• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.87-96
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• 2014

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.5
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• pp.485-500
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• 2016

We examined acidity state of inorganic aerosol and oxidation state of organic aerosol by High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS) at Baengnyeong Super site from Jan 2012 to Dec 2013. Additionally, we carried out the analysis for the aerosol component group of organic matter ($C_xH_y$, $C_xH_yO_1$, $C_xH_yO_z$, $C_xH_yO_zN_p$) and elemental composition to calculate H/C, O/C, N/C, OM/OC and identify the oxidation state. The aerosol chemical composition in this study is dominated by sulfate ($SO_4{^{2-}}$), nitrate ($NO_3{^-}$) plays a smaller role in aerosol acidity. Ammonium ($NH_4{^+}$) was found in a formation of $(NH_4)_3H(SO_4)_2$. However, the binding formations of $NH_4NO_3$ and $NH_4Cl$ increase in the winter. $C_xH_yO_1$ indicating the oxidized state of $PM_{1.0}$ has the highest ratio of 41% while $C_xH_y$ indicating the non-oxidized state has a lower ratio of 36%, meaning that the oxidation level of $PM_{1.0}$ in Baengnyeong Island is high. The ratio between H/C and O/C was 1.33 and 0.78 respectively, showing the characteristic of LV-OOA (Low volatility-Oxygenated Organic Aerosol). Acidic and oxidized aerosols sampled during this field study were largely anthropogenic in origin from Chinese continent and photochemically aged.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.13-30
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• 1997

Recent improvements of satellite remote sensing sensors which are represented by hyperspectral imaging sensors and high spatial resolution sensors provide a large amount of data, typically several hundred megabytes per one scene. Moreover, increasing information exchange via internet and information super-highway requires the developments of more active service systems for processing and analysing of remote sensing data in order to provide value-added products. In this sense, an advanced satellite data processing system is being developed to achive high performance in computing speed and efficieney in processing a huge volume of data, and to make possible network computing and easy improving, upgrading and managing of systems. JAVA internet programming language provides several advantages for developing software such as object-oriented programming, multi-threading and robust memory managent. Using these features, a satellite data processing system named as GeoPixel has been developing using JAVA language. The GeoPixel adopted newly developed techniques including object-pipe connect method between each process and multi-threading structure. In other words, this system has characteristics such as independent operating platform and efficient data processing by handling a huge volume of remote sensing data with robustness. In the evaluation of data processing capability, the satisfactory results were shown in utilizing computer resources(CPU and Memory) and processing speeds.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.71.1-71.1
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• 2013

Among quadruples or higher multiplicity stars, only a few binary systems have been discovered. They are important targets to understand the formation and evolution of multiple stellar systems because we can obtain accurate stellar parameters from photometric and spectroscopic studies. We present the observational results of this kind of rare object 1SWASP J093010.78+533859.5, for which the doubly eclipsing feature had been detected previously from the SuperWASP photometric archive. Individual PSF photometry for two objects with a separation of about 1.9 arcsec was performed for the first time in this study. Our time-series photometric data show that the brighter object A is an Algol-type detached eclipsing binary with an orbital period of 1.3 days and the fainter B is a W UMa-type contact eclipsing binary with a period of 0.23 days. Using the high-resolution optical spectra, we obtained well-defined radial velocity variations of the system A. Furthermore, stationary spectral lines were detected and should have originated from the other stellar component, which was confirmed by the third object contribution from the light curve analysis. No spectral feature of the system B was detected, probably due to its faintness. We obtained the binary parameters and the absolute dimensions from each light curve synthesis. The primary and secondary components of the system A have a spectral type of K1 and K5 main sequences, respectively. Two components of system B have nearly the same type of K3 main sequence. Light variations at out of eclipses were appeared in both systems, interpreting as the effect of stellar spots on these late spectral type stars. We estimated the distances to the systems A and B individually. They may have similar distances of about 70 pc and seem to be gravitationally bound with a separation of about 130 AU. In conclusion, we suggest that 1SWASP J093010.78+533859.5 is a quintuple stellar system with a hierarchical structure of a triple system A(ab)c and a binary system B(ab).

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.