• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.189-195
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• 2008

The OFDMA Physical layer in the WiMAX standard of IEEE 802.16e adopts 114 LDPC codes with various code rates and block sizes as a channel coding scheme to meet varying channel environments and different requirements for transmission performance. In this paper, the performances of the LDPC codes are evaluated according to various code rates and block-lengths throueh simulation studies using min-sum decoding algorithm in AWGN chamois. As the block-length increases and the code rate decreases, the BER performance improves. In the cases with code rates of 2/3 and 3/4, where two different codes ate specified for each code rate, the codes with code rates of 2/3A and 3/4B outperform those of 2/3B and 3/4A, respectively. Through the statistical analyses of the number of decoding iterations the decoding complexity and the word error rates of LDPC codes are estimated. The results can be used to trade-off between the performance and the complexity in designs of LDPC decoders.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.2
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• pp.32-45
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• 2000

This paper presents the analytic derivation of the SINR, when a linear array antenna is accommodated into the cellular CDMA basestation receiver, in relation to the two major performance effecting factors in beamforming(BF) applications, i. e., the direction selectivity, which refers to the narrowness of the mainbeam width, and the direction-of-arrival(DOA) estimation accuracy. The analytically derived results are compared with the operation simulation of the receiver realized with the several BF algorithms and their agreements are confirmed, consequently verifying the correctness of the analysis and the operation simulation. In order to investigate separately the effects of the errors occurring in the direction estimation and in the interference suppression, which are the two major functional components of general BF algorithms, both the algorithms of steering BF and the minimum- variance- distortionless-response(MVDR) BF are applied to the analysis. A signal model to reflect the spatially scattering phenomenon of the RF waves entering into the .:nay antenna, which directly affects on the accuracy of the BF algorithm's direction estimation, is also suggested in this paper and applied to the analysis and the operation simulation. It is confirmed from the results that the enhancement of the direction selectivity of the away antenna is not desirable in view of both the implementation economy and the BF algorithm's robustness to the erroneous factors. Such a trade-off characteristics is significant in the sense that it can be capitalized to obtain an economic means of BF implementation that does not severely deteriorate its performance while ensuring the robustness to the erroneous effects, consequently manifesting the significance of the analysis results of this paper that can be used as a design reference in developing BF algorithms to the cellular CDMA system.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.136-144
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• 2010

Exhaust gas recirculation (EGR) is an emission control technology allowing significant NOx emission reduction from light-and heavy duty diesel engines. The future EGR type, dual loop EGR, combining features of high pressure loop EGR and low pressure loop EGR, was developed and optimized by using a commercial engine simulation program, GT-POWER. Some variables were selected to control dual loop EGR system such as VGT (Variable Geometry Turbocharger)performance, especially turbo speed, flap valve opening diameter at the exhaust tail pipe, and EGR valve opening diameter. Applying the dual loop EGR system in the light-duty diesel engine might cause some problems, such as decrease of engine performance and increase of brake specific fuel consumption (BSFC). So proper EGR rate (or mass flow) control would be needed because there are trade-offs of two types of the EGR (HPL and LPL) features. In this study, a diesel engine under dual loop EGR system was optimized by using design of experiment (DoE). Some dominant variables were determined which had effects on torque, BSFC, NOx, and EGR rate. As a result, optimization was performed to compensate the torque and BSFC by controlling start of injection (SOI), injection mass and EGR valves, etc.

• Progress in Medical Physics
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• v.27 no.4
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• pp.272-276
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• 2016

To investigate the optimum x-ray tube design for the dental radiology, factors affecting x-ray beam characteristics such as tungsten target thickness and anode angle were evaluated. Another goal of the study was to addresses the anode heel effect and off-axis spectra for different target angles. MCNPX has been utilized to simulate the diagnostic x-ray tube with the aim of predicting optimum target angle and angular distribution of x-ray intensity around the x-ray target. For simulation of x-ray spectra, MCNPX was run in photon and electron using default values for PHYS:P and PHYS:E cards to enable full electron and photon transport. The x-ray tube consists of an evacuated 1 mm alumina envelope containing a tungsten anode embedded in a copper part. The envelope is encased in lead shield with an opening window. MCNPX simulations were run for x-ray tube potentials of 70 kV. A monoenergetic electron source at the distance of 2 cm from the anode surface was considered. The electron beam diameter was 0.3 mm striking on the focal spot. In this work, the optimum thickness of tungsten target was $3{\mu}m$ for the 70 kV electron potential. To determine the angle with the highest photon intensity per initial electron striking on the target, the x-ray intensity per initial electron was calculated for different tungsten target angles. The optimum anode angle based only on x-ray beam flatness was 35 degree. It should be mentioned that there is a considerable trade-off between anode angle which determines the focal spot size and geometric penumbra. The optimized thickness of a target material was calculated to maximize the x-ray intensity produced from a tungsten target materials for a 70 keV electron energy. Our results also showed that the anode angle has an influencing effect on heel effect and beam intensity across the beam.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.1-14
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• 2015

Climate change caused by elevated greenhouse gases would affect crop production through different pathways in agricultural ecosystems. Because an agricultural ecosystem has complex interactions between societal and economical environment as well as organisms, climate, and soil, adaptation measures in response to climate change on a specific sector could cause undesirable impacts on other sectors inadvertently. An integrated system, which links individual models for components of agricultural ecosystems, would allow to take into account complex interactions existing in a given agricultural ecosystem under climate change and to derive proper adaptation measures in order to improve crop productivity. Most of models for agricultural ecosystems have been used in a separate sector, e.g., prediction of water resources or crop growth. Few of those models have been desiged to be connected to other models as a module of an integrated system. Threfore, it would be crucial to redesign and to refine individual models that have been used for simulation of individual sectors. To improve models for each sector in terms of accuracy and algorithm, it would also be needed to obtain crop growth data through construction of super-sites and satellite sites for long-term monitoring of agricultural ecosystems. It would be advantageous to design a model in a sector from abstraction and inheritance of a simple model, which would facilitate development of modules compatible to the integrated prediction system. Because agricultural production is influenced by social and economical sectors considerably, construction of an integreated system that simulates agricultural production as well as economical activities including trade and demand is merited for prediction of crop production under climate change.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.