• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.634-637
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• 2005

Multichannel seismic data acquired in Ulleung Basin of East Sea for gas hydrate exploration. The seismic sections of this area show strong BSR(bottom simulating reflections) associated with methane hydrate occurrence in deep marine sediments. Very limited information is available from deep sea drilling as the risk of heating and destabilizing the initial hydrate conditions during the processing of drilling is considerably high. Not so many advanced status of gas hydrate exploration in Korea, the most of information of gas hydrate characteristics and properties are inferred from seismic reflection data. In this study, The AVO analysis using the long offset seismic data acquired in Ulleung Basin used to explain the characteristics and structure of gas hydrate. It is used primarily P-wave velocity accessible from seismic data. To make a good quality of AVO analysis input data, seismic preprocessing including 'true gain correction', 'source signature deconvolution', twice velocity analysis and some kinds of multiple rejection and enhancing the signal to noise ratio processes is carried out very carefully. The results of AVO analysis, the eight kinds of AVO attributes are estimated basically and some others of AVO attributes are evaluated for interpretation of AVO analysis additionally. The impedance variation at the boundary of gas hydrate and free gas is estimated for investing the BSR characteristics and properties. The complex analysis is performed also to verifying the amplitude variation and phase shift occurrence at BSR. Type III AVO anomaly appearance at saturated free gas area is detected on BSR. It can be an important evidence of gas hydrate deposition upper the BSR.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.203-205
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• 2006

This paper presents a novel soft-switching PWM utility frequency AC to high frequency AC power conversion circuit incorporating boost-half-bridge inverter topology, which is more suitable and acceptable for cost effective consumer induction heating applications. The operating principle and the operation modes are presented using the switching mode and the operating voltage and current waveforms. The performances of this high-frequency inverter using the latest IGBTs are illustrated, which includes high frequency power regulation and actual efficiency characteristics based on zero voltage soft switching (ZVS) operation ranges and the power dissipation as compared with those of the previously developed high-frequency inverter. In addition, a dual mode control scheme of this high frequency inverter based on asymmetrical pulse width modulation (PWM) and pulse density modulation (PDM) control scheme is discussed in this paper in order to extend the soft switching operation ranges and to improve the power conversion efficiency at the low power settings. The power converter practical effectiveness is substantially proved based on experimental results from practical design example.

• Journal of the Korean Society of Combustion
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• v.22 no.4
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• pp.19-26
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• 2017

This study investigates the gasification of coal char by $CO_2$ under high pressures in a drop tube furnace(DTF). The rate constants are derived for the shrinking core model using the conventional method based on the set reactor conditions. The computational fluid dynamic(CFD) simulations adopting the rate constants revealed that the carbon conversion was much slower than the experimental results, especially under high temperature and high partial pressure of reactants. Three reasons were identified for the discrepancy: i) shorter reaction time because of the entry region for heating, ii) lower particle temperature by the endothermic reaction, and iii) lower partial pressure of $CO_2$ by its consumption. Therefore, the rate constants were corrected based on the actual reaction conditions of the char. The CFD results updated using the corrected rate constants well matched with the measured values. Such correction of reaction conditions in a DTF is essential in deriving rate constants for any char conversion models by $H_2O$ and $O_2$ as well as $CO_2$.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.387-390
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• 2006

This paper presents a novel soft-switching PWM utility frequency AC to high frequency AC power conversion circuit Incorporating boost-half-bridge inverter topology, which is more suitable and acceptable for cost effective consumer induction heating applications. The operating principle and the operation modes are presented using the switching mode and the operating voltage and current waveforms. The performances of this high-frequency inverter using the latest IGBTs are illustrated, which includes high frequency power regulation and actual efficiency characteristics based on zero voltage soft switching (ZVS) operation ranges and the power dissipation as compared with those of the previously developed high-frequency inverter. In addition, a dual mode control scheme of this high frequency inverter based on asymmetrical pulse width modulation (PWM) and pulse density modulation (PDM) control scheme is discussed in this paper in order to extend the soft switching operation ranges and to improve the power conversion efficiency at the low power settings. The power converter practical effectiveness is substantially proved based on experimental results from practical design example.

