• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.787-796
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• 2015

When the temperature of a structure varies, there is a tendency to produce changes in the shape of the structure. The resulting actions may be of considerable importance in the analysis of the structures having non-prismatic members. The computation of design forces for the non-prismatic beams having symmetrical parabolic haunches (NBSPH) is fairly difficult because of the parabolic change of the cross section. Due to their non-prismatic geometrical configuration, their assessment, particularly the computation of fixed-end horizontal forces and fixed-end moments becomes a complex problem. In this study, the efficiency of the Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) in predicting the design forces and the design moments of the NBSPH due to temperature changes was investigated. Previously obtained finite element analyses results in the literature were used to train and test the ANN and ANFIS models. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients ($R^2$). In addition to this, the comparison of ANN and ANFIS with traditional methods was made by setting up Linear-regression (LR) model.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.65.1-65.1
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• 2011

We built a simple Adaptive Optics (AO) system at laboratory. This AO system is a step toward developing AO system for astronomical use. In this step, the AO system consists of He-Ne laser as a artificial light source, wavefront sensor, MEMS (Micro electro mechanical system) type deformable mirror and several lenses. MEMS deformable mirror allows the compact system at low cost and the only several mm sized collimated beam. We made Shack-Hartmann wavefront sensor using a lenslet array and a fast frame CCD. Its performance is verified using an artificial phase disturber and noting the movement of spot images by the lenslet array. The frame rate of the driving software is about 70 fps, depending on the control parameters. The characteristics of MEMS deformable mirror was measured which includes the voltage-to-deflection relation, influence function, and cross-talk. The total system is operated under closed-loop control for the artificial phase disturber and the wavefront is found to be compensated successfully.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Physical Therapy Korea
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• v.29 no.3
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• pp.200-207
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• 2022

Background: The oxygen uptake efficiency slope (OUES) is the most important index for accurately measuring cardiopulmonary function in patients with acute ischemic heart disease. However, the relationship between the OUES variables and important cardiopulmonary function parameters remain unelucidated for patients with acute ischemic heart disease, which accounts for the largest proportion of heart disease. Objects: The present cross sectional clinical study aimed to determine the multiple relationships among the cardiopulmonary function variables mentioned above in adults with acute ischemic heart disease. Methods: A convenience sample of 110 adult inpatients with ischemic heart disease (age: 57.4 ± 11.3 y; 95 males, 15 females) was enrolled at the hospital cardiac rehabilitation center. The correlation between the important cardiopulmonary function indicators including peak oxygen uptake (VO₂ peak), minute ventilation (VE)/carbon dioxide production (VCO₂) slope, heart rate recovery (HRR), and ejection fraction (EF) and OUES was confirmed. Results: This study showed that OUES was highly correlated with VO₂ peak, VE/VCO₂ slope, and HRR parameters. Conclusion: The OUES can be used as an accurate indicator for cardiopulmonary function. There are other factors that influence aerobic capacity besides EF, so there is no correlation with EF. Effective cardiopulmonary rehabilitation programs can be designed based on OUES during submaximal exercise in patients with acute ischemic heart disease.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.203-208
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• 2023

The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.

• The Journal of Bigdata
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• v.7 no.2
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• pp.195-204
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• 2022

The tourism industry is facing a crisis due to the recent COVID-19 pandemic, and it is vital to improving profitability to overcome it. In situations such as COVID-19, it would be more efficient to sell additional products other than guest rooms to customers who have visited to increase the unit price rather than adopting an aggressive sales strategy to increase room occupancy to increase profits. Previous tourism studies have used machine learning techniques for demand forecasting, but there have been few studies on cross-selling forecasting. Also, in a broader sense, a resort is the same accommodation industry as a hotel. However, there is no study specialized in the resort industry, which is operated based on a membership system and has facilities suitable for lodging and cooking. Therefore, in this study, we propose a cross-selling prediction model using various machine learning techniques with an actual resort company's accommodation data. In addition, by applying the explainable artificial intelligence XAI(eXplainable AI) technique, we intend to interpret what factors affect cross-selling and confirm how they affect cross-selling through empirical analysis.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.22 no.3
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• pp.158-163
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• 2012

The purpose of this study is to improve recycling rate of the coal reject ash by investigating bloating mechanism for artificial lightweight aggregate of reject ash. In this study, we use reject ash (R/A) and dredged soil (D/S) as raw materials. The artificial lightweight aggregates were formed by plastic forming (${\phi}$ = 10 mm) and sintered by temperature raising method at different temperatures (between 1200 and $1275^{\circ}C$). The physical properties of the aggregates such as bulk specific gravity, adsorption and microstructure of surface and cross-section are investigated with the sintering temperature and rate of R/A-D/S contents. As the result of the bulk specific gravity graphs, we can found out the inflection point at content of R/A 80 wt.%. From the microstructure images, we considered the artificial lightweight aggregates content of R/A over 80 wt.% are distributed numerous uniform micro-pores by vitrification without Black Core and the artificial lightweight aggregates of R/A below 80wt.% are distributed macro-pores with Black Core.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.931-939
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• 2014

In this paper we propose the iteratively reweighted least squares procedure to solve the quadratic programming problem of support vector expectile regression with an asymmetrically weighted squares loss function. The proposed procedure enables us to select the appropriate hyperparameters easily by using the generalized cross validation function. Through numerical studies on the artificial and the real data sets we show the effectiveness of the proposed method on the estimation performances.