• Applied Chemistry for Engineering
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• v.22 no.5
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• pp.461-466
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• 2011

In this study, the fluorine doped $TiO_2$ was prepared as a photoelectrode in order to improve the efficiency of dye-sensitized solar cells and estimated the electrochemical characterizations. The energy conversion efficiency of the prepared dye-sensitized solar cells using fluorine doped $TiO_2$ was calculated from a current-voltage curve. The efficiency of prepared dye-sensitized solar cells was improved by about maximum three times by F-doping on $TiO_2$. It was suggested that the efficiency of dye-sensitized solar cells was improved by hybrid semiconductors of $TiO_2/TiOF_2$ in photoelectrode based on reduced $TiOF_2$ energy level via fluorine doping. It can be confirmed that the electron transport was faster but the electron recombination was slower by doping fluorine on $TiO_2$ in photoelectrode through intensity-modulated photocurrent spectroscopy and intensity-modulated photovoltage spectroscopy analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.212-212
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• 2023

비점오염원관리와 같이 장기적인 유역 관리 계획에서 유역 내 오염원 평가는 정말 중요하다. 유역 내 오염원 평가는 강우 유출에 의한 비점오염 발생원이 어디서 얼마나 발생시키는지에 대한 정량적인 조사가 필요하다. 유역 내의 오염원에 대한 정량적인 조사는 많은 비용과 시간이 필요하다. 이러한 비용과 시간을 줄이기 위해 유역단위 수리 수문 모델을 사용하고 있다. 유역단위 수리수문 모델은 HSPF (Hydrological Simulation Program in Fortran), SWAT (Soil and Water Assessment Tool), L-THIA ACN-WQ(The Long-term Hydrologic Impact Assessment Model with Asymptotic Curve Number Regression Equation and Water Quality model)등 다양한 모델이 사용되고 있다. 하지만 유역 모델을 통한 모의는 다양한 연산 과정을 진행하여 모의까지 많은 시간이 필요하다는 단점이 있다. 이에 따라 데이터 기반 모델링 기법(머신러닝/딥러닝)을 이용한 유출 및 수질 예측 연구가 많이 이루어지고 있다. 단순 머신러닝/딥러닝 기반 모델링 기법은 점(최종유출구)에서의 예측만 가능하여 최적관리 기법 적용 등과 같은 유역관리 방안을 적용하기 힘들다는 문제점이 있다. 따라서 본 연구에서 머신러닝/딥러닝을 통해 일부 수문 프로세스를 대체하고 소유역별 하도추적 기법을 연계하여 유량 및 수질 항목들의 모의가 가능한 하이브리드 모델을 개발하였다. 이는 머신러닝/딥러닝이 유역 모델의 일부 연산 과정을 대체하여 모의시간이 빠르며, 기존 머신러닝/딥러닝 예측 모델에서 평가가 어려웠던 유역 관리 방안 및 최적관리기법 적용 평가에도 활용이 가능할 것으로 판단이 된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.307-307
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• 2022

최근 기후변화와 댐 상류 토지이용 변화 등과 같은 다양한 원인에 의해 댐 유입량의 변동성이 증가하면서 댐 관리 및 운영조작 의사 결정에 어려움이 발생하고 있다. 따라서 이러한 댐 유입량의 변동 특성을 반영하여 댐 유입량을 정확하고 효율적으로 예측할 수 있는 방안이 필요한 실정이다. 머신러닝 기술이 발전하면서 Auto-ML(Automated Machine Learning)이 다양한 분야에서 활용되고 있다. Auto-ML은 데이터 전처리, 최적 알고리즘 선택, 하이퍼파라미터 튜닝, 모델 학습 및 평가 등의 모든 과정을 자동화하는 기술이다. 그러나 아직까지 수문 분야에서 댐 유입량을 예측하기 위한 모델을 개발하는데 있어서 Auto-ML을 활용한 사례는 부족하고, 특히 댐 유입량의 예측 정확성을 확보하기 위해 High-inflow and low-inflow 의 변동 특성을 고려한 하이브리드 결합 방식을 통해 Auto-ML 기반 앙상블 모델을 개발하고 평가한 연구는 없다. 본 연구에서는 Auto-ML의 패키지 중 Auto-sklearn을 통해 홍수기, 비홍수기 유입량 변동 특성을 반영한 하이브리드 앙상블 댐 유입량 예측 모델을 개발하였다. 소양강댐을 대상으로 적용한 결과, 하이브리드 Auto-sklearn 앙상블 모델의 댐 유입량 예측 성능은 R² 0.868, RMSE 66.23 m³/s, MAE 16.45 m³/s로 단일 Auto-sklearn을 통해 구축 된 앙상블 모델보다 전반적으로 우수한 것으로 나타났다. 특히 FDC (Flow Duration Curve)의 저수기, 갈수기 구간에서 두 모델의 유입량 예측 경향은 큰 차이를 보였으며, 하이브리드 Auto-sklearn 모델의 예측 값이 관측 값과 더욱 유사한 것으로 나타났다. 이는 홍수기, 비홍수기 구간에 대한 앙상블 모델이 독립적으로 구축되는 과정에서 각 모델에 대한 하이퍼파라미터가 최적화되었기 때문이라 판단된다. 향후 본 연구의 방법론은 보다 정확한 댐 유입량 예측 자료를 생성하기 위한 방안 수립뿐만 아니라 다양한 분야의 불균형한 데이터셋을 이용한 앙상블 모델을 구축하는데도 유용하게 활용될 수 있을 것으로 사료된다.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.1
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

