• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.2
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• pp.34-43
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• 2022

In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Smart Structures and Systems
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• v.28 no.6
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• pp.827-838
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• 2021

The dynamic displacement is considered to be an important indicator of structural safety, and becomes an indispensable part of Structural Health Monitoring (SHM) system for high-speed railway bridges. This paper proposes an indirect strain based dynamic displacement reconstruction methodology for high-speed railway box girders. For the typical box girders under eccentric train load, the plane section assumption and elementary beam theory is no longer applicable due to the bend-torsion coupling effects. The monitored strain was decoupled into bend and torsion induced strain, pre-trained multi-output support vector regression (M-SVR) model was employed for such decoupling process considering the sensor layout cost and reconstruction accuracy. The decoupled strained based displacement could be reconstructed respectively using box girder plate element analysis and mode superposition principle. For the transformation modal matrix has a significant impact on the reconstructed displacement accuracy, the modal order would be optimized using particle swarm algorithm (PSO), aiming to minimize the ill conditioned degree of transformation modal matrix and the displacement reconstruction error. Numerical simulation and dynamic load testing results show that the reconstructed displacement was in good agreement with the simulated or measured results, which verifies the validity and accuracy of the algorithm proposed in this paper.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.119-124
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• 2021

In a situation where Moore's law, which states that the performance of semiconductor integrated circuits doubles every two years, is showing a limit from a certain point, and it is difficult to increase the performance due to the limitations of exposure technology.In this study, a wafer hybrid method that can increase the degree of integration Various research on bonding technology is currently in progress. In this study, in order to achieve rotational precision between wafers in wafer hybrid bonding technology, modeling of θ_z alignment stage and VCM actuator modeling used for rotational alignment, magnetic field analysis and desgin, control, and evaluation are performed. The system of this study was controlled by VCM actuator, capactive sensor, and dspace, and the working range was ±7200 arcsec, and the in-position and resoultion were ±0.01 arcsec. The results of this study confirmed that safety and precise control are possible, and it is expected to be applied to the process to increase the integration.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.479-487
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• 2022

This study evaluates the performance of gravel impact compaction piers system (GICPs) in strengthening retrofitting a very loose silty sand layer with a very high liquefaction risk with a thickness of 3.5 meters in a multilayer coastal soil located in Bushehr, Iran. The liquefiable sandy soil layer was located on clay layers with moderate to very stiff relative consistency. Implementation of gravel impact compaction piers is a new generation of aggregate piers. After technical and economic evaluation of the site plan, out of 3 experimental distances of 1.8, 2 and 2.2 meters between compaction piers, the distance of 2.2 meters was selected as a winning option and the northern ring of the site was implemented with 1250 gravel impact compaction piers. Based on the results of the standard penetration test in the matrix soil around the piers showed that the amount of (N₁)₆₀ in compacted soils was in the range of 20-27 and on average 14 times the amount of (1-3) in the initial soil. Also, the relative density of the initial soil was increased from 25% to 63% after soil improvement. Also the safety factor of the improved soil is 1.5-1.7 times the minimum required according to the two risk levels in the design.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Journal of Digital Convergence
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• v.15 no.10
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• pp.455-465
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• 2017

This study focuses on design development and verification through virtual simulation based on 3D model data in the cloud platform as a method of utilization of engineering technology of design in the fourth industrial revolution era. The goal of research is to develop and examine a design for the needs of the target that has never been met before through virtual simulations that can be conducted in practice. As a research method, we analyzed secondary data to identify the needs of the target, and did literature research for the ergonomic data and target body development stages. In addition, the design development process of this study was shown meaningful result in design, structure, safety, material, durability through loop test of 7 virtual simulations. This study can be applied to the automated process system based on 3D model data in the 4th industrial revolution era and can be used as an element of the cyber physics system for the additional research.

• Proceedings of KOSOMES biannual meeting
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• 2007.11a
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• pp.97-102
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• 2007

The piggery wastewater is the major source of the water pollution problem in the rural area. The treatment alternatives for piggery wastewater are limited by the characteristics of both high organic and nitrogen(N) content. In order to investigate an efficient N removal system, the thermophilic aerobic digestion process was examined. The experiment was investigated organic and nitrogen removal efficiency at various HRTs and air supply volume. The results of semi-continuous experiment indicated that a higher removal of the soluble portion of COD was achieved with the longer HRTs. However, the inert portion of COD in piggery wastewater was not much changed by thermophilic aerobic digestion. In addition, with the higher HRT of 3 days, up to 79% of NH4-N removal efficiency was achieved. Lower the HRTs, a decrease of NH4-N removal was founds. The gas samples from the lab reactor were analyzed along with the N content in influent and effluent. The N2O formation in our system indicates a novel aerobic deammonification process occurred during the thermophilic aerobic digestion. Both N02 and N03 were not presented in the effluent of thermophilic aerobic digester. With the HRT of 3 days, 36.4% of influent N(or 57.5% removal N) was aerobically converted to N2O gas. The ammonium conversion to N2O gas significantly decrease to 4.5% at low HRT of .05 day..

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.1
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• pp.59-67
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• 2013

Complex-shaped tall building causes many structural challenges due to its structural characteristics regarding inclined members and complexed shape. This paper is aimed at development of design process using computational-platform which is effective design tool for responding frequent design changes, particularly as to overseas projects. StrAuto, a parametric structural modeling and optimizing system, provides the optimized alternatives according to design intent and realize a swift process converting a series of structural information necessary to nonlinear analytical models. The application of the process was to a 45-story hotel building in Ulanbator, Mongolia adopting shear wall and special moment frame with outrigger systems. To investigate the safety of lateral force resisting system against maximum considered earthquake(MCE), nonlinear response history analysis was conducted using StrAuto.