• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.85-97
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• 2003

This study was aimed at suggest a proper logging method of mountain forest using geographic information system(GIS) based on digital terrain model(DTM) in the National Forest at Mt. Kumsan in Namhae-gun, Gyungsangnam-do, which has about 2,948 ha in area. The areal percentage of 201 to 250m in the elevation was about 15.5%, elevation of 251 to 300m was 14.5%, and 78.75% for higher than 400m. The accumulated areal percentage of below 30% in the gradient was 17.2%, and 81.0% for steeper than 60%. The area for tractor skidding was 17.2%(511.7ha), the area for tractor attached winch skidding was 63.8%(1,896.3ha) and 18.4%(545.5ha) for cable yarding. It is important to choose the proper logging machines for timber harvesting. In general, the selection of logging operation system was affected several major environmental factors like as terrain conditions(slope gradient, slope length) and stand factors. The rate of middle slope gradients in terrain conditions showed higher than that of steep slope gradients in this area. Therefore, it considered that the logging operation system in this area could apply to tractor+winch operating machine according to terrain conditions.

• Journal of the Korea Concrete Institute
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• v.21 no.4
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• pp.473-480
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• 2009

The 180 minutes fire test based on the standard curve of ISO-834 were conducted on three RC column specimens with different constant axial loading ratios to evaluate the fire performance of fiber cocktail (polypropylene+steel fiber) reinforced high strength concrete column. The columns were tested under three loading levels as 40%, 50%, and 61% of the design load. No explosive spalling has been observed and the original color of specimen surface has been changed to light pinkish grey. The maximum axial displacements of three specimens were 1.5~2.2 mm. There was no reduction in load bearing capacity of each specimen exposed to fire and no effect were observed on the fire performance within 61% of the design load. The tendencies of the results with loading, such as the temperature distribution of in concrete and the changes in temperature rise due to the water vaporization in concrete, are very similar to those without loading. The final temperatures of steel rebar after 180 minutes of fire test resulted in 491.4${^{\circ}C}$ for corner rebar, 329.0${^{\circ}C}$ for center rebar, and 409.8${^{\circ}C}$ for total mean of steel rebar. The difference of mean temperature between corner and center rebar was 153.7${^{\circ}C}$ㅍ. The tendency of temperature rise in concrete and steel rebar changed after 30~50 minutes from the starting time of the fire test because the heat energy influx into corner rebar is larger than that into center rebar. The cause of decrease in temperature rise was due to the water vaporization in concrete, the lower temperature gradient of the concrete with steel and polypropylene fiber cocktails, the moisture movement toward steel rebars and the moisture clogging.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.187-194
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• 2012

The strength of particle-reinforced metal matrix composites is, in general, known to be increased by the geometrically necessary dislocations punched around a particle that form during cooling after consolidation because of coefficient of thermal expansion (CTE) mismatch between the particle and the matrix. An additional strength increase may also be observed, since another type of geometrically necessary dislocation can be formed during extensive deformation as a result of the strain gradient plasticity due to the elastic-plastic mismatch between the particle and the matrix. In this paper, the magnitudes of these two types of dislocations are calculated based on the dislocation plasticity. The dislocations are then converted to the respective strengths and allocated hierarchically to the matrix around the particle in the axisymmetric finite-element unit cell model. The proposed method is shown to be very effective by performing finite-element strength analysis of $SiC_p$/Al2124-T4 composites that included ductile failure in the matrix and particlematrix decohesion. The predicted results for different particle sizes and volume fractions show that the length scale effect of the particle size obviously affects the strength and failure behavior of the particle-reinforced metal matrix composites.

