• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.601-602
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• 2003

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.200-200
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• 2003

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.4
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• pp.352-360
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• 2017

Polygeneration process is widely used to pursuit high efficiency by sharing electricity, utility, refrigeration and the utilization of product chemicals. In this paper, performance analysis of the 450 MW Class polygeneration process was conducted with various syngas generated from coal and biomass gasifier. WGSR and PSA process were employed for hydrogen production and separation. Process modeling and dynamic simulation was carried out, and the results were compared with NETL report. Net power of the polygeneration process was 439 MW considering power consumption. More than 90% of CO was converted at WGSR and the hydrogen purity of PSA was more than 99.99%.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1054-1061
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher quality of material, These includs numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experimens conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a healthy bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• Applied Science and Convergence Technology
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• v.26 no.6
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• pp.164-173
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• 2017

Across the most industry, the demand for nanoparticles is increasing. Therefore, many studies have been carried out to synthesize nanoparticles using various methods. The aim of this paper is to introduce an industry-applicable as well as financially and environmentally effective solution plasma process. The solution plasma process involves fewer chemicals than the traditional kit, and can be used to replace many of the chemical agents employed in previous synthesis of nanoparticles into plasma. In this study, this process is compared to the wet-reaction process that has thus far been widely used in the most industry. Furthermore, the solution plasma process has been classified into four different types (in-solution, out of solution, direct type, and remote type), according to its plasma occurrence position and plasma types. Thus, the source of radicals, nanoparticle synthesis, and modification methods are explained for each design. Lastly, unlike nanoparticles with hydrophilic functional groups that are made inside the solution, a nanoparticle synthesis and modification method to create a hydrophobic functional group is also proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4285-4299
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• 2019

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.6
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• pp.466-473
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• 2018

Most frequency domain-based approaches assume that structural response should be a Gaussian random process. But a lot of non-Gaussian processes caused by multi-excitation and non-linearity in structural responses or load itself are observed in many real engineering problems. In this study, the effect of non-Normality on fatigue damages are discussed through case study. The accuracy of four frequency domain methods for non-Gaussian processes are compared in the case study. Power-law and Hermite models which are derived for non-Gaussian narrow-banded process tend to estimate fatigue damages less accurate than time domain results in small kurtosis and in case of large kurtosis they give conservative results. Weibull model seems to give conservative results in all environmental conditions considered. Among the four methods, Benascuitti-Tovo model for non-Gaussian process gives the best results in case study. This study could serve as background material for understanding the effect of non-normality on fatigue damages.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.849-859
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• 2022

Feed-water valves (FWVs) are used to regulate the flow rate of water entering steam generators, which are very important devices in nuclear power plants. Due to the working environment of relatively high pressure and temperature, there is strength failure problem of valve body in some cases. Based on the thermo-fluid-solid coupling model, the valve body stress of the feed-water valve in the opening process is investigated. The flow field characteristics inside the valve and temperature change of the valve body with time are studied. The stress analysis of the valve body is carried out considering mechanical stress and thermal stress comprehensively. The results show that the area with relatively high-velocity area moves gradually from the bottom of the cross section to the top of the cross section with the increase of the opening degree. The whole valve body reaches the same temperature of 250 ℃ at the time of 1894 s. The maximum stress of the valve body meets the design requirements by stress assessment. This work can be referred for the design of FWVs and other similar valves.

• Journal of the Korean institute of surface engineering
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• v.55 no.3
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• pp.143-155
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• 2022

Ceramic oxide layers successfully were formed on the surface of cast Al alloys with high Si contents using plasma electrolytic oxidation (PEO) process in electrolytes containing Na₂SiO₃, NaOH, and additives. The microstructure of the oxide layers was systematically analyzed using scanning electron microscopy (SEM), cross-sectional transmission electron microscopy (TEM), X-ray diffraction patterns (XRD), and energy X-ray dispersive spectroscopy (EDS). XRD analysis indicated that the PEO untreated high-silicon Al alloys (i.e., 17.1 and 11.7 wt.% Si) consist of Al, Si and Al₂Cu phases whereas Al₂Cu phase selectively disappeared after PEO treatment. PEO process yielded an amorphous oxide layer with few second phases including γ-Al₂O₃ and Fe-rich phases. The corrosion behaviors of high-silicon Al alloys treated by PEO process were investigated using electrochemical impedance spectroscopy (EIS) and other electrochemical techniques (i.e., open circuit potential and polarization curve). Electroanalytical studies indicated that high-silicon Al alloys treated by PEO process have greater corrosion resistance than high-silicon alloys untreated by PEO process.