• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.451-456
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• 2003

The behavior of concrete deep beams in shear is substantially influenced by beam size and shape, loading conditions, reinforcement details, and material properties. Therefore, it is not easy to predict the ultimate response of beams correctly and take into account all those factors in practical shear design. In this study, a grid softened strut-tie model approach for determining the shear strengths of various reinforced concrete deep beams is proposed. The validity of the approach is examined through the strength analysis of numerous reinforced concrete deep beams tested to failure. The approach can be further developed to improve the current deep beam design procedures by incorporating the actual shear resisting mechanisms of deep beams.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.188-191
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• 2009

A high efficiency deep blue phosphorescent organic light-emitting diode (PHOLED) was developed using a new wide triplet bandgap host material (PPO1) with a phenylcarbazole and a phosphine oxide unit. The wide triplet bandgap host material was synthesized by a phosphornation reaction of 2-bromo-Nphenylcarbazole with chlorodiphenylphosphine. A deep blue emitting phosphorescent dopant, tris((3,5-difluoro-4-cyanophenyl)pyridine)iridium (FCNIr), was doped into the PPO1 host and a high quantum efficiency of 17.1 % and a current efficiency of 19.5 cd/A with a color coordinate of (0.14,0.15) were achieved in the blue PHOLED. The quantum efficiency of the deep blue PHOLED was better than any other quantum efficiency value reported up to now.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1126-1137
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• 2020

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.458-476
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• 2024

To address the issue of class imbalance in network traffic data, which affects the network intrusion detection performance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGAN are used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encode traffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network model combining a bidirectional gated current unit and deep neural network is proposed, which is used to extract long-dependence features. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detection accuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusion detection performance, thereby improving the communication security of network data.

• Journal of IKEEE
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• v.26 no.2
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• pp.306-312
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• 2022

We investigated electrical properties and deep level traps in 4H-SiC merged PiN Schottky (MPS) diodes with different barrier heights by different PN ratios and metallization annealing temperatures. The barrier heights of MPS diodes were obtained in IV and CV characteristics. The leakage current increased with the lowering barrier height, resulting in 10 times larger current. Additionally, the deep level traps (Z_1/2 and RD_1/2) were revealed by deep level transient spectroscopy (DLTS) measurement in four MPS diodes. Based on DLTS results, the trap energy levels were found to be shallow level by 22~28% with lower barrier height It could confirm the dependence of the defect level and concentration determined by DLTS on the Schottky barrier height and may lead to incorrect results regarding deep level trap parameters with small barrier heights.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of IKEEE
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• v.28 no.1
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• pp.104-109
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• 2024

In this research, we explored the influence of post-annealing atmospheres on the electrical properties of Ga₂O₃/SiC heterojunction diodes. We fabricated Ga₂O₃/SiC heterojunction diodes by RF sputtering and after the fabrication the post-annealing in various gas atmospheres was performed. We measured the changes in deep-level defects using Deep Level Transient Spectroscopy (DLTS) and we conducted an electrical characteristic of J-V measurement and Hall measurement to analyzed the effects of annealing atmosphere on Ga₂O₃/SiC heterojunction diode. In the N₂ annealed devices, the highest on-state current was measured as 3.06 × 10^-2 A/cm^2, and an increase in carrier concentration of 3.8 × 10¹⁴ cm^-3 was observed. This confirms that the variations in deep level defects due to the post-annealing atmosphere can influence the electrical properties.

• Ocean and Polar Research
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• v.26 no.2
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• pp.341-349
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• 2004

Hyrdography and deep currents were measured from 1997 to 1999 to investigate deep-sea environments in the KODOS (Korea Deep Ocean Study) area of the northeastern tropical Pacific. KODOS area is located meridionally from the North Equatorial Current to the boundary between the North Equatorial Current and the Equatorial Counter Current. Strong thermocline exists between 10 m and 120 m depths at the study area. Since that strong thermocline does hardly allow vertical mixing between surface and lower layer waters, vertical distributions of temperature, salinity, dissolved oxygen and nutrients drastically change near the thermocline. Salinity-minimum layer, which indicate the North Pacific Intermediate Water (NPIW) and the Antartic Intermediate Water (AAIW), vertically occupies vertically at the depths from 500 m down to 1400 m. The NPIW and the AAIW horizontally occur to the north and to the south of $7^{\circ}N$, respectively. The near-bottom water shows the physical characteristics of $1.05^{\circ}C$ and 34.70 psu at the depths of 10 m to 110 m above the bottom (approximately 4000-5000 m), which was originated from the Antarctic Circumpolar Water. It flows northeastwards for 2 to 4 months at the study area, and its mean velocity was 3.1-3.7 cm/s. Meanwhile, reverse (southwestward) currents appear for about 15 days with the average of 1.0-6.1 cm/s every 1 to 6 months. Dominant direction of the bottom currents obtained from the data for more than 6 months is northeastward with the average speeds of 1.7-2.1 cm/s. Therefore, it seems that deep waters from the Antarctica flow northwards passing through the KODOS area in the northeastern tropical Pacific.

• Ocean and Polar Research
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• v.36 no.4
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• pp.487-494
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• 2014

Deep-seabed test miner operated by a self-propelled mining system moving on soft soil is an essential device to secure floating and towing performances. The performances of the tracked vehicle are seriously influenced by noise factors such as the shear strength of the seafloor, bottom current, seafloor slope, speed of tracked vehicle, reaction forces of flexible hose, steering ratio, etc. Due to uncertainties related to noise factors, the design of a deep-sea manganese nodules test miner that satisfies target reliabilities is difficult. Therefore, reliability-based design optimization (RBDO) is required to guarantee system reliability under circumstances where uncertainties related to noise factors prevail. Among noise factors, the bottom current, a bimodal distribution, is censored due to the observation limit of measurement devices. Therefore, estimated distribution of the bottom current is inaccurate without considering these characteristics and the result of RBDO cannot be guaranteed. In this paper, we define censored data as unknown values over the limit of observation. If this data is estimated by using Akaike information criterion (AIC) that cannot consider the characteristics of censored data, the distribution of estimated data cannot guarantee accurate reliability. Therefore, censored AIC that can consider the characteristics of data is used to estimate accurate distribution of the bottom current. Finally, RBDO, under circumstances where uncertainties related to noise factors combined censored data are present, is performed on the mobility of a deep-sea manganese nodules test miner.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.223-226
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• 1998

In this paper, we have investigated how morphology and electrical properties in Polyphenylene sulfide(PPS) are changed by uniaxial elongation. XRD pattern shows that interplanar distance and crystallinities are decreased by increasing elongation ratio. Electrical conduction mechanism of PPS is explained as schottky emission from analysis of electrical current. The electrical current is decreased by increasing elongation ratio. The conductivity is changed remarkably above the glass transition temperature around $(82^{\circ}C)$. The band gap of PPS is evaluated as 3.9-4(eV) from the results of photoconductivity. Increarnent of elongation ratio gives us some information about deep trap formation from photocurrent.