• Journal of The Korean Astronomical Society
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• v.55 no.4
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• pp.111-121
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• 2022

Based on the light an exoplanet blocks from its host star as it passes in front of it during a transit, the mid-transit time can be determined. Periodic variations in mid-transit times can indicate another planet's gravitational influence. We investigate 83 transits of TrES-1 b as observed from 6-inch telescopes in the MicroObservatory robotic telescope network. The EXOTIC data reduction pipeline is used to process these transits, fit transit models to light curves, and calculate transit midpoints. This paper details the methodology for analyzing transit timing variations (TTVs) and using transit measurements to maintain ephemerides. The application of Lomb-Scargle period analysis for studying the plausibility of TTVs is explained. The analysis of the resultant TTVs from 46 transits from MicroObservatory and 47 transits from archival data in the Exoplanet Transit Database indicated the possible existence of other planets affecting the orbit of TrES-1 and improved the precision of the ephemeris by one order of magnitude. We now estimate the ephemeris to be (2 455 489.66026 BJD_TDB ± 0.00044 d) + (3.0300689 ± 0.0000007) d × epoch. This analysis also demonstrates the role of small telescopes in making precise midtransit time measurements, which can be used to help maintain ephemerides and perform TTV analysis. The maintenance of ephemerides allows for an increased ability to optimize telescope time on large ground-based telescopes and space telescope missions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.433-439
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• 2022

Muscle fatigue models to predict maximum endurance time (MET) are broadly classified as either 'empirical' or 'theoretical'. Empirical models are based on fitting experimental data and theoretical models on mathematical representations of physiological process. This paper examines the effectiveness of dynamic muscle fatigue model as theoretical model to predict maximum endurance time during forearm isometric contraction. Forty volunteers (20 females, 20 males) are participated in this study. Empirical models (exponential model and power model) and theoretical model (dynamic muscle fatigue model) are used to compare. Mean absolute deviation (MAD), correlation coefficient (r) and intraclass correlation (ICC) are calculated between theoretical model and empirical models. MAD are below 3.5%p, r and ICC are above 0.93 and 0.87, respectively. This results demonstrate that dynamic muscle fatigue model as theoretical model is valid to predict MET.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.241-248
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• 2023

The value of experimentally obtained data becomes highest when they are properly analyzed based on valid logics. Many physical and chemical properties such as electrical and magnetic properties, chemical reaction rates, etc. are known to be thermally activated; thus, a proper understanding of thermally-activated processes is of importance. However, there are still a number of papers published with falsely analyzed data. In this contribution, we would like to revisit the meaning of thermally-activated processes, and then reanalyze a data set published misinterpreted. By showing a step-by-step procedure for the reanalysis, we would like to help researchers who may come across such data in the future not to make mistakes in their analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Current Optics and Photonics
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• v.8 no.3
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• pp.230-238
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• 2024

The refractive index is a key material-design parameter, especially for high-refractive-index glasses, which are used for precision optics and devices. Increased demand for high-precision optical lenses produced by the glass-mold-press (GMP) process has spurred extensive studies of proper glass materials. B₂O₃, SiO₂, and multiple heavy-metal oxides such as Ta₂O₅, Nb₂O₅, La₂O₃, and Gd₂O₃ mostly compose the high-refractive-index glasses for GMP. However, due to many oxides including up to 10 components, it is hard to predict the refractivity solely from the composition of the glass. In this study, the refractive index of optical glasses based on the B₂O₃-La₂O₃-Ta₂O₅-SiO₂ system is predicted using machine learning (ML) and compared to experimental data. A dataset comprising up to 271 glasses with 10 components is collected and used for training. Various ML algorithms (linear-regression, Bayesian-ridge-regression, nearest-neighbor, and random-forest models) are employed to train the data. Along with composition, the polarizability and density of the glasses are also considered independent parameters to predict the refractive index. After obtaining the best-fitting model by R² value, the trained model is examined alongside the experimentally obtained refractive indices of B₂O₃-La₂O₃-Ta₂O₅-SiO₂ quaternary glasses.

• Journal of Magnetics
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• v.9 no.1
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• pp.17-22
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• 2004

We studied the magnetotransport properties of tunnel junctions with AlO and AlN barriers fabricated using microwave-excited plasma. The plasma nitridation process provided wider controllability than the plasma oxidization for the formation of MTJs with ultra-thin insulating layer, because of the slow nitriding rate of metal Al layers, comparing with the oxidizing rate of them. High tunnel magnetoresistance (TMR) ratios of 49 and 44% with respective resistance-area product $(R{\times}A) of 3 {\times} 10^4 and 6 {\times} 10^3 {\Omega}{\mu}m^2$ were obtained in the Co-Fe/Al-N/Co-Fe MTJs. We conclude that AlN is a hopeful barrier material to realize MTJs with high TMR ratio and low $R{\times}A$ for high performance MRAM cells. In addition, in order to clarify the annealing temperature dependence of TMR, the local transport properties were measured for Ta $50{\AA} /Cu 200 {\AA}/Ta 50 {\AA}/Ni_{76}Fe_{24} 20 {\AA}/Cu 50 {\AA}/Mn_{75}Ir_{25} 100 {\AA}/Co_{71}Fe_{29} 40 {\AA}/Al-O$ junction with $d_{Al}= 8 {\AA} and P_{O2}{\times}t_{0X}/ = 8.4 {\times} 10^4$ at various temperatures. The current histogram statistically calculated from the electrical current image was well in accord with the fitting result considering the Gaussian distribution and Fowler-Nordheim equation. After annealing at $340^{\circ}C$, where the TMR ratio of the corresponding MTJ had the maximum value of 44%, the average barrier height increased to 1.12 eV and its standard deviation decreased to 0.1 eV. The increase of TMR ratio after annealing could be well explained by the enhancement of the average barrier height and the reduction of its fluctuation.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.235-246
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• 2018

The main purpose of this study is to suggest a methodology for identifying vulnerable region in Choyang creek basin susceptible to soil losses based on runoff aggregation structure and energy expenditure pattern of natural river basin within the framework of power law distribution. To this end geomorphologic factors of every point in the basin of interest are extracted by using GIS, which define tractive force and stream power as well as drainage area, and then their complementary cumulative distributions are graphically analyzed through fitting them to power law distribution to identify the sensitive points within the basin susceptible to soil losses with respect to scaling regimes of tractive force and stream power. It is observed that the range of vulnerable region by scaling regime of tractive force is much narrower than by scaling regime of stream power. This result seems to be due to the tractive force is a kind of scale dependent factor which does not follow power law distribution and does not adequately reflect energy expenditure pattern of river basins. Therefore, stream power is preferred to be a more reasonable factor for the evaluation of soil losses. The methodology proposed in this study can be validated by visualizing the path of soil losses, which is generated from hill-slope process characterized by local slope, to the valley through fluvial process characterized by drainage area as well as local slope.