• Design & Manufacturing
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• 제15권3호
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Steel and Composite Structures
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• 제42권6호
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• Journal of Mechanical Science and Technology
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• 제19권spc1호
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• pp.255-264
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• 2005

In this paper, a practical bushing model is proposed to improve the accuracy of the vehicle dynamic analysis. The results of the rubber bushing are used to develop an empirical bushing model with an artificial neural network. A back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model. Then, a full car dynamic model with artificial neural network bushings is simulated to show the feasibility of the proposed bushing model.

• 한국농공학회지
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• 제45권2호
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• International journal of advanced smart convergence
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• 제8권2호
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• pp.56-67
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• 2019

The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 봄 학술논문 발표대회
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• pp.16-17
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• 2020

In this paper, we intended to present a model for estimating carbon dioxide emissions by work of construction equipment using Artificial Neural Network(ANN) analysis. In this study, data of excavators and trucks are classified according to the work carried out, and carbon dioxide emissions are predicted through ANN based on equipment information and work information. As a result, the effect of each model was validated, and a carbon dioxide emission prediction model was derived for each work. This has the expected effect of establishig an eco-friendly process plan using this model from the construction planning stage.

• International journal of advanced smart convergence
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• 제7권1호
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• 사물인터넷융복합논문지
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• 제9권1호
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• pp.49-55
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• 2023

디지털 신기술의 진화가 빠르게 진행이 되고 있다. 특히 교육 관련 분야에서는 소프트웨어와 인공지능에 대한 많은 변화가 빠르게 진행이 되고 있다. 교육부에서는 소프트웨어와 인공지능 정규교육과정으로 연계에 의한 교육프로그램을 계획하고 있다. 정규교과로 적용하기 전에 다양한 소프트웨어와 인공지능 관련 체험 캠프를 추진하고 있다. 본 연구는 디지털 신기술을 기반으로 고등학생을 대상으로 소프트웨어와 인공지능 교육프로그램을 위한 교육 모델을 구성하고자 한다. 소프트웨어와 인공지능 교육을 확대 보급함으로써 고등학생들의 소프트웨어와 인공지능 기초역량 높이고자 한다. 고등학교에서의 소프트웨어와 인공지능의 개념을 정의하고 소프트웨어와 인공지능 학습요인을 정규교육과정으로 연계하는 모델을 제안하고자 한다.

• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• 열처리공학회지
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• 제31권1호
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• pp.1-5
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• 2018

This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.