• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.171-178
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• 1991

For the polynomial spline regression with fixed knots, some properties of the D-optimal design are discussed. Also the D-optimal design for some cases are found analytically by using a normalized B-spline basis for $S(P_m : k : \Delta)$. Based on the Kiefer-Wolfowitz equivalence theorem, the D-optimal design for some cases are found by numerical methods.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.619-627
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• 2002

It is unpractical for the optimal design theory based on the given model and assumption to be applied to the real-world experimentation. Particularly, when the experimenter feels it necessary to consider multiple objectives in experimentation, its modified version of optimality criteria is indeed desired. The constrained optimal design is one of many methods developed in this context. But when the number of constraints exceeds two, there always exists a problem in specifying the lower limit for the efficiencies of the constraints because the “infeasible solution” issue arises very quickly. In this paper, we developed a sequential approach to tackle this problem assuming that all the constraints can be ranked in terms of importance. This approach has been applied to the polynomial regression model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.452-456
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• 2002

The sensor based control system has some sensor fault while operating in the field. In this paper, a sensor fault detection and reconstruction system for a sun tracking controller has been researched by using polynomial regression and principle component analysis approach. The developed sun tracking system controls tow actuators with sensor based mechanism as on-line control and sun orbit information as off-line control, alternatively. To show the validity of the developed system, several experiments were illustrated.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.109-112
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• 2001

실세계의 움직이는 여러 이동객체들은 시공간적인 특성을 지니고 있다. 이들 객체는 실세계의 공간 즉, 점들의 집합 내에 위치해 있으며 이들을 데이터베이스로 표현 및 관리하기 위해서는 점 흑은 영역 형태로 표현하고 저장하게 된다. 이 논문에서는 샘플링되지 않은 시점에 대한 이동객체의 위치 질의시 발생할 수 있는 이동객체의 불확실성을 처리하는 데 있어서, 기존의 선형 보간법 대신 이동객체의 위치값 자체의 오차범위까지 고려하는 다항회함수(polynomial regression function)을 이용한 이동객체의 불확실한 이동위치 추정 방법을 제시하였으며, 이동객체의 이동경로를 구현하였다. 다항회귀모형을 이용할 경우 선형 보간법 보다 추정된 위치간에 대한 오차를 줄일 수 있으며, 이동객체의 과거 및 미래 위치값을 사용자에게 반환해 줄 수 있는 장점을 가진다.

• Journal of Korean Society for Quality Management
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• v.8 no.1
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• pp.3-7
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• 1980

In many industrial processes, there are more than two responses (i.e., yield, percent impurity, etc.) of interest, and it is desirable to determine the optimal levels of the factors (i.e., temperature, pressure, etc.) that influence the responses. Suppose the response relationships are assumed to be approximated by second-order polynomial regression models. The problems considered in this paper is, first, to propose how to select polynomial terms to fit the multivariate regression surfaces for a given set of data, and, second, to propose how to analyze the data to obtain an optimal operating condition for the factors. The proposed techniques were applied for empirical process optimization in a tire company in Korea. This case is presented as an illustration.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.310-312
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• 2001

샘플링되지 않은 불확실한 이동객체의 위치값을 추정하기 위한 기존의 연구방범 중 가장 보편적으로 사용하고 있는 방법은 선형 보간법이다. 선형 보간법을 사용할 경우 샘플링 구간은 좁게하여 오차를 줄일 수 있고 계산 시간을 단축할 수 있지만, 연속적인 이동객체의 경로는 직선이라기 보다는 곡선으로 나타내어지므로 샘플링되지 않은 이동객체의 위치값에 대해 불확실한 위치정보를 사용자에게 반환하게 된다. 따라서 이 논문에서는 샘플링된 이동객체의 위치값에 오차가 없다는 가정하에서 모든 위치점을 지나는 보간 다항식을 구해서 처리하는 선형 보간법 대신 이동객체의 위치값 자체의 오차범위까지 고려하는 다항회귀모형(polynomial regression model)을 이용한 이동객체의 불확실한 이동위치 추정방법을 제시한다. 다항회지모형은 이용할 경우 선형 보간법 보다 추정된 위치값에 대한 오차를 줄일 수 있으며, 이동객체의 과거 및 미래 위치값을 사용자에게 반환해 줄 수 있는 장점을 가진다.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.263-272
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• 2018

For the design of hydraulic structures, the design flood discharge corresponding to a specific frequency is generally used by using the design storm calculated according to the rainfall-runoff relationship. In the past, empirical equations such as rational equations were used to calculate the peak flow rate. However, as the duration of rainfall is prolonged, the outflow patterns are different from the actual events, so the accuracy of the temporal distribution of the probability rainfall becomes important. In the present work, Huff's quartile method is used for the temporal distribution of rainfall, and the third quartile is generally used. The regression equation for Huff's quadratic curve applies a sixth order polynomial equation because of its high accuracy throughout the duration of rainfall. However, in statistical modeling, the regression equation needs to be concise in accordance with the principle of simplicity, and it is necessary to determine the regression coefficient based on the statistical significance level. Therefore, in this study, the statistical significance test for regression equation for temporal distribution of the Huff's quartile method, which is used as the temporal distribution method of design rainfall, is conducted for 69 rainfall observation stations under the jurisdiction of the Korea Meteorological Administration. It is statistically significant that the regression equation of the Huff's quartile method can be considered only up to the 4th order polynomial equation, as the regression coefficient is significant in most of the 69 rainfall observation stations.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.585-596
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• 2000

We developed a macro for prediction and classification using profection pursuit regression based on Friedman (1984b) and Hwang, et al. (1994). In the macro, the order of the Hermite functions can be selected automatically. In projection pursuit regression, we compare several smoothing methods such as super smoothing, smoothing with the Hermite functions. Also, classification methods applied to German credit data are compared.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.55-67
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• 1998

Recent work by Shin and Sarkar (1996) examines model misspecification problems for nonstationary time series. Shin and Sarkar introduce a general regression model with integrated errors and one system of integrated regressors and discuss the limiting distributions of the OLS estimators and the usual OLS statistics such as $\hat{\sigma^2}$t, DW and $R^2$. We analyze three different model misspecification problems through a Monte Carlo study and investigate each model misspecification problem. Our Monte Carlo experiments show that DW and $R^2$ can be in general used as diagnostic tools to detect spurious regression, misspecification of nonstationary autoregressive and polynomial regression models.