• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.141-141
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• 2017

본 연구에서는 대하천의 8개의 수질인자(수온, 용존산소, 수소이온농도, 전기전도도, 총질소, 총인, 탁도, 클로로필-a)를 예측할 수 있는 인공신경망모델을 개발하였다. 인공신경망모델(ANN)은 수질데이터가 가지는 불확실성 및 비정상성, 복잡한 상호관련성에 효과적으로 대응할 수 있는 데이터기반 모델이다. 데이터기반 모델의 특성상 예측정확도를 높이기 위해서 양질의 입력데이터를 구성하는 것이 가장 중요하다. 때문에 각각의 수질인자뿐만 아니라 기상학적 인자 또한 예측을 위한 입력자료로 사용하였으며, 요인분석 및 층화표층추출법을 적용하여 입력데이터를 구성하였고 앙상블기법을 이용하여 추가적으로 예측의 정확도를 향상시켰다. 개발된 모델을 이용하여 지천유입이 있는 북한강의 수질자료를 예측한 결과 탁도를 제외한 7개의 수질인자 모두 0.85 이상의 설명력을 보였으며, 실측값과 예보값을 비교해본 결과 평균적으로 10% 미만의 에러값을 나타냈다. 요인분석을 통하여 연관성있는 인자를 입력인자로 추가한 경우 향상된 결과값을 보였주었으며, 앙상블기법을 적용한 결과 정확도 면에서 큰 향상을 보여주었다.

• Journal of Powder Materials
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• v.26 no.4
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• pp.275-281
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• 2019

An artificial neural network (ANN) model is developed for the analysis and simulation of correlation between flake powder metallurgy parameters and properties of AA2024-SiC nanocomposites. The input parameters of the model are AA 2024 matrix size, ball milling time, and weight percentage of SiC nanoparticles and the output parameters are density and hardness. The model can predict the density and hardness of the unseen test data with a correlation of 0.986 beyond the experimental data. A user interface is designed to predict properties at new instances. We have used the model to simulate the individual as well as the combined influence of parameters on the properties. Moreover, we have analyzed the calculated results from the powder metallurgical point of view. The developed model can be used as a guide for further composite development.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.31-39
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• 2019

A robot usually adopts ANN (artificial neural network)-based object detection and instance segmentation algorithms to recognize objects but creating datasets for these algorithms requires high labeling costs because the dataset should be manually labeled. In order to lower the labeling cost, a new scheme is proposed that can automatically generate a training images and label them for specific objects. This scheme uses an instance segmentation algorithm trained to give the masks of unknown objects, so that they can be obtained in a simple environment. The RGB images of objects can be obtained by using these masks, and it is necessary to label the classes of objects through a human supervision. After obtaining object images, they are synthesized with various background images to create new images. Labeling the synthesized images is performed automatically using the masks and previously input object classes. In addition, human intervention is further reduced by using the robot arm to collect object images. The experiments show that the performance of instance segmentation trained through the proposed method is equivalent to that of the real dataset and that the time required to generate the dataset can be significantly reduced.

• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.639-647
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• 2019

The performance of precast concrete structures is greatly influenced by the behaviour of beam-to-column connections. A single connection may be required to transfer several loads simultaneously so each one of those loads must be considered in the design. A good connection combines practicality and economy, which requires an understanding of several factors; including strength, serviceability, erection and economics. This research work focuses on the performance aspect of a specific type of beam-to-column connection using partly hidden corbel in precast concrete structures. In this study, the results of experimental assessment of the proposed beam-to-column connection in precast concrete frames was used. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) for moment-rotation prediction of precast beam-to-column connections. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.717-729
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• 2021

This paper examines the efficacy of the default risk factor in an emerging market context using the Fama-French five-factor model. Our aim is to test whether the Fama-French five-factor model augmented with a default risk factor improves the predictability of returns of portfolios sorted on the firm's characteristics as well as on industry. The default risk factor is constructed by estimating the probability of default using a hybrid version of dynamic panel probit and artificial neural network (ANN) to proxy default risk. This study also provides evidence on the temporal stability of risk premiums obtained using the Fama-MacBeth approach. Using a sample of 3,806 firm-year observations on non-financial listed companies of Pakistan over 2006-2015 we found that the augmented model performed better when tested across size-investment-default sorted portfolios. The investment factor contains some default-related information, but default risk is independently priced and bears a significantly positive risk premium. The risk premiums are also found temporally stable over the full sample and more recent sample period 2010-2015 as evidence by the Fama-MacBeth regressions. The finding suggests that the default risk factor is not a useless factor and due to mispricing, default risk anomaly prevails in the Pakistani equity market.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.113-118
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• 2021

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.65-75
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• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.148-151
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• 2010

분류 기법은 데이터 마이닝 기술 중 가장 잘 알려진 방법으로서, Decision tree, SVM(Support Vector Machine), ANN(Artificial Neural Network) 등 기법을 포함한다. 분류 기법은 이미 알려진 상호 배반적인 몇 개 그룹에 속하는 다변량 관측치로부터 각각의 그룹이 어떤 특징을 가지고 있는지 분류 모델을 만들고, 소속 그룹이 알려지지 않은 새로운 관측치가 어떤 그룹에 분류될 것인가를 결정하는 분석 방법이다. 분류기법을 수행할 때에 기본적으로 특징 공간이 잘 표현되어 있다고 가정한다. 그러나 실제 응용에서는 단일 특징으로 구성된 특징공간이 분명하지 않기 때문에 분류를 잘 수행하지 못하는 문제점이 있다. 본 논문에서는 이 문제에 대한 해결방안으로써 많은 정보를 포함하면서 빈발패턴에 대한 정보의 순실이 없는 닫힌 빈발패턴 기반 분류에 대한 연구를 진행하였다. 본 실험에서는 ${\chi}^2$(Chi-square)과 정보이득(Information Gain) 속성 선택 척도를 사용하여 의미있는 특징 선택을 수행하였다. 그 결과, 이 연구에서 제시한 척도를 사용하여 특징 선택을 수행한 경우, C4.5, SVM 과 같은 분류기법보다 더 향상된 분류 성능을 보였다.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.