• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Steel and Composite Structures
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• v.51 no.4
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.439-453
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• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.9
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• pp.1081-1090
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• 2017

The purpose of this study was to develop a functional porridge prepared with mulberry leaf and mulberry fruit powder, which can ameliorate hypertension. The experiment was designed according to the central composite design. For optimization of the mixture ratio of mulberry leaf powder (MLP) and mulberry fruit powder (MFP), the independent variables were defined as MLP (X1) and MFP (X2) and the dependent variables were defined as K (Y1), Na (Y2), ${\gamma}$-aminobutyric acid (GABA) (Y3), cyanidin-3-glycoside (C3G) (Y4), rutin (Y5), and flavonoid (Y6). The optimal MLP to MFP mixture ratio according to the response surface method were 5.41 g of MLP and 2.65 g of MFP. The amounts of K, Na, GABA, C3G, rutin, and flavonoid in the optimal MLP and MFP mixture were 1,844.22 mg/100 g, 52.74 mg/100 g, 139.98 mg/100 g, 1,134.89 mg/100 g, 101.56 mg/100 g, and 201.28 mg/100 g, respectively. The amounts of Ca, K, Mg, and Na in the functional porridge at this optimal point were 27.66 mg/100 g, 131.32 mg/100 g, 19.57 mg/100 g, and 3.59 mg/100 g, respectively. Overall, this functional porridge can help reduce hypertension.

• Tuberculosis and Respiratory Diseases
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• v.43 no.5
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• pp.728-735
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• 1996

Background : Recognition and ingestion of opsonized microorganisms by neutrophils induces the burst of oxidative metabolic activity. Products of the respiratory burst activity provide powerful oxygen dependent killing mechanism. Measurement of respiratory burst activity has been a major indicator of the functional capacity of neutrophils. We determined the respiratory burst activity of neutrophils in patients with pneumonia and observed the changes during the clinical course of pneumonia. Methods: The EDTA blood was drawn from 24 normal controls and same numbers of pneumonia patients. The respiratory burst activity(with the production of $H_2O_2$ which changes nonfluorescent DCF-DA to green fluorescent DCF) in the non-stimulated state and the stimulated state with fMLP and PMA of neutrophils was measured by flowcytometry at day 1, 3, 5, 7 and 9 of admission. Results: The respiratory burst activity of neutrophils was mildly increased by stimulation with fMLP. But there was no statistical significance between normal control and patients with pneumonia. The respiratory burst activity of neutrophils was markedly increased by stimulation with PMA in both groups. There was a significant difference in response to PMA between normal control and patients with pneumonia. The production of hydrogen peroxide from neutrophils was decreased during early course of pneumonia and it was recuperated gradually to normal level in 9 days. Conclusion : Hydrogen peroxide production from neutrophils was suppressed during early course of pneumonia and restored after treatment. It is suggested that the production of oxygen radical in response to PMA stimulation from each neutrophils is decreased rather than increased during the early course of pneumonia.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.699-709
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• 2017

This study was performed to develop and evaluate functional prepared with optimum mixing of mulberry leaf and fruit powder using response surface method (RSM). In order to develop the optimized functional Nokdujukr using RSM, mulberry leaf powder (MLP:X1) and mulberry fruit powder (MLF:X2) were set as independent variables, and pH (Y1), sweetness (Y2), viscosity (Y3), L (Y4), a (Y5), b (Y6), color (Y7), flavor (Y8), taste (Y9), overall quality (Y10), TPC (Y11), and DPPH radical scavenging ability ($IC_{50}$)(Y12) were set as dependent variables. The optimum mixing ratio of MLP and MLF was determined to be 3.88 g of MLP and 6 g of MLF. The values of color, flavor, taste, overall quality, TPC, and DPPH radical scavenging ability ($IC_{50}$) of optimized Nokdujuk were 5.20, 5.85, 6.00, 6.22, 330.99 mg TAE/g and 650.10 g/mL, respectively. In conclusion, this study has led to the development of an improved version of Nokdujuk that has antioxidative properties and good sensory evaluation and, will likely serve as a functional meal replacement for the busy modern world.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.40-46
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• 2010

In this paper, we use the Raman spectra measured from platelet to the diagnosis of Alzheimer's disease(AD). The Raman spectra used in the experiments were preprocessed with the following method and then fed into the classifier. The first step of the preprocessing is a simple smoothing followed by background elimination to the original spectra to make it easy to measure the intensity of the peaks. The last step of the preprocessing was peak alignment with the reference peak. After the inspection of the preprocessed spectra, we found that proportion of two peak intensity at 743 and 757 $cm^{-1}$ and peak intensity at 1658 $cm^{-1}$ are the most discriminative features. Then we apply mapstd method for normalization. The method returned data with means to 0 and deviation to 1. With these two features, the classification result involving 278 spectra showed about 95.5% true classification in case of MLP(multi-layer perceptron). It means that the Raman spectra measured from platelet would be effectively used to the diagnosis of Alzheimer's disease.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.