• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6290-6296
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• 2015

More informative ones of heuristics can help to conduct search more efficiently to obtain solution plan. However, in general, to derive highly informative heuristics from problem specifications requires lots of computational effort. To address this problem, we propose an State-Action based Planning Graph(SAPG) and Action-based heuristics for solving planning problems more efficiently. The SAPG is an extended one to be applied to can find interactions between subgoal & goal conditions from the relaxed planning graph which is a common means to get heuristics for solving the planning problems, Action-based heuristics utilizing SAPG graphs can find interactions between subgoal & goal conditions in an effective way, and then consider them to estimate the goal distance. Therefore Action-based heuristics have more information than the existing max and additive heuristics, also requires less computational effort than the existing overlap heuristics. In this pager. we present the algorithm to compute Action-based heuristics, and then explain empirical analysis to investigate the accuracy and the efficiency of the Action-based heuristics.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.1
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• pp.60-70
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• 1995

In the field of marine transportation the energy saving is one of the most important factors for profit. In order to reduce the fuel oil consumption the ship's propulsion efficiency must be increased as much as possible. The propulsion efficiency depends upon a combination of an engine and a propeller. The propeller has better efficiency as lower rotational speed. This situation led the engine manufacturers to design the engine that has lower speed, longer stroke and a small number of cylinders. Consequently the variation of rotational torque became larger than before because of the longer delay-time in the fuel oil injection process and an increased output per cylinder. As this new trends the conventional mechanical-hydrualic governors for engine speed control have been replaced by digital speed controllers which adopted the PID control or the optimal control algorithm. But these control algorithms have not enough robustness to suppress the variation of the delay-time and the parameter pertubation. In this paper we consider the delay-time and the perturbation of engine parameters as the modeling uncetainties. Next we design the controller which has zero offset in steady state engine speed, based on the two-degree-of-freedom control theory and $\mu$-synthesis. Thd validity of the controller is investigated through the response simulation. We use a personal computer and an analog computer as the digital controller and the engine (plant) part respectively. And, we certify that the designed controller maintains its performance even though the engine parameters may vary.

• Design & Manufacturing
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• v.13 no.1
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• pp.5-12
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• 2019

In this paper, we studied the micro tool deflection, micro cutting with low temperature, and deformation of micro ribs caused by cutting forces. First, we performed an integrated machining error compensation method based on captured images of tool deflection shapes in micro cutting process. In micro cutting process, micro tool deflection generates very serious problems in contrast to macro tool deflection. To get the real images of micro tool deflection, it is possible to estimate tool deflection in cutting conditions modeled and to compensate for machining errors using an iterative algorithm correcting tool path. Second, in macro cutting fields, the cryogenic cutting process has been applied to cut the refractory metal but, the serious problem may be generated in micro cutting fields by the cryogenic environment. However, if the proper low temperature is applied to micro cutting area, the cooling effect of cutting heat is expected. Such effect can make the reduction of tool wear and burr formation. For verifying this passibility, the micro cutting experiment at low temperature was performed and SEM images were analyzed. Third, the micro pattern was deformed by the cutting forces and the shape error occurred in the sidewall multi-step cutting process were minimized. As the results, the relationship between the cutting conditions and the deformation of micro-structure during micro cutting process was investigated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.41-45
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• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.151-158
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• 2006

In this paper, we proposed a new audio fingerprint extraction method, based on Philips' music retrieval algorithm, which uses the energy difference of neighboring filter-bank and probabilistic characteristics of music. Since Philips method uses too many filter-banks in limited frequency band, it may cause audio fingerprints to be highly sensitive to additive noises and to have too high correlation between neighboring bands. The proposed method improves robustness to noises by reducing the number of filter-banks while it maintains the discriminative power by representing the energy difference of bands with 2 bits where the quantization levels are determined by probabilistic characteristics. The correlation which exists among 4 different levels in 2 bits is not only utilized in similarity measurement. but also in efficient reduction of searching area. Experiments show that the proposed method is not only more robust to various environmental noises (street, department, car, office, and restaurant), but also takes less time for database search than Philips in the case where music is highly degraded.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.