• Steel and Composite Structures
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• v.52 no.4
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• pp.405-418
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• 2024

To study the influence of different reduced beam section (RBS) on the mechanical performance of modular boltedwelded hybrid connection joints (MHCJs), this article uses ABAQUS to establish and verify the finite element model (FEM) of the test specimens on the basis of quasi-static test research. Based on, 14 joint models featuring different RBS are devised to evaluate their influence on seismic behavior, such as joint failure mode, bending moment (M)-rotation angle (θ) curve, ductility, and energy consumption. The results indicate that when the flange and web are individually weakened, they alleviate to some extent the concentrated stress of the core module (CM) and column end steel skeleton in the joint core area, but both increase the stress on the flange connecting plate (FCP). At the same time, the impact of both on seismic performance such as bearing capacity, stiffness, and energy consumption is relatively small. When simultaneously weakening the flange and web of the steel beam, forming plastic hinges at the weakened position of the beam end, significantly alleviated the stress concentration of the CM and the damage at the FCP, improving the overall deformation and energy consumption capacity of joints. But as the weakening size of the web increases, the overall bearing capacity of the joint shows a decreasing trend.

• Steel and Composite Structures
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• v.35 no.6
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• pp.799-813
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• 2020

Fatigue cracks of rib-to-deck (RD) joints have been frequently observed in the orthotropic steel decks (OSD) using conventional U-ribs (CU). Thickened edge U-rib (TEU) is proposed to enhance the fatigue strength of RD joints, and its effectiveness has been proved through fatigue tests. In-depth full-scale tests are further carried out to investigate both the fatigue strength and fractography of RD joints. Based on the test result, the mean fatigue strength of TEU specimens is 21% and 17% higher than that of CU specimens in terms of nominal and hot spot stress, respectively. Meanwhile, the development of fatigue cracks has been measured using the strain gauges installed along the welded joint. It is found that such the crack remains almost in semi-elliptical shape during the initiation and propagation. For the further application of TEUs, the design curve under the specific survival rate is required for the RD joints using TEUs. Since the fatigue strength of welded joints is highly scattered, the design curves derived by using the limited test data only are not reliable enough to be used as the reference. On this ground, an experiment-numerical hybrid approach is employed. Basing on the fatigue test, a probabilistic assessment model has been established to predict the fatigue strength of RD joints. In the model, the randomness in material properties, initial flaws and local geometries has been taken into consideration. The multiple-site initiation and coalescence of fatigue cracks are also considered to improve the accuracy. Validation of the model has been rigorously conducted using the test data. By extending the validated model, large-scale databases of fatigue life could be generated in a short period. Through the regression analysis on the generated database, design curves of the RD joint have been derived under the 95% survival rate. As the result, FAT 85 and FAT 110 curves with the power index m of 2.89 are recommended in the fatigue evaluation on the RD joint using TEUs in terms of nominal stress and hot spot stress respectively. Meanwhile, FAT 70 and FAT 90 curves with m of 2.92 are suggested in the evaluation on the RD joint using CUs in terms of nominal stress and hot spot stress, respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.9
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• pp.1-9
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• 2007

