• Nuclear Engineering and Technology
• /
• 제53권1호
• /
• pp.44-60
• /
• 2021

In this paper, we validate the decay heat calculation capability via a two-step method to analyze spent nuclear fuel (SNF) discharged from pressurized water reactors (PWRs). The calculation method is implemented with a lattice code STREAM and a nodal diffusion code RAST-K. One of the features of this method is the direct consideration of three-dimensional (3D) core simulation conditions with the advantage of a short simulation time. Other features include the prediction of the isotope inventory by Lagrange non-linear interpolation and the use of power history correction factors. The validation is performed with 58 decay heat measurements of 48 fuel assemblies (FAs) discharged from five PWRs operated in Sweden and the United States. These realistic benchmarks cover the discharge burnup range up to 51 GWd/MTU, 23.2 years of cooling time, and spanning an initial uranium enrichment range of 2.100-4.005 wt percent. The SNF analysis capability of STREAM is also employed in the code-to-code comparison. Compared to the measurements, the validation results of the FA calculation with RAST-K are within ±4%, and the pin-wise results are within ±4.3%. This paper successfully demonstrates that the developed decay heat calculation method can perform SNF back-end cycle analyses.

• Journal of Astronomy and Space Sciences
• /
• 제36권1호
• /
• pp.35-43
• /
• 2019

The Optical Wide-field patroL-Network (OWL-Net) is a global optical network for Space Situational Awareness in Korea. The primary operational goal of the OWL-Net is to track Low Earth Orbit (LEO) satellites operated by Korea and to monitor the Geostationary Earth Orbit (GEO) region near the Korean peninsula. To obtain dense measurements on LEO tracking, the chopper system was adopted in the OWL-Net's back-end system. Dozens of angle-only measurements can be obtained for a single shot with the observation mode for LEO tracking. In previous work, the reduction process of the LEO tracking data was presented, along with the mechanical specification of the back-end system of the OWL-Net. In this research, we describe an integrity assessment method of time-position matching and verification of results from real observations of LEO satellites. The change rate of the angle of each streak in the shot was checked to assess the results of the matching process. The time error due to the chopper rotation motion was corrected after re-matching of time and position. The corrected measurements were compared with the simulated observation data, which were taken from the Consolidated Prediction File from the International Laser Ranging Service. The comparison results are presented in the In-track and Cross-track frame.

• 한국전자통신학회논문지
• /
• 제15권6호
• /
• pp.987-994
• /
• 2020

본 논문은 음향잡음감쇠기에서 CNN(: Convolutional Neural Network) 계층의 커널 사이즈가 성능에 미치는 영향을 위한 연구하였다 이 시스템은 기존의 적응필터를 이용하는 대신 신경망 적응예측필터를 이용한 심층학습 알고리즘으로 잡음감쇠 성능을 개선한다. 100-neuron, 16-filter CNN 필터와 오차 역전파(back propagation) 알고리즘을 이용하여 잡음이 포함된 단일입력 음성신호로부터 음성을 추정한다. 이는 음성신호가 갖는 유성음 구간에서의 준주기적 성질을 이용하는 것이다. 본 연구에서 커널 사이즈에 대한 잡음감쇠기의 성능을 검증하기 위하여 Tensorflow와 Keras 라이브러리를 사용한 시뮬레이션 프로그램을 작성하고 모의실험을 수행하였다. 모의실험 결과, 커널 사이즈가 16 정도일 때 평균자승오차(MSE: Mean Square Error) 및 평균절대값오차(MAE: Mean Absolute Error) 값이 가장 작은 것으로 나타났으며 사이즈가 이보다 더 작거나 커지면 MSE 및 MAE 값이 증가하는 것을 볼 수 있다. 이는 음성신호의 경우 커널 사이즈가 16 정도일 때 특성을 가장 잘 포집할 수 있음을 알 수 있다.

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

• 한국지반신소재학회논문집
• /
• 제21권2호
• /
• pp.21-30
• /
• 2022

전단강도는 암반 비탈면 안정성 평가에서 가장 중요한 지표이다. 일반적으로 기존 문헌자료, 역해석, 실험 등의 결과를 비교하여 산정한다. 암반 비탈면에서의 전단강도는 불연속면의 상태와 관련된 변수를 추가로 고려해야 한다. 이 변수들은 시추조사를 통해 여부를 파악하는 것이 어려울뿐더러 전단강도와의 정확한 관계를 찾아내기도 어렵다. 본 연구에서는 역해석을 통해 산정된 데이터를 이용했다. 기존 고려되었던 변수들의 관계를 딥러닝에 접목시켜 전단강도 산정에 적합한지 그 가능성을 모색하였다. 비교를 위해 기존에 사용되는 간단한 선형회귀(Linear Regression) 모델과 딥러닝 알고리즘인 심층인공신경망(DNN) 모델을 사용하였다. 각 분석 모델은 비슷한 예측결과를 도출해내었지만 미세한 차이로 DNN의 설명력이 개선된 결과를 나타내었다.

