• Clean Technology
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• v.27 no.1
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• pp.61-68
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• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.445-453
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• 2021

CT scan is reported to have a high risk of cancer due to a relatively high dose among medical radiological examinations. In particular, exposure to radiation to the breast, which is sensitive to radiation, is inevitable during a chest CT scan for female patient. In this study, the dose reduction effect of wearing a compression band during chest CT scans in women was evaluated, and the lifetime attributable risk due to the effective dose exposed during the CT scan was estimated. As a result, when the compression band was used, the effective tube current decreased as the outer perimeter of the chest became smaller, and it was analyzed that the CT dose index and effective dose were also reduced. In addition, the lifetime attributable risk by chest CT scan was found to reduce the cancer risk by 3.2 per 100,000 for all cancers, 0.2 per 100,000 for solid cancer, and 0.8 per 100,000 for breast cancer, based on women in their 30s when using a compression band. It is judged that the risk of cancer can be reduced through the use of appropriate scan parameters and dose optimization measures such as compression bands for future CT examinations.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.6
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• pp.251-261
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• 2022

This paper describes the finite element analysis and die design change of spring retainer forging process to reduce the cold forging load and plastic forming stress concentration. Plastic deformation analysis was carried out in order to understand the forming process of workpieces and elastic stress analysis of the die set was performed in order to get basic data for the die fatigue life estimation. Cold forging die design was set up to each process with different four types analysis progressing, the upper and lower dies shapes with combination of fillets and chamfers shapes of cold forging dies. This study suggested optimal cold forging die geometry to reduce cold forging load. The design parameters of fillets and chamfers are selected geometry were selected to apply optimization with the DoE (design of experiment) and Taguchi method. DoE and Taguchi method was performed to optimize the workpiece preform shape for spring retainer forging process, it was possible to expect an increase in cold forging die life due to the 20 percentage forging load reduction.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.31-38
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• 2023

In this study, runoff characteristics analysis was conducted as a basic research to establish a forecasting and warning system for flood risk areas in small mountainous basins in South Korea. The Danyang 1 Bridge basin located in Danyang-gun, Chungcheongbuk-do was selected as the study basin, and the watershed characteristic factors were calculated using Q-GIS based on the digital elevation model (DEM) of the basin. In addition, nine heavy rainfall events were selected from 2020 to 2023 using hydrometeorological data provided by the National Water Resources Management Comprehensive Information System. HEC-HMS rainfall-runoff model was used to analyze the runoff characteristics of small mountainous basins, and rainfall-runoff model simulation was performed by reflecting 9 heavy rainfall events and calculated basin characteristic factors. Based on the rainfall-runoff model, parameter optimization was performed for six heavy rain events with large error rates among the simulated events, and the appropriate parameter range for the Danyang 1 Bridge basin, a small mountainous basin, was calculated to be 0.8 to 3.4. The results of this study will be utilized as foundational data for establishing flood forecasting and warning systems in small mountainous basin, and further research will be conducted to derive the range of parameters according to basin characteristics.

• Geotechnical Engineering
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• v.8 no.1
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• pp.81-102
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• 1992

Landslides on hillside slopes with shallow soil cover over a sloping bedrock are frequently caused by increases in porewater pressures following of heavy rainfall and it is one of the most important factors of assessing the risk of landslide to predict the groundwater level fluctuations in hillslopes. This paper presents the comparative study of three unsaturated flow models developed by Sloan et al., Reddi, L.N., and Thomas, H.A., Jr., respectively, which are used to predict the increase of groundwater levels in hillside slopes. The parametric study for each of models is also presented. The Kinematic Storage Model(KSM) developed by Sloan et at. is utilized to predict the saturated groundwater flow. They are applied to the two sites in Korea so as to examine the possibility of use in the groundwater flow model. The results show that two unsaturated models developed by Sloan et al. and Reddi, L. N. are largely affected by the uncertain parameters like saturated permeability and saturated water content : the abed model has the potential of use in unsaturated flow model with the optimal estimates of model parameters utilizing available optimization techniques. And it is also found that the KSM must be modified to account for the time delay effect in the saturated zone. The results of this paper are able to be utilized in developing the predictive model of groan dwater level fluctuations in a hillslope.

• Journal of the Korea Concrete Institute
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• v.21 no.2
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• pp.179-186
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• 2009

The peculiarity pointed out for high strength concrete is the occurrence of spalling during a fire. Recently, there are many efforts such as development of all types of spalling reducing materials and other innovative materials in various fields. Need is now to examine the adequate mixing proportions of these materials. This study intended to derive experimentally and statistically mix proportions that can represent the basic quality requirements as well as the optimal effects on the fire-resistance for 4 types of functional materials that are metakaolin, waste tire chip, polypropylene fiber and steel fiber. Here, the tests were planned through an optimal test method using an orthogonal array table with 4 parameters and 3 levels. The statistical analysis adopted the response surface analysis method. Results verified mutual complementary contribution between the materials when using a combination of the functional materials selected as parameters for the strengthening of the fire-resistance of 80 MPa-class high strength concrete. Besides, the optimal conditions of the fire-resistance strengthening materials derived through response surface analysis were a volumetric replacement of silica fume by 80% of metakaolin, a volumetric replacement of fine aggregates by 3% of tire waste chip, and an addition of 0.2% of the whole volume by polypropylene fiber without mixing of steel fiber. In such cases, the basic characteristics as well as the fire-resistant characteristics of high strength concrete were also satisfied.

• Korean Chemical Engineering Research
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• v.51 no.2
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• pp.226-232
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• 2013

To produce biobutanol, fermentation processes using clostridia that mainly produce acetone, butanol and ethanol are used. In this work, a dynamic model describing the metabolic reactions in an acetone-butanol-ethanol (ABE)-producing clostridium, Clostridium acetobutylicum ATCC824, was proposed. To estimate the 58 kinetic parameters of the metabolic network model with experimental data obtained from a batch fermentor, we used an efficient optimization method combining a genetic algorithm and the Levenberg-Marquardt method because of the complexity of the metabolism of the clostridium. For the verification of the determined parameters, the developed metabolic model was evaluated by experiments where genetically modified clostridium was used and the initial concentration of glucose was changed. Consequently, we found that the developed kinetic model for the metabolic network was considered to describe the dynamic metabolic state of the clostridium sufficiently. Thus, this dynamic model for the metabolic reactions will contribute to designing the clostridium as well as the fermentor for higher productivity.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.