• Geophysics and Geophysical Exploration
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• v.23 no.1
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• pp.13-21
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• 2020

The acquisition and processing of long-offset data are essential for imaging deep geological structures in marine seismic surveys. It is challenging to derive an accurate subsurface image by employing conventional data processing to long-offset data owing to the normal moveout (NMO) stretch and non-hyperbolic moveout phenomena induced by seismic anisotropy. In 2017, the Korea Institute of Geoscience and Mineral Resources conducted a simultaneous two-dimensional multichannel streamer and ocean-bottom seismic survey using a 5.7-km streamer and an ocean-bottom seismometer to identify the deep geological structure of the Ulleung Basin. Herein, the actual geological subsurface structure was obtained via the sequential iterative updating of the velocity and anisotropic parameters of the long-offset data obtained using a multichannel streamer, and anisotropic prestack Kirchhoff migration was performed using the updated velocity and anisotropic parameters as input parameters. As a result, the reflection energy in the long-offset traces, which showed non-hyperbolic moveout owing to seismic anisotropy, was well aligned horizontally and NMO stretches were also reduced. Thus, a more precise and accurate migrated image was obtained, minimizing the distortion of reflectors and mispositioned reflection energy.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.637-658
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• 2022

TBM tunnel is simple with the iterative process of excavating the ground, building a segment ring-build, and backfilling. Drill & Blast, a conventional tunnel construction method, is more complicated than the TBM tunnel and has some restrictions because it repeats the inspection, drilling, charging, blasting, ventilation, muck treatment, and installation of support materials. However, the preparation work for excavation requires time and cost based on a very detailed plan compared to Drill & Blasting, which reinforces the ground and forms a tunnel after the formation of tunnel portal. This is because the TBM equipment for excavating the target ground determines the success or failure of the construction. If the TBM, an expensive order-made equipment, is incorrectly configured at the assembly stage, it becomes difficult to excavate from the initial stage as well as the main excavation stage. When the assembled shield TBM equipment is dismantled again, and a situation of re-assembly occurs, it is difficult throughout the construction period due to economic loss as well as time. Therefore, in this study, the layout and plan of the site and the assembly process for each major part of the TBM equipment were reviewed for the assembly of slurry shield TBM to construct the largest diameter road tunnel in domestic passing through the Han River and minimized interference with other processes and the efficiency of cutter head assembly and transport were analyzed and improved to suit the site conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.279-285
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• 2019

Transverse vibrations of a ship's aft end and deckhouse are mainly induced by transverse exciting forces from the main engine. Resonance should be avoided in the initial design stages when there is a prediction of resonance between the main engine and transverse modes of the deckhouse. Estimates of frequencies for resonance avoidance are possible from the specifications of the main engine and propeller, but the inherent vibration frequency of the structure around the engine room is not easy to estimate due to the variation in the shape. Experience-oriented vibration design is also carried out, which results in many problems, such as process delay, over-injection of on-site personnel, and iterative performance of the design. For the flexible design of 8,600 TEU container vessels, this study addressed the resonance problem caused by the transverse vibration of the main engine when only the main engine was changed from 12 cylinders to 10 cylinders without modification of the hull structure layout. Efficient structural reinforcement design guidelines are presented for avoiding resonances with the main engine lateral vibration and the structure around the engine room. The guidelines are expected to be used as practical design guidelines at design sites.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.136-143
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• 2018

Cellular automata have been applied to simulations in many fields such as astrophysics, social phenomena, fire spread, and evacuation. Using cellular automata, this study develops a model for consequence analysis of the dispersion of hazardous chemicals, which is required for risk assessments of and emergency responses for frequent chemical accidents. Unlike in cases of detailed plant safety design, real-time accident responses require fast and iterative calculations to reduce the uncertainty of the distribution of damage within the affected area. EPA ALOHA and KORA of National Institute of Chemical Safety have been popular choices for these analyses. However, this study proposes an initiative to supplement the model and code continuously and is different in its development of free software, specialized for small and medium enterprises. Compared to the full-scale computational fluid dynamics (CFD), which requires large amounts of computation time, the relative accuracy loss is compromised, and the convenience of the general user is improved. Using Python open-source libraries as well as meteorological information linkage, it is made possible to expand and update the functions continuously. Users can easily obtain the results by simply inputting the layout of the plant and the materials used. Accuracy is verified against full-scale CFD simulations, and it will be distributed as open source software, supporting GPU-accelerated computing for fast computation.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2805-2812
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• 2018

