• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.103-110
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• 2005

Software Development is converting from structural to object oriented method. The later software development prefers the iterative process applications, not aterfall process and based on use case model, the requirements are expressed and based on this, analysis, design and coding are accomplished. Therefore, size of the software to be developed is estimated basing on use case and it is only possible to maintain the project success by estimating development effort, cost and development period. Even though development effort estimation models related current use case point. there is no appropriate development effort estimating. This paper shows, as a result of applying the development effort estimating model about UCP to the growth curve, a superior performance improvement to current statistical models. Therefore, estimation of development effort by applying this model, project development maintenance can be appropriately carried out.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.81-90
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• 2006

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.113-125
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• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.442-442
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• 2021

하천과 호수 등 공공수역에서 측정되는 수질자료는 수자원 및 수생태계의 실태를 파악하기 위한 가장 중요한 요소이며, 측정망 운영으로 장기간 측정된 방대한 수질자료를 이용하여 신뢰성 있는 장기 경향 추세를 분석하는 것 또한 매우 중요하다. 장기간 생산된 수질자료에 대한 통계적 경향분석을 실시함으로써 정량적으로 수질자료를 분석할 수 있는데 우선 일정한 장소에서 동일한 측정기법을 통해 비교적 장기간 동안 일정간격으로 측정된 수질자료가 필요하다. 우리나라의 수질자료는 1990년도 이후부터 잘 축적되어 왔으므로 비로소 시간에 따른 수질변화 경향을 파악하는 것이 용이해졌다. 이러한 수질경향 분석을 통하여 수체 내부에서 일어나는 여러 가지 수질 변화 과정을 이해하고 적절한 수질관리 대책을 마련할 수도 있다. 본 연구에서는 수질자료의 경향을 분석하기 위하여 비모수적 통계기법의 수질 경향을 분석하는데 많이 활용되는 맨-켄달 검정기법(Mann-Kendall Test)과 LOWESS(LOcally WEighted Scatter polt Smoother) 경향분석법을 적용하였다. 맨-켄달 검정기법은 선형 경향을 기본 가정으로하기 때문에 대상 기간 동안 경향성이 변할 경우에는 이를 적절히 반영할 수 없는 단점이 있으나 LOWESS 경향분석법은 이를 보완하기 위하여 특정회귀모델을 가정하지 않고 이동 직선에 대한 수질자료 점들을 통해 회귀모델을 적합 시키는 방법으로 기간 내 변화하는 경향성을 파악 할 수 있는 대상지점은 영산강본류 중심으로 지류지천을 포함하여 18개 지점에 대하여 분석하였으며, p-value값은 0.05를 기준으로 미만일 경우 경향성이 있고, 이상일 경우 경향성이 없는 것으로 분류하였으며, Trend는 경향성이 있다고 판단될 경우 S값이 양수이면 증가하는 경향으로, S값이 음수이면 감소하는 경향으로 판단하였다. 경향분석을 통해 영산강 18개 지점을 분석한 결과 영산강 상류와 중류, 지류에 대한 전체적인 경향을 판단할 수 있었다.

• Analytical Science and Technology
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• v.24 no.6
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• pp.544-555
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• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.608-609
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• 2011

전력수요 예측 오차가 큰 추석 연휴 및 전, 후일 전력수요 예측의 정확성을 향상시키기 위해 과거 추석 연휴 및 전, 후일에 대한 전력수요 특성을 분석하고 최대/최소 전력 예측을 위한 퍼지 입력데이터 선정 방법과 24시간 예측을 위한 정규화에 필요한 입력 데이터 선정방법을 개발하여 퍼지 선형회귀분석 모델을 사용하여 2006년에서 2010년까지 5개년의 사례연구를 통해 알고리즘의 우수성을 검증하였다.

• Journal of the Korean Solar Energy Society
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• v.35 no.5
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• pp.31-38
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• 2015

It is important to improve the energy performance in the industrial sector, and therefore most of the manufacturing companies need the energy performance indicators to identify the target and to verify the energy savings. However, the conventional energy performance indicators such as the total energy consumption and the energy intensity are not proper to use. The reason is that they do not consider adequate relevant variables including productions in the boundary of the manufacturing companies. Therefore, the study provides the advanced energy performance indicator using by the linear regression model according to each energy source to manage the target and to verify the energy performance properly.