• Geomechanics and Engineering
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• 제28권6호
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• 대한핵의학회지
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• 제22권1호
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• pp.39-45
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• 1988

To evaluate diagnostic accuracy of liver scintigraphy we analysed liver scans of 143 normal and 258 patients with various liver diseases. Three ROC curves for SOL, liver cirrhosis and diffuse liver disease were fitted using rating methods and areas under the ROC curves and their standard errors were calculated by the trapezoidal rule and the variance of the Wilcoxon statistic suggested by McNeil. We compared these results with that of National Institute of Radiological Science in Japan. 1) The sensitivity of liver scintigraphy was 74.2% in SOL, 71.8% in liver cirrhosis and 34.0% in diffuse liver disease. The specificity was 96.0% in SOL, 94.2% in liver cirrhosis and 87.6% in diffuse liver diasease. 2) ROC curves of SOL and liver cirrhosis approached the upper left-hand corner closer than that of diffuse liver disease. Area (${\pm}$ standard error). under the ROC curve was $0.868{\pm}0.024$ in SOL and $0.867{\pm}0.028$ in liver cirrhosis. These were significantly higher than $0.658{\pm}0.043$ in diffuse liver disease. 3) There was no interobserver difference in terms of ROC curves. But low sensitivty and high specificity of authors' SOL diagnosis suggested we used more strict decision threshold.

• Steel and Composite Structures
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• 제48권2호
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Computers and Concrete
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• 제32권2호
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• 대기
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• 제33권5호
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• pp.549-560
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• 2023

This study evaluates three distinct observation methods, CRDS, OA-ICOS, and OF-CEAS, in greenhouse gas monitoring equipment for atmospheric CO₂ and CH₄ concentrations. The assessment encompasses fundamental performance, high-concentration measurement accuracy, calibration methods, and the impact of atmospheric humidity on measurement accuracy. Results indicate that within a range of approximately 500 ppm, all three devices demonstrate high accuracy and linearity. However, beyond 1000 ppm, CO₂ accuracy sharply declines (84%), emphasizing the need for caution when interpreting high-concentration CO₂ data. An analysis of calibration methods reveals that both CO₂ and CH₄ measurements achieve high accuracy and linearity through 1-point calibration, suggesting that multi-point calibration is not imperative for precision. In dynamic atmospheric conditions with significant CO₂ and CH₄ concentration variations, a 1-point calibration suffices for reliable data (99% accuracy). The evaluation of humidity impact demonstrates that humidity removal devices significantly reduce air moisture levels, yet this has a negligible effect on dry CO₂ concentrations (less than 0.5% relative error). All three observation method instruments, which have integrated humidity correction to calculate dry CO₂ concentrations, exhibit minor sensitivity to humidity removal devices, implying that additional removal devices may not be essential. Consequently, this study offers valuable insights for comparing data from different measurement devices and provides crucial information to consider in the operation of monitoring sites.

• 한국구조물진단유지관리공학회 논문집
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• 제12권6호
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• pp.129-139
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• 2008

콘크리트 구조물의 내구성 해석 변수들의 변동성과 불확실성으로 인해 확률론적인 접근법의 사용이 증가되어 왔다. 특히, 몬테칼로시뮬레이션 방법(Level III 방법)은 접근성의 용이함으로 인해 많은 내구신뢰성 해석에 사용되어왔지만, 결과를 얻기위해서는 수 십만번의 반복계산이 필요하다. Level II 수준의 신뢰성 해석법인 일계이차모멘트법(FOSM)은 MCS법과 비교할 수 없을 정도의 짧은 시간에 신뢰도지수나 파괴확률을 계산할 수 있어, 유효성만 검증된다면 편리성과 신속성으로 인해 폭넓은 사용이 가능할 것이다. 본 연구에서는 FOSM법과 MCS법에 의한 부식확률(내구성 파괴확률)을 서로 비교하여 FOSM법의 유효성을 검증하고 각 내구성 해석변수들의 변동성이 부식확률에 미치는 영향을 검토하였다.

• 대한환경공학회지
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• 제31권9호
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• pp.825-830
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• 2009

의약물질은 다양한 경로를 통해 수질환경으로 유입된다. 수계에 의약물질은 ppt에서 ppb 단위의 낮은 농도로 종종 검출되고 있으므로 적절한 관리방안과 기술적 대안을 찾기 위해 최적화된 미량분석기술을 개발하는 것이 필요하다. LC-MS/MS 최적화에 있어서 단변수 변화분석이 선호되어 왔다. 그러나 분석기기의 독립변수들은 서로 영향을 주고받기 때문에 여러 독립변수를 동시에 변화시키는 방법을 통해 최적조건을 탐색해야 한다. 본 연구에서는 반응표면 분석법을 최근 문제가 되고 있는 항생제 설파메톡사졸의 LC-MS/MS 분석에 활용하였다. 먼저 선별실험을 통해 최적화 대상 독립변수를 조각화에너지(Fragmentation Energy)와 충돌전압(Collision Voltage)으로 선정하였다. 조각화에너지와 충돌전압을 동시변화시키고 각 조건의 반응을 다항식으로 모사하였다. 회귀분석결과 상관계수 $R^2$값은 0.9947를 나타내어 높은 정확도를 보였으며, 무작위 조건에서 반응의 예측값과 관측값 사이의 오차율이 3.41%로 작은 차이를 보였다. 따라서 RSA에 의해 도출된 모델이 조각화에너지와 충돌전압의 변화에 의한 LC-MS/MS의 반응을 성공적으로 모사하는 것으로 사료되었다. 이때 모델을 통해 확인된 최적조건은 조각화에너지 116.6과 충돌전압 10.9 eV이다. 이러한 반응표면분석법은 고체상 추출조건 및 액체크로마토그래피 조건의 최적화에 확장되어 활용될 수 있다.