The methylation score, expressed as a percentage of the methylation status data derived from the iterative sequencing process, has a value between 0 and 1. It is contrary to the assumption of normal distribution that simply applying the t-test to examine the difference in population-specific methylation scores in these data. In addition, since the result may vary depending on the number of repetitions of sequencing in the process of methylation score generation, a method that can analyze such errors is also necessary. In this paper, we introduce the symbolic data analysis and the interval K-S test method which convert observation data into interval data including uncertainty rather than one numerical data. In addition, it is possible to analyze the characteristics of methylation score by using Beta distribution without using normal distribution in the process of converting into interval data. For the data analysis, the nature of the proposed method was examined using sequencing data of actual patients and normal persons. While the t-test is only possible for the location test, it is found that the interval type K-S statistic can be used to test not only the location parameter but also the heterogeneity of the distribution function.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.384-393
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• 2019

This study was conducted to investigate the factors influencing the contraceptive behavior of male and female university students. Data were collected after receiving the written consent of 238 university students in G city during the period from September 22 to November 23 in 2017. Data analysis was performed using the SPSS PASW 18.0 program. The results revealed that 56.7% of the subjects used contraceptives, and 23.9% used condoms. Male students were more exposed to sexual content than female students (t=6.02, p=0.000) and had an open sexual attitude (t=5.38, p=0.000). Female students showed high scores on subjective norms (t=-3.51 p=0.000) and acceptable and positive contraceptive attitudes (t=-4.21, p=0.000). Among factors influencing the contraceptive attitude of males, the subjective norm was 3.6%. Female students had a 25.5% influence on contraceptive attitude, contraception, sexual education frequency, and sexual content exposure. It is suggested that sexual education and sexual counseling programs be developed to form positive attitudes toward contraceptives through iterative research.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.427-438
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• 2021

Two-stage sampling allows us to estimate population characteristics by both unit and cluster level together. Given a complex auxiliary information, integrated calibration weighting would better reflect the level-wise characteristics as well as multivariate characteristics between levels. This paper explored the integrated calibration weighting methods by Estevao and Särndal (2006) and Kim (2019) through a simulation study, where the efficiency of those weighting methods was compared using an artificial population data. Two weighting methods among others are shown efficient: single step calibration at the unit level with stacked individualized auxiliary information and iterative integrated calibration at each level. Under both methods, cluster calibrated weights are defined as the average of the calibrated weights of the unit(s) within cluster. Both were very good in terms of the goodness-of-fit of estimating the population totals of mutual auxiliary information between clusters and units, and showed small relative bias and relative mean square root errors for estimating the population totals of survey variables that are not included in calibration adjustments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.403-408
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• 2021

Currently, in the design process of civil structures such as bridges, it is common to make final products by repeating the process of redesigning, if the initial design is found to not meet the standards after a structural review. This iterative process extends the design time, and causes inefficient consumption of engineering manpower, which should be put into higher-level design, on simple repetitive mechanical work. This problem can be resolved by automating the design process, but the external analysis program used in the design process has been the biggest obstacle to such automation. In this study, we constructed an AI-based automation system for the bridge design process, including an interface that could control both a reinforcement learning algorithm, and an external analysis program, to replace the repetitive tasks in the current design process. The prototype of the system built in this study was developed for a 2-span RC Rahmen bridge, which is one of the simplest bridge systems. In the future, it is expected that the developed interface system can be utilized as a basic technology for linking the latest AI with other types of bridge designs.