• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.1
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• pp.21-28
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• 2009

Detailed information on soil characteristics is of great importance for the use and conservation of soil resources that are essential for human welfare and ecosystem sustainability. This paper introduces soil inventory of Korea focusing on national soil database establishment, information systems, use, and future direction for natural resources management. Different scales of soil maps surveyed and soil test data collected by RDA (Rural Development Administration) were computerized to construct digital soil maps and database. Soil chemical properties and heavy metal concentrations in agricultural soils including vulnerable agricultural soils were investigated regularly at fixed sampling points. Internet-based information systems for soil and agro-environmental resources were developed based on 'National Soil Survey Projects' for managing soil resources and for providing soil information to the public, and 'Agroenvironmental Change Monitoring Project' to monitor spatial and temporal changes of agricultural environment will be opened soon. Soils data has a great potential of further application in estimation of soil carbon storage, water capacity, and soil loss. Digital mapping of soil and environment using state-of-the-art and emerging technologies with a pedometrics concept will lead to future direction.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.573-588
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• 2009

In this paper, we propose a novel video quality degradation monitoring scheme titled VR-VQMS(Visual Rhythm based Video Quality Monitoring Scheme) over an IPTV service prone to packet losses during network transmission. Proposed scheme quantifies the amount of quality degradation due to packet losses, and can be classified into a RR(reduced-reference) based quality measurement scheme exploiting visual rhythm data of H.264-encoded video frames at a media server and reconstructed ones at an Set-top Box as feature information. Two scenarios, On-line and Off-line VR-VQMS, are proposed as the practical solutions. We define the NPSNR(Networked Peak-to-peak Signal-to-Noise Ratio) modified by the well-known PSNR as a new objective quality metric, and several additional objective and subjective metrics based on it to obtain the statistics on timing, duration, occurrence, and amount of quality degradation. Simulation results show that the proposed method closely approximates the results from 2D video frames and gives good estimation of subjective quality(i.e.,MOS(mean opinion score)) performed by 10 test observers. We expect that the proposed scheme can play a role as a practical solution to monitor the video quality experienced by individual customers in a commercial IPTV service, and be implemented as a small and light agent program running on a resource-limited set-top box.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.109-124
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• 2020

Using panel data on freight rates and ship prices in the dry freighter market from January 2015 to December 2019, this study investigates the characteristics of shipping industry fluctuations. The analysis aims at two aspects of academic contribution. First, this study analyzes the relationship between shipping indicators and ship price based on separate dry-bulk ships, while the previous research considered the overall shipping index and weighted average ship prices. Second, the VAR model for the causality test is extended to a heterogeneous mixed panel model capable of limiting coefficients. There is a peak estimated by removing the cross-correlation problem, which is mainly raised in panel data analysis, using bootstrap estimation and solving the problem of information loss due to differences in non-stationary data. An empirical investigation of the causal relationship between economic fluctuations and ship price shows that the effect on the ship price from the freight is significant at the 1% level. This implies that there is a one-way relationship with demand in the shipping industry rather than a bilateral relationship.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.1
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• pp.66-74
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• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.310-318
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• 2008

The H.264/AVC standard achieves higher coding efficiency than previous video coding standards with the rate-distortion optimization (RDO) technique which selects the best coding mode and reference frame for each macroblock. As a result, the complexity of the encoder have been significantly increased. In this paper, a fast intra-mode decision algorithm is proposed to reduce the computational load of intra-mode search, which is based on the inverse tree-structure edge prediction algorithm. First, we obtained the dominant edge for each $4{\times}4$ block from local edge information, then the RDO process is only performed by the mode which corresponds to dominant edge direction. Then, for the $8{\times}8$ (or $16{\times}16$) block stage, the dominant edge is calculated from its four $4{\times}4$ (or $16{\times}16$) blocks' dominant edges without additional calculation and the RDO process is also performed by the mode which is related to dominant edge direction. Experimental results show that proposed scheme can significantly improve the speed of the intra prediction with a negligible loss in the peak signal to noise ratio (PSNR) and a little increase of bits.

• Environmental and Resource Economics Review
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• v.14 no.1
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• pp.75-100
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• 2005

This study attempts to empirically investigate the respondents' preference uncertainty involved in stating their willingness to pay (WTP). In the contingent valuation (CV) survey, we employed two approaches using two split samples. The respondents of one sample were given the opportunity to express intensity of preference through polychotomous choice (PC) WTP question. Those of the other sample were given a follow-up question of confidence measure (0~100%). By incorporating the two elicited degrees of preference uncertainty into examining the WTP responses, we take a comparison of the two approaches in terms of the goodness-of-fit of the examination and the efficiency of the mean WTP estimates. In comparing the DC model with the PC models, the DC model provides more efficient estimates. Moreover, the conventional DC model give some gains in terms of the goodness-of-fit and efficiency in comparing with the PC model most similar to this model. In this specific study, incorporating the preference uncertainty in DC model results greater estimates than conventional DC model without loss of goodness-of-fit and efficiency. This implies that the consideration of preference uncertainty on DC model could correct underestimating. We conclude that DC model provides a better estimate of WTP and preference uncertainty could be a critical information on the DC-CV estimation.

• Journal of the Korean Geotechnical Society
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• v.18 no.3
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• pp.51-59
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• 2002

This paper deals with development of a damage risk assessment system for adjacent buildings to under-passing tunnel face considering 3D-ground movement. The system consists of building and ground information module, monitoring data module, settlement evaluation module, and building damage risk assessment module. The major modules, settlement evaluation module and building damage assessment module, are based on settlement estimation model suggested by Attewell et al (1982) and the building damage assessment method by Mair et al. (1996). After estimating 3D-ground movements due to tunneling with settlement evaluation module, damage assessment far buildings is performed using building damage risk assessment module. The developed system has two major functions; 1) calculation of 3D-settlement with ground loss ($V_{s}$)or maximum settlement ($w_{max}$) and inflection point (i) using various empirical formulae, monitoring data, numerical results, and so on; 2) assessment of damage risk for adjacent buildings of arbitrary section with position change of tunnel face. The field data given by Boscadin and Cording (1989) leer the case of two-storied masonry building near the Metro tunnel in Washington D.C. was simulated to verify the applicability of the developed system.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.1-10
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• 2023

This paper proposes a method to extend Inter-Carrier Interference (ICI) canceling Orthogonal Frequency Division Multiplexing (OFDM) receivers for 5G mobile systems to spatial multiplexing 2×2 MIMO (Multiple Input Multiple Output) systems to support high-speed ground transportation services by linear motor cars traveling at 500 km/h. In Japan, linear-motor high-speed ground transportation service is scheduled to begin in 2027. To expand the coverage area of base stations, 5G mobile systems in high-speed moving trains will have multiple base station antennas transmitting the same downlink (DL) signal, forming an expanded cell size along the train rails. 5G terminals in a fast-moving train can cause the forward and backward antenna signals to be Doppler-shifted in opposite directions, so the receiver in the train may have trouble estimating the exact channel transfer function (CTF) for demodulation. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceller is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number n to receiver sub-carrier number l is generated. In case of n≠l, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 8 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, 2×2MIMO QPSK and 16QAM modulation schemes, BER (Bit Error Rate) improvement was observed when the number of taps in the multi-tap equalizer was set to 31 or more taps, at a moving speed of 500 km/h and in an 8-pass reverse doppler shift environment.