• Atmosphere
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• v.25 no.1
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• pp.117-127
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• 2015

The presences of clouds significantly influence the accuracy of ozone retrievals from satellite measurements. This study focuses on the influence of clouds on Ozone Monitoring instrument (OMI) ozone profile retrieval based on an optimal estimation. There are two operational OMI cloud products; OMCLDO2, based on absorption in $O_2-O_2$ at 477 nm, and OMCLDRR, based on filling in Fraunhofer lines by rotational Raman scattering (RRS) at 350 nm. Firstly, we characterize differences between $O_2-O_2$ and RRS effective cloud pressures using MODIS cloud optical thickness (COT), and then compare ozone profile retrievals with different cloud input data. $O_2-O_2$ cloud pressures are significantly smaller than RRS by ~200 hPa in thin clouds, which corresponds to either low COT or cloud fraction (CF). On the other hand, the effect of Optical centroid pressure (OCP) on ozone retrievals becomes significant at high CF. Tropospheric ozone retrievals could differ by up to ${\pm}10$ DU with the different cloud inputs. The layer column ozone below 300 hPa shows the cloud-induced ozone retrieval error of more than 20%. Finally, OMI total ozone is validated with respect to Brewer ground-based total ozone. A better agreement is observed when $O_2-O_2$ cloud data are used in OMI ozone profile retrieval algorithm. This is distinctly observed at low OCP and high CF.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.307-316
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• 2008

Real life problems can be expressed as a congruence modulus n and split into a system of congruence equations in modulus factors of n. A system of congruence equations can be combined into a congruence equation under certain conditions. This paper uniquely presents and critically reviews the generalized Chinese Remainder Theorem (CRT) for combining systems of congruence equations into single congruence equations. Sequential and parallel implementation strategies of the generic CRT are outlined. A variety of unique applications of the CRT are discussed.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.221-227
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• 2010

Aerosol size distribution provides good information for predicting weather changes and understanding cloud formation. Aerosol extinction coefficient and backscattering coefficient are measured by many scientists, but these parameters depend not only on aerosol size but on aerosol concentrations. An algorithm has been developed to measure aerosol parameters such as ${\AA}$ngstr$\ddot{o}$m exponent, color ratio, and LIDAR ratio without any assumptions by using two wavelength rotational Raman LIDAR signals. These parameters are good indicators for the aerosol size. And we can find ${\AA}$ngstr$\ddot{o}$m exponent, color ratio, and LIDAR ratio under various weather conditions. Finally, it can be seen that the ${\AA}$ngstr$\ddot{o}$m exponent has an inverse relationship to the particle size of the aerosol and the color ratio is linearly dependent on the aerosol size. An ${\AA}$ngstr$\ddot{o}$m exponent from 1.2 to 3.1, a color ratio from 0.28 to 1.04, and a LIDAR ratio 66.9 sr at 355 nm and 32.6 sr at 532 nm near the cloud were obtained.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2258-2274
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• 2020

In this paper we first propose a new cryptosystem based on our data hiding scheme (2,9,8) introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography. We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem. Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses ⌈(1-ε)m)⌉ processors, where ε, m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively. In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.12-16
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• 2002

The clear sky radiative forcings of aerosols were evaluated over East Asia. We first investigated optical characteristics of aerosol using sky radiation measurements. An algorithm of Nakajima et al. (1996) is used for retrieving aerosol parameters such as optical thickness, ${\AA}$ngstr$\"{O}$m exponent, single scattering albedo, and size distribution from sky-radiation measurements, which then can be used for examining spatial and temporal variations of aerosol. Obtaining aerosol radiative forcing at TOA and surface, a radiative transfer model is used with inputs of obtained aerosol parameters and GMS-5 satellite-based cloud optical properties. Results show that there is a good agreement of simulated downwelling radiative flux at the surface with observation within 10 W m$^{-2}$ rms errors under the clear sky condition. However, a relatively large difference up to 40 W m$^{-2}$ rms error is found under the cloudy sky condition. The computed aerosol radiative forcing at the surface shows downward flux changes ranging from -100 to -170 W m$^{-2}$ per unit aerosol optical thickness at 0.7 $\mu$m. The different values of aerosol radiative forcing among the stations is mainly due to the differences in single scattering albedo ($\omega$$_{0.7}$) and asymmetric parameter (g$_1$) related to the geographical and seasonal variations.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.559-569
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• 2008

The Geostationary Ocean Color Imager (GOCI) on board the Communication Ocean Meteorological Satellite (COMS), the first geostationary ocean color sensor, requires accurate atmospheric correction since its eight bands are also affected by atmospheric constituents such as gases, molecules and atmospheric aerosols. Unlike gases and molecules in the atmosphere, aerosols can interact with sunlight by complex scattering and absorption properties. For the purpose of qualified ocean remote sensing, understanding of aerosol-radiation interactions is needed. In this study, we show micro-physical and optical properties of aerosols using the Optical Property of Aerosol and Cloud (OPAC) aerosol models. Aerosol optical properties, then, were used to analysis the relationship between theoretical satellite measured radiation from radiative transfer calculations and aerosol optical thickness (AOT) under various environments (aerosol type and loadings). It is found that the choice of aerosol type makes little different in AOT retrieval for AOT<0.2. Otherwise AOT differences between true and retrieved increase as AOT increases. Furthermore, the differences between the AOT and angstrom exponent from standard algorithms and this study, and the comparison with ground based sunphotometer observations are investigated. Over the northeast Asian region, these comparisons suggest that spatially averaged mean AOT retrieved from this study is much better than from standard ocean color algorithm. Finally, these results will be useful for aerosol retrieval or atmospheric correction of COMS/GOCI data processing.