• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.398-404
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• 2019

Objective : Recently, three-dimensional (3D) printed models of the intracranial vascular have served as useful tools in simulation and training for cerebral aneurysm clipping surgery. Precise and realistic 3D printed aneurysm models may improve patients' understanding of the 3D cerebral aneurysm structure. Therefore, we created patient-specific 3D printed aneurysm models as an educational and clinical tool for patients undergoing aneurysm clipping surgery. Herein, we describe how these 3D models can be created and the effects of applying them for patient education purpose. Methods : Twenty patients with unruptured intracranial aneurysm were randomly divided into two groups. We explained and received informed consent from patients in whom 3D printed models-(group I) or computed tomography angiography-(group II) was used to explain aneurysm clipping surgery. The 3D printed intracranial aneurysm models were created based on time-of-flight magnetic resonance angiography using a 3D printer with acrylonitrile-butadiene-styrene resin as the model material. After describing the model to the patients, they completed a questionnaire about their understanding and satisfaction with aneurysm clipping surgery. Results : The 3D printed models were successfully made, and they precisely replicated the actual intracranial aneurysm structure of the corresponding patients. The use of the 3D model was associated with a higher understanding and satisfaction of preoperative patient education and consultation. On a 5-point Likert scale, the average level of understanding was scored as 4.7 (range, 3.0-5.0) in group I. In group II, the average response was 2.5 (range, 2.0-3.0). Conclusion : The 3D printed models were accurate and useful for understanding the intracranial aneurysm structure. In this study, 3D printed intracranial aneurysm models were proven to be helpful in preoperative patient consultation.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.1-8
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• 2019

In December 2017, many collision accidents of small vessels, such as those between oil refineries and fishing boats, occurred near Yeonghung-do in Incheon. In order to prevent marine casualties from small vessels, the government is striving to improve the safety capabilities of ship operators by strengthening education and improving the working environment. They are providing education and refining training regulations for fishermen operating vessels under 5 tons. However, the situation includes certain vulnerabilities. In this study, we propose a collision prevention algorithm for small vessels using the Wireless Access in Vehicular Environments (WAVE) communication system, which is a new communication technique to prevent collisions with small ships. The collision avoidance algorithm used is based on DCPA/TCPA. Research analyses, simulation experiments and questionnaires have been conducted to define the criteria of DCPA/TCPA. As a result, the standard for DCPA was $8(L_a+L_b)$ and for TCPA was 2.5 min. Three different accident cases were selected, and this algorithm was applied to confirm alarm responses at certain times. This algorithm can provide information to the operators of small ships in advance to help them recognize potential collision situations.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.37-45
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• 2019

In recent years, the emergence of virtual reality (VR Virtual Reality) technology has provided a new model of safety education, enabling users to learn and respond to disasters in a virtual safety education environment. However, the related VR products related to domestic and foreign R & D are relatively simple, there is no practical training on specific accident, and it is not practical enough to play a sufficient role in safety education. In this paper, the problems and disadvantages of VR technology applied in the field of automobile safety education as an example of automobile accident among the types of disasters are examined, and a system framework of automotive safety education based on VR technology is proposed. The vehicle safety education system proposed in this paper will help users to improve driving safety consciousness, to acquire safety knowledge in driving, and to acquire driving safety skill which is very important for automobile safety education. In addition, the design and production methods of safety education based on VR technology are considered to have important reference implications for the application of modern teaching and teaching theory by integrating with VR technology and developing related teaching materials products and finally introducing education.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.81-92
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• 2021

