• Steel and Composite Structures
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• 제25권1호
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• pp.45-55
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• 2017

Stainless steel exhibits high ductility and strain hardening capacity in comparison with carbon steel widely used in constructions. To analyze the particular behaviour of stainless steel cover-plate joints, an experimental study was conducted. It showed large ductility and complex failure modes of the joints. A non-linear finite element model was developed to predict the main parameters influencing the behaviour of these joints. The results of this deterministic model allow us to built a meta-model by using the quadratic response surface method, in order to allow for efficient reliability analysis. This analysis is then applied to the assessment of design formulae in the currently used codes of practice. The reliability analysis has shown that the stainless steel joint design according to Eurocodes leads to much lower failure probabilities than the Eurocodes target reliability for carbon steel, which incites revising the resisting model evaluation and consequently reducing stainless steel joint costs. This approach can be used as a basis to evaluate a wide range of steel joints involving complex failure modes, particularly bearing failure.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.266-268
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• 2003

This is based on the AGENT-LUC model framework. Luangprabang Province has the largest percentage of shifting cultivation area in Laos PDR. The model simulates the spatial and temporal patterns of the shifting cultivation in the study area, using a GIS database while the total area of shifting cultivation is controlled by supply and demand balance of food. The model simulation period is from 1990 to 1999, at a spatial resolution of 500m. The results are evaluated using statistical data and remote sensing images. Through the validation, it is concluded that the trends simulated agrees to that of statistical data and the spatial and temporal patterns are also replicated satisfactorily.

• 대기
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• 제21권4호
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• pp.373-389
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• 2011

This research focuses on the sensitivity of the WRF(Weather Research and Forecasting) Model according to three different land cover data(USGS(United States Geological Survey), MODIS(Moderate Resolution Imaging Spectroradiometer)30s+USGS, and KLC (Korea Land Cover)) for an event of sea breeze, occurred over the Gangwon Yeongdong region on 13 May 2009. Based on the observation, the easterly into Gangneung, due to the sea-breeze circulation, was identified between 1000 LST and 1640 LST. It did not reach beyond the Taebaek Mountain Range and thus the easterly was not observed near Daegwallyeong. On the other hand, the numerical simulations utilizing land cover data of USGS, MODIS30s+USGS, and KLC showed easterlies beyond the Taebaek Mountain Range up to Daegwallyeong. In addition, rather different penetration distances of each easterly, and different timings of beginning and ending of sea breeze were identified among the simulations. The Bias, MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of the wind from WRF simulation using MODIS30s+USGS land cover data were the least among the simulations particularly over Gangwon Yeongdong coastal area(Sokcho, Gangneung and Donghae), while those of the wind over the Gangwon Mountain area(Daegwallyeong and Jinbu) from the simulation using KLC land cover data were the least among them. The wind field over Gangwon Yeongdong coastal area from the simulation using USGS land cover data was rather poor among them.

• 한국지리정보학회지
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• 제27권1호
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• pp.115-127
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• 2024

본 연구는 Transformer 모듈을 기반으로 다양한 구조의 모델을 구성하고, 토지피복 분류를 수행하여 Transformer 모듈의 활용방안 검토를 목적으로 하였다. 토지피복 분류를 위한 딥러닝 모델은 CNN 구조를 가진 Unet 모델을 베이스 모델로 선정하였으며, 모델의 인코더 및 디코더 부분을 Transformer 모듈과 조합하여 총 4가지 딥러닝 모델을 구축하였다. 딥러닝 모델의 학습과정에서 일반화 성능 평가를 위해 같은 학습조건으로 10회 반복하여 학습을 진행하였다. 딥러닝 모델의 분류 정확도 평가결과, 모델의 인코더 및 디코더 구조 모두 Transformer 모듈을 활용한 D모델이 전체 정확도 평균 약 89.4%, Kappa 평균 약 73.2%로 가장 높은 정확도를 보였다. 학습 소요시간 측면에서는 CNN 기반의 모델이 가장 효율적이었으나 Transformer 기반의 모델을 활용할 경우, 분류 정확도가 Kappa 기준 평균 0.5% 개선되었다. 차후, CNN 모델과 Transformer의 결합과정에서 하이퍼파라미터 조절과 이미지 패치사이즈 조절 등 다양한 변수들을 고려하여 모델을 고도화 할 필요가 있다고 판단된다. 토지피복 분류과정에서 모든 모델이 공통적으로 발생한 문제점은 소규모 객체들의 탐지가 어려운 점이었다. 이러한 오분류 현상의 개선을 위해서는 고해상도 입력자료의 활용방안 검토와 함께 지형 정보 및 질감 정보를 포함한 다차원적 데이터 통합이 필요할 것으로 판단된다.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• 한국터널지하공간학회 논문집
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• 제24권1호
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• pp.1-13
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• 2022

