• Title/Summary/Keyword: 가우스 커널 함수

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Heating-Plan Heuristics for Forming Curved Shell Plate of Ship Structure (선체 외판 부재의 곡 성형을 위한 가열 계획 생성 휴리스틱)

  • Gang, Byeong-Ho;Park, Gi-Yeok;Kim, Ung;Ryu, Gwang-Ryeol;Lee, Jeong-Hwan;Do, Yeong-Chil;Kim, Dae-Gyeong;Kim, Se-Hwan
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2007.11a
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    • pp.570-578
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    • 2007
  • 선체 외판 부재의 곡 성형 과정은 주로 가열(열간가공)에 의해 수행된다. 이 가열 작업은 작업자의 경험과 지식에 크게 의존하는 매우 어려운 작업이다. 본 논문에서는 선체 외판의 곡 성형을 위한 가열 계획을 자동으로 수립할 수 있는 휴리스틱을 소개한다. 현장 전문가의 지식에 기반한 이 휴리스틱은 크게 가열 선을 생성하는 부분과 외력을 주는 도구를 배치하는 부분으로 구성된다. 가열 선은 대상 부재의 현재 곡면과 설계된 목적곡면과의 비교를 통해 생성되고, 가우스 커널 함수를 통해 스무딩(smoothing)된다. 현장에서는 열간가공 시 의도하지 않은 변형을 막으면서 작업시간을 줄이고자 외력을 이용한다. 외력의 위치와 방향은 가열 선 군집화를 통해 추출된 대표 가열 선을 기준으로 결정된다. 가상의 인공 곡면과 현장의 실제 부재를 대상으로 실험한 결과, 이 휴리스틱이 숙련된 전문가가 수립한 가열 계획과 유사한 가열 계획을 수립할 수 있음을 확인하였다.

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Daily maximum power demand analysis using machine learning model (기계학습 모델을 활용한 일일 최대 전력 수요 분석)

  • Lee, Tae-Ho;Kim, Min-Woo;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2019.07a
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    • pp.157-158
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    • 2019
  • 발전소 관리의 단기 전력 수요에 대한 정확한 예측은 전력 시스템의 안전하고 효율적인 작동을 보장하는데 필수적이다. 따라서 본 연구는 가우스 커널 함수 네트워크 (GKFNs)의 심층 구조를 이용하여 일일 최대 전력 수요를 예측하는 새로운 방법을 제시한다. 제안 된 GKFN의 깊이 구조는 표준 GKFN에 비해 예측 정확도를 향상시킨다. 한국의 일일 최대 전력 수요를 예측하기위한 시뮬레이션은 제안 된 예측 모델이 GKFN 모델, k-NN 및 SVR과 같은 다른 예측 모델에 비해 예측 성능에 이점이 있음을 보여준다. GKFN의 제안된 심층 구조는 시계열 예측 및 회귀 문제의 다양한 문제에 적용될 수 있다.

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Estimation of GARCH Models and Performance Analysis of Volatility Trading System using Support Vector Regression (Support Vector Regression을 이용한 GARCH 모형의 추정과 투자전략의 성과분석)

  • Kim, Sun Woong;Choi, Heung Sik
    • 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.