In this study, we investigated the effects of five EMS lumbar back pressure belts produced on an anatomical basis on lumbar spine stabilization. Five core muscles were selected, including the urinal, vertebral column, endotracheal, external abdominal, and large back muscles, and patterns were designed using a conductive fabric considering the appropriate muscle shape and pain-causing points. We experimented with four motions to examine the effects of different EMS abdominal compression belts on lumbar spine stabilization. Five healthy men in their 20s were selected. The selection conditions include no back pain history for the past three months, no restricted movements through pre-inspection, and the muscular strength of the body should belong to the normal grade. Using SLR, the sequence of experimental actions was chosen from the following but not limited to left-hand, body-hand, and back-line forces. Resting between movements lasted for 2 min, and the experiments were conducted after wearing the EMS abdominal pressure belt. Electrical stimulation was applied for 10 min to increase blood flow and muscle activation. The statistics of the experimental results were analyzed for specific differences by conducting the Wilcoxon and Friedman tests with nonparametric tests. The ranking results of each pattern were successfully assessed in the order of 5, 4, 3, 1, 2 for the five patterns, and we could identify slightly more significant results for experimental behavior associated with each muscle movement. Patterns produced based on anatomy showed differentiated effects when electric stimulation was applied to each muscle in different shapes, which could improve the stabilization of the lumbar spine in everyday life or training to the public. Based on these results, subsequent research would focus on developing smart healthcare clothing that is practical in daily life by employing different anatomical mechanisms, depending on the back pain, to utilize trunk-type tights.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.487-495
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• 2021

The voltages mainly used in ships are 450 [V], 6.6 [kV], and 11 [kV], and an earthed system is applied to ensure the stability of the power distribution system. In general, low-voltage ships using 450 [V] apply an unearthed system, while high-voltage ships using 6.6 [kV] or 11 [kV] use a high-resistance earthed system. When an earth fault occurs in a ship's power distribution system, the voltage of the healthy phase increases to the line-to-line voltage or higher, which causes an excessive impact on the insulation of the cable. Thus, analyzing this behavior is very important. In this paper, we investigate the characteristics of the line-to-earth voltage variation according to earth faults and a recognition procedure of a faulty phase using the symmetrical coordinate method for a high-resistance earthed system and unearthed system. A mathematical model of the line-to-earth voltage was derived through the symmetric coordinate method, and the ship voltage for simulations was selected as 6.6 [kV] and 450 [V]. A MATLAB simulation proved that this method can determine the highest increase of the line-to-earth voltage, which leads by 120° on the faulty phase, and it accurately judges the faulty phase in both earthed systems.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.469-481
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• 2022

This study is for increasing evacuation safety by analyzing RSET(the required safe escape time) through the arrangement of personnel by floor and by room while evacuating in a Girls' High School Dormitory. For this study, PAPS(Physical Activity Promotion System) results that have not been studied so far were analyzed and reflected in evacuation simulations on the premise that individual student's physical strength can affect evacuation. Based on the PAPS results, four scenarios were applied. In addition, evacuation simulation using the pathfinder program was conducted in two situations: the evacuation route was assigned or not. Scenario 4 was the fastest at 168.5 seconds of RSET in assigning evacuation routes among scenarios. As a result of this study, the arrangement of students focusing on improving their academic ability and student life guidance excluding student physical strength has problem. In order to solve this problem, it is effective to place C group students(low grade on PAPS) on low floors and A group students(high grade on PAPS) on high floors and to assign evacuation routes in each room. In the future, the following ways need to be more studied. A study on how to increase evacuation safety through practical evacuation training, the way of assessing evacuation safety reflecting the lifestyle and physical strength of girls, the evacuation route assignment according to the fire occurrence point, and the method to secure evacuation routes in the event of a fire near stairs or entrances should be conducted.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.47-56
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• 2022

Recently, water resources and energy policies such as integrated water management and carbon neutrality are changing rapidly. There is an opinion that the value of hydropower reservoirs related to these policies should be re-evaluated. In the past, they have contributed to flood control in addition to electricity generation, such as operating at a limited water level during the flood season, but loss of power generation is inevitable with this operation. Therefore, this study introduced the concept of resilience to the hydropower generation system to minimize the power loss. A framework for evaluating the power generation performance of them was presented by defining the maximization of electricity sales as performance. Based on the current procedure of multiple operation plan, a scenario was established and simulation was performed using HEC-5. As a result of applying to the framework, it was confirmed that the power generation performance according to each scenario was evaluated as an important factor. And it was confirmed that the performance of flood control and water use could also be evaluated.

• 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.