• The Journal of the Petrological Society of Korea
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• v.13 no.4
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• pp.179-190
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• 2004

Precambrian metamorphic rocks of Yeongyang-Uljin area, which is located in the eastern part of Sobaegsan Massif, Korea, are composed of Pyeonghae, Giseong, Wonnam Formations and Hada leuco granite gneisses. These show a zonal distribution of WNW-ESE trend, and are intruded by Mesozoic igneous rocks and are unconformably overlain by Mesozoic sedimentary rocks. This study clarifies the deformation history of Precambrian metamorphic rocks after the formation of gneissosity or schistosity on the basis of the geometric and kinematic features and the forming sequence of multi-deformed rock structures, and suggests that the geological structures of this area experienced at least four phases of deformation i.e. ductile shear deformation, one deformation before that, at least two deformations after that. (1) The first phase of deformation formed regional foliations and WNW-trending isoclinal folds with subhorizontal axes and steep axial planes dipping to the north. (2) The second phase of deformation occurred by dextral ductile shear deformation of top-to-the east movement, forming stretching lineations of E-W trend, S-C mylonitic structure foliations, and Z-shaped asymmetric folds. (3) The third phase deformation formed I-W trending open- or kink-type recumbent folds with subhorizontal axes and gently dipping axial planes. (4) The fourth phase deformation took place under compression of NNW-SSE direction, forming ENE-WSW trending symmetric open upright folds and asymmetric conjugate kink folds with subhorizontal axes, and conjugate faults thrusting to the both NNW and SSE with drag folds related to it. These four phases of deformation are closely connected with the orientation of regional foliation in the Yeongyang-Uljin area. 1st deformation produced regional foliation striking WNW and steeply dipping to the north, 2nd deformation locally change the strike of regional foliation into N-S direction, and 3rd and 4th deformations locally change dip-angle and dip-direction of regional foliation.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• 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.