• Geomechanics and Engineering
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• 제10권3호
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• pp.297-314
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• 2016

With the mining depth continuously increasing, gas emission behaviors become more and more complex. Gas emission is an important basis for choosing the method of gas drainage, gas controlling. Thus, the accurate prediction of gas emission is of great significance for coal mine. In this work, based on the sources of gas emission from the heading faces and the fluid-solid coupling process, we established a gas continuous dynamic emission model, numerically simulated and applied it to the engineering. The result was roughly consistent with the actual situation and shows the model is correct. We proposed the measures of reducing the excavation distance and borehole gas drainage based on the model. The measures were applied and the result shows the overproof problem of gas emission disappears. The model considered the influence factors of gas emission wholly, and has a wide applicability, promotional value. The research is of great significance for the controlling of gas disaster, gas drainage and pre-warning coal and gas outbursts based on gas emission anomaly at the heading face.

• 한국지진공학회논문집
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• 제20권7_spc호
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• pp.485-494
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• 2016

The risk-based assessment, also called time-based assessment of structure is usually performed to provide seismic risk evaluation of a target structure for its entire life-cycle, e.g. 50 years. The prediction of collapse probability is the estimator in the risk-based assessment. While the risk-based assessment is the key in the performance-based earthquake engineering, its application is very limited because this evaluation method is very expensive in terms of simulation and computational efforts. So the evaluation database for many archetype structures usually serve as representative of the specific system. However, there is no such an assessment performed for building stocks in Korea. Consequently, the performance objective of current building code, KBC is not clear at least in a quantitative way. This shortcoming gives an unresolved issue to insurance industry, socio-economic impact, seismic safety policy in national and local governments. In this study, we evaluate the comprehensive seismic performance of an low-rise residential buildings with discontinuous structural walls, so called piloti-type structure which is commonly found in low-rise domestic building stocks. The collapse probability is obtained using the risk integral of a conditioned collapse capacity function and regression of current hazard curve. Based on this approach it is expected to provide a robust tool to seismic safety policy as well as seismic risk analysis such as Probable Maximum Loss (PML) commonly used in the insurance industry.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.976-984
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• 2009

This study proposes a simple method that uses a simple mass-spring model to predict the natural frequency of a soil-pile-structure system in sandy soil. This model includes a pair of matrixes, i.e., a mass matrix and a stiffness matrix. The mass matrix is comprised of the masses of the pile and superstructure, and the stiffness matrix is comprised of the stiffness of the pile and the spring coefficients between the pile and soil. The key issue in the evaluation of the natural frequency of a soil-pile system is the determination of the spring coefficient between the pile and soil. To determine the reasonable spring coefficient, subgrade reaction modulus, nonlinear p-y curves and elastic modulus of the soil were utilized. The location of the spring was also varied with consideration of the infinite depth of the pile. The natural frequencies calculated by using the mass-spring model were compared with those obtained from 1-g shaking table model pile tests. The comparison showed that the calculated natural frequencies match well with the results of the 1-g shaking table tests within the range of computational error when the three springs, whose coefficients were calculated using Reese's(1974) subgrade reaction modulus and Yang's (2009) dynamic p-y backbone curves, were located above the infinite depth of the pile.

• 대한기계학회논문집A
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• 제39권11호
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• pp.1105-1111
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• 2015

배기계는 엔진 및 현가계와 함께 차량의 주요 가진원 중의 하나이다. 배기계는 엔진과 직접적으로 연결된 시스템으로서 차체와의 연결 마운트를 통하여 엔진 구동 시의 진동을 차체로 전달한다. 따라서 배기계로부터 전달되는 진동을 저감하기 위해서는 배기계 진동특성을 예측해야 하고, 배기계의 주요부품인 벨로우즈의 정확한 특성을 묘사해야 한다. 그러나 벨로우즈는 복잡한 형상으로 인하여 특성을 예측하는 데에 어려움이 있었다. 설계 단계에서 벨로우즈에 대한 등가 변환 이론이 적용되었지만, 구간에 따라 주름의 크기가 달라지는 차량용 벨로우즈에 적용하기에는 부족함이 있다. 본 연구에서는 유한요소 해석기법을 이용하여 차량용 벨로우즈의 모델링 기법을 제시하고, 그 정확성을 실험결과와 비교하여 입증하였다.

• 한국음향학회지
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• 제39권4호
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• pp.342-350
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• 2020

본 논문은 우주 발사체 구조 개발을 위한 음향/진동 연구의 개요와 음향 해석 및 시험 기술의 국내 현황을 소개하고 있다. 먼저 발사체 운용중에 받는 동하중에 대하여 요약, 정리하고 위성체를 보호하기 위한 페이로드 페어링의 음향 하중 저감 설계 및 해석 방법을 소개하였다. 나로호부터 현재 한국형발사체 페이로드 페어링까지 음향 보호 시스템의 최적 설계를 위해 구조 진동-음향 연성 해석 성능의 향상을 도모하였으며, 이를 위한 연구 활동을 살펴보았다. 구체적으로 적층 구조가 다른 복합재료 실린더에 대한 음향 하중 저감 성능 해석 및 검증 시험, 음향 공명기 배열을 적용하기 위한 인클로저 음향 시험, 나로호 페어링 실린더부에 대한 음향 가진 시험 및 해석 등의 결과를 소개하였다. 현재 개발중인 한국형 발사체(누리호)의 페이로드 페어링 음향 하중 저감 해석 및 시험 결과를 소개하였으며 해석 결과가 실험 결과를 잘 예측함을 보였다.

• 한국항해항만학회지
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• 제41권5호
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• pp.297-302
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• 2017

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

• Structural Engineering and Mechanics
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• 제77권4호
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• pp.441-461
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• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• 한국항행학회논문지
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• 제23권6호
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• pp.597-604
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• 2019

인공신경망은 뇌의 뉴런들에서 상호 작용과 경험을 통해 학습해 나가는 것을 모사해 만든 알고리즘으로, 데이터의 특성이 반영된 학습을 통하여 정확한 결과를 산출하는데 사용할 수 있는 방법이다. 본 연구에서 기상 역학 모델에서 예측된 풍속 값의 개선을 위하여 심층신경망을 이용한 모델을 제시하였다. 연구에서 제시한 심층신경망을 이용한 풍속 예측 개선 모델은 기상 역학 모델의 예측 값을 재 보정하는 모델을 구축하고 이에 대한 검증과 시험 과정 후 별도의 데이터를 통한 예측의 정확도를 높일 수 있는 것을 확인하였다. 풍속 예측의 개선을 위하여 예측 시간, 온도, 기압, 습도, 대기상태변수, 풍속 등과 같은 일반적 기상 현상 자료의 예측 값을 활용한 심층신경망을 구축하였고, 전체 데이터 중 일부 데이터는 모델의 적정성 확인용 데이터로 구분하여, 모델 구축 및 학습에 사용하지 않고 별도의 정확도를 확인하여 연구에서 제시한 방법의 적합성을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.