• Steel and Composite Structures
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• 제48권2호
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• pp.163-177
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• 2023

Complex and intricate preparation techniques, the imperative for utmost precision and sensitivity in instrumentation, premature sample failure, and fragile specimens collectively contribute to the arduous task of measuring the fracture toughness of concrete in the laboratory. The objective of this research is to introduce and refine an equation based on the gene expression programming (GEP) method to calculate the fracture toughness of reinforced concrete, thereby minimizing the need for costly and time-consuming laboratory experiments. To accomplish this, various types of reinforced concrete, each incorporating distinct ratios of fibers and additives, were subjected to diverse loading angles relative to the initial crack (α) in order to ascertain the effective fracture toughness (Keff) of 660 samples utilizing the central straight notched Brazilian disc (CSNBD) test. Within the datasets, six pivotal input factors influencing the Keff of concrete, namely sample type (ST), diameter (D), thickness (t), length (L), force (F), and α, were taken into account. The ST and α parameters represent crucial inputs in the model presented in this study, marking the first instance that their influence has been examined via the CSNBD test. Of the 660 datasets, 460 were utilized for training purposes, while 100 each were allotted for testing and validation of the model. The GEP model was fine-tuned based on the training datasets, and its efficacy was evaluated using the separate test and validation datasets. In subsequent stages, the GEP model was optimized, yielding the most robust models. Ultimately, an equation was derived by averaging the most exemplary models, providing a means to predict the Keff parameter. This averaged equation exhibited exceptional proficiency in predicting the Keff of concrete. The significance of this work lies in the possibility of obtaining the Keff parameter without investing copious amounts of time and resources into the CSNBD test, simply by inputting the relevant parameters into the equation derived for diverse samples of reinforced concrete subject to varied loading angles.

• ALGAE
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• 제30권2호
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• pp.121-137
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• 2015

Studies on carbon flux in the oceans have been highlighted in recent years due to increasing awareness about climate change, but the coastal ecosystem remains one of the unexplored fields in this regard. In this study, the dynamics of carbon flux in a vegetative coastal ecosystem were examined by an evaluation of net and gross ecosystem production (NEP and GEP) and $CO_2$ exchange rates (net ecosystem exchange, NEE). To estimate NEP and GEP, community production and respiration were measured along different habitat types (eelgrass and macroalgal beds, shallow and deep sedimentary, and deep rocky shore) at Gwangyang Bay, Korea from 20 June to 20 July 2007. Vegetative areas showed significantly higher ecosystem production than the other habitat types. Specifically, eelgrass beds had the highest daily GEP ($6.97{\pm}0.02g\;C\;m^{-2}\;d^{-1}$), with a large amount of biomass and high productivity of eelgrass, whereas the outer macroalgal vegetation had the lowest GEP ($0.97{\pm}0.04g\;C\;m^{-2}\;d^{-1}$). In addition, macroalgal vegetation showed the highest daily NEP ($3.31{\pm}0.45g\;C\;m^{-2}\;d^{-1}$) due to its highest P : R ratio (2.33). Furthermore, the eelgrass beds acted as a $CO_2$ sink through the air-seawater interface according to NEE data, with a carbon sink rate of $0.63mg\;C\;m^{-2}\;d^{-1}$. Overall, ecosystem production was found to be extremely high in the vegetated systems (eelgrass and macroalgal beds), which occupy a relatively small area compared to the unvegetated systems according to our conceptual diagram of a carbon-flux box model. These results indicate that the vegetative ecosystems showed significantly high capturing efficiency of inorganic carbon through coastal primary production.

• Nuclear Engineering and Technology
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• 제48권6호
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• pp.1315-1320
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• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

• Clinical and Experimental Pediatrics
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• 제51권1호
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• pp.54-61
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• 2008

