• The Journal of the Acoustical Society of Korea
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• v.31 no.3
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• pp.188-196
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• 2012

In this paper, we propose a new approach to speaker identification technique which uses an ensemble of multiple classifiers (speaker identifiers). KPCA (kernel principal component analysis) enhances features for each classifier. To reduce the processing time and memory requirements, we select limited number of samples randomly which are used as estimation set for each KPCA basis. The experimental result shows that the proposed approach gives a higher identification accuracy than GKPCA (greedy kernel principal component analysis).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1630-1638
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• 2015

The finite element analysis has become an essential process to investigate the structural performance in many industry fields. In addition, the computer's performance is improving rapidly, but in large design problems, there is a limit to apply the optimal design techniques. For this, it is general to introduce a metamodel based optimization technique. The method to generate an approximate model can be classified into curve fitting and interpolation, and each representative one is response surface model and kriging interpolation method. This study proposes an ensemble model made of RSM and kriging to solve a structural design problem. The suggested method is applied to the designs of two bar and automobile outer tie rod.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.388-393
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• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.263-269
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• 2016

The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1033-1042
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• 2023

This paper proposes a network intrusion detection system that identifies abnormal flows within the network. The majority of datasets commonly used in research lack time-series information, making it challenging to improve detection rates for attacks with fewer instances due to a scarcity of sample data. However, there is insufficient research regarding detection approaches. In this study, we build upon previous research by using the Artificial neural network(ANN) model and a stack ensemble technique in our approach. To address the aforementioned issues, we incorporate temporal information by leveraging adjacent flows and enhance the learning of samples from sparse attacks, thereby improving both the overall detection rate and the detection rate for sparse attacks.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1169-1175
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• 2008

The validation technique is classified with two methods whether to demand of additional experimental points. The method which requires additional experimental points such as RSME is actually impossible in engineering field. Therefore, the method which only use experimented points such as the cross validation technique is only available. But the cross validation not only requires considerable computational costs for generating metamodel each iterations, but also cannot measure quantitatively the fidelity of metamodel. In this research we propose a new validation technique for representative metamodels using an variance of metamodel and confidence interval information. The proposed validation technique computes confidence intervals using a variance information from the metamodel. This technique will have influence on choosing the accurate metamodel, constructing ensemble of each metamodels and advancing effectively sequential sampling technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.433-438
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• 2003

Recently, some studies of a web-based information recommendation technique which provides users with the most necessary information through websites like a web-based shopping mall have been conducted vigorously. In most cases of web information recommendation techniques which rely on a user profile and a specific feedback from users, they require accurate and diverse profile information of users. However, in reality, it is quite difficult to acquire this related information. This paper is aimed to suggest an information prediction technique for a web information service without depending on the users'specific feedback and profile. To achieve this goal, this study is to design and implement a Dynamic Web Information Prediction System which can recommend the most useful and necessary information to users from a large volume of web data by designing and embodying Ensemble Support Vector Machine and hybrid SOM algorithm and eliminating the scarcity problem of web log data.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices