• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.587-594
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• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• ETRI Journal
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• v.26 no.6
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• pp.653-656
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• 2004

This paper presents an effective algorithm for inserting an MPEG-2 transport stream (TS) into a Digital Audio Broadcasting (DAB) ensemble without any bandwidth waste in a Digital Multimedia Broadcasting (DMB) transmission system. The key technologies of this algorithm include packet rate control and program clock reference correction, which are important for TS processing. The proposed algorithms are applied to the various DMB transmission systems based on Eureka-147, and the performance of the proposed algorithm is confirmed through the experimental DMB broadcasting.

• ETRI Journal
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• v.32 no.5
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• pp.766-773
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• 2010

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.7
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• pp.595-601
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• 2001

We applied PIV method to obtain instantaneous and ensemble averaged velocity fields from the first row to the fifth row of a staggered tube bundle. The Reynolds number based on the tube diameter and the maximum velocity was set to be 4,000. Remarkably different natures are observed in the developing bundle flow. Such differences are depicted in the mean recirculating bubble length and the vorticity distributions. The jet-like flow seems to be a dominant feature after the second row and usually skew. However, the ensemble averaged fields show symmetric profiles and the flow characteristics between the third and fourth measuring planes are not so different. comparison between the PIV data and the RANS simulation yields severe disagreement in spite of the same Reynolds number. It can be explained that the distinct jet-like unsteady motions are not to be accounted in th steady numerical analysis.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• The International Journal of Costume Culture
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• v.4 no.1
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• pp.9-17
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• 2001

The purposes of the study are to find out the effect of clothing colors on perception of stimulus ages, occupations and images using stereotyping and color vision theories. The research method is a qualitative study and materials developed for the study are a set of stimuli and open-ended responses. The subjects were 1138 undergraduate students in Taejoen city, Chungbuk province. The data is analyze using content analysis, supplementary frequency and χ²analysis. The results are as follows : 1) The colors in Korean traditional costumes affected on the wearer's age perception : The red ensemble give the wearer the youngest look while the gray give the oldest look. 2) Mono-color ensemble wearers tend to give older look than bi-color ensemble wearers. 3) The chima colors and the jogori colors have similar impact on the wearer's age perception. 4) On image perception the jogori colors have more impact than the chima colors. 5) The colors in Korean traditional costumes are the clues to estimate the wearer's occupation.

• IE interfaces
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• v.25 no.2
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• pp.178-186
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• 2012

The DRAM module is an important part of servers, workstations and personal computer. Its malfunction causes a lot of damage on customer system. Therefore, customers demand the highest quality products. The company applies DRAM module Outgoing Quality Assurance Inspection(OQA) to secures the highest quality. It is the key process to decides shipment of products through sample inspection method with customer oriented tests. High fraction of defectives entering to OQA causes inevitable high quality cost. This article proposes the application of ensemble learning to classify the lot status to minimize the ratio of wrong decision in OQA, observing a potential in reducing the wrong decision.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• Advances in nano research
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• v.1 no.3
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• pp.153-158
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• 2013

An approach to achieving of spatially homogeneous, ordered ensemble of semiconductor quantum rods in polymer film of polyvinyl butyral is reported. The CdSe/ZnS quantum rods are embedded to the polymer film. Obtained film is stretched up to four times to its initial length. A concentration of quantum rods in the samples is around $2{\times}10^{-5}$ M. The absorption spectra, obtained in the light with orthogonal polarization, confirm the occurrence of spatial ordering in a quantum rod ensemble. Anisotropy of the optical properties in the ordered quantum rod ensemble is examined. The presented method can be used as a low-cost solution for preparing the nanostructured materials with anisotropic properties and high concentration of nanocrystals.