• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3717-3723
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• 2022

For the first time, two-dimensional temporal behavior of the edge localized mode (ELM) filament is measured in the edge tokamak plasma with a multi-channel electrical probe array (MCEP). MCEP, which has 16 floating probes (4 × 4), is mounted at the far scrape-off layer (SOL) region in the KSTAR. An electron temperature and an ion flux are measured by sideband method (SBM), which can achieve two-dimensional measurements with high time resolution. Furthermore, temporal evolutions of the electron temperature and the ion flux are obtained during the ELM occurrence. In the H-mode period, short spikes from ELM bursts are observed in measured plasma parameters, and the trend is similar to that of typical Hα signal. Interestingly, when blob-like ELM filaments crash the probe, the heat flux is significantly higher in a local region of the probe array. The results show that our probe array using the SBM can measure the ELM behavior and the plasma parameters without the effect of the stray current caused by the huge device. This study can provide valuable data needed to understand the interaction between the SOL plasma and the plasma facing components (PFCs).

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.118-127
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• 2016

An improved sample balanced genetic algorithm and Extreme Learning Machine (iSBGA-ELM) was designed for accurate diagnosis of Alzheimer disease (AD) and identification of biomarkers associated with AD in this paper. The proposed AD diagnosis approach uses a set of magnetic resonance imaging scans in Open Access Series of Imaging Studies (OASIS) public database to build an efficient AD classifier. The approach contains two steps: "voxels selection" based on an iSBGA and "AD classification" based on the ELM. In the first step, the proposed iSBGA searches for a robust subset of voxels with promising properties for further AD diagnosis. The robust subset of voxels chosen by iSBGA is then used to build an AD classifier based on the ELM. A robust subset of voxels keeps a high generalization performance of AD classification in various scenarios and highlights the importance of the chosen voxels for AD research. The AD classifier with maximum classification accuracy is created using an optimal subset of robust voxels. It represents the final AD diagnosis approach. Experiments with the proposed iSBGA-ELM using OASIS data set showed an average testing accuracy of 87%. Experiments clearly indicated the proposed iSBGA-ELM was efficient for AD diagnosis. It showed improvements over existing techniques.

• Structural Engineering and Mechanics
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• v.71 no.6
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• pp.739-749
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• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1728-1729
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• 2011

본 연구에서는 안정적인 물 공급과 에너지의 효율적 사용을 위한 단기 물 수요예측알고리즘 개발에 있어서, 지방 소도시 지역의 물 공급패턴에 대한 영향인자를 도출하기 위하여 기상환경인자와 과거 물 공급량에 대한 상관성 분석을 실시하였다. 그리고, 신경회로망 이론 중 ELM알고리즘을 적용한 단기 물 수요예측알고리즘을 개발하여 현장 적용성을 검토하고자 한다.

• Mycobiology
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• v.40 no.2
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• pp.94-99
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• 2012

During an investigation of fungi from an elm tree infested with bark beetles in Korea, one isolate, DUCC401, was isolated from elm wood. Based on morphological characteristics and phylogenetic analysis of the internal transcribed spacer and 28S rDNA (large subunit) sequences, the isolate, DUCC401, was identified as Mariannaea samuelsii. Mycelia of the fungus grew faster on malt extract agar than on potato dextrose agar and oatmeal agar media. Temperature and pH for optimal growth of fungal mycelia were 25oC and pH 7.0, respectively. The fungus demonstrated the capacity to degrade cellobiose, starch, and xylan. This is the first report on isolation of Mariannaea samuelsii in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.481-486
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• 2002

In this paper, we propose CELM(Cascade ELM) to improve stability. We could realize as cascade connected each other key value with N degree equation which has a initial value. And we could know to be improved in stability with the nature of Chaos in simulation result. In efficiency, this CELM algorithm identified sire of encrypted code with size of source code and we could know more efficient than existing RSA and ECC. In speed, CELM took average 0.18㎳ degree to encrypt a file. Although it was slower than DES, it was faster than ECC of RSA.

• Korean Journal of Microbiology
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• v.7 no.3
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• pp.91-106
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• 1969

From a review of the literature it was found that 96 species of fungi have been reported as occurring on the wood of Ulmus americana L., the Amercan elm. In an intensive study of the fungi growing on one American elm log, 60 species were found. Only one had been reported previously on American elm. A second fungus proved to be a hyperparasite of a slime mold. Three members of the Fungi Imperfecti could not be identified and is believed that they may constitute new taxa. In the past, Nasidiomycetes constituted the main group of fungi on American elm wood according to the literature. The Fungi Imperfecti were the largest group in this study in that over half of the species found are imperfect fungi. All of the species encountered in the study were illustrated.