• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Earthquakes and Structures
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• v.23 no.3
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• pp.259-269
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• 2022

In this study, we introduce a canonical correlation analysis method to accurately assess the tunnel damage potential of ground motion. The proposed method can retain information relating to the initial variables. A total of 100 ground motion records are used as seismic inputs to analyze the dynamic response of three different profiles of tunnels under deep and shallow burial conditions. Nine commonly used ground motion parameters were selected to form the canonical variables of ground motion parameters (GMP_CCA). Five structural dynamic response parameters were selected to form canonical variables of structural dynamic response parameters (DRP_CCA). Canonical correlation analysis is used to maximize the correlation coefficients between GMP_CCA and DRP_CCA to obtain multivariate ground motion parameters that can be used to comprehensively assess the tunnel damage potential. The results indicate that the multivariate ground motion parameters used in this study exhibit good stability, making them suitable for evaluating the tunnel damage potential induced by ground motion. Among the nine selected ground motion parameters, peck ground acceleration (PGA), peck ground velocity (PGV), root-mean-square acceleration (RMSA), and spectral acceleration (Sa) have the highest contribution rates to GMP_CCA and DRP_CCA and the highest importance in assessing the tunnel damage potential. In contrast to univariate ground motion parameters, multivariate ground motion parameters exhibit a higher correlation with tunnel dynamic response parameters and enable accurate assessment of tunnel damage potential.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Korean Journal of Exercise Nutrition
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• v.23 no.3
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• pp.22-28
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• 2019

[Purpose] The purpose of this study was to investigate the effect of diet plus exercise training and detraining for 12 weeks on body composition, aerobic performance, and stress-related variables in obese women. [Methods] Twenty-five women in their 20s-40s with 30% body fat and body mass indices above 25 kg/m² were divided into HR_LT (heart rate at lactate threshold) and HR_LT + 5% groups. Dietary intervention of 70% recommended dietary allowance (RDA) and exercise treatment composed of aerobic exercises on a bicycle (30 min) and treadmill (30 min) were then performed. These interventions were performed three times a week for 12 weeks. [Results] Dietary intake was significantly decreased, while daily activity significantly increased within the 12-week intervention period, and this effect was sustained after 12 weeks of detraining. Exercise training based on dietary intake and daily activity presented a significantly decreased weight and % body fat, improvement of aerobic performance, and a significant increase in heart rate variability (HRV) (e.g., average of all RR intervals and the square root mean squared differences of successive RR intervals) as stress-related variables. It was also confirmed that the improvement of body composition and stress-related variables were maintained even after detraining. [Conclusion] Our results suggest that 70% RDA of dietary intervention and exercise training corresponding to HR_LT and HR_LT + 5% for 12 weeks were effective in improving body composition and aerobic performance, and relieving stress. In particular, enhanced HRV persisted for up to 12 weeks after the end of exercise training in obese women.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Journal of Technologic Dentistry
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• v.45 no.3
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• pp.67-73
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• 2023

Purpose: This in vitro study aimed to evaluate the clinical acceptability of precision of fit of the support thickness of Co-Cr alloy copings fabricated using selective laser melting (SLM). Methods: Thirty dental stone models of maxillary left molar abutments were manufactured, images were taken using a scanner, and a computer-aided design program was used to design the form of a conventional metal ceramic crown coping. Overall, 30 single copings were made from Co-Cr alloy using SLM and divided into three support radius groups (0.1, 0.25, and 0.35 mm) of 10 for each. Digitized data were superimposed with three-dimensional inspection software to quantitatively obtain the machinability of a ceramic crown coping, and visual differences were confirmed using a color map. The root mean square values of the ceramic crown coping group were statistically analyzed using one-way analysis of variance (α=0.05). Results: The precision of fit was superior with 0.25 mm compared with 0.1 mm and 0.35 mm, and the results exhibited significant differences (p<0.05). All specimens showed that various support thicknesses did not exceed the clinically permitted value of 120 ㎛, which mean that more than 0.1 mm and 0.35 mm of support radius for SLM was adequate. Conclusion: The support thickness of Co-Cr alloy restoration fabricated using SLM is shown to affect the adaptation.

