• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1423-1431
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• 2022

Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.435-443
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• 2007

Computing the interior spectrum of large sparse generalized eigenvalue problems $Ax\;=\;{\lambda}Bx$, where A and b are large sparse and SPD(Symmetric Positive Definite), is often required in areas such as structural mechanics and quantum chemistry, to name a few. Recently, CG-type methods have been found useful and hence, very amenable to parallel computation for very large problems. Also, as in the case of linear systems proper choice of preconditioning is known to accelerate the rate of convergence. After the smallest eigenpair is found we use the orthogonal deflation technique to find the next m-1 eigenvalues, which is also suitable for parallelization. This offers advantages over Jacobi-Davidson methods with partial shifts, which requires re-computation of preconditioner matrx with new shifts. We consider as preconditioners Incomplete LU(ILU)(0) in two variants, ever-relaxation(SOR), and Point-symmetric SOR(SSOR). We set m to be 5. We conducted our experiments on matrices from discretizations of partial differential equations by finite difference method. The generated matrices has dimensions up to 4 million and total number of processors are 32. MPI(Message Passing Interface) library was used for interprocessor communications. Our results show that in general the Multi-Color ILU(0) gives the best performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.584-590
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• 2013

Depending on the operating characteristics, track circuit is installed for the purpose of control directly or indirectly of the signal device, point switch machine and other security device. These are mainly used for train detection, transmission of information, broken train detection and transmission of return current. Especially, the return current is related to signal system, power system and catenary line, and track circuit systems. It is one of the most important component shall be dealt for the safety of track side staff and for the protection of railway-related electrical system according to electrification. Therefore, an accurate analysis of the return current is needed to prevent the return current unbalance and the system induced disorder and failure due to an over current condition. Also, if the malfunction occurred by the return current harmonics, it can cause problems including train operation interruption. In this paper, we presented measurement and analysis method at return current and it's harmonics by train operation. By the test criteria, we evaluated for safety. Hereafter, it is expected to contribute to the field associated with it.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3444-3449
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• 2013

The control module controlling points has become high-tech. but the introduction of relevant company's inspection intervals and methods, and the adoption of the way which is used in relay interlock system became the cause of a failure by excessive and incorrect maintenance. The Human error in failure recovery process can cause vital accidents including train derailment, the points monitoring system could prevent this problem by monitoring points' operation condition in real time. After conducting the changed inspection intervals that applied the results of the criticality of each failure type, MTBF, MTTR, availability, maintainer's opinion, the work became simplified, and, the failure did not occur for 4 consecutive years in contrast to the previous annual average of 11 failures.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1387-1392
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• 2006

본 논문에서는 SVM (Support Vector Machine)을 기반으로 하여 인체의 뇌 하부구조인 해마에 대한 지능적 형상분석 방법을 제공한다. 일반적으로 의료 영상으로부터 해마의 형상 분석을 하기 위해서는 충분한 임상 데이터를 필요로 한다. 하지만 현실적으로 많은 양의 표본들을 얻는 것이 쉽지 않기 때문에 전문가의 지식을 기반으로 한 작업이 수반되어야 한다. 결국 이러한 요소들이 분석 작업을 어렵게 한다. 의학 기술이 복잡해 지면서 최근의 형상 분석 연구는 점차 통계적 모델을 기반으로 진행되고 있다. 본 연구에서는 해마로부터 고해상도의 매개변수형 모델을 만들어 형상 표현으로 이용하고, 집단간 분류 작업에 SVM 알고리즘을 적용하는 지능적 분석 방법을 구현한다. 우선 메쉬 데이터로부터 물리변형모델 기반의 매개변수 모델을 구축하고, PDM (point distribution model) 방법을 적용하여 두 집단을 대표하는 평균 모델을 생성한다. 마지막으로 SVM 기반의 이진 분류기를 구축하여 집단간 분류 작업을 수행한다. 구현한 모델링 방법과 분류기의 성능을 평가하기 위하여 본 연구에서는 네 가지 커널 함수 (linear, radial basis function, polynomial, sigmoid)들을 적용한다. 본 논문에서 제시한 매개변수형 모델은 다양한 형태의 의료 데이터로부터 보편적인 3차원 모델을 생성하고, 또한 모델의 전역적, 국부적인 특징들을 복합적으로 표현할 수 있기 때문에 통계적 형상분석에 적합하다. 그리고 SVM 기반의 분류기는 적은 수의 학습 데이터로부터 정상인 해마 집단과 간질 환자 집단간의 정확한 분류를 가능하게 한다.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.2
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• pp.123-130
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• 2013

Autonomous agricultural machines that are operated in small-scale farmland frequently experience turning and changes in direction. Thus, unlike when they are operated in large-scale farmland, the steering control systems need to be controlled precisely so that travel errors can be minimized. This study aims to develop a control algorithm for improving the path tracking performance of a steering system by analyzing the effect of the setting of the waypoint, which serves as the reference point for steering when an autonomous agricultural machine moves along a path or a coordinate, on control errors. A simulation was performed by modeling a 26-hp tractor steering system and by applying the equations of motion of a tractor, with the use of a computer. Path tracking errors could be reduced using an algorithm which sets the waypoint for steering on a travel path depending on the radius of curvature of the path and which then controls the speed and steering angle of the vehicle, rather than by changing the steering speed or steering ratio which are dependent on mechanical performance.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.869-874
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• 2004

The inverse kinematics of five-axis milling machines produce large errors near stationary points of the required surface. When the tool travels cross or around the point the rotation angles may jump considerably leading to unexpected deviations from the prescribed trajectories. We propose three new algorithms to repair the trajectories by adjusting the rotation angles in such a way that the kinematics error is minimized. Given the tool orientations and the inverse kinematics of the machine, we first eliminate the jumping angles exceeding ${\pi}$ by using the angle adjustment algorithm, leaving the jumps less than ${\pi}$ to be further optimized. Next, we propose to apply an angle switching algorithm to compute the rotations and identify an optimized sequence of rotations by the shortest path scheme. Further error reduction is accomplished by the angle insertion algorithm based an o special interpolation to obtain the required rotations near the singularity. We have verified the algorithms by five-axis milling machines, namely, MAHO600E at the CIM Lab of Asian Institute of Technology and HERMLE UWF902H at the CIM Lab of Kasetsart University.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.772-775
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• 2004

Generally, arterial pulse waves are measured at the radial arterial of wrist or carotid arterial of neck using a sensor such as pressure sensor, piezoelectric sensor or optic sensor. But in this paper, arterial pulse wave is measured at the temple using PZT piezo sensor which is attached on the temple in form of a hair-band. Arterial Pulse waves are generally measured when a reagent is in a static state. But in this paper, we implemented the arterial pulse wave measurement system, as a previous stage of the arterial pulse wave measurement system for running at outdoors or on a running machine, that measures arterial pulse waves at the temple, which is the least moving part when running. Thorough the continuous study, if the motion artifact when running is possible to be removed, the system will be able to perform monitoring of running men's states and especially emergency signals such as serious pulse waves of an/old and feeble persons and handicapped persons.

• Computers and Concrete
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• v.19 no.6
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• pp.651-658
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• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.