본 연구에서는 기존에 개발된 개착식 철도 터널의 지진취약도 모델들을 가중 조합하여 새로운 모델을 제시하고 제시한 모델의 적정성을 평가하였다. 지진취약도 함수의 형태는 최대지반가속도의 대수정규분포형태로, 누적확률분포로 표현된다. 독립적으로 개발된 각 모델을 선형 가중 조합하는 것으로 모델의 불확실성을 줄일 수 있기에 4개의 모델에 대하여 25%씩 동등하게 선형가중을 부여하였다. 조합된 지진취약도 곡선에 최대 지반가속도에 대한 피해발생확률을 이용하여 지진취약도 곡선의 중앙값과 표준편차를 결정하여 새로운 지진취약도 함수를 개발하였다. 개발된 지진취약도 함수의 적합성을 평가하기 위하여 다양한 터널의 지진취약도 곡선과 비교 분석을 진행하였다. 개발된 곡선은 상대적으로 지진피해에 안전한 굴착식 터널의 지진취약도 함수와 비슷한 취약도를 갖는 것으로 나타나는데, 대상 터널은 국내 고속철도 개착식 터널로 높은 내진설계 기준에 의해 기인하는 것으로 판단된다.

• Geomechanics and Engineering
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• 제34권5호
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• pp.491-505
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• 2023

The practical usage of underground space and demand for vehicular tunnels necessitate the construction of non-circular wide rectangular tunnels. However, constructing large tunnels in soft clayey soil conditions with no ground improvement can lead to excessive ground deformations and collapse. In recent years, in situ ground improvement techniques such as jet grouting and deep cement mixing are often utilized to perform cement-stabilisation around the tunnel boundary to prevent large deformations and failure. This paper discusses the stability characteristics and failure behaviour of a wide rectangular tunnel in cement-treated soft clays. First, the plane strain finite element model is developed and validated with the results of centrifuge model tests available in the past literature. The critical tunnel support pressures computed from the numerical study are found to be in good agreement with those of centrifuge model tests. The influence of varying strength and thickness of improved soil surround, and cover depth are studied on the stability and failure modes of a rectangular tunnel. It is observed that the failure behaviour of the tunnel in improved soil surround depends on the ratio of the strength of improved soil surround to the strength of surrounding soil, i.e., q_ui/q_us, rather than just q_ui. For low q_ui/q_us ratios,the stability increases with the cover; however, for the high strength improved soil surrounds with q_ui >> q_us, the stability decreases with the cover. The failure chart, modified stability equation, and stability chart are also proposed as preliminary design guidelines for constructing rectangular tunnels in the improved soil surrounded by soft clays.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.706-710
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• 2008

The experiment of dome port cover under shock impact is performed with shock tube. The dome port cover blocked intake air duct up from the solid propellant during air breathing vehicle speed reach Mach 2.0. When the air breathing vehicle reach Mach 2.0, the inlet cover is removed and the dome port cover is broken to pieces by detonator or pressure of inlet air. Thus the dome port cover not only must stand the pressure of combustion chamber but also easy to break from the RAM pressure. In this study, a fracture evaluation on the $Al_2O_3$ ceramic spherical dome and circular plate port under impact has been presented. Ceramic were supported by the rigid body and a couple of O-ring. The Mooney-Rivlin model have been used to describe behaviors of both O-ring. And spherical dome and circular plate fracture results of the LS-DYNA code using Johnson-Holmquist(JH-2) constitutive equation was compared.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.580-583
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• 2007

Various matching methods have been introduced by investigators to improve digital elevation model (DEM) accuracy of satellite imagery. This study proposed an area-based matching method according to land cover property using correlation coefficient of pixel brightness value between the two images for DEM generation from IKONOS stereo imagery. For this, matching line (where "matching line" implies straight line that is approximated to complex nonlinear epipolar geometry) is established by exterior orientation parameters to minimize search area. The matching is carried out based on this line. Land cover classes are divided off into water, urban land, forest and agricultural land. Matching size is selected using a correlation-coefficient image in the four areas. The selected sizes are $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively. And hence, DEM is generated from IKONOS stereo imagery using the selected matching sizes and land cover map on the four types.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.