For an efficient implementation of cryptosystems based on arithmetic in a finite field $GF(2^n)$, their hardware implementation is an important research topic. To construct a multiplier with low area complexity, the divide-and-conquer technique such as the original Karatsuba-Ofman method and multi-segment Karatsuba methods is a useful method. Leone proposed an efficient parallel multiplier with low area complexity, and Ernst at al. proposed a multiplier of a multi-segment Karatsuba method. In [1], the authors proposed new $MSK_5$ and $MSK_7$ methods with low area complexity to improve Ernst's method. In [3], the authors proposed a method which combines $MSK_2$ and $MSK_3$. In this paper we propose an efficient multiplication method by combining $MSK_2,\;MSK_3\;and\;MSK_5$ together. The proposed method reduces $116{\cdot}3^l$ gates and $2T_X$ time delay compared with Gather's method at the degree $25{\cdot}2^l-2^l with l>0.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.25-34
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• 2008

A new hybrid oedometer cell is designed and manufactured to investigate a behavior of soft soils by using elastic and electromagnetic waves during consolidation test. Bender elements, which generate and detect shear waves, are placed in the top cap and the bottom plate and mounted on the oedometer wall. Double wedge type electrical resistance probe, which measures local void ratio change, is positioned onto the top cap of the oedometer cell. The bender elements and the electrical resistance probe are anchored into a nylon set screw with epoxy resin. The nylon set screw with epoxy resin minimizes directly transmited elastic waves through the oedometer cell due to impedence mismatch and allows for easy replacement of defected bender elements and electrical resistance probe. Primary consolidation time can be estimated from the slope of electrical resistance versus log time curve and the evolution of shear wave velocity. The shear wave velocity can be used to assess inherent anisotropy when disturbance effects are minimized because particle alignment affects the shear wave velocity. The void ratios evaluated by the electrical resistance probe are similar to those by the settlement during consolidation. This study suggests that the shear wave velocity and the electrical resistance can provide complementary imformations to understand consolidation characteristics such as primary consolidation, anisotropy, and void ratio.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.697-705
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• 2021

Objective: To investigate the feasibility and the accuracy of the coronary CT angiography (CCTA)-derived Registry of Crossboss and Hybrid procedures in France, the Netherlands, Belgium and United Kingdom (RECHARGE) score (RECHARGECCTA) for the prediction of procedural success and 30-minutes guidewire crossing in percutaneous coronary intervention (PCI) for chronic total occlusion (CTO). Materials and Methods: One hundred and twenty-four consecutive patients (mean age, 54 years; 79% male) with 131 CTO lesions who underwent CCTA before catheter angiography (CA) with CTO-PCI were retrospectively enrolled in this study. The RECHARGECCTA scores were calculated and compared with RECHARGECA and other CTA-based prediction scores, including Multicenter CTO Registry of Japan (J-CTO), CT Registry of CTO Revascularisation (CT-RECTOR), and Korean Multicenter CTO CT Registry (KCCT) scores. Results: The procedural success rate of the CTO-PCI procedures was 72%, and 61% of cases achieved the 30-minutes wire crossing. No significant difference was observed between the RECHARGECCTA score and the RECHARGECA score for procedural success (median 2 vs. median 2, p = 0.084). However, the RECHARGECCTA score was higher than the RECHARGECA score for the 30-minutes wire crossing (median 2 vs. median 1.5, p = 0.001). The areas under the curve (AUCs) of the RECHARGECCTA and RECHARGECA scores for predicting procedural success showed no statistical significance (0.718 vs. 0.757, p = 0.655). The sensitivity, specificity, positive predictive value, and the negative predictive value of the RECHARGECCTA scores of ≤ 2 for predictive procedural success were 78%, 60%, 43%, and 87%, respectively. The RECHARGECCTA score showed a discriminative performance that was comparable to those of the other CTA-based prediction scores (AUC = 0.718 vs. 0.665-0.717, all p > 0.05). Conclusion: The non-invasive RECHARGECCTA score performs better than the invasive determination for the prediction of the 30-minutes wire crossing of CTO-PCI. However, the RECHARGECCTA score may not replace other CTA-based prediction scores for predicting CTO-PCI success.