• Nuclear Engineering and Technology
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• 제54권10호
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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).

• 한국음향학회지
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• 제28권1호
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• pp.32-43
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• 2009

본 논문은 자동 분할과 extreme learning machine (ELM)을 이용하여 연속 심음신호에 의한 심장질환 분류의 성능을 개선한다. 자동 분할을 위한 전처리 단계에서 비정상적인 심음신호는 심잡음 (murmur)과 클릭음 (click)을 포함하고 있기 때문에 제1음 (S1)과 제2음 (S2) 시작점 검출 결과가 부정확하거나 누락되어 기존의 심장질환 분류 시스템의 정확도를 저하시키게된다. 이러한 분할 오류에 의한 성능 저하를 감소하기 위해 S1 및 S2의 위치를 찾고, S1 및 S2의 시간 차이를 이용하여 부정확한 시작점을 교정한 다음 한 주기 심음 신호를 추출한다. 특징벡터로는 단일 주기의 심음 신호로부터 추출된 멜척도 필터뱅크 로그 에너지 계수와 포락선을 사용한다. 심장질환을 분류하기 위하여 한 개의 은닉층을 가진 ELM 알고리듬을 사용한다. 9가지 심장질환 분류 실험을 수행한 결과, 제안 방법은 81.6%의 분류 정확도를 나타내며, multi-layer perceptron(MLP), support vector machine (SVM), hidden Markov model (HMM) 중에서 가장 높은 분류 정확도를 보여준다.

• 한국지능시스템학회논문지
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• 제17권6호
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• pp.807-812
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• 2007

최근 단일 은닉층을 갖는 전방향 신경회로망 구조로, 기존의 경사 기반 학습알고리즘들보다 학습 속도가 매우 우수한 ELM(Extreme Learning Machine)이 제안되었다. ELM 알고리즘은 입력 가중치들과 은닉 바이어스들의 초기 값을 무작위로 선택하고 출력 가중치들은 Moore-Penrose(MP) 일반화된 역행렬 방법을 통하여 구해진다. 그러나 입력 가중치들과 은닉층 바이어스들의 초기 값 선택이 어렵다는 단점을 갖고 있다. 본 논문에서는 최적화 알고리즘 중 박테리아 생존(Bacterial Foraging) 알고리즘의 수정된 구조를 이용하여 ELM의 초기 입력 가중치들과 은닉층 바이어스들을 선택하는 개선된 방법을 제안하였다. 실험을 통하여 제안된 알고리즘이 많은 입력 데이터를 가지는 문제들에 대하여 성능이 우수함을 보였다.

• Journal of Computing Science and Engineering
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• 제10권4호
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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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• 제71권6호
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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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• 제22권2호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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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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• 제40권2호
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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.

• 한국정보통신학회논문지
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• 제6권3호
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• pp.481-486
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• 2002

본 논문에서는 카오스에 기반을 툰 ELM(Expanding Logistic Map) 암호화 알고리즘을 개선하기 위해 CELM(Cascade ELM)을 제안한다. 제안된 암호화 알고리즘은 3차 방정식에 기반을 둔 ELM 의 차수를 증가시켜 키의 범위를 확대하고, 서로 다른 Key 값과 초기 값의 함수를 Cascade 연결한 것으로 시뮬레이션결과 키의 랜덤성이 보장되면서 안정성이 국제 기준에 부합됨을 알 수 있었다.

• 미생물학회지
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• 제7권3호
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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.