• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.794-800
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• 2018

The proliferation resistance (PR) of Th/U and Th/Pu fuels used in CANada Deuterium Uranium for the deep geological repository was assessed by combining the multiattribute utility analysis proposed by Chirayath et al., 2015 with the transport model of radionuclides in the repository and comparing with that of the used natural U fuel case. It was found that there was no significant advantage for Th/U and Th/Pu fuels from the viewpoint of the PR in the repository. It was also found that the PR values for used nuclear fuels in the repository of Th/U, Th/Pu, and natural U was comparable with those for enrichment and reprocessing facilities in the pressurized water reactor (PWR) nuclear fuel cycle. On the other hand, the PR values considering the transport of radionuclides in the repository were found to be slightly smaller than those without their transport after the used nuclear fuels started dissolving after 1,000 years.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.1-7
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• 2018

The image-to-image translation is one of the deep learning applications using image data. In this paper, we aim at improving the performance of object transfiguration which transforms a specific object in an image into another specific object. For object transfiguration, it is required to transform only the target object and maintain background images. In the existing results, however, it is observed that other parts in the image are also transformed. In this paper, we have focused on the structure of artificial neural networks that are frequently used in the existing methods and have improved the performance by adding constraints to the exiting structure. We also propose the advanced structure that combines the existing structures to maintain their advantages and complement their drawbacks. The effectiveness of the proposed methods are shown in experimental results.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.33-43
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• 2022

This study presents a method to restore an optical satellite image with distortion and occlusion due to fog, haze, and clouds to one that minimizes degradation factors by referring to the same type of peripheral image. Specifically, the time and cost of re-photographing were reduced by partially occluding a region. To maintain the original image's pixel value as much as possible and to maintain restored and unrestored area continuity, a simulation restoration technique modified with the Cycle Generative Adversarial Network (CycleGAN) method was developed. The accuracy of the simulated image was analyzed by comparing CycleGAN and histogram matching, as well as the pixel value distribution, with the original image. The results show that for Site 1 (out of three sites), the root mean square error and R² of CycleGAN were 169.36 and 0.9917, respectively, showing lower errors than those for histogram matching (170.43 and 0.9896, respectively). Further, comparison of the mean and standard deviation values of images simulated by CycleGAN and histogram matching with the ground truth pixel values confirmed the CycleGAN methodology as being closer to the ground truth value. Even for the histogram distribution of the simulated images, CycleGAN was closer to the ground truth than histogram matching.

• Information Display
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• v.6 no.2
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• pp.5-9
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• 2005

LG.Philips Displays(LPD)의 SyperSlim은 125도의 편향각과 Deep Gun insertion을 적용하여 개발한 길이 350 mm의 초슬림 CRT이다. SuperSlim은 놀랄만한 디자인으로 브라운관의 주력시장으로써 CRT의 life cycle을 오랫동안 연장시킬 것이다.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.4
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• pp.301-312
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• 2017

The concept of deep borehole disposal (DBD) for high-level nuclear wastes has been around for about 40 years. Now, the Department of Energy (DOE) in the United States (U.S.) is re-examining this concept through recent studies at Sandia National Laboratory and a field test. With DBD, nuclear waste will be emplaced in boreholes at depths of 3 to 5 km in crystalline basement rocks. Thinking is that these settings will provide nearly intact rock and fluid density stratification, which together should act as a robust geologic barrier, requiring only minimal performance from the engineered components. The Nuclear Waste Technical Review Board (NWTRB) has raised concerns that the deep subsurface is more complicated, leading to science, engineering, and safety issues. However, given time and resources, DBD will evolve substantially in the ability to drill deep holes and make measurements there. A leap forward in technology for drilling could lead to other exciting geological applications. Possible innovations might include deep robotic mining, deep energy production, or crustal sequestration of $CO_2$, and new ideas for nuclear waste disposal. Novel technologies could be explored by Korean geologists through simple proof-of-concept experiments and technology demonstrations.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1151-1157
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• 2012

Ocean Thermal Energy Conversion(OTEC) technology is one of the new and renewable energy that utilizes the natural temperature gradient that exists in the tropical ocean between warm surface water and the deep cold water, to generate electricity. The selection of working fluid and the OTEC cycle greatly influence the effect on the system operation, and it's energy efficiency and impacts on the environment. Working fluids of the OTEC are ammonia, R22, R407C, and R410A. In this paper, we compared boiling pressure to optimize OTEC system at $25^{\circ}C$. Also, this paper showed net-power and efficiency according to working fluids for closed cycle and regeneration cycle.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1254-1266
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• 2024

This study investigates the application of supercritical carbon dioxide (S-CO₂) direct-cycle micro modular reactors (MMRs) in primary frequency control (PFC), which is a scenario characterized by significant load fluctuations that has received less attention compared to secondary load-following. Using a modified GAMMA + code and a deep neural network-based turbomachinery off-design model, the authors conducted an analysis to assess the behavior of the reactor core and fluid system under different PFC scenarios. The results indicate that the acceptable range for sudden relative electricity output (REO) fluctuations is approximately 20%p which aligns with the performance of combined-cycle gas turbines (CCGTs) and open-cycle gas turbines (OCGTs). In S-CO₂ direct-cycle MMRs, the control of the core operates passively within the operational range by managing coolant density through inventory control. However, when PFC exceeds 35%p, system control failure is observed, suggesting the need for improved control strategies. These findings affirm the potential of S-CO₂ direct-cycle MMRs in PFC operations, representing an advancement in the management of grid fluctuations while ensuring reliable and carbon-free power generation.

• ALGAE
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• v.18 no.2
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• pp.129-134
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• 2003

In order to examine the effects of deep seawater (mesopelagic water in the broad sense) on the growth of macroalgae, the growth and nutrient uptake (nitrate and phosphate) of Ulva sp. (Ulvophyceae, Chlorophyta) were investigated by cultivation in deep seawater (taken from 687 m depth at Yaizu, central Japan, in August 2001), surface seawater (taken from 24 m depth), and a combination of the two. Culture experiments were carried out in a continuous water supply system and an intermittent water supply system, in which aerated 500-mL flasks with 4 discs of Ulva sp. (cut sections of ca. 2 $cm_2$) were cultured at 20$^{\circ}C$ water temperature, 100 $\mu$mol photons $m^{-2}{\cdot}s^{-1}$ light intensity, and a 14:10 light:dark cycle. Nutrient uptake by Ulva sp. was high in all seawater media in both culture systems. The frond area, dry weight, chlorophyll a content, dry weight per unit area, and chlorophyll a content per unit area of Ulva sp. at the end of the experimental period were the highest in deep seawater and the lowest in surface seawater in both culture systems. These values, except for dry weight per unit area and chlorophyll a content per unit area, for each seawater media in the intermittent water supply system were higher than those in the continuous water supply system. We conclude that not only deep seawater as the culture medium but also the seawater supply system is important for effective cultivation of macroalgae.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.