• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.591-610
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• 2019

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

• 한국정보전자통신기술학회논문지
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• 제16권5호
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• pp.325-330
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• 2023

본 논문은 머신러닝을 활용하여 교량 데이터 설계 시 기존 엔지니어의 구조해석결과 또는 경험 및 주관에 따라 슬래브 두께를 예측하여 왔던 프로세스를 머신러닝 기법을 적용하여 디지털 기반 의사결정이 가능하도록 제시한다. 본 연구에서는 슬래브 두께 선정을 구조해석 외에 머신러닝 기법을 활용하여 엔지니어에게 가이드 값을 제공하게 함으로써 신뢰성 있는 설계 환경을 구축하고자 한다. 교량 데이터 중 가장 많은 비중을 차지하고 있는 거더교를 기준으로 상부구조물 중 슬래브 두께를 예측하기 위한 예측모델 프로세스를 정의 하였다. 각 프로세스 별 예측 값을 산출하기 위하여 다양한 머신러닝 모델 (Linear Regress, Decision Tree, Random Forest, Muliti-layer Perceptron)을 프로세스별 경합하여 최적의 모델을 도출하였다. 본 연구를 통해 기존 구조해석을 통해서만 슬래브 두께 예측을 하였던 영역에 머신러닝 기법의 적용 가능성을 확인하였으며 정확도 또한 95.4%를 도출하였다, 향후 프로세스 확장 및 데이터를 지속 확보하여 예측모델 정확도를 향상시킨다면 공사 환경에 머신러닝 모델이 지속 활용될 수 있을 것으로 기대된다.

• Computers and Concrete
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• 제32권6호
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Computers and Concrete
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• 제33권6호
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권3호
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• pp.85-90
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• 2021

본 논문에서는 고령자를 위한 개별 웨어러블(Wearable) 기기를 이용한 낙상 감지에 대해 논한다. 신뢰할 수 있는 낙상 감지를 위한 저비용 웨어러블 기기를 설계하기 위해서 대표적인 두 가지 모델을 종합적으로 분석하여 제시한다. 기계 학습 모델인 의사결정 나무(Decision Tree), 랜덤 포래스트(Random Forest), SVM(Support Vector Machine)과 심층 학습 모델인 일차원(One-Dimensional) 합성곱 신경망(Convolutional Neural Network)을 사용하여 낙상 감지 학습 능력을 정량화하였다. 또한 입력 데이터에 적용하기 위한 데이터 분할, 전처리, 특징 추출 방법 등을 고려하여 검토된 모델의 유효성을 평가한다. 실험 결과는 전반적인 성능 향상을 보여주며 심층학습 모델의 유효성을 검증한다.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of electromagnetic engineering and science
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• 제17권4호
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• pp.186-190
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• 2017

Visible-near-infrared (VIS-NIR) spectroscopy is a fast and non-destructive method for analyzing materials. However, most commercial VIS-NIR spectrometers are inappropriate for use in various locations such as in homes or offices because of their size and cost. In this paper, we classified eight food powders using a portable VIS-NIR spectrometer with a wavelength range of 450-1,000 nm. We developed three machine learning models using the spectral data for the eight food powders. The proposed three machine learning models (random forest, k-nearest neighbors, and support vector machine) achieved an accuracy of 87%, 98%, and 100%, respectively. Our experimental results showed that the support vector machine model is the most suitable for classifying non-linear spectral data. We demonstrated the potential of material analysis using a portable VIS-NIR spectrometer.

• Design & Manufacturing
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• 제17권1호
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.