• 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 Korea Water Resources Association
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• v.54 no.11
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• pp.955-968
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• 2021

A water resource plan is routinely based on a natural flow and can be estimated using observed streamflow data or a long-term continuous rainfall-runoff model. However, the watershed with the natural flow is very limited to the upstream area of the dam. In particular, for the ungauged watershed, a rainfall-runoff model is established for the gauged watershed, and the model is then applied to the ungauged watershed by transferring the associated parameters. In this study, the GR4J rainfall-runoff model is mainly used to regionalize the parameters that are estimated from the 14 dam watershed via an optimization process. In terms of optimizing the parameters, the Bayesian approach was applied to consider the uncertainty of parameters quantitatively, and a number of parameter samples obtained from the posterior distribution were used for the regionalization. Here, the relationship between the estimated parameters and the topographical factors was first identified, and the dependencies between them are effectively modeled by a Copula function approach to obtain the regionalized parameters. The predicted streamflow with the use of regionalized parameters showed a good agreement with that of the observed with a correlation of about 0.8. It was found that the proposed regionalized framework is able to effectively simulate streamflow for the ungauged watersheds by the use of the regionalized parameters, along with the associated uncertainty, informed by the basin characteristics.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.371-383
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• 2021

Steel-concrete single-leaf blast-resistant doors, having steel box and slab inside, are installed on the wall using supporting members such as hinges and latches. Several studies have been conducted on their deflection behavior in the same direction as that of the blast pressure, but studies on their deflection behavior in the opposite direction, that is, studies on negative deflection behavior are relatively insufficient. In this study, we conducted a parameter analysis using finite element analysis on blast-resistant doors, on their rebound behavior in the negative deflection phase. Results revealed that the plastic deformation of the door, and the change in momentum and kinetic energy during rebound, were major factors influencing the rebound behavior. Greater rebound force was developed on the supporting members in the impulsive region, than in the quasi-static region; due to the characteristics in the impulsive region, where the kinetic energy developed relatively greater than the strain energy. In the design process, it is necessary to consider excessive deformation that could occur in the supporting members as the rebound behavior progresses. Additionally, it was found that in the case of steel-concrete blast doors, the rebound force increased relatively more, when the effects of both rebound and negative blast pressure contributed to the negative deflection of the door. Since conditions for the occurrence of this superposition effect could vary depending on structural characteristics and explosion conditions, further investigation may be required on this topic.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.203-212
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• 2021

The research purpose is analyzing the relationship between the distributive justice and turnover intention of the workers in community rehabilitation centers. This study also examines the mediating effect of job satisfaction between the distributive justice and the turnover intention. For this purpose, the survey was conducted to workers of 14 community rehabilitation centers in Daejeon and Chungnam area, and the 365 data were used for analysis. The results of analysing as follows: 1) the distributive justice of the workers in community rehabilitation centers effected the turnover intention negatively and the job satisfaction positively. In addition, the partial mediating effect of job satisfaction was proved between the distributive justice and the turnover intention. The analysis results show that the distributive justice is the important independent variable of the turnover intention and the job satisfaction is the important parameter between the two variables. According to these analysis results, several suggestions were made to reduce the turnover intention of the workers being engaged in community rehabilitation centers in terms of the improving their distributive justice and job satisfaction.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.39-55
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• 2022

This study carried out the laboratory tests for AE signal attenuation to determine the attenuation coefficient (α) of silo concrete in Gyeongju low and intermediate-level disposal environments. The concrete samples were prepared by satisfying the concrete mixing ratio used in the Gyeongju disposal silo, and these samples were additionally exposed depending on the temperature conditions and saturation and, dry condition. As a result of attenuation tests according to the transmission distance on three concrete specimens for each disposal condition, the AE amplitude and absolute energy measured on the saturated concrete were higher than that of the dry concrete in the initial range of the signal transmission distance, but the α of the saturated concrete was higher than that of the dry concrete. Regardless of the saturation and dry conditions, the α tended to decrease as the temperature increases. The α had a more major influence on the saturation and dry condition than the temperature condition, which means that the saturation and dry condition is the main consideration in measuring the signal attenuation of a concrete disposal structure. The α of concrete in the disposal environment expect to be used to predict the integrity of silos concrete in Gyeongju low and intermediate-level disposal environments by estimating the actual AE parameter values at the location of cracks and to determine the optimum location of sensors.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.1-10
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• 2022

Deep neural networks are widely used to solve various problems. In a fully connected neural network, the nonlinear activation function is a function that nonlinearly transforms the input value and outputs it. The nonlinear activation function plays an important role in solving the nonlinear problem, and various nonlinear activation functions have been studied. In this study, we propose a combined parametric activation function that can improve the performance of a fully connected neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a parameter that converts the scale and location of the activation function according to the input data. By combining the parametric activation functions, more diverse nonlinear intervals can be created, and the parameters of the parametric activation functions can be optimized in the direction of minimizing the loss function. The performance of the combined parametric activation function was tested through the MNIST classification problem and the Fashion MNIST classification problem, and as a result, it was confirmed that it has better performance than the existing nonlinear activation function and parametric activation function.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.655-665
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• 2022

This study evaluated the validity of a newly developed mobility protocol examining the comfort functions and requirements of personal protective equipment (PPE) for COVID-19 healthcare workers. Eight males (age: 24.7 ± 3.0 y, height: 173.4 ± 2.3 cm, and body weight 69.9 ± 3.7 kg) participated in the following three PPE conditions: (1) Plastic gown ensemble, (2) Level D ensemble, and (3) Powered air purifying respirator (PAPR) ensemble. The mobility protocol consisted of 10 different tasks in addition to donning and doffing. The 10 tasks were repeated twice at an air temperature of 25oC with 74% RH. The results showed significant differences among the three PPE conditions in mean skin temperature, local skin temperatures (the forehead, thigh, calf, and foot), clothing microclimate (the chest and back), thermal sensation, thermal comfort, and humidity sensation, while there were no significant differences in heart rate or total sweat rate. At rest, the subjects felt less warm and more comfortable in the PAPR than in the Level D condition (P<0.05). However, subjective perceptions in the PAPR and Level D conditions became similar as the tasks progressed and mean skin and leg temperature became greater for the PAPR than the Level D condition (P<0.05). An interview was conducted just after completing the mobility test protocol, and suggestions for improving each PPE item were obtained. To sum up, the mobility test protocol was valid for evaluating the comfort functions of PPE for healthcare workers and obtaining requirements for improving the mobility of each PPE item.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.823-833
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• 2022

This study aims to improve the runoff modeling accuracy of a basin using Hydrological Simulation Program-FORTRAN (HSPF) model by considering nonhomogeneous characteristics of a basin. By entering classified values according to the various types of land cover and soil to the parameters in HSPF-roughness coefficient (NSUR), infiltration (INFILT), and evapotranspiration (LZETP)- the heterogeneity of the Yongdam Dam basin was reflected in the model. The results were analyzed and compared with the one where the parameters were set as a single value throughout the basin. The flow rate and water quality simulation results showed improved results when classified parameters were used by land cover and soil type than when single values were used. The parameterization changed not only the flow rate, but also the composition ratio of each hydrologic components such as surface runoff, baseflow, and evapotranspiration, which shows the impact of the value set to a parameter on the entire hydrological process. This implies the importance of considering the heterogeneous characteristics of the land cover and soil of the basin when setting the parameters in a model.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.363-372
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• 2022

Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.