• 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.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.119-126
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• 2023

Acquiring experimental data for PIV verification or machine learning training data is resource-demanding, leading to an increasing interest in synthetic particle images as simulation data. Conventional synthetic particle image generation algorithms do not follow physical laws, and the use of CFD is time-consuming and requires computing resources. In this study, we propose a new method for synthetic particle image generation, based on a Physics-Informed Neural Networks(PINN). The PINN is utilized to infer the flow fields, enabling the generation of synthetic particle images that follow physical laws with reduced computation time and have no constraints on spatial resolution compared to CFD. The proposed method is expected to contribute to the verification of PIV algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.476-486
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• 2022

Synthetic dynamic infrared image generation from the given virtual environment is being the primary goal to simulate the output of the infra-red(IR) camera installed on a vehicle to evaluate the control algorithm for various search & reconnaissance missions. Due to the difficulty to obtain actual IR data in complex environments, Artificial intelligence(AI) has been used recently in the field of image data generation. In this paper, CycleGAN technique is applied to obtain a more realistic synthetic IR image. We added the Structural Similarity Index Measure(SSIM) loss function to the L1 loss function to generate a more realistic synthetic IR image when the CycleGAN image is generated. From the simulation, it is applicable to the guided-missile flight simulation tests by using the synthetic infrared image generated by the proposed technique.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.95-102
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• 1998

This paper proposes a synchronization method of synthetic facial iamge sequences and synthetic speech. The LP-PSOLA synthesizes the speech for each demi-syllable. We provide the 3,040 demi-syllables for unlimited synthesis of the Korean speech. For synthesis of the Facial image sequences, the paper defines the total 11 fundermental patterns for the lip shapes of the Korean consonants and vowels. The fundermental lip shapes allow us to pronounce all Korean sentences. Image synthesis method assigns the fundermental lip shapes to the key frames according to the initial, the middle and the final sound of each syllable in korean input text. The method interpolates the naturally changing lip shapes in inbetween frames. The number of the inbetween frames is estimated from the duration time of each syllable of the synthetic speech. The estimation accomplishes synchronization of the facial image sequences and speech. In speech synthesis, disk memory is required to store 3,040 demi-syllable. In synthesis of the facial image sequences, however, the disk memory is required to store only one image, because all frames are synthesized from the neutral face. Above method realizes synchronization of system which can real the Korean sentences with the synthetic speech and the synthetic facial iage sequences.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1410-1419
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• 1989

In this paper, we present a new synthetic high coding method which gives high image compression ratio. Given an image, only its low-pass component is transmitted by DCT coding` the high-pass component is not transmitted but synthesized using edge sharpening on the reconstructed low-pass image at the receiver. For the DCT coding which is used to encode the low-pass image, we used an improved version of Cox's variance estimator. Also, introduced are new image quality measures called GSNR and EPR which emphasize perceptual aspects of image quality. Experimental results show that the performance of the proposed synthetic high coding is better in various quality measures than that of Cox's adaptive transform coding. Also, it yields acceptable image quality with neither apparent block effect nor visible granular noise even at high compression ratio of about 30:1.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.4
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• pp.97-103
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• 2006

A synthetic infrared image can be effectively utilized in various fields such as the recognition and tracking of targets as long as its quality is good enough to reflect the real situations. One way to improve its quality is to use the reference atmosphere which best describes atmospheric properties of regional areas. The east asian reference atmosphere has been developed to represent atmospheric properties of the east asia including Korean peninsula. However, few research has been conducted to examine the effects of this east asian reference atmosphere on the modeling and simulation. In this regard, this paper analyzes the effects of the east asian reference atmosphere on a synthetic infrared image. The research compares the atmospheric transmittance, the surface temperature, and the radiance obtained by using the east asian reference atmosphere with those of the midlatitude reference atmosphere which has been widely applied in the east asia. The results show that the differences of the atmospheric transmittance, the surface temperature, and the radiance between the east asian reference atmosphere and the midlatitude reference atmosphere are significant especially during the daytime. Therefore, it is recommended to apply the east asian reference atmosphere for generating a synthetic infrared image with targets in the east asia.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• Journal of Microbiology and Biotechnology
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• v.32 no.8
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• pp.1026-1033
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• 2022

This study presents a novel DNA part characterization technique that increases throughput by combinatorial DNA part assembly, solid plate-based quantitative fluorescence assay for phenotyping, and barcode tagging-based long-read sequencing for genotyping. We confirmed that the fluorescence intensities of colonies on plates were comparable to fluorescence at the single-cell level from a high-end, flow-cytometry device and developed a high-throughput image analysis pipeline. The barcode tagging-based long-read sequencing technique enabled rapid identification of all DNA parts and their combinations with a single sequencing experiment. Using our techniques, forty-four DNA parts (21 promoters and 23 RBSs) were successfully characterized in 72 h without any automated equipment. We anticipate that this high-throughput and easy-to-use part characterization technique will contribute to increasing part diversity and be useful for building genetic circuits and metabolic pathways in synthetic biology.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.835-842
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• 2022

This study is aimed to evaluate the effects of a synthetic MR technique in reducing metal artifacts. In the experiment, the in-plane and through-plane images were acquired by applying a synthetic MR technique and a high-speed spin echo technique to a phantom manufactured with screw for spinal surgery. The area of the metal artifact was compared. The metal artifacts were measured by dividing the signal-loss and the signal pile-up areas, and the area of the final artifact was calculated through the sum of the two. As a result, the metal artifacts were relatively reduced when the synthetic MR techniques were applied to both in-plane and through-plane. Comparing by sequence, the in-plane T1 images decreased by 23.45%, T2 images by 20.85%, PD images by 19.67%, and FLAIR images by 22.12%. Also, in the case of the through-plane, the T1 image decreased by 62.95%, the T2 image decreased by 73.93%, the PD image decreased by 74.68%, and the FLAIR image decreased by 66.43%. The cause of this result is that when the synthetic MR technique is applied, the distortion is due to the signal pile-up and does not occur and the size of the entire metal artifact is reduced. Therefore, synthetic MR technique can very effectively reduce metal artifacts, which can help to increase the diagnostic value of images.