• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.437-444
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• 2017

Since land pixels often generate false alarms in ship detection using Synthetic Aperture Radar (SAR), land masking is a necessary step which can be processed by a land area map or water database. However, due to the continuous coastline changes caused by newport, bridge, etc., an updated data should be considered to mask either the land or the oceanic part of SAR. Furthermore, coastal concrete facilities make noise signals, mainly caused by side lobe effect. In this paper, we propose two methods. One is a semi-automatic water body data generation method that consists of terrain correction, thresholding, and median filter. Another is a dynamic land masking method based on water database. Based on water database, it uses a breadth-first search algorithm to find and mask noise signals from coastal concrete facilities. We verified our methods using Sentinel-1 SAR data. The result shows that proposed methods remove maximum 84.42% of false alarms.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.275-284
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• 2023

Gene expression data can be utilized in various studies, including the prediction of disease prognosis. However, there are challenges associated with collecting enough data due to cost constraints. In this paper, we propose a gene expression data generation model based on Conditional Variational Autoencoder. Our results demonstrate that the proposed model generates synthetic data with superior quality compared to two other state-of-the-art models for gene expression data generation, namely the Wasserstein Generative Adversarial Network with Gradient Penalty based model and the structured data generation models CTGAN and TVAE.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1343-1345
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• 2003

An internal wave observation experiment by SAR in South China Sea is described. Two scenes of Radarsat ScanSAR images were acquired. Internal solitary waves are found in all the two images. It is concluded that these internal waves are generated in Bashi channel. Relationship between internal wave generation and tide is studied based on analyzing of tidal data of Legaspi in Philippine. Using ocean environmental data of this sea area internal waves’ amplitude and wave speed are detected by SAR images.

• Journal of Korea Water Resources Association
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• v.32 no.3
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• pp.291-300
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• 1999

The Snyder's synthetic unit hydrograph method is selected to apply the concept of the fractal dimension by stream order for the practicable rainfall-runoff generation, and fourth types of the Snyder's relation are derived from topographic and observed unit hydrograph data of twenty-nine basins. As a result of the analysis of twenty-nine basins and the verification of two basins, the Snyder's relation which considers the fractal dimension of the stream length and uses calculated unit hydrograph data shows the best result. The concept of the fractal dimension by stream order is applied to the Snyder's synthetic unit hydrograph method. The topographic factors, used in the Snyder's synthetic unit hydrograph method, which have a property of the stream length like $L_{ma}$ (mainstream length) and $L_{ca}$ (length along the mainstream to a point nearest the watershed centroid) were considered. In order to simplify the fractal property of stream length, it is supposed that $L_{ma}$ has not the fractal dimension and the stream length between $L_{ma}$ and ($L_{ma}\;-\;L_{ca}$) has the fractal dimension of 1.027. From the utilization of this supposition, a new Snyder's relation which consider the fractal dimension of the stream length occurred by the map scale used was finally suggested.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.95-101
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• 2005

In this paper, some performance characteristics of multimedia traffic for mobile ad-hoc networks is studied with simulations. Synthetic streaming video is considered as the multimedia traffic for MPEG-coded video in the simulation. The synthetic video stream is generated with a video stream generation algorithm. The algorithm generate VBR traffics for MPEG video streams with special predefined GOP(group of pictures) patterns that is consisted of a sequence of I(intra-coded), P(predicted-coded) and B(bidirectional-coded) frames. The synthetic VBR streams is transmitted through UDP protocol with on-demand mobile ad-hoc network routing protocols like as AODV and DSR. And performances for video streams through mobile ad-hoc networks is evaluated, the throughputs is compared between data and video traffics.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.69-80
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• 2023

Ship collision accidents not only cause loss of life and property damage, but also cause marine pollution and can become national disasters, so prevention is very important. Most of these ship collision accidents are caused by human factors due to the navigation officer's lack of vigilance and carelessness, and in many cases, they can be prevented through the support of a system that helps with situation awareness. Recently, artificial intelligence has been used to develop systems that help navigators recognize the situation, but the sea is very wide and deep, so it is difficult to secure maritime traffic datasets, which also makes it difficult to develop artificial intelligence models. In this paper, to solve these difficulties, we propose a method to build a dataset with characteristics similar to actual maritime traffic datasets. The proposed method uses segmentation and inpainting technologies to build a foreground and background dataset, and then applies compositing technology to create a synthetic dataset. Through prototype implementation and result analysis of the proposed method, it was confirmed that the proposed method is effective in overcoming the difficulties of dataset construction and complementing various scenes similar to reality.

• Wind and Structures
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• v.10 no.6
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• pp.511-532
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• 2007

The present study aims to generate turbulent inflow data to more accurately represent the turbulent flow around a square cylinder when the inflow turbulence level is significant. The modified random flow generation (RFG) technique in conjunction with a previously developed LES code is successfully adopted into a finite element based fluid flow solver to generate the required inflow turbulence boundary conditions for the three-dimensional (3-D) LES computations of transitional turbulent flow around a square cylinder at Reynolds number of 22,000. The near wall region is modelled without using wall approximate conditions and a wall damping coefficient is introduced into the calculation of sub-grid length scale in the boundary layer of the cylinder wall. The numerical results obtained from simulations are compared with each other and with the experimental data for different inflow turbulence boundary conditions in order to discuss the issues such as the synthetic inflow turbulence effects on the 3-D transitional flow behaviour in the near wake and the free shear layer, the basic mechanism by which stream turbulence interacts with the mean flow over the cylinder body and the prediction of integral flow parameters. The comparison among the LES results with and without inflow turbulence and the experimental data emphasizes that the turbulent inflow data generated by the present RFG technique for the LES computation can be a viable approach in accurately predicting the effects of inflow turbulence on the near wake turbulent flow characteristics around a bluff body.