• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.309-324
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• 2008

In this study the retrieval algorithms have been developed to retrieve total precipitable water (TPW) from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) infrared measurements using a physical iterative retrieval method and a split-window technique over East Asia. Retrieved results from these algorithms were validated against Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) over ocean and radiosonde observation over land and were analyzed for investigating the key factors affecting the accuracy of results and physical processes of retrieval methods. Atmospheric profiles from Regional Data Assimilation and Prediction System (RDAPS), which produces analysis and prediction field of atmospheric variables over East Asia, were used as first-guess profiles for the physical retrieval algorithm. We used RTTOV-7 radiative transfer model to calculate the upwelling radiance at the top of the atmosphere. For the split-window technique, regression coefficients were obtained by relating the calculated brightness temperature to the paired radiosonde-estimated TPW. Physically retrieved TPWs were validated against SSM/I and radiosonde observations for 14 cases in August and December 2004 and results showed that the physical method improves the accuracy of TPW with smaller bias in comparison to TPWs of RDAPS data, MODIS products, and TPWs from split-window technique. Although physical iterative retrieval can reduce the bias of first-guess profiles and bring in more accurate TPWs, the retrieved results show the dependency upon initial guess fields. It is thought that the dependency is due to the fact that the water vapor absorption channels used in this study may not reflect moisture features in particular near surface.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.31-45
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• 1995

The values of brightness temperature difference (BTD) between 11um and 12um infrared channels may reflect amounts of low-level water vapor and cloud types due to the different absorptivity for water vapor between two channels. A simple method of classifying cloud types at night was proposed. Two-dimensional histograms of brightness temperature of the 11um channel and the BTD between the split window data over subareas around characteristic clouds such as Cb(cumulonimbus), Ci(cirrus), and Sc(stratocumulus) was constructed. Cb, Ci and Sc can be classified by seleting appropriate thresholds in the two-dimensional histograms. And we can see amounts of low-level water vapor in clear area as well as cloud types in cloudy area in the BTD image. The map of cloud types and low-level water vapor generated by this method was compared with 850hPa and 1000hPa relative humidity(%) of numerical analysis data and nephanalysis chart. The comparisons showed reasonable agreement.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.165-168
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• 2005

In this study, emissivity and land surface temperature (LST) were retrieved using the previously developed algorithms and Aqua/MODIS data. And sensitivity of estimated emissivity and LST to the predefined values, such as land cover, normalized difference vegetation index (NOVI) and spectral emissivity were investigated. The methods used for emissivity and LST were vegetation cover method (VCM) and four different split-window algorithms. The spectral emissivity retrieved by VCM was not sensitive to the NOVI error but more sensitive to the land cover error. The comparison of LST showed that the LST was systematically different without regard to the land cover and season. And the LST was very sensitive to the emissivity error excepting the Uliveri et al. This preliminary result indicates that more works are needed for the retrieval of reliable LST from satellite data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1650-1657
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• 2007

In this paper, we propose an Overlapped Block and an Hanning Window to prevent a Block Discontinuity when we use an algorithm to eliminate ringing artifact which is based on a block structure. The algorithm to eliminate ringing artifact operates with a block structure and 24-RGB data and is based on a modified K-means algorithm. The proposed overlapped block method is piled up one on another per an half of the size of unit-block when an input image is split into several unit-blocks. Therefore, we define a data unit as the unit-block of the block size, $16{\times}16$ pixels. We reconstruct the processed data units into the original form of input image by using an isotropic form of Hanning Window. Finally, in order to evaluate the performance of the abovementioned algorithms, we compare three image, an input image with ringing artifact, an image result obtained by conventional method (non-overlapped), and an image result obtained the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.546-555
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• 2013

In this paper, we propose a novel method to enhance the harmonic components from the frequency lines of the passive sonar signals. For this, we first separate the stable frequency lines from unstable ones using mean and difference of spectral bins in the vernier analysis. Then we emphasize the harmonic components using autocorrelation-based postprocessing, and enhance them by reducing the background noise with the split-window two pass mean algorithm. Experimental results for real underwater acoustic data are presented with our discussions.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.65-78
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• 2008

This study compares the relative accuracy and consistency of four split-window land surface temperature (LST) algorithms (Becker and Li, Kerr et ai., Price, Ulivieri et al.) using 24 sets of Terra (Aqua)/Moderate Resolution Imaging Spectroradiometer (MODIS) data, observed ground grass temperature and air temperature over South Korea. The effective spectral emissivities of two thermal infrared bands have been retrieved by vegetation coverage method using the normalized difference vegetation index. The intercomparison results among the four LST algorithms show that the three algorithms (Becker-Li, Price, and Ulivieri et al.) show very similar performances. The LST estimated by the Becker and Li's algorithm is the highest, whereas that by the Kerr et al.'s algorithm is the lowest without regard to the geographic locations and seasons. The performance of four LST algorithms is significantly better during cold season (night) than warm season (day). And the LST derived from Terra/MODIS is closer to the observed LST than that of Aqua/MODIS. In general, the performances of Becker-Li and Ulivieri et al algorithms are systematically better than the others without regard to the day/night, seasons, and satellites. And the root mean square error and bias of Ulivieri et al. algorithm are consistently less than that of Becker-Li for the four seasons.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.427-435
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• 2002

This paper presents a development result for image processed tracking system of multiple moving objects based on Kalman filter and a simple window tracking method. The proposed algorithm of foreground detection and background adaptation (FDBA) is composed of three modules: a block checking module(BCM), an object movement prediction module(OMPM), and an adaptive background estimation module (ABEM). The BCM is processed for checking the existence of objects. To speed up the image processing time and to precisely track multiple objects under the object's mergence, a concept of a simple window tracking method is adopted in the OMPM. The ABEM separates the foreground from the background in the reset simple tracking window in the OMPM. It is shown through experimental results that the proposed FDBA algorithm is robustly adaptable to the background variation in a short processing time. Furthermore, it is shown that the proposed method can solve the problems of mergence, cross and split that are brought up in the case of tracking multiple moving objects.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.884-885
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• 2007

This paper presents an window based user-friendly designed characteristics analysis program of permanent-split capacitor single-phase induction motor. For the analysis, equivalent magnetic circuit and symmetrical coordinate method are used. The saturation effect and iron loss of stator and rotor core are considered. The analysis program is made to GUI type which can be used easily by many elementary designer. The accuracy of analysis is verified by comparison with experimental results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.48-55
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• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.