• Steel and Composite Structures
• /
• 제46권1호
• /
• pp.75-92
• /
• 2023

The novel composite cold-formed steel (CFS)/engineered cementitious composites (ECC) beams have been recently presented. The new composite section exhibited superior structural performance as a flexural member, benefiting from the lightweight thin-walled CFS sections with improved buckling and torsional properties due to the restraints provided by thinlayered ECC. This paper investigated the shear performance of the new composite CFS/ECC section. Twenty-eight simply supported beams, with a shear span-to-depth ratio of 1.0, were assembled back-to-back and tested under a 3-point loading scheme. Bare CFS, composite CFS/ECC utilising ECC with Polyethylene fibres (PE-ECC), composite CFS/MOR, and CFS/HSC utilising high-strength mortar (MOR) and high-strength concrete (HSC) as replacements for PE-ECC were compared. Different failure modes were observed in tests: shear buckling modes in bare CFS sections, contact shear buckling modes in composite CFS/MOR and CFS/HSC sections, and shear yielding or block shear rupture in composite CFS/ECC sections. As a result, composite CFS/ECC sections showed up to 96.0% improvement in shear capacities over bare CFS, 28.0% improvement over composite CFS/MOR and 13.0% over composite CFS/HSC sections, although MOR and HSC were with higher compressive strength than PE-ECC. Finally, shear strength prediction formulae are proposed for the new composite sections after considering the contributions from the CFS and ECC components.

• Geomechanics and Engineering
• /
• 제37권4호
• /
• pp.307-321
• /
• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• 대한토목학회논문집
• /
• 제14권1호
• /
• pp.217-229
• /
• 1994

Lade의 Double surface와 Single surface 구성모델의 변형을 예측의 정도를 비교평가하기 위하여 백마강모래로 두 구성식의 토질매개변수를 다수 구하고 각 변수의 통계치를 분석하였다. 이 통계치를 이용하여 일반함수의 변동계수를 산정하는 1계근사법으로 두 구성모델의 변형율에 대한 변동계수를 해석하였다. 그 결과 각 토질매개변수의 결정에는 Single surface 구성모델의 변수가 Double surface 구성모델의 변수보다 변동계수가 작게 나타나므로 매개변수결정에 일관성이 있는 반면 확률론으로 해석한 축 변형율의 변동계수는 Double surface 구성모텔에서 안정된 값을 나타내고 있으며, 체적 변형율에서는 두 구성모델 모두 안정된 해석결과를 보인다. 이는 두 구성모델의 특성을 비교한 다른 연구 결과와 일치하는 경향으로서 확률론에 의한 구성식의 평가가 효과적인 수단임을 알 수 있었다.

• 한국의류산업학회지
• /
• 제21권6호
• /
• pp.789-799
• /
• 2019

This study classified and analyzed the upper body types of 7-13 years old elementary school boys, using 3D data from the 6th Size Korea. The results of this study are as follows. Seven factors were extracted from the factorial analysis as an independent factor for a cluster analysis. The cluster analysis generated four body types. Type 1 has large ratio of front and back depth as well as circumference, with a front protrusion. In Type 2, the vertical value of upper torso is longer than average; in addition, its flatness is the largest and produces a thin body type. Type 3 has a smaller flatness in the bust, waist, abdomen and hip than other types, while also having the largest BMI. Type 4 is characterized by a greater shoulder angle than other types and its other factors are close to average. As a result of the logistic regression analysis, the prediction model used eight variables to generate and its accuracy is 88.679%. The classification of upper body types from this study can be used as basic data to improve patternmaking for each body type. The generated prediction model is also expected to be used as a method to help classify upper body types using the eight variables.

• 한국해안해양공학회지
• /
• 제16권2호
• /
• pp.64-74
• /
• 2004

SCP공법(Sand Compaction Pile Method)은 안벽구조물 하부 지반이 연약할 경우에 압밀침하 속도를 증가시키고, 침하를 감소시키며, 지지력을 증가시키기 위하여 널리 적용되어 왔다. 연약지반에 타설 된 모래말뚝과 주변 연약지반으로 구성된 SCP 개량지반 상부에 상부 캐이슨과 상치구조물이 설치됨에 따라, SCP 복합지반에서는 모래말뚝에 의한 즉시침하와 함께 주변 점토지반의 압밀침하가 복합적으로 유발하게 된다. 본 연구에서는 SCP 복합지반에 대한 기존의 탄성침하 및 압밀이론에 근거하여 침하 예측기법을 제안하였다. 제안된 침하모델의 모델정수값들의 산정하기 위하여 유전자 알고리즘에 근거한 역해석 기법을 적용하여 최적화 과정을 수행하였다. 국내 SCP 복합지반에 대한 예제해석을 수행하여 제안된 침하예측기법의 적용성을 검토하였다.