• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.809-817
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• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.163-171
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• 2005

I have developed a processor for synthetic aperture radar (SAR) raw data compression using range-doppler algorithm for educational purpose. The program realized a generic SAR focusing algorithm so that it can deal with any SAR system if the specification is known. It can run efficiently on a low-cost computer by selecting minimum size out of a whole dataset, and can produce intermediate images during the process. Especially, the program is designed for educational purpose in such a way that Doppler centroid and azimuth ambiguity can be determined graphically by the user. By distributing the source code and the algorithm to public, I intend to maximize the educational effect on understanding and utilizing SAR data. This paper introduces the principle of SAR focusing algorithm embedded on the eSAR processor and shows an example of data processing using ERS-1 raw data.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.355-358
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• 1998

This paper investigate the interaction of electromagnetic fields(EF's) in the human head with respect to the radiated waves from 1.8〔GHz〕 PCS terminal that the titled angles of a monopole antenna between the head and the horizontal axis of the terminal are 45, 60, 75 and 90 degrees respectively. And then, it is found the fact that the induced SAR distri bution in the model of the head gradually decreases by in creasing the titled angle.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1246-1248
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• 2003

Internal wave with a soliton-like, large amplitudes within several kilometers, is frequently observed in the sea surface caused by tidal rectification due to sill or rough topographic changes. Internal waves can perturb current and density field, initiate bottom sediment re -suspension and mix nutrients to photic zone. Previous studies indicate that the appearance of internal wave in the Lombok Strait have been detected in SAR image data. This paper studies effect of internal wave in the Lombok Strait to chlorophyll distribution in the surrounded areas using SeaWiFS and ERS SAR images data during 1996-2001 periods. The preliminary result concludes that the internal waves presumably affect phytoplankton distribution spreading southeastward in the coast off Bali Island. The distribution of phytoplankton at southern coastline off Bali Island when internal wave occurred is elongated and distributed further to westward (from 8.8$^{\circ}$ to 10.7$^{\circ}$LS) than the area when internal wave did not occur on August 2000 (from 9.25$^{\circ}$ to 10.25$^{\circ}$LS) as shown in figure 3. It shown that the surface phytoplankton concentration near coastal area, i.e. from 8.8$^{\circ}$ to 9.25$^{\circ}$ LS, increased when internal wave is occurred.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.121-130
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• 2000

For the land-cover classification we have usually used imagery of the optical sensors only. But currently a number of the satellite with various sensors are operating and the availability of using the data acquired from them are increasing. SAR sensors, in particular, can produce additional informations on the land-cover which has not been available from optical sensors. On this study, I have applied the SAR Image to the SPOT XS image in the classification procedures, and analysed the classified results. In this procedure I have extracted texture informations from SAR intensity images, then applied both intensity and texture informations. From the accuracy analysis, overall accuracy are increased slightly when the SAR texture was applied. In case of the Built-up class the results showed higher accuracy than those of when only the SPOT XS image was used. From this result I can show that overall accuracy was increased slightly but the spatial distribution of classes was visibly improved.

• Journal of Microbiology and Biotechnology
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• v.24 no.5
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• pp.592-596
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• 2014

HMO-2011, a recently isolated lytic phage that infects the SAR116 bacterial clade, represents one of the most abundant phage types in the oceans. In this study, the HMO-2011 genome sequence was compared with virome sequences obtained from various depths of the Pacific Ocean regions using metagenome binning. HMO-2011 was confirmed to be one of the most highly assigned viruses, with a maximum of 7.6% of total reads assigned. The HMO-2011-type phages demonstrated a depth-specific distribution, showing more abundance in the euphotic zone of coastal, transition, and open ocean regions as compared with the dark ocean.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.126-129
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel type s as states of molecules in a lattice-like physical system defined on a GRF. Because of the MRFGRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular mteractions. The proposed adaptive iterative method was evaluated using simulation data generated by the Monte Carlo method. In the extensive experiments of this study, the proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.272-275
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• 1997

Noninvasive multifrequency microwave radiometry using coaxial waveguide antenna has been investigated for a homogeneous and our layer human body model. We derived finite-difference time-domain(FDTD) algorithm and equation of MUR and generalized perfectly matched layer(GPML) absorbing boundary conditions(ABCs) in cylindrical coordination. The coupling between coaxial waveguide antenna and a biological object was analyzed by use of the FDTD method using MUR and GPML ABCs to obtain the absorbed power patterns in the media. The specific absorption rates(SAR) distribution which was corresponding to the temperature distribution was calculated in each region by use of the steady-state response in FDTD method. The SAR patterns of FDTD method using MUR ABCs was compared with those of FDTD method using GPML ABCs.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.973-983
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• 2020

Recently, SAR (Synthetic Aperture Radar) is being highlighted as a solution to the coarse spatial resolution of remote sensing data in water resources research field. Spatial resolution up to 10 m of SAR backscattering coefficient has facilitated more elaborate analyses of the spatial distribution of soil moisture, compared to existing satellite-based coarse resolution (>10 km) soil moisture data. It is essential, however, to multilaterally analyze how various hydrological and environmental factors affect the backscattering coefficient, to utilize the data. In this study, soil moisture estimated by WCM (Water Cloud Model) and linear regression is compared with in-situ soil moisture data at 5 soil moisture observatories in the Korean peninsula. WCM shows suitable estimates for observing instant changes in soil moisture. However, it needs to be adjusted in terms of errors. Soil moisture estimated from linear regression shows a stable error range, but it cannot capture instant changes. The result also shows that the effect of soil moisture on backscattering coefficients differs greatly by land cover, distribution of vegetation, and water content of vegetation, hence that there're still limitations to apply preexisting models directly. Therefore, it is crucial to analyze variable effects from different environments and establish suitable soil moisture model, to apply SAR to water resources fields in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.125-132
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• 2024

Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis.