• Journal of Korean Society of Water and Wastewater
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• v.35 no.4
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• pp.259-275
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• 2021

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.617-626
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• 2022

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

• Advances in concrete construction
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• v.13 no.1
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.576-590
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• 2023

Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.113-117
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• 2023

As sputter equipment becomes more complex, it becomes increasingly difficult to understand the parameters that affect the thickness uniformity of thin metal film deposited by sputter. To address this issue, we verified a deep learning model that can predict complex relationships. Specifically, we trained the model to predict the height of 36 magnets based on the thickness of the material, using Support Vector Machine (SVM), Multilayer Perceptron (MLP), 1D-Convolutional Neural Network (1D-CNN), and 2D-Convolutional Neural Network (2D-CNN) algorithms. After evaluating each model, we found that the MLP model exhibited the best performance, especially when the dataset was constructed regardless of the thin film material. In conclusion, our study suggests that it is possible to predict the sputter equipment source using film thickness data through a deep learning model, which makes it easier to understand the relationship between film thickness and sputter equipment.

• Journal of Drive and Control
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• v.20 no.3
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• pp.25-34
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• 2023

In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R²), the test samples showed an R² of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.114-114
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• 2022

딥러닝의 학습 및 예측성능을 개선하기 위해서는 딥러닝 기법 내 연산과정의 개선과 함께 학습 및 예측에 사용되는 데이터의 전처리 과정이 중요하다. 본 연구에서는 딥러닝의 성능을 개량하기 위해 제안된 메타휴리스틱 최적화 알고리즘-딥러닝 결합모형과 데이터 전처리 기법을 통해 댐의 수위를 예측하였다. 수위예측을 위해 Multi-Layer Perceptron(MLP), 메타휴리스틱 최적화 알고리즘인 Harmony Search(HS)와 딥러닝을 결합한 MLP using a HS(MLPHS) 및 Exponential Bandwidth Harmony Search with Centralized Global Search(EBHS-CGS)와 딥러닝을 결합한MLP using a EBHS-CGS(MLPEBHS)를 통해 댐의 수위를 예측하였다. 메타휴리스틱 최적화 알고리즘-딥러닝 결합모형의 학습 및 예측성능을 개선하기 위해 학습 및 예측을 위한 자료를 기반으로 데이터 전처리기법을 적용하였다. 적용된 데이터 전처리 기법은 정규화, 수위구간별 사상(Event)분리 및 수위 변동에 대한 자료의 구분이다. 수위예측을 위한 대상유역은 금강유역에 위치한 대청댐으로 선정하였다. 대청댐의 수위예측을 위해 대청댐 상류에 위치하는 수위관측소 3개소를 선정하여 수위자료를 취득하였다. 각 수위관측소에서 취득한 수위자료를 입력자료로 설정하였으며, 대청댐의 수위자료를 출력자료로 설정하여 메타휴리스틱 최적화 알고리즘-딥러닝 모형의 학습을 진행하였다. 각 수위관측소 및 대청댐에서 취득한 수위자료는 2010년부터 2020년까지 총 11년의 일 단위 수위자료이며, 2010년부터 2019년까지의 자료를 학습자료로 사용하였으며, 2020년의 자료를 예측 및 검증자료로 사용하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.144-146
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• 2021

Idiopathic pulmonary fibrosis (IPF) is one of the most dreadful lung diseases which effects the performance of the lung unpredictably. There is no any authentic natural history discovered yet pertaining to this disease and it has been very difficult for the physicians to diagnosis this disease. With the advent of Artificial intelligent and its related technologies this task has become a little bit easier. The aim of this paper is to develop and to explore the machine learning models for the prediction and diagnosis of this mysterious disease. For our study, we got IPF dataset from Haeundae Paik hospital consisting of 2425 patients. This dataset consists of 502 features. We applied different data preprocessing techniques for data cleaning while making the data fit for the machine learning implementation. After the preprocessing of the data, 18 features were selected for the experiment. In our experiment, we used different machine learning classifiers i.e., Multilayer perceptron (MLP), Support vector machine (SVM), and Random forest (RF). we compared the performance of each classifier. The experimental results showed that MLP outperformed all other compared models with 91.24% accuracy.

• Steel and Composite Structures
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• v.45 no.4
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• pp.501-511
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• 2022

In this study, a two-dimensional model of the contact problem has been examined using the finite element method (FEM) based software ANSYS and based on the multilayer perceptron (MLP), an artificial neural network (ANN). For this purpose, a functionally graded (FG) half-infinite layer (HIL) with a crack pressed by means of two rigid blocks has been solved using FEM. Mass forces and friction are neglected in the solution. Since the problem is analyzed for the plane state, the thickness along the z-axis direction is taken as a unit. To check the accuracy of the contact problem model the results are compared with a study in the literature. In addition, ANSYS and MLP results are compared using Root Mean Square Error (RMSE) and coefficient of determination (R2), and good agreement is found. Numerical solutions are made by considering different values of external load, the width of blocks, crack depth, and material properties. The stresses on the contact surfaces between the blocks and the FG HIL are examined for these values, and the results are presented. Consequently, it is concluded that the considered non-dimensional quantities have a noteworthy influence on the contact stress distributions, and also, FEM and ANN can be efficient alternative methods to time-consuming analytical solutions if used correctly.