• 대한토목학회논문집
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• 제28권6C호
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• pp.349-358
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• 2008

본 연구에서는 고베 공항 해저 충적 점토를 대상으로 한 신뢰성 침하 해석을 위해 각종 입력 물성치의 불확실성을 확률 통계 이론에 근거하여 조사하였고, Terzaghi 압밀 방정식을 목적 함수로 AFOSM 법을 적용하여 파괴 확률을 정식화하였다. 신뢰성 해석 결과, 목표침하량을 평균침하량 ${\pm}10%,\;{\pm}25%$로 설정한 경우, 발생확률은 각각 30~50%, 60%~90%로 나타났다. 이는 대상 지반의 확률변수의 변동계수가 과거의 연구보고 범위 내에 있음을 고려할 때, 목적함수로 Terzaghi 압밀방정식을 이용한 경우 침하량의 허용 오차 범위는 평균침하량 ${\pm}10%$가 적절할 것으로 사료된다. 또한, 감도 분석 결과 해석에 크게 영향을 미치는 인자는 압축 계수, 모델, 압밀 항복 응력의 불명확성으로 나타났다. 이는 정밀도가 높은 사전 침하량의 예측을 위해서는 현장의 응력 변형 조건을 충실하게 반영한 시험을 수행하여 신뢰도가 높은 물성치를 구하는 것이 매우 중요한 것임을 설명한다.

• Structural Engineering and Mechanics
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• 제84권2호
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• 한국항행학회논문지
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• 제27권5호
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• pp.629-634
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• 2023

본 논문에서는 여러 종류의 부채꼴 구조를 조합한 슬롯을 이용한 소형 광대역 테이퍼드 슬롯 안테나 설계에 대하여 연구하였다. 테이퍼드 슬롯 안테나를 소형화하고 낮은 주파수에서 동작하도록 하기 위하여 접지면에 부채꼴 구조를 조합한 슬롯을 추가하였다. 각 부채꼴 구조가 추가될 때 입력 반사 계수와 이득 변화를 슬롯이 없을 때와 비교하여 최종 제안된 안테나의 소형화 설계 과정을 체계적으로 설명하였다. 제안된 부채꼴 구조를 조합한 슬롯을 이용한 소형 광대역 테이퍼드 슬롯 안테나를 RF-35 기판 상에 제작하여 특성을 시뮬레이션 결과와 비교하였다. 실험 결과, 전압 정재파비(VSWR; voltage standing wave ratio)가 2 이하인 대역은 2.59-11.39 GHz이고, 대역내에서 이득이 3.3- 7.0 dBi로 측정되었다. 제안된 소형광대역 테이퍼드 슬롯 안테나는 접지면에 슬롯이 없을 때와 비교하여 크기를 36.9% 소형화할 수 있다.

• 한국방사선학회논문지
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• 제17권5호
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• pp.709-715
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• 2023

본 연구의 목적은, U-net 딥러닝 모델을 이용하여 CT 영상에서의 노이즈 감소 효과를 다양한 하이퍼 파라미터를 적용하여 평가하였다. 노이즈가 포함된 입력 영상 생성을 위하여 Gaussian 노이즈를 적용하였고, 총 1300장의 CT 영상에서 train, validation, test 셋의 비율을 8:1:1로 유지하여 U-net 모델을 적용하여 학습하였다. 연구에서 적용된 하이퍼파라미터는 최적화 함수 Adagrad, Adam, AdamW와 학습횟수 10회, 50회, 100회와 학습률 0.01, 0.001, 0.0001을 적용하였으며, 최대 신호 대 잡음비와 영상의 변동계수 값을 계산하여 정량적으로 분석하였다. 결과적으로 U-net 딥러닝 모델을 적용한 노이즈 감소는 영상의 질을 향상시킬 수 있으며 노이즈 감소 측면에서 유용성을 입증하였다.

• 디지털정책학회지
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• 제2권4호
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• pp.25-32
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• 2023

논문은 PPG 기반 센서에서 측정한 심박수(HR), 심박변이도(HRV) 데이터를 기반으로 DNN(Deep Neural Network) 혈당예측 모델을 개발하는 연구이다. 혈당 예측은 다층퍼셉트론(MLP) 신경망을 이용하였다. DNN 심층학습은 11의 독립변수가 있는 입력층, 은닉층, 출력층으로 구성된다. 혈당 예측모델의 학습결과는 MAE=0.3781, MSE=0.8518, 및 RMSE=0.9229이며, 결정계수(R²)는 0.9994이다. PPG기반의 디지털기기를 이용한 비채혈적 생체신호를 이용하여 혈당관리의 가능성을 확인하였다. PPG기반의 표준화된 활력신호 획득 및 해석법, 다량의 데이터기반 심층학습(Deep Learning)의 데이터셋, 정확성를 실증하는 연구가 이어진다면 개의 혈당관리에 편이성과 대안적인 방법을 제공할 수 있을 것이다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.53-58
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• 2024

