• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.344-355
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, Multilayer Perceptron (MLP) has been used in wide applications. But, it is known that Error Backpropagation (EBP) algorithm which MLP uses in learning has a defect that requires relatively long leaning time. Because learning data in pattern recognition contain abundant redundancies, in order to increase learning speed it is very effective to use online-based teaming methods, which update parameters of MLP pattern by pattern. Typical online EBP algorithm applies fixed learning rate for each update of parameters. Though a large amount of speedup with online EBP can be obtained by choosing an appropriate fixed rate, fixing the rate leads to the problem that the algorithm cannot respond effectively to different leaning phases as the phases change and the learning pattern areas vary. To solve this problem, this paper defines learning as three phases and proposes a Instant Learning by Varying Rate and Skipping (ILVRS) method to reflect only necessary patterns when learning phases change. The basic concept of ILVRS is as follows. To discriminate and use necessary patterns which change as learning proceeds, (1) ILVRS uses a variable learning rate which is an error calculated from each pattern and is suppressed within a proper range, and (2) ILVRS bypasses unnecessary patterns in loaming phases. In this paper, an experimentation is conducted for speaker verification as an application of pattern recognition, and the results are presented to verify the performance of ILVRS.

• Journal of the Korean Geotechnical Society
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• v.38 no.8
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• pp.39-52
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• 2022

Multilayer perceptron neural network was trained to determine the factor of safety and slip surface of the slope. Slope geometry is a simple slope based on Korean design standards, and the case of dry and existing groundwater levels are both considered, and the properties of the soil composing the slope are considered to be sandy soil including fine particles. When curating the data required for model training, slope stability analysis was performed in 42,000 cases using the limit equilibrium method. Steady-state seepage analysis of groundwater was also performed, and the results generated were applied to slope stability analysis. Results show that the multilayer perceptron model can predict the factor of safety and failure arc with high performance when the slope's physical properties data are input. A method for quantitative validation of the model performance is presented.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.85-94
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• 2018

The change of land covers in 2026 was prediceted based on the change of urbanization in 1996, 2006 and 2016 in Seoul and surrounding areas in this study. Landsat images were used as the basic data, and MLP (Multilayer Perceptron) and MCA (Markov Chain Analysis) were integrated for future prediction for the study area. The land cover transition potentials were calculated by setting up sub-models in MLP and the driving factors of land cover transition from 1996 to 2006 and transition probabilities were calculated using MCA to generate the land cover map of 2016. This was compared to the land cover map of 2016 from Landsat. MLP and MCA were verified and the future land covers of 2026 were predicted using the land cover map from Landsat in 2006 and 2016. As a result, it was predicted that the major land cover changes from 1996 to 2006 were from Barren Land and Grass Land to Builtup Area, and the same trend of transition will be remained for 2026. This study is meaningful in that it is applied for the first time to predict the future coating change in Seoul and surrounding areas by the MLP-MCA method.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.555-567
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• 2022

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.

• 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 Sensor Science and Technology
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• v.11 no.4
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• pp.239-246
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• 2002

In this paper a new design of multi-layer perceptron(MLP) for camera calibration is proposed. Most existing techniques determine a transformation from 3D spatial points to their image points and camera parameters are tried to be estimated from the transformation. The technique proposed here, on the other hand, determines rays of sight uniquely from image points and parameters are estimated from the relationship using an MLP. By this approach projection and back-projection can be done more straightforwardly. Being based on a geometric model, a network design process becomes less ambiguous, which is a clear merit compared to other neural net based techniques. An MLP designed according to the technique proposed showed fast and stable learning in tests under various conditions.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.