• 한국농공학회지
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• 제32권3호
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• pp.87-101
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• 1990

The purpose of this study is to estimate the flood discharge and runoff volume at a stream by using geomorphologic parameters obtained from the topographic maps following the law of stream classification and ordering by Horton and Strahier. The present model is modified from Cheng' s model which derives the geomorphologic instantaneous unit hydrograph. The present model uses the results of Laplace transformation and convolution intergral of probability density function of the travel time at each state. The stream flow velocity parameters are determined as a function of the rainfall intensity, and the effective rainfall is calculated by the SCS method. The total direct runoff volume until the time to peak is estimated by assuming a triangular hydrograph. The model is used to estimate the time to peak, the flood discharge, and the direct runoff at Andong, Imha. Geomchon, and Sunsan basin in the Nakdong River system. The results of the model application are as follows : 1.For each basin, as the rainfall intensity doubles form 1 mm/h to 2 mm/h with the same rainfall duration of 1 hour, the hydrographs show that the runoff volume doubles while the duration of the base flow and the time to peak are the same. This aggrees with the theory of the unit hydrograph. 2.Comparisions of the model predicted and observed values show that small relative errors of 0.44-7.4% of the flood discharge, and 1 hour difference in time to peak except the Geomchon basin which shows 10.32% and 2 hours respectively. 3.When the rainfall intensity is small, the error of flood discharge estimated by using this model is relatively large. The reason of this might be because of introducing the flood velocity concept in the stream flow velocity. 4.Total direct runoff volume until the time to peak estimated by using this model has small relative error comparing with the observed data. 5.The sensitivity analysis of velocity parameters to flood discharge shows that the flood discharge is sensitive to the velocity coefficient while it is insensitive to the ratio of arrival time of moving portion to that of storage portion of a stream and to the ratio of arrival time of stream to that of overland flow.

• 한국위성정보통신학회논문지
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• 제5권1호
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• pp.58-62
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• 2010

본 논문에서는 갈릴레오 수신기 구조의 요구사항을 검토한 후 시뮬레이션을 통해 RF 성능 파라미터들이 갈릴레오 수신기 성능에 어떠한 영향을 주는지 알아보았다. 먼저 갈릴레오 시스템의 일반사항과 갈릴레오 수신기의 구조 및 특성에 대해 고찰하였고, 갈릴레오 수신기의 성능 분석을 위해 에질런트사의 ADS(Advanced Design System)를 이용하여 15 % EVM에 상응하는 16 dB C/N의 갈릴레오 수신기 성능 요구 규격에 초점을 맞춰 갈릴레오 수신기를 설계하였다. AGC(Automatic Gain Control) 동작을 확인하기 위해 수신 파워에 따른 출력 IF의 변화량을 확인하였으며, 일정한 IF 출력을 통해 정상적인 AGC 동작을 확인하였다. 수신기 입력 파워에 의한 성능 분석과 수신기 국부 발진기의 위상 잡음 변경에 따른 성능 열화 분석을 통해 -127 dBm의 입력 파워에서 EVM(Error Vector Magnitude) 변화를 알아보았다. 또한 AGC의 이득 범위(-2.5 dB ~ +42.5 dB)에 의해 결정된 -92 dBm ~ -139 dBm의 입력 파워에서 ADC(Analog to Digital Converter)의 비트 변경에 따른 성능 분석을 하였으며, LO의 위상 잡음이 감소하고 ADC의 비트가 증가함에 따라 EVM이 향상 됨을 알 수 있었다.

• 대한원격탐사학회지
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• 제37권5_1호
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• pp.1013-1027
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• 2021

KOMPSAT-3로 촬영한 영상은 일반 카메라로 촬영한 영상과 달리 가시광선 대역의 RGB 영역뿐만 아니라 NIR, PAN Band를 추가적으로 가지고 있다. 또한, 지상 685 km의 높은 고도에서 약 17 km 이상이 되는 넓은 반경의 지역을 촬영하기 때문에 이에 따른 전기적, 광학적 특성을 고려해야 한다. 즉, KOMPSAT-3의 카메라 센서는 각 CCD 픽셀 별, 각 band 별 특성, 감도 및 시간에 따른 변화, CCD Geometry 등에 의해 왜곡 현상이 발생하는데, 왜곡 현상을 해결하기 위해 센서보정이 필수적으로 필요하다. 본 논문에서는 KOMPSAT-3 사이드 슬리더 촬영 기반 영상에서 세그먼트 기반 노이즈 분석을 통한 균일 영역을 검출하는 기법을 제안한다. 해당 알고리즘을 통해 균일 영역을 검출 후 비 균일 보정 알고리즘 적용을 위해 각 센서별로 보정 테이블을 생성한 후 생성된 보정 테이블을 이용하여 위성 영상 보정을 수행하였다. 그 결과 기존 기법 대비 제안한 기법을 통해 수직 노이즈와 같은 위성 영상의 왜곡을 감소하였으며, 영상 품질의 척도인 상대적 방사 정확성 지표에 대해서는 평균 제곱 오차를 사용한 지표(RA)와 절대오차를 이용한 지표(RE)에 대해서 기존 방법에 대비하여 각각 0.3%, 0.15% 평가 지표에서 비교 우위에 있음을 확인하였다.