This paper presents a novel miniaturized 3-D cubic antenna for use in wireless sensor network (WSN) application. The geometry of this antenna is designed as a cube including a meander dipole antenna. A truly omnidirectional pattern is produced by this antenna in both E-plane and H-plane, which allows for non-intermittent communication that is orientation independent. The operating frequency lies in the ISM band (centered in 2.45 GHz). The dimensions of this ultra-compact cubic antenna are 1.25*1.12*1cm3 which features a length dimension λ/11. The coefficient which presents the overall antenna structure is Ka=0.44. The cubic shape of the antenna is allowing for smart packaging, as sensor equipment may be easily integrated into the cube hallow interior. The major constraint of WSN is the energy consumption. The power consumption of radio communication unit is relatively high. So it is necessary to design an antenna which improves the energy efficiency. The parameters considered in this work are the resonant frequency, return loss, efficiency, bandwidth, radiation pattern, gain and the electromagnetic field of the proposed antenna. The specificity of this geometry is that its size is relatively small with an excellent gain and efficiency compared to previously structures (reported in the literature). All results of the simulations were performed by CST Microwave Studio simulation software and validated with HFSS. We used Advanced Design System (ADS) to validate the equivalent scheme of our conception. Input here the part of summary.

• 한국간호교육학회지
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• 제30권2호
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• pp.113-123
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• 2024

Purpose: This study aimed to examine the mediating effect of learning immersion in clinical simulations on the relationship between nursing student learning motivation and achievement in clinical simulation. Methods: This study was conducted using a cross-sectional survey with 184 nursing students from two universities who participated in clinical simulation between September and December 2022. The participants completed a self-administered questionnaire, and the collected data were analyzed using independent an independent t-test, Mann-Whitney U-test, one-way ANOVA, Pearson's correlation coefficient, and multiple regression analysis to identify the mediating effects of learning immersion on the relationship between nursing student learning motivation and achievement. Results: Among the subvariables of nursing student learning motivation, task value and self-efficacy for learning and performance had a significant effect on learning immersion (respectively, β=.36, p=.001; β=.31, p<.001) and learning achievement (respectively, β=.48, p<.001; β=.38, p<.001). With the input of learning motivation variables, the direct effect of learning immersion on learning achievement was significant (β=.20, p=.003), and the effects of learning motivation and task value and self-efficacy on learning achievement was reduced after controlling for learning immersion, which is a mediating variable (respectively, β=.41, p<.001; β=.32, p<.001). The bootstrapping test to confirm the mediating effect of learning immersion was also significant (task value 95% confidence interval [95% CI], 0.02~0.20; self-efficacy 95% CI, 0.01~0.12). Conclusion: The results of this study suggest that simulation educators should consider learners' motivation and immersion when organizing and operating clinical simulations.

• Archives of Plastic Surgery
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• 제51권1호
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• pp.30-35
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• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

• Advances in materials Research
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• 제13권1호
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

• 터널과지하공간
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• 제34권2호
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• pp.143-153
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• 2024

TBM 공법은 발파 공법에 비해 굴착 중 소음과 진동 수준이 낮고, 안정성이 높은 터널 굴착 공법이며, 전세계적으로 터널 프로젝트에 TBM 공법을 적용하는 사례가 증가하는 추세이다. 디스크 커터는 TBM의 커터헤드에 장착되는 굴착 도구로 지속적으로 막장면 지반과 상호작용하며, 이때 필연적으로 마모가 발생한다. 본 연구에서는 지질 조건과 TBM 운영파라미터, 머신러닝 알고리즘들을 이용하여 디스크 커터 마모를 정량적으로 예측하였다. 디스크커터 마모 예측의 입력변수 중 UCS 데이터의 수가 다른 기계 데이터 및 마모 데이터에 비해 매우 부족하기 때문에, 먼저 TBM 기계 데이터를 이용하여 전체 구간에 대한 UCS 추정을 진행하고, 완성된 전체 데이터로 마모율 계수 예측을 수행하였다. 마모율 계수 예측 모델의 성능을 비교해 본 결과 XGBoost 모델의 성능이 가장 높게 나타났으며, 복잡한 예측 모델의 해석을 위해 SHapley Additive exPlanation (SHAP) 분석을 진행하였다.