• Journal of the Korean Institute of Gas
• /
• v.4 no.4 s.12
• /
• pp.25-33
• /
• 2000

In recent years, the subject of remaining life assessment has drawn considerable attention in power plants, where various structural components typically operate at high temperature and pressure. Thus a life prediction methodology accounting for high temperature creep fracture is increasingly needed for the components. Critical defects in such structures are generally found in the form of semi-elliptical surface crack, and the analysis of which is consequently an important problem in engineering fracture mechanics. On this background, we first develop an auto mesh generation program for detailed 3-D finite element analyses of axial and circumferential semi-elliptical surface cracks in a piping system. A high temperature creep fracture parameter $C^{\ast}$-integral is obtained from the finite element analyses of generated 3-D models. Post crack growth module is further appended here to calculate the amount of crack growth. Finally the remaining lives of surface cracked pipes for various analytical parameters are assessed using the developed life assessment program.

• Korean Chemical Engineering Research
• /
• v.54 no.4
• /
• pp.479-486
• /
• 2016

The efficient steam drum should be required to reduce carbon oxide emissions and heat recovery in oxygen converter hood system. However, steam generation is limited to the time of the oxygen blowing period, which is intermittent or cyclical in operation of steel-making process. Thus, steam drum should be optimized for an effective steam generation during the oxygen blowing portion of the converter cycle. In this study, a three-dimensional computational fluid dynamics (CFD) model has been developed to describe the impacts of changing various operating conditions and geometric shape on thermo-fluid characteristics and performance of the steam drum. This model encompasses not only fluid flow and heat transfer but also evaporation and condensation at the interfacial surface in the steam drum by using VOF (Volume of Fluid) method. To validate the prediction performance of this model, comparison of the steam flow rate between numerical and experimental result has been performed, resulting in the accuracy of the relative error by less than 3.2%.

• Journal of KIBIM
• /
• v.5 no.4
• /
• pp.53-62
• /
• 2015

Korea has been at the forefront of green growth initiatives. In 2008, the government declared the new vision toward 'low-carbon society and green growth'. The government subsidies and Feed-in Tariff (FIT) increased domestic usage of solar power by supplying photovoltaic housing and photovoltaic generation systems. Since 2000, solar power industry has been the world's fastest growing source with the annual growth rate of 52.5%. Especially, BIPV(Building Integrated Photovoltaic) systems are capturing a growing portion of the renewable energy market due to several reasons. BIPV consists of photovoltaic cells and modules integrated into the building envelope such as a roof or facades. By avoiding the cost of conventional materials, the incremental cost of photovoltaics is reduced and its life-cycle cost is improved. When it comes to atypical building, numerous problems occur because PV modules are flat, stationary, and have its orientation determined by building surface. However, previous studies mainly focused on improving installations of solar PV technologies on ground and rooftop photovoltaic array and developing prediction model to estimate the amount of produced electricity. Consequently, this paper discusses the problem during a planning and design stage of BIPV systems and suggests the method to select optimal design of the systems by applying the national strategy and economic policies. Furthermore, the paper aims to develop BIM tool based on the engineering knowledge from experts in order for non-specialists to design photovoltaic generation systems easily.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.12
• /
• pp.1772-1781
• /
• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• Biomolecules & Therapeutics
• /
• v.27 no.2
• /
• pp.201-209
• /
• 2019

Mixed lineage leukemia proteins (MLL) are the key histone lysine methyltransferases that regulate expression of diverse genes. Aberrant activation of MLL promotes leukemia as well as solid tumors in humans, highlighting the urgent need for the development of an MLL inhibitor. We screened and isolated MLL1-binding ssRNAs using SELEX (${\underline{S}}ystemic$ ${\underline{E}}volution$ of ${\underline{L}}igands$ by ${\underline{E}}xponential$ enrichment) technology. When sequences in sub-libraries were obtained using next-generation sequencing (NGS), the most enriched aptamers-APT1 and APT2-represented about 30% and 26% of sub-library populations, respectively. Motif analysis of the top 50 sequences provided a highly conserved sequence: 5'-A[A/C][C/G][G/U][U/A]ACAGAGGG[U/A]GG[A/C] GAGUGGGU-3'. APT1, APT2, and APT5 embracing this motif generated secondary structures with similar topological characteristics. We found that APT1 and APT2 have a good binding activity and the analysis using mutated aptamer variants showed that the site information in the central region was critical for binding. In vitro enzyme activity assay showed that APT1 and APT2 had MLL1 inhibitory activity. Three-dimensional structure prediction of APT1-MLL1 complex indicates multiple weak interactions formed between MLL1 SET domain and APT1. Our study confirmed that NGS-assisted SELEX is an efficient tool for aptamer screening and that aptamers could be useful in diagnosis and treatment of MLL1-mediated diseases.