• Korean Journal of Materials Research
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• v.33 no.11
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• pp.491-496
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• 2023

As the demand for p-type semiconductors increases, much effort is being put into developing new p-type materials. This demand has led to the development of novel new p-type semiconductors that go beyond existing p-type semiconductors. Copper iodide (CuI) has recently received much attention due to its wide band gap, excellent optical and electrical properties, and low temperature synthesis. However, there are limits to its use as a semiconductor material for thin film transistor devices due to the uncontrolled generation of copper vacancies and excessive hole doping. In this work, p-type CuI semiconductors were fabricated using the chemical vapor deposition (CVD) process for thin-film transistor (TFT) applications. The vacuum process has advantages over conventional solution processes, including conformal coating, large area uniformity, easy thickness control and so on. CuI thin films were fabricated at various deposition temperatures from 150 to 250 ℃ The surface roughness root mean square (RMS) value, which is related to carrier transport, decreases with increasing deposition temperature. Hall effect measurements showed that all fabricated CuI films had p-type behavior and that the Hall mobility decreased with increasing deposition temperature. The CuI TFTs showed no clear on/off because of the high concentration of carriers. By adopting a Zn capping layer, carrier concentrations decreased, leading to clear on and off behavior. Finally, stability tests of the PBS and NBS showed a threshold voltage shift within ±1 V.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.415-421
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• 2023

In this study, the piezoresistive properties of cementitious composites enhanced with carbon nanotubes for improved electrical conductivity were analyzed using a deep learning-based transformer algorithm. Experimental execution was performed in parallel for acquisition of training data. Previous studies on mixture design, specimen fabrication, chemical composition analysis, and piezoresistive performance testing are also reviewed in this paper. Notably, specimens in which fly ash substituted 50% of the binder material were fabricated and evaluated in this study, in addition to carbon nanotube-infused specimens, thereby exploring the potential enhancement of piezoresistive characteristics in conductive cementitious materials. The experimental results showed more stable piezoresistive responses in specimens with fly-ash substituted binder. The transformer model was trained using 80% of the gathered data, with the remaining 20% employed for validation. The analytical outcomes were generally consistent with empirical measurements, yielding an average absolute error and root mean square error between 0.069 to 0.074 and 0.124 to 0.132, respectively.

• Smart Structures and Systems
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• v.32 no.5
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• pp.281-295
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• 2023

The purpose of this study is to demonstrate and verify the application of phase-control absolute-acceleration-feedback active tuned mass dampers (PCA-ATMD) to multiple-degree-of-freedom (MDOF) building structures. In addition, servo speed control technique has been developed as a replacement for force control in order to mitigate the negative effects caused by friction and inertia. The essence of the proposed PCA-ATMD is to achieve a 90° phase lag for a structure by implementing the desired control force so that the PCA-ATMD can receive the maximum power flow with which to effectively mitigate the structural vibration. An MDOF building structure with a PCA-ATMD and a real-time filter forming a complete system is modeled using a state-space representation and is presented in detail. The feedback measurement for the phase control algorithm of the MDOF structure is compact, with only the absolute acceleration of one structural floor and ATMD's velocity relative to the structure required. A discrete-time direct output-feedback optimization method is introduced to the PCA-ATMD to ensure that the control system is optimized and stable. Numerical simulation and shaking table experiments are conducted on a three-story steel shear building structure to verify the performance of the PCA-ATMD. The results indicate that the absolute acceleration of the structure is well suppressed whether considering peak or root-mean-square responses. The experiment also demonstrates that the control of the PCA-ATMD can be decentralized, so that it is convenient to apply and maintain to real high-rise building structures.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.522-527
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• 2022

Object tracking is a field of signal processing that sequentially tracks the location of an object based on the previous-time location estimations and the present-time observation data. In this paper, we propose an adaptive scaling neural network that can track and adjust the scale of the input data with three recursive neural network (RNN) submodules. To evaluate object tracking performance, we compare the proposed system with the Kalman filter and the maximum likelihood object tracking scheme under an one-dimensional object movement model in which the object moves with piecewise constant acceleration. We show that the proposed scheme is generally better, in terms of root mean square error (RMSE) performance, than maximum likelihood scheme and Kalman filter and that the performance gaps grow with increased observation noise.