• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Steel and Composite Structures
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• v.46 no.4
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• pp.565-575
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• 2023

This study represents a numerical research in vibration and buckling of functionally graded material (FGM) beam comprising edge crack by using finite element method (FEM) and multilayer perceptron (MLP). It is assumed that the material properties change only according to the exponential distributions along the beam thickness. FEM and MLP solutions of the natural frequencies and critical buckling load are obtained of the cracked FGM beam for clamped-free (C-F), hinged-hinged (H-H), and clamped-clamped (C-C) boundary conditions. Numerical results are obtained to show the effects of crack location (c/L), material properties (E₂/E₁), slenderness ratio (L/h) and end supports on the bending vibration and buckling properties of cracked FGM beam. The FEM analysis used in this paper was verified with the literature, and the fundamental frequency ratio ($\overline{P_{cr}}$) and critical buckling load ratio ($\overline{{\omega}}$) results obtained were compared with FEM and MLP. The results obtained are quite compatible with each other.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Structural Engineering and Mechanics
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• v.92 no.2
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• pp.189-196
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• 2024

Metal-based materials used in ships are built by welding plates and profiles of various sizes and shapes together. Although various methods are currently used during the production of ships, studies are ongoing on alternative welding methods. When alternative methods are examined, it is seen that friction stir welding (FSW) is advantageous in applying plate-type materials and obtaining high mechanical properties after application. In this study, FSW was applied to the steel used in ships, and after the application, hardness, tensile, and bending tests were performed, and mechanical properties were determined. Afterward, the bending test results, which are of great importance for the formability of welded structures, were transferred to finite element analysis (FEA) and multilayer perceptron (MLP) models, and the data obtained in these models were mutually analyzed with the mechanical test data. As a result of the analyses, it was determined that models with appropriate results obtained with experimental data could be created after both FEA and MLP, and thus the bending behavior of welded structures could be determined without the need for experimental data.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.11-21
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• 2020

Cardio-cerebrovascular diseases (CCD) account for 24% of the causes of death to Koreans and its proportion is the highest except cancer. Currently, the risk of the cardiovascular disease for domestic patients is based on the Framingham risk score (FRS), but accuracy tends to decrease because it is a foreign guideline. Also, it can't score the risk of cerebrovascular disease. CCD is hard to predict, because it is difficult to analyze the features of early symptoms for prevention. Therefore, proper prediction method for Koreans is needed. The purpose of this paper is validating IG-MLP (Information Gain - Multilayer Perceptron) evaluation based feature selection method using CCD data with simulation. The proposed method uses the raw data of the 4^th ~ 7^th of The Korea National Health and Nutrition Examination Survey (KNHANES). To select the important feature of CCD, analysis on the attributes using IG-MLP are processed, finally CCD prediction ANN model using optimize feature set is provided. Proposed method can find important features of CCD prediction of Koreans, and ANN model could predict more accurate CCD for Koreans.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Tuberculosis and Respiratory Diseases
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• v.43 no.1
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• pp.46-53
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• 1996

Background: Endotoxin or lipopolysaccharide(LPS) can prime phagocytic cells such as polymorphonuclear leukocytes, monocytes or animal peritoneal macrophages to generate increased amounts of secretory products such as oxygen free radicals and tumor necrosis factor, which play an important role in developing adult respiratory distress syndrome in gram negative sepsis. Human alveolar macrophages(HAM) are continuously exposed to various stimuli inhaled into the alveoli, and the response to LPS might be different in HAM. Therefore, we investigated the effect of LPS pre-exposure on HAM adhered to plastic surface and A549 cell(type II human alveolar epithelial cell line) monolayer. Methods: HAM were isolated from bronchoalveolar lavage fluid from normal lung of the patients with localized lung cancer and esophageal cancer. LPS was exposed to HAM for 2hrs before or after adherence to plastic surface of 24-well Linbro plate and A549 cell monolayer. And then HAM was stimulated with PMA(phorbol myristate acetate) or fMLP(N-formyl-methionylleucyl-phenylalanine). The amount of hydrogen peroxide($H_2O_2$) production in the supernatant was measured on the principle of peroxidase-dependent oxidation of phenol red by hydrogen peroxide. Results: LPS pre-exposure could not enhance $H_2O_2$ production in neither HAM adhered to plastic surface nor one to A549 cell monolayer. But LPS even in the absence of PMA or fMLP stimulation directly increased $H_2O_2$ release in HAM if added after the adherence to A549 cell monolayer. Conclusion: Endotoxin does not prime HAM, but may directly activate HAM adhered to alveolar epithelial cells. Further investagation will be necessary.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.1
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• pp.67-82
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• 2011

Calculation of accurate wall heat flux for high enthalpy flows requires a dense grid system, which leads to significantly large computational time. A high-order scheme can improve the efficiency of calculation because wall heat flux can be obtained accurately even with a relatively coarse grid system. However, conventional high order schemes have some drawbacks such as oscillations near a discontinuity and instability in multi-dimensional problem. To resolve these problems, enhanced Multi-dimensional Limiting Process(e-MLP) was applied as a high-order scheme. It could provide robust and accurate solutions with high order accuracy in calculation of high enthalpy flows within a short time. We could confirm the efficiency of the high order e-MLP scheme through grid convergence tests with different grid densities in a hypersonic blunt nose problem.