• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1462-1467
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• 2013

A pseudo 480-Hz drive method has been proposed to reduce the dynamic false contour noise that occurs on flat panel displays with displaying grayscale image in the digital mode, such as plasma display panels. The proposed method makes the image movements nearly continuous by rearranging the 8-bit image data displayed for 1 TV field into 8 subfields. The position of the image data rearranged in subfields has been optimized on the basis of the speed of the moving image by computer simulations for the dynamic false contour noise. It is verified that a significant reduction in the dynamic false contour noise is achieved with the proposed method as compared to the conventional noise reduction technologies. Moreover, to reduce the noise in digital mode displays, the proposed technology requires only 8 subfields. Therefore, there is no reduction in the brightness of the image.

• Advances in environmental research
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• 제1권2호
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• pp.143-151
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• 2012

Nitrate removal from aqueous solution was investigated using $ZnCl_2$ and phosphoric acid activated carbon developed from pomegranate peel with particle size 0.4 mm. Potassium nitrate solution was used in batch adsorption experiments for nitrate removal from water. The effects of activated carbon dosage, time of contact, and pH were studied. The equilibrium time was fond to be 45 min. Two theoretical adsorption isotherms namely Langmuir and Freundlich were used to describe the experimental results. The Langmuir fit the isotherm with the theoretical adsorption capacity ($q_t$) was fond 78.125 mg g-1. Adsorption kinetics data were modeled using the pseudo-first, pseudo-second order, and intraparticle diffusion models. The results indicate that the second-order model best describes adsorption kinetic data. Results show activated carbon produced from pomegranate is effective for removal of nitrate from aqueous solution.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.899-904
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• 2012

High-dimensional categorical data models with small sample sizes have not been used extensively in genomic sequences that involve count (or discrete) or purely qualitative responses. A basic task is to identify differentially expressed genes (or positions) among a number of genes. It requires an appropriate test statistics and a corresponding multiple testing procedure so that a multivariate analysis of variance should not be feasible. A family wise error rate(FWER) is not appropriate to test thousands of genes simultaneously in a multiple testing procedure. False discovery rate(FDR) is better than FWER in multiple testing problems. The data from the 2002-2003 SARS epidemic shows that a conventional FDR procedure and a proposed test statistic based on a pseudo-marginal approach with Hamming distance performs better.

• 대한자원환경지질학회:학술대회논문집
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• 대한자원환경지질학회 2002년도 춘계 공동학술발표회
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• pp.156-158
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• 2002

Reverse-time migration has no dip limitations and one of the most promising methods to preserve true amplitudes. We applied 3-D prestack reverse time migration based on a pseudo-spectral implementation of the acoustic wave equation to the SEG/EAGE salt dome synthetic data set. We were able to illuminate sub salt reflectors of the SEG/EAGE salt model that were barely observable in the Kirchhoff migration images. Using the pseudo-spectral modeling technique, we could implement reverse-time migration within the core memory, which could be equipped to a personal computer.

• Carbon letters
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• 제23권
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• pp.30-37
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• 2017

Dyes are widely used in various industries including textile, cosmetic, paper, plastics, rubber, and coating, and their discharge into waterways causes serious environmental and health problems. Four different carbon nanostructures, graphene oxide, oxidized multi-walled carbon nanotubes, activated carbon and multi-walled carbon nanotubes, were used as adsorbents for the removal of Nile Blue A (NBA) dye from aqueous solution. The four carbon nanostructures were characterized by scanning electron microscope and X-ray diffractometer. The effects of various parameters were investigated. Kinetic adsorption data were analyzed using the first-order model and the pseudo-second-order model. The regression results showed that the adsorption kinetics were more accurately represented by the pseudo-second-order model. The equilibrium data for the aqueous solutions were fitted to Langmuir and Freundlich isotherms, and the equilibrium adsorption of NBA was best described by the Langmuir isotherm model. This is the first research on the removal of dye using four carbon nanostructures adsorbents.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.410-413
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• 2023

레이블링 작업은 데이터 분석 시 필요한 사전 작업중 하나이다. 모든 데이터들에 대해 레이블링 작업은 시간/인적 자원을 필요로 하기에, 해당 작업을 보완할 방법이 존재한다면 요구되는 리소스를 줄여 효율성을 크게 향상시킬 수 있다. 본 논문에서는 통신회사에서 적재된 데이터 셋에 대하여 레이블이 없는 데이터(Unlabeled-data)에 대해 의사 레이블링(Pseudo-labeling), SMOTE 를 통한 데이터 증강을 활용하여 기존에 활용되지 못한 데이터를 추가하여 모델에 학습시킨다. 실험을 통해 의사 레이블을 통한 모델 학습 방법이 기존 도메인 지식의 레이블 방법보다 효율적이고 성능이 우수함을 확인하였다.

• 자동차안전학회지
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• 제15권4호
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• pp.58-64
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• 2023

The adoption of hydrogen-powered Elec is expanding globally. Hydrogen is recognized as a potentially hazardous energy source, and safety assessment is crucial for the development of plans to supply hydrogen-powered electric buses. Hydrogen gas leakage can have a significant impact during bus operations, and continuous hydrogen leakage in hydrogen-powered vehicles can result in fatal accidents. In this study, information about hydrogen leakage is collected through sensors installed within the vehicles and is measured when the sensor detects a leak. The study also proposes the use of Pseudo Fuel Leakage (PFL, %) as an additional indicator for evaluating and monitoring hydrogen safety and leakage.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.237-246
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• 1998

지금까지 알려진 의사난수 생성기에서는 혼합 합동 생성기, 승산 합동 생성기, 유니버셜 난수 생성기, 역함수 합동 생성기, 양의 역함수 난수 생성기 등 여러 가지가 있다. 이러한 의사난수 생성기에 대하여 각각 20, 40, 60, 80, 100개의 자료를 생성하여 유의수준(${\alpha}$) 0.1, 0.05, 0.01 기준으로 10,000번의 시행 과정을 통하여 난수의 특성인 일양성과 독립성을 만족하는지를 검정하였다.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.