• Journal of Korea Society of Waste Management
• /
• v.35 no.8
• /
• pp.777-784
• /
• 2018

Since climate change increases the risk of extreme rainfall events, concerns on flood management have also increased. In order to rapidly recover from flood damages and prevent secondary damages, fast collection and treatment of flood debris are necessary. Therefore, a quick and precise estimation of flood debris generation is a crucial procedure in disaster management. Despite the importance of debris estimation, methodologies have not been well established. Given the intrinsic heterogeneity of flood debris from local conditions, a regional-scale model can increase the accuracy of the estimation. The objectives of this study are 1) to identify significant damage variables to predict the flood debris generation, 2) to ascertain the difference in the coefficients, and 3) to evaluate the accuracy of the debris estimation model. The scope of this work is flood events in Ulsan city region during 2008-2016. According to the correlation test and multicollinearity test, the number of damaged buildings, area of damaged cropland, and length of damaged roads were derived as significant parameters. Key parameters seems to be strongly dependent on regional conditions and not only selected parameters but also coefficients in this study were different from those in previous studies. The debris estimation in this study has better accuracy than previous models in nationwide scale. It can be said that the development of a regional-scale flood debris estimation model will enhance the accuracy of the prediction.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.4
• /
• pp.328-337
• /
• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.2
• /
• pp.181-186
• /
• 2022

Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

• Journal of the Korean Institute of Gas
• /
• v.26 no.5
• /
• pp.49-57
• /
• 2022

If the hydrogen industry is activated in the future, the LH2 receiving terminal with membrane storage tank is a major way to store and send large capacity hydrogen. Since such a LH2 receiving terminal does not currently exist, the process simulation model of it was completed by referring to the design data on existing LNG receiving terminal with same typed storage tank. Based on this model, the amount of BOG generation according to change of design conditions, which is a very important factor in the operation of LH2 receiving terminal, was predicted. Through this, it was attempted to review the appropriate operating conditions to minimize the amount of BOG generated during unloading in LH2 receiving terminal with membrane storage tank.

• Atmosphere
• /
• v.34 no.2
• /
• pp.97-106
• /
• 2024

The seasonal forecast skill of tropical cyclones (TCs) in the Northern Hemisphere from the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 6 (GloSea6) hindcast has been verified for the period 1993 to 2016. The operational climate prediction system at KMA was upgraded from GloSea5 to GloSea6 in 2022, therefore further validation was warranted for the seasonal predictability and variability of this new system for TC forecasts. In this study, we examine the frequency, track density, duration, and strength of TCs in the North Indian Ocean, the western North Pacific, the eastern North Pacific, and the North Atlantic against the best track data. This methodology follows a previous study covering the period 1996 to 2009 published in 2020. GloSea6 indicates a higher frequency of TC generation compared to observations in the western North Pacific and the eastern North Pacific, suggesting the possibility of more TC generation than GloSea5. Additionally, GloSea6 exhibits better interannual variability of TC frequency, which shows relatively good correlation with observations in the North Atlantic and the western North Pacific. Regarding TC intensity, GloSea6 still underestimates the minimum surface pressures and maximum wind speeds from TCs, as is common among most climate models due to lower horizontal resolutions. However, GloSea6 is likely capable of simulating slightly stronger TCs than GloSea5, partly attributed to more frequent 6-hourly outputs compared to the previous daily outputs.