• Smart Structures and Systems
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• 제30권4호
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• 천문학회보
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• 제46권2호
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• pp.44.3-45
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• 2021

일반화된 excursion set 이론과 자기 유사 구형 유입(Self-similar spherical infall) 모형에 기반하여 Splashback 질량함수에 대한 해석적 단일 매개변수 모델을 착안하였다. Planck/WMAP7 관측결과를 토대로 구축된 EREBOS N-Body 시뮬레이션의 수치적 결과의 해석적 모델을 이용한 회귀분석을 통해 단일 매개변수이자 Splashback 경계의 확산적 특성을 수치화하는 확산계수(Diffusion Coefficient)의 추정치를 계산하였다. 계산된 확산계수를 적용한 해석적 모델과 수치적 결과가 5 ≤ M/(10¹²h^-1 M_⊙) < 10³의 질량범위에서 매우 근접히 일치하는 것을 보였으며 Baysian and Akaike Information Criterion 검정을 통해 0.3 ≤ z ≤ 3의 범위에서 기존의 모델들보다 본 모델이 선호 돼야함을 확인하였다. 또한 확산계수가 적색편이에 대하여 선형진화에 근접한 변화를 보임을 발견하였으며, 특정 임계 적색편이(z_c)를 기준으로 확산계수가 0에 수렴함을 발견하였다. 더 나아가 두 Planck모델과 WMAP7모델에서 도출된 확산계수는 서로 상당한 차이를 보였다. 이 결과는 암흑물질 헤일로의 splashback 질량함수가 z ≥ z_c에서 매개변수가 없는 온전한 해석적 모델로 설명되고 z_c가 독립적으로 우주의 초기조건을 독립적으로 특정지을 수 있는 가능성을 지님을 시사한다. 이 초록은 The Astrophysical Journal의 Ryu & Lee 2021, ApJ, 917, 98 (arxiv:2103.00730) 논문을 바탕으로 작성되었다.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• 한국의학물리학회지:의학물리
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• 제32권4호
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• pp.137-144
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• 2021

Purpose: This study aimed to investigate the accuracy of head scatter factor (S_c) by applying a developed multi-leaf collimator (MLC) scatter source model for an unflattened photon beam. Methods: Sets of S_c values were measured for various jaw-defined square and rectangular fields and MLC-defined square fields for developing dual-source model (DSM) and MLC scatter model. A 6 MV unflattened photon beam has been used. Measurements were performed using a 0.125 cm³ cylindrical ionization chamber and a mini phantom. Then, the parameters of both models have been optimized, and S_c has been calculated. The DSM and MLC scatter models have been verified by comparing the calculated values to the three S_c set measurement values of the jaw-defined field and the two S_c set measurement values of MLC-defined fields used in the existing modeling, respectively. Results: For jaw-defined fields, the calculated S_c using the DSM was consistent with the measured S_c value. This demonstrates that the DSM was properly optimized and modeled for the measured values. For the MLC-defined fields, the accuracy between the calculated and measured S_c values with the addition of the MLC scatter source appeared to be high, but the only use of the DSM resulted in a significantly bigger differences. Conclusions: Both the DSM and MLC models could also be applied to an unflattened beam. When considering scattered radiation from the MLC by adding an MLC scatter source model, it showed a higher degree of agreement with the actual measured S_c value than when using only DSM in the same way as in previous studies.

• 한국재료학회지
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• 제21권11호
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• pp.604-610
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• 2011

The effect of $BaF_2$ flux in $Y_3Al_5O_{12}:Ce^{3+}$(YAG:Ce) formation was investigated. Phase transformation of $Y_3Al_5O_{12}$(YAG) was characterized by using XRD, SEM, and TEM-EDS, and it was revealed that the sequential formation of the $Y_4Al_2O_9$(YAM), $YAlO_3$(YAP) and $Y_3Al_5O_{12}$(YAG) in the temperature range of 1000-1500$^{\circ}C$. Single phase of YAG was revealed from 1300$^{\circ}C$. In order to find out the effect of $BaF_2$ flux, three modeling experiments between starting materials (1.5$Al_2O_3$-2.5$Y_2O_3$, $Y_2O_3$-$BaF_2$, and $Al_2O_3$-$BaF_2$) were done. These modeling experiments showed that the nucleation process occurs via the dissolution-precipitation mechanism, whereas the grain growth process is controlled via the liquid-phase diffusion route. YAG:Ce phosphor particles prepared using a proposed technique exhibit a spherical shape, high crystallinity, and an emission intensity. According to the experimental results conducted in this investigation, 5% of $BaF_2$ was the best concentration for physical, chemical and optical properties of $Y_3Al_5O_{12}:Ce^{3+}$(YAG:Ce) that is approximately 10-15% greater than that of commercial phosphor powder.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2017년도 추계학술논문요약집
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• pp.55-56
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• 2017

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.

• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.119-124
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• 2021

In a situation where Moore's law, which states that the performance of semiconductor integrated circuits doubles every two years, is showing a limit from a certain point, and it is difficult to increase the performance due to the limitations of exposure technology.In this study, a wafer hybrid method that can increase the degree of integration Various research on bonding technology is currently in progress. In this study, in order to achieve rotational precision between wafers in wafer hybrid bonding technology, modeling of θ_z alignment stage and VCM actuator modeling used for rotational alignment, magnetic field analysis and desgin, control, and evaluation are performed. The system of this study was controlled by VCM actuator, capactive sensor, and dspace, and the working range was ±7200 arcsec, and the in-position and resoultion were ±0.01 arcsec. The results of this study confirmed that safety and precise control are possible, and it is expected to be applied to the process to increase the integration.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.