• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.797-800
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• 2021

인공지능을 활용한 주가 예측 모형을 실제 금융 서비스에 도입한 사례가 많아지고 있다. 주식 데이터는 일반적인 시계열 데이터와 다르게 예측을 어렵게 하는 복합적인 요소가 존재하며 주식은 리스크가 큰 자산 상품 중 하나이다. 주가 예측 모형의 활용 가능성을 높이기 위해선 성능을 향상시키는 것과 함께 모델을 해석 가능한 형태로 제시해 신뢰성을 향상시킬 필요성이 있다. 본 논문은 주가 전망 결정 방법에 따른 예측 결과를 비교하고, 설명 가능성을 부여해 모형 개선했다는 것에 의의가 있다. 연구 결과, 주가 전망을 장기적으로 결정할수록 정확도가 증가하고, XAI 기법을 통해 모형의 개선 근거를 제시할 수 있음을 알 수 있었다. 본 연구를 통해 인공지능 모형의 신뢰성을 확보하고, 합리적인 투자 결정에 도움을 줄 수 있을 것으로 기대한다.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 1999.04a
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• pp.53-57
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• 1999

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.233-233
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• 2017

현재의 기술과 전문가들의 지식을 바탕으로 수치 예보 모델의 해상도가 점차 증가하고 있으나 한편으로는 해상도가 높아질수록 신뢰성 있는 장기 예보를 제공하는데 어려움이 있다. 즉, 고해상도 모델의 경우 미세한 오차가 발생 하더라도, 실제 기상학적 관점에서 시공간적으로 변동성이 크게 발생할 개연성이 크며, 이로 인해 모델에서 발생하는 불확실성은 더욱 커질 수 있다. 한국 기상청(KMA)에서는 영국기상청으로부터 도입한 통합모델(UM)을 현업 운영하고 있다. 본 연구에서 기상청 통합모델인 UM3.0 예보모델의 예측정확성을 다양한 관점에서 평가하고자 한다. 기상청 UM3.0 모델은 3km의 공간해상도와 1시간 시간해상도를 가지며, 예보시작시점기준 7일간의 예보정보를 제공한다. 강수량 예측정보의 활용성을 평가하기 위해서 예측 시계열에 대해 RMSE, 편의 및 등 다양한 통계지표와 공간적인 강수량 발생 특성을 평가하기 위해서 FSS 방법을 적용하였다. 본 연구 결과를 통해 UM3.0 모델의 1시간 및 3km의 시공간해상도와 선행예보 기간을 그대로 수문학적으로 활용하는 데에는 다소 무리가 있는 것으로 평가되었으며, 이러한 점에서 수문학적 활용관점에서 최적의 시공간적 규모와 선행예보 시간을 분석하였다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.541-546
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• 2021

Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.

• Journal of Navigation and Port Research
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• v.28 no.1
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• pp.17-22
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• 2004

The paper describes implementation of three-dimensional visualization system that is to provide comprehensive meaning of the statistical prediction results on the marine casualties. Graphical User Interface (GUI) and Web based Virtual Reality (VR) technology are mainly introduced in the system development. To provide daily forecasting, time based casualty prediction model and risk level index are developed in this work. As operating test results of the system, complicated statistical meaning can be shown in the three-dimensional virtual space using simple color. In addition, daily risk levels can be shown on the bar-graph.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.183-200
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• 2008

Research for efficient management of the National Pension has been emphasized as the current society trends toward aging and low birth rate. In this article, we suggest a statistical model for effective classification and prediction of the reserve for the survivor's pension in Korea. Logistic regression model is incorporated; correct classification rate, and distribution of the posterior probability for the reserve of survivor's pension are investigated and compared with the results from the general logistic models. Assessment of predictive model is also done with lift graph, ROC curve and K-S statistic. We suggest strategies for reducing financial risks in managing and planning the pension as an application of the suggested model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.4
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• pp.457-465
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• 2010

In this study, a Taylor series (T-S) model based on the Arrhenius, McVetty, and Monkman-Grant equations was developed using a mathematical analysis. In order to reduce fitting errors, the McVetty equation was transformed by considering the first three terms of the Taylor series equation. The model parameters were accurately determined by a statistical technique of maximum likelihood estimation, and this model was applied to the creep data of alloy 617. The T-S model results showed better agreement with the experimental data than other models such as the Eno, exponential, and L-M models. In particular, the T-S model was converted into an isothermal Taylor series (IT-S) model that can predict the creep strength at a given temperature. It was identified that the estimations obtained using the converted ITS model was better than that obtained using the T-S model for predicting the long-term creep life of alloy 617.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2002.11a
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• pp.85-88
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• 2002

기상학적으로 안개는 지상에서 발생하는 응결 현상으로, 시정이 1km 이하일 때로 정의된다. 안개 발생은 기후 인자의 영향을 많이 받는다. 따라서 각 지역마다의 발생 특성을 따로 통계해야 할 필요가 있다. 특히 항공 교통의 장애가 되는 위험 요소로서의 역할이 중시되어 각 비행장마다 발생 특성이 따로 통계 분석되고 이용되어 왔다.(중략)

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.301-310
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• 2021

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.