on the new algorithm, the new algorithm, the new board level BIST architecture which has a relatively small area overhead is developed. The new BIST architecture can reduce the test application time since it can shift the test patterns and the test responses of two scan paths at a time. In addition, it can reduce the costs for the test pattern generation and the test response analysis.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.