• Journal of the Korea Institute of Building Construction
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• v.19 no.6
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• pp.539-547
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• 2019

Recently, the international community signed a climate change agreement to prevent global warming. Yet currently, the fossil fuels have been widely used in to supply building energy for cooling and heating. The Green Building certification (G-SEED), an energy efficiency rating for new or existing buildings requires that buildings meet certain conditions. Insulation is used as a building material to reduce the energy supply to buildings and to improve the thermal insulation, and it accounts for more than 90% of the total heat resistance provided by the building surface components that meet the energy-saving design standards of new buildings. In this investigation, a performance evaluation study was conducted through an experimental study by directly extracting the foam polystyrene insulation on-site during the remodeling of a building that was in the range of 22~38 years old. Through tests, it was found that the thermal conductivity of the extrusion method insulation (XPS) was reduced by 48% and the compressive strength of XPS decreased by 36% compared to KS M 3808, which is the initial quality standard. For bead method insulation (EPS) with a thickness of 50mm, the thermal conductivity, the compressive strength, and flexural failure load were similar to the initial quality standard. Therefore, in the calculation of the primary energy requirement per unit area per year, the performance of bead method insulation can be estimated simply by considering the thickness of the insulation, while a correction factor that considers its performance deterioration should be applied when extrusion method insulation is used.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.71-80
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• 2001

Blood and urine mercury level of three workers were monitored during 60~80 days after high exposure to mercury at the silver refining plant. Mercury was used to form silver-mercury amalgam from plating sludge. Workers were exposed to mercury about 70 days at the several processes, such as hand held weaving, vibration table, and heating from the furnace. mercury was analysed by atomic absorption spectroscopy-vapor generation technique. Recovery from the biological sample was 95.51% and pooled standard deviation was 0.033. At the time of study, there was no work at the workplace. So, airborne mercury concentration was measured with area sampling 5 days after the work, ranged from 0.1459 to 1.2351 mg/㎥(Arithmatic mean 0.4711 mg/㎥, Geometric mean 0.3566 mg/㎥) at the inside of the plant, that is far above the ACGIH's TLV(0.025 mg/㎥) and ranged from 0.0073 to 0.0330 mg/㎥ at the outdoor. Blood mercury levels at the beginning of the monitoring were 4~14 times greater than the American Conference of Governmental Industrial Hygienists Biological Exposure Index(ACGIH BEI, 15 ug/L). Blood mercury levels were decreased logarithmically, that is, rapidly at the high level and slowly at the low level but sustained above the level of the ACGIH BEI 60~80 days after the work. Urine mercury levels at the beginning of the monitoring were 8~16 times greater than the ACGIH BEI(35 ug/g creatinine). Urine mercury levels were decreased logarithmically, but correlation between urine level and off-days were lower than those of blood. Decreasing pattern of blood mercury levels were little affected than that of urine levels when the chelating agent, D-penicillamine, was administered. There was correlation between blood mercury level and urine mercury level(0.81~0.83) but it didn\`t mean that the highest blood mercury level corresponded the highest urine mercury level. In our study, Case 1 always shows the highest level in urine but case 3 always shows the highest level in blood. Creatinine correction represented better correlations between urine mercury levels and blood levels, and between urine levels and off-days rather than by urine volume. Spot urine sampling had a wide variation than that of whole day urine sampling. So, We recommend spot urine sampling for screening and whole day urine sampling for exact diagnosis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.135-145
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• 2019

Analysis of a long cycle or a trend of time series data based on a long-term observation would require comparability between data observed in the past and the present. In the present study, we proposed an approach to ensure the compatibility among the instruments used for the long-term observation, which would allow to secure continuity of the data. An open-path gas analyzer (Model LI-7500, LI-COR, Inc., USA) has been used for eddy covariance flux measurement in the Gwangneung deciduous forest for more than 10 years. The open-path gas analyzer was replaced by an enclosed-path gas analyzer (Model EC155, Campbell Scientific, Inc., USA) in July 2015. Before completely replacing the gas analyzer, the carbon dioxide ($CO_2$) and latent heat fluxes were collected using both gas analyzers simultaneously during a five-month period from August to December in 2015. It was found that the $CO_2$ fluxes were not significantly different between the gas analyzers under the condition that the daily mean temperature was higher than $0^{\circ}C$. However, the $CO_2$ flux measured by the open-path gas analyzer was negatively biased (from positive sign, i.e., carbon source, to 0 or negative sign, i.e., carbon neutral or sink) due to the instrument surface heating under the condition that the daily mean temperature was lower than $0^{\circ}C$. Despite applying the frequency response correction associated with tube attenuation of water vapor, the latent heat flux measured by the enclosed-path gas analyzer was on average 9% smaller than that measured by the open-path gas analyzer, which resulted in >20% difference of the sums over the study period. These results indicated that application of the additional air density correction would be needed due to the instrument heat and analysis of the long-term observational flux data would be facilitated by understanding the underestimation tendency of latent heat flux measurements by an enclosed-path gas analyzer.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.