• Journal of Broadcast Engineering
• /
• v.27 no.1
• /
• pp.3-12
• /
• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1D
• /
• pp.17-23
• /
• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1A
• /
• pp.9-18
• /
• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• International Area Studies Review
• /
• v.15 no.1
• /
• pp.173-193
• /
• 2011

Most of the research regarding economic effects of policy loans has thus far been focused on whether policy loans can improve the financial status or the management performance of small and medium enterprises (SMEs). Unlike previous researches, this study implemented an empirical analysis focused on the contribution of policy loans to easing the liquidity restriction of investment. To analyze whether investment liquidity restriction can be alleviated or not, this study attempted an empirical analysis utilizing the nonlinear Euler equation induced through optimization of investment and GMM (generalized method of moments) as its analysis methodology. With the SMEs that received policy financing from the Small and medium Business Corporation (SBC) in 2004, this study analyzed three years of panel data before(2001~2003) and after(2004~2006) receipt of policy loans. According to the empirical results, it appears that policy loans had effects on resolving liquidity restriction of investment, implying that policy financing eases the liquidity restriction of SME investment and would contribute to the growth and development of SMEs. Further, I checked robustness of empirical results using Tobin's q model. The empirical results also support that policy loans help to resolve liquidity constraint. With these results, it is understood that the critical view to date, which has emphasized the ineffectiveness of policy financing due to it having no or insignificant economic effects, may be wrong.

• Phonetics and Speech Sciences
• /
• v.15 no.3
• /
• pp.75-82
• /
• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.

• Membrane Journal
• /
• v.33 no.6
• /
• pp.362-368
• /
• 2023

This research article explores the application of Polymer of Intrinsic Microporosity (PIM-1) as a cutting-edge material for CO₂ gas separation membranes in response to the escalating global concern over climate change and the imperative to reduce greenhouse gas emissions. The study delves into the synthesis, molecular weight control, and fabrication of PIM-1 membranes, providing comprehensive insights through various characterization techniques. The intrinsic microporosity of PIM-1, arising from its unique crosslinked and rigid structure, is harnessed for selective gas permeation, particularly of carbon dioxide. The article emphasizes the tunable chemical properties of PIM-1, allowing for customization and optimization of gas separation membranes. By controlling the molecular weight, higher molecular weight (H-PIM-1) membranes are demonstrated to exhibit superior CO₂ permeability and selectivity compared to lower molecular weight counterparts (L-PIM-1). The study's findings highlight the critical role of molecular weight in tailoring PIM-1 membrane properties, contributing to the advancement of next-generation membrane technologies for efficient and selective CO₂ capture-an essential step in addressing the pressing global challenge of climate change.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.7
• /
• pp.971-979
• /
• 2023

Small ships (<499 GT) constitute 46% of the existing ships, therefore, it can be concluded that they produce relatively high CO₂ gas emissions. Operating in optimal trim conditions can reduce the resistance of the ship, which results in fewer greenhouse gases. An affordable way for trim optimization is to adjust the weight distribution to obtain an optimum longitudinal center of gravity (LCG). Therefore, in this study, the effect of LCG changes on the resistance of a small planing ship is studied using empirical and numerical analyses. The Savitsky method employing Maxsurf resistance and the STAR-CCM+ commercial computational fluid dynamics (CFD) software is used for the empirical and numerical analyses, respectively. Finally, the total resistance from the ship design process is compared to obtain the optimum LCG. To summarize, using numerical analysis, optimum LCG is achieved at the 46.2% length overall (LoA) at Froude Number 0.56, and 43.4% LoA at Froude Number 0.63, which provides a significant resistance reduction of 41.12 - 45.16% compared to the reference point at 29.2% LoA.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.6
• /
• pp.536-543
• /
• 2023

Speaker diarization, which labels for "who spoken when?" in speech with multiple speakers, has been studied on a deep neural network-based end-to-end method for labeling on speech overlap and optimization of speaker diarization models. Most deep neural network-based end-to-end speaker diarization systems perform multi-label classification problem that predicts the labels of all speakers spoken in each frame of speech. However, the performance of the multi-label-based model varies greatly depending on what the threshold is set to. In this paper, it is studied a speaker diarization system using single-label classification so that speaker diarization can be performed without thresholds. The proposed model estimate labels from the output of the model by converting speaker labels into a single label. To consider speaker label permutations in the training, the proposed model is used a combination of Permutation Invariant Training (PIT) loss and cross-entropy loss. In addition, how to add the residual connection structures to model is studied for effective learning of speaker diarization models with deep structures. The experiment used the Librispech database to generate and use simulated noise data for two speakers. When compared with the proposed method and baseline model using the Diarization Error Rate (DER) performance the proposed method can be labeling without threshold, and it has improved performance by about 20.7 %.

• Journal of the Korean institute of surface engineering
• /
• v.56 no.6
• /
• pp.412-419
• /
• 2023

Anion exchange membrane electrolysis is considered a promising next-generation hydrogen production technology that can produce low-cost, clean hydrogen. However, anion exchange membrane electrolysis technology is in its early stages of development and requires intensive research on electrodes, which are a key component of the catalyst-system interface. In this study, we optimized the pressure conditions of the hot-pressing process to manufacture cobalt oxide electrodes for the development of a high uniformity and high adhesion electrode production process for the oxygen evolution reaction. As the pressure increased, the reduction of pores within the electrode and increased densification of catalytic particles led to the formation of a uniform electrode surface. The cobalt oxide electrode optimized for pressure conditions exhibited improved catalytic activity and durability. The optimized electrode was used as the anode in an AEMWE single cell, exhibiting a current density of 1.53 A cm^-2 at a cell voltage of 1.85 V. In a durability test conducted for 100 h at a constant current density of 500 mA cm^-2, it demonstrated excellent durability with a low degradation rate of 15.9 mV kh^-1, maintaining 99% of its initial performance.

• Information Systems Review
• /
• v.24 no.4
• /
• pp.23-40
• /
• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.