• Journal of the Korean Magnetics Society
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• v.21 no.2
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• pp.43-47
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• 2011

It is known that the Fe-Al transition metal compounds have a lot of disagreement about structural stability and magnetism. In this study, the correlation between magnetism and atomic structure of ordered $B_2$, $L1_2$, and $D0_3$ structured Fe-Al compounds has been investigated using the all-electron full-potential linearized augmented plane wave (FLAPW) method based on the generalized gradient approximation (GGA). We found that considered all the structures were calculated to be stabilized in a ferromagnetic state. The calculated spin magnetic moments of the Fe atoms for B2 and $L1_2$ structures were 0.771 and 2.373 ${\mu}_B$, respectively, and that of Fe(I) and Fe(II) in $D0_3$ structure calculated to be 2.409 ${\mu}_B$, 1.911 ${\mu}_B$, respectively. In order to investigate structural stability between $L1_2$ and $D0_3$ structures, we performed the formation enthalpy calculations. As a result, the $D0_3$ structure is found to be more favorable than $L1_2 one by energy difference 16 meV/atom, which is well consistent with the experimental observation. We understood about structural stability and magnetism for Fe-Al compounds in terms of analysis of their atomic and electronic structures.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.34-43
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• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• The Journal of Korean Society for Radiation Therapy
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• v.18 no.1
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• pp.1-5
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• 2006

Purpose: IMRT quality assurance(Q.A) is consist of the absolute dosimetry using ionization chamber and relative dosimetry using the film. We have in general used 0.015 cc ionization chamber, because small size and measure the point dose. But this ionization chamber is too small to give an accurate measurement value. In this study, we have examined the degree of calculated to measured dose difference in intensity modulated radiotherapy(IMRT) based on the observed/expected ratio using various kinds of ion chambers, which were used for absolute dosimetry. Materials and Methods: we peformed the 6 cases of IMRT sliding-window method for head and neck cases. Radiation was delivered by using a Clinac 21EX unit(Varian, USA) generating a 6 MV x-ray beam, which is equipped with an integrated multileaf collimator. The dose rate for IMRT treatment is set to 300 MU/min. The ion chamber was located 5cm below the surface of phantom giving 100cm as a source-axis distance(SAD). The various types of ion chambers were used including 0.015cc(pin point type 31014, PTW. Germany), 0.125 cc(micro type 31002, PTW, Germany) and 0.6 cc(famer type 30002, PTW, Germany). The measurement point was carefully chosen to be located at low-gradient area. Results: The experimental results show that the average differences between plan value and measured value are ${\pm}0.91%$ for 0.015 cc pin point chamber, ${\pm}0.52%$ for 0.125 cc micro type chamber and ${\pm}0.76%$ for farmer type 0.6cc chamber. The 0.125 cc micro type chamber is appropriate size for dose measure in IMRT. Conclusion: IMRT Q.A is the important procedure. Based on the various types of ion chamber measurements, we have demonstrated that the dose discrepancy between calculated dose distribution and measured dose distribution for IMRT plans is dependent on the size of ion chambers. The reason is small size ionization chamber have the high signal-to-noise ratio and big size ionization chamber is not located accurate measurement point. Therefore our results suggest the 0.125 cc farmer type chamber is appropriate size for dose measure in IMRT.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.3
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• pp.211-220
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• 2020

When domestic sewage and rainwater runoff are discharged into a single sewer pipe, it is called a "combined sewer system." The sewage design standards in Korea specify the flow velocity based only on the volume of rainfall; therefore, sedimentation occurs on non-rainy days owing to the reduced flow rate and velocity. This sedimentation reduces the discharge capacity, causes unpleasant odors, and exacerbates the problem of combined sewer overflow concentration. To address this problem, the amount of sewage on non-rainy days, not just the volume of rainfall, should also be considered. There are various theories on sedimentation in sewer movement. This study introduces a self-cleansing velocity based on tractive force theory. By applying a self-cleansing velocity equivalent to the critical shear stress of a sand particle, sedimentation can be reduced on non-rainy days. The amount of sewage changes according to the water use pattern of citizens. The design hourly maximum wastewater flow was considered as a representative value, and the velocity of this flow should be more than the self-cleansing velocity. This design method requires a steeper gradient than existing design criteria. Therefore, the existing sewer pipelines need to be improved and repaired accordingly. In this study, five types of improvement and repair methods that can maximize the use of existing pipelines and minimize the depth of excavation are proposed. The key technologies utilized are trenchless sewer rehabilitation and complex cross-section pipes. Trenchless sewer rehabilitation is a popular sewage repair method. However, it is complex because the cross-section pipes do not have a universal design and require continuous research and development. In an old metropolis with a combined sewer system, it is difficult to carry out excavation work; hence, the methods presented in this study may be useful in the future.