• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.159-169
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• 2003

In the ACI Code, the empirical equations governing deep beam design are based on low-strength concrete specimens with $f_{ck}$ in the range of 14 to 40MPa. As high-strength concrete(HSC) is becoming more and more popular, it is timely to evaluate the application of HSC deep beam. For the shear strength prediction of HSC deep beams, this paper proposed Softened Strut-and-Tie Model(SSTM) considered HSC and bending moment effect. The shear strength predictions of the proposed model, the Appendix A Strut-and-Tie Model of ACI 318-02, and Eq. of ACI 318-99 11.8 are compared with the experimental test results of 4 deep beams and the collected experimental data of 74 HSC deep beams, compressive strength in the range of 49~78MPa. The proposed SSTM performance consistently reproduced 74 HSC deep beam measured shear strength with reasonable accuracy for a wide range of concrete strength, shear span-depth ratio, and ratio of horizontal and vertical reinforcement.

• Atmosphere
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• v.25 no.1
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• Progress in Medical Physics
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• v.28 no.4
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• pp.171-180
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• 2017

The purpose of this study is to install a system that compensated for the respiration motion using an articulated robotic manipulator couch which enables a wide range of motions that a Stewart platform cannot provide and to evaluate the performance of various prediction algorithms including proposed algorithm. For that purpose, we built a miniature couch tracking system comprising an articulated robotic manipulator, 3D optical tracking system, a phantom that mimicked respiratory motion, and control software. We performed simulations and experiments using respiratory data of 12 patients to investigate the feasibility of the system and various prediction algorithms, namely linear extrapolation (LE) and double exponential smoothing (ES2) with averaging methods. We confirmed that prediction algorithms worked well during simulation and experiment, with the ES2-averaging algorithm showing the best results. The simulation study showed 43% average and 49% maximum improvement ratios with the ES2-averaging algorithm, and the experimental study with the $QUASAR^{TM}$ phantom showed 51% average and 56% maximum improvement ratios with this algorithm. Our results suggest that the articulated robotic manipulator couch system with the ES2-averaging prediction algorithm can be widely used in the field of radiation therapy, providing a highly efficient and utilizable technology that can enhance the therapeutic effect and improve safety through a noninvasive approach.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.166-176
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• 2012

Purpose: This study was conducted to investigate the potential of interactance mode of NIR spectroscopy technology for the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from local greenhouses in three different harvesting seasons (experiments 1, 2, and 3). The fruit attributes were measured at the 6 points on an equator of each sample where the spectral data were collected. The prediction models were developed using the original spectral data and the spectral data sets preprocessed by 20 methods. The performance of the models was compared. Results: In the prediction of SSC, the highest coefficient of determination ($R_{cv}{^2}$) values of the cross-validation was 0.755 (standard error of prediction, SEP=$0.89^{\circ}Brix$) with the preprocessing of normalization with range in experiment 1. The highest coefficient of determination in the robustness tests, $R_{rt}{^2}$=0.650 (SEP=$1.03^{\circ}Brix$), was found when the best model of experiment 3 was evaluated with the data set of experiment 2. The best $R_{cv}{^2}$ for the prediction of firmness was 0.715 (SEP=3.63 N) when no preprocessing was applied in experiment 1. The highest $R_{rt}{^2}$ was 0.404 (SEP=5.30 N) when the best model of experiment 3 was applied to the data set of experiment 1. Conclusions: From the test results, it can be concluded that the interactance mode of VIS/NIR spectroscopy technology has a great potential to measure SSC and firmness of thick-skinned muskmelons.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.84-91
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• 2008

This paper suggests the robust localization system in the application of ambient display with multiple ultrasonic range sensors. The ambient display provides the interactive image and video to improve the quality of life, especially for low mobility elders. Due to the limitation of indoor localization, this paper employs linear prediction algorithm to recover the missing information based on AR(Autoregressive) model by using Yule-Walker method. Numerous speculations from prediction error and computation load are considered to decide the optimal length of referred data and order. The results of these analyses demonstrate that the linear prediction algorithm with the 16th order and 50 reference data can improve reliability of the system in average 74.39% up to 97.97% to meet the performance of interactive system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• The korean journal of orthodontics
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• v.46 no.3
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• pp.171-179
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• 2016

Objective: The primary aim of the study was to generate new prediction equations for the estimation of maxillary and mandibular canine and premolar widths based on mandibular incisors and first permanent molar widths. Methods: A total of 2,340 calculations (768 based on the sum of mandibular incisor and first permanent molar widths, and 1,572 based on the maxillary and mandibular canine and premolar widths) were performed, and a digital stereomicroscope was used to derive the the digital models and measurements. Mesiodistal widths of maxillary and mandibular teeth were measured via scanned digital models. Results: There was a strong positive correlation between the estimation of maxillary (r = 0.85994, $r^2=0.7395$) and mandibular (r = 0.8708, $r^2=0.7582$) canine and premolar widths. The intraclass correlation coefficients were statistically significant, and the coefficients were in the strong correlation range, with an average of 0.9. Linear regression analysis was used to establish prediction equations. Prediction equations were developed to estimate maxillary arches based on $Y=15.746+0.602{\times}sum$ of mandibular incisors and mandibular first permanent molar widths (sum of mandibular incisors [SMI] + molars), $Y=18.224+0.540{\times}(SMI+molars)$, and $Y=16.186+0.586{\times}(SMI+molars)$ for both genders, and to estimate mandibular arches the parameters used were $Y=16.391+0.564{\times}(SMI+molars)$, $Y=14.444+0.609{\times}(SMI+molars)$, and $Y=19.915+0.481{\times}(SMI+molars)$. Conclusions: These formulas will be helpful for orthodontic diagnosis and clinical treatment planning during the mixed dentition stage.

• Korean Journal of Food Science and Technology
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• v.38 no.2
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• pp.165-168
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• 2006

Fourier transform-near infrared spectroscopy (FT-NIRS) was evaluated for determination of total dietary fiber (TDF) content of brown rice. Enzymatic-gravimetric method was suitable to obtain reference values for calibration of NIR at 1,000-2,500 nm range. Standard error of laboratory procedure ranged 0.17 to 0.72%. Partial least square (PLS) regression was used to develop the calibration equations. Regression was performed automatically using NIRCal chemometric software. Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP), showing 0.9780, 0.0636, and 0.0642, respectively. This prediction model can be used for determination of TDF in brown rice and would be useful for real-time analysis in food industry.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2003.03a
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• pp.13-30
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• 2003

In this paper, a new systematic method will be introduced, in which a Rock-mass Prediction System(RPS) predicts the geological conditions and rock mass movements before tunnel excavation and the appropriate counter-measures are taken in the expected weak zones during tunnel construction. The Rock-mass Prediction System(RPS) consists of the LIM, a horizontal core drilling and a seismic exploration method(TSP/HSP). In the Rock-mass Prediction System(RPS), the seismic exploration method (TSP/HSP) gives information on the locations of the weak zones such as major faults and voids in wide-range, and the horizontal core drillings are utilized to find exact location and widths of the faults or voids near the weak zones which was predicted by the seismic exploration method (TSP/HSP). The LIM is used to find the hardness of the rock mass and small weak zones near the excavation face. The Rock-mass Prediction System (RPS) was successfully applied to the Sol-An Tunnel and the effectiveness of the system was verified.

• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.105-113
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• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.