목 적 : 특발성 신증후군의 연구를 위하여 병태생리 및 임상소견과 유사한 실험적 puromycin aminonucleoside(PAN) 신증을 이용하는데 본 연구는 배양한 사구체 상피세포에 대한 PAN의 영향을 통하여 신증후군의 주 원인 병태생리인 단백뇨의 기전을 밝히고자 하였다. 방 법 : 사구체 상피세포를 배양한 후 다양한 농도의 PAN과 항산화제를 투여하여 전자현미경관찰, 반응성 산소종 투과율 변화, confocal microscopy 등을 통하여 족세포성분의 변화를 관찰하였다. 결 과 : 사구체 상피세포의 초고배율소견에서 PAN에 의해 세포간극이 벌어지고 표면의 미세돌기가 단축되는 변화를 볼 수 있었다. 이러한 세포간극의 변화는 세포막부분의 ZO-1에 대한 면역형광검사에서도 확인할 수 있었다. DCF-DA로 측정한 반응성 산소종은 PAN에 의하여 농도에 따라 투여 2시간에 이미 유의한 증가를 보이나, 이러한 변화는 항산화제인 EGCG, probucol, vitamin C에 의해 감소하였다. 또한, 세포단층모델에서 투과율은 PAN에 의하여 농도에 따라 증가하나 항산화제에 의해 증가가 억제되었다. 세포골격구조인 ${\alpha}-actinin$은 사구체 상피세포의 세포질과 바깥 세포막부분으로 actin과 같이 분포하나 고농도의 PAN에 의해 세포질 바깥쪽의 일부분에 집중하는 형상으로 변하였다. 그러나 이러한 변화는 항산화제인 vitamin C의 처치에 의해 예방될 수 있었다. 세극막성분인 ZO-1는 고농도의 PAN에 의해 안쪽으로 이동하고 집중하는 형상으로 변하였으나, vitamin C의 처치에 의해 예방되었다. 이와 함께 ${\alpha}-actinin$과 ZO-1은 PAN에 의해 단백양이 감소하였으나 이는 항산화제에 의해 예방할 수 있었다. 결 론 : PAN은 사구체 상피세포의 반응성 산소종 생성을 증가시키고, 구조성분의 변화를 통하여 형태학적인 변화를 초래하며 이는 투과율의 증가로 나타났다. 이러한 변화들은 항산화제에 의해 어느 정도 억제할 수 있었음으로, PAN은 생체 외 사구체 상피세포에 산화스트레스기전을 통하여 구조적 변화와 이에 따른 단백뇨를 유발시키는 것으로 사료된다.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.67-81
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• 2008

Last decade, IT industry in Korea has been developed greatly. The game industry as an international leader has given good value added to its country. Game industry is one of the speedy improved one and it showed over 20% of growth rate from 2002. Most distressings in Game industry, there are no established costing system in spite that there should be in emerging market. In 2004, Game Contents Costing Model using Mission and Event was developed and also the study of Game Elements weight was done in 2005. The cost of Game Contents can be calculated by GEP and its unit price. The study of Game Contents Sizing Model was done in 2005. The costing of Game Contents by single Game Element which represents software which is one of 3 Game Elements-plan, graphic and software-and it is counted by mission and event. If the software element only can not well represent Game Contents volume, we can include plan and graphic elements for Game Contents costing. And we can say above two methods as a costing model of Game Contents. In this paper, these models were tested empirically and proved as usable.

• Computers and Concrete
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• 제31권3호
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.

• 한국공간정보시스템학회 논문지
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• 제11권3호
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• pp.50-54
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• 2009

최근 3차원 실내 외 공간정보데이터 모델에 대한 많은 연구가 진행되고 있다. 이러한 데이터 모델을 기반으로 구축된 3차원 공간데이터는 양이 방대하고 비교적 복잡한 구조를 갖는다. 따라서 이를 효과적으로 저장 및 관리, 응용하기 위해서는 DBMS를 활용하는 것이 유리하다. 이러한 필요에 의해 Gep-DBMS에서 데이터를 저장하고 응용하는 연구가 많이 이루어지고 있는데 Oosterom, Arens 등이 3차원 건물, 지표의 Geometry와 Topology를 DBMS에 저장하는 방법을 연구하였다. 본 논문은 GML3 기반의 3차원 도시 모델의 저장 및 교환을 위한 포맷인 CityGML 1.0을 따르는 구조로 데이터를 데이터베이스에 저장하였으며, 상용 DBMS인 Oracle Spatial 11g를 사용하였다.

• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• 한국산학기술학회논문지
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• 제18권4호
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• pp.86-92
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• 2017

본 연구에서는 사이드 빔이 1차 현가장치 역할을 수행하도록 설계된 복합소재 대차프레임의 적용성 검토를 위해 동특성 해석과 성능평가를 수행하였다. 대차프레임에 적용된 소재는 GEP224 유리섬유/에폭시 프리프레그이다. 성능검증을 위해 복합소재 대차프레임의 사이드 빔의 두께를 50mm, 80mm, 150mm로 변화시키면서 강성을 조절한 모델에 대해서 주행성능을 해석적으로 평가하였다. 주행성능평가에서 사이드 빔의 두께가 80mm인 모델은 모든 성능조건을 만족하였고, 사이드 빔 두께가 50mm인 경우 역시 모든 성능조건을 만족하지만 임계속도가 요구조건에 2%정도의 여유 밖에 없어 적합하지 않았다. 사이드 빔 두께가 150mm인 모델의 경우 공차시 수직방향 승차감 지수가 기준을 만족하지 못해 부적합한 것을 확인하였다. 또한, 사이드 빔의 두께가 80mm인 모델을 제작하여 대차에 설치하고, 주행시험대 시험을 통해 임계속도를 시험적으로 평가하였다. 주행시험대 시험에서는 휠세트에 횡방향 가진을 부과하고, 목표속도까지 증속과정에서 횡방향 가진에 의한 휠세트 횡변위의 발산현상은 발생하지 않았다. 또한, 횡방향 가진이 제거된 이후 휠세트의 횡변위 역시 수렴하여 최대 임계속도는 차량 동역학 해석에서 예측된 최대 임계속도와 유사함을 확인 할 수 있었다.

• Steel and Composite Structures
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• 제52권3호
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.