• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.73-80
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• 2024

In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index. The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.5 s.170
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• pp.55-62
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• 2005

This paper presents mainly a procedure to get the mathematical form of the manufactured aspherical lens. Generally Schulz formula describes the aspherical lens profile. Therefore, the base curvature, conic constant. and high-order polynomial coefficient should be set to get the approximated design equation. To find the best-approximated aspherical form, lens profile is measured by a commercial stylus profiler, which has a sub-micrometer measurement resolution. The optimization tool is based on the minimization of the root mean square of error sum to get the estimated aspherical surface equation from the scanned aspherical profile. Error minimization step uses the Nelder-Mead simplex (direct search) method. The result of the lens refractive power measurement shows the experimental consistency with the curvature distribution of the best-approximated aspherical surface equation

• Smart Structures and Systems
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• v.16 no.3
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• pp.401-414
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• 2015

A developed hybrid method for crack identification of beams is presented. Based on the Euler-Bernouli beam theory and concepts of fracture mechanics, governing equation of the cracked beams is reformulated. Finite element (FE) method as a powerful numerical tool is used to discritize the equation in space domain. After transferring the equations from time domain to frequency domain, frequencies and mode shapes of the beam are obtained. Efficiency of the governed equation for free vibration analysis of the beams is shown by comparing the results with those available in literature and via ANSYS software. The used equation yields to move the influence of cracks from the stiffness matrix to the mass matrix. For crack identification measured data are produced by applying random error to the calculated frequencies and mode shapes. An objective function is prepared as root mean square error between measured and calculated data. To minimize the function, hybrid genetic algorithms (GAs) and particle swarm optimization (PSO) technique is introduced. Efficiency, Robustness, applicability and usefulness of the mixed optimization numerical tool in conjunction with the finite element method for identification of cracks locations and depths are shown via solving different examples.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Journal of Korean Academy of Nursing
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• v.48 no.5
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• pp.588-600
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• 2018

Purpose: The purpose of this study was to test a model of nursing competencies of nursing simulation learners. The conceptual model was based on the theory of Jeffries's simulaton theory. Methods: Data collection was conducted in October 2017 for 310 students from two nursing universities in Kyungbuk area for 20 days. Data analysis methods were covariance structure analysis using SPSS 21.0 and AMOS 22.0 statistical programs. Results: The hypothetical model was a good fit for the data. The model fit indices were comparative fit index=.97, normed fit index=.94, Tucker-Lewis Index=.97, root mean square error of approximation=.44, and standardized root mean square residual=.04. Teacher factors were directly related to simulation design characteristics, and it was confirmed that the curriculum, classroom operation and teaching method of the instructors were important factors. Learner factors were found to have a direct effect on nursing competence, self-confidence, and clinical performance that belong to nursing capacity. In particular, the results of this study indicate that the simulation design characteristics have a partial mediating effect on learner factors and clinical performance, and a complete mediating effect on learner factors and clinical judgment ability. Conclusion: In order to improve the learner's clinical performance and clinical judgment ability, it is necessary to conduct practical training through nursing simulation besides preparing the learner and the educator.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.2
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• pp.148-160
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• 2023

Purpose: The purpose of this study was to construct and test a structural model of nursing students' performing communication skills. Methods: The data collection was conducted from October 13 to October 20, 2020. The participants were 286 students from nursing colleges located in three cities. The data analysis method was a covariance structure analysis with using IBM SPSS statistics version 23.0 and AMOS 21.0 statistical programs. Results: The hypothetical model showed a proper fit with the data: root mean square error of approximation=.08, standardized root mean square residual=.06, adjusted goodness of fit=.85, normed fit index=.91, and comparative fit index=.94. The model fit indices were normed to fit index=2.96. Statistically significant explanatory variables for the performing communication skills of nursing students were peer support, emotional intelligence, ethical sensitivity, and communication self-efficacy. The variables accounted for 66.1% of the performing communication skills of nursing students. Conclusion: Based on the above results, it appears necessary to develop strategies for improving the performing communication skills of nursing students, and having positive effects on health outcomes of the subjects by considering the variables of peer support, emotional intelligence, ethical sensitivity, and communication self-efficacy. Such strategies could potentially have positive effects on the health outcomes of the patients.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.493-504
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• 2008

Space-Based Augmentation System (SBAS), which is one of the GPS augmentation systems, is a Wide-Area Differential GPS that provides differential GPS corrections and integrity data. In this study, we did performance analysis of kinematic SBAS surveying by conducting Real-Time Kinematic (RTK), DGPS, standalone, and SBAS surveys. Considering static survey results as truth, 2-D Root Mean Square (RMS) error and 3-D RMS error were computed to evaluate the positioning accuracy of each survey method. As a result, the 3-D positioning error of RTK was 13.1cm, DGPS 126.0cm, standalone (L1/L2) 135.7cm, standalone (C/A) 428.9cm, and SBAS 109.2cm. The results showed that the positioning accuracy of SBAS was comparable to that of DGPS.

• Journal of Biosystems Engineering
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• v.37 no.5
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• pp.327-334
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• 2012

Purpose: The color changes in red pepper during far infrared drying were studied in order to establish a color change model. Methods: The far infrared drying experiments of red pepper were conducted at two temperature levels of 60, $70^{\circ}C$ and two air velocity levels of 0.6 and 0.8 m/s. The results were compared with the hot-air drying method. The surface color changes parameters of red pepper were measured qualitatively based on L (lightness), a (redness), b (yellowness) and total color changes (${\Delta}E$). The goodness of fit of model was estimated using the coefficient of determination ($R^2$), the root mean square error (RMSE), the mean relative percent error (P) and the reduced chi-square (${\chi}^2$). Results: The results show that an increase in drying temperature and air velocity resulted in a decrease in drying time, the values of L (lightness) and a (redness) decreased with drying time during far infrared drying. The developed model showed higher $R^2$ values and lower RMSE, P and ${\chi}^2$ values. Conclusions: The model in this study could be beneficial to describe the color changes of red pepper by far infrared drying.

• Wind and Structures
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• v.31 no.4
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• pp.311-319
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• 2020

India is a developing nation and heavily spends on the development of wind power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of Ennore site using wind data collected over a period of two years by three parameter Weibull distribution. The Weibull parameters are estimated using maximum likelihood, least square method and moment method and the accuracy is determined using R² and root mean square error values. The site specific capacity factor is calculated by the mathematical model developed by three parameter Weibull distribution at different hub heights above the ground level. At last, the wind energy economic analysis is carried out using capacity factor at 30 m, 40 m and 50 m height for different wind turbine models. The analysis showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m. This research provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.