• 전자공학회논문지
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• 제50권1호
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• pp.269-275
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• 2013

본 논문에서는 Extreme Learning Machine(ELM)을 이용한 자기부상시스템 모델링 기법을 제안한다. 자기부상시스템의 모델링을 위하여 일반적으로 테일러 급수를 이용한 선형화 모델이 사용되어져 왔으나, 이런 수학적 기법의 경우 자기부상시스템의 비선형 반영에 한계가 있다는 단점을 가지고 있다. 이러한 단점을 극복하기 위해 본 논문에서는 학습시간이 빠른 특성을 가진 ELM을 이용한 자기부상시스템의 모델링 기법을 제안한다. 제안된 기법은 입력 가중치들과 은닉 바이어스들의 초기값을 무작위로 선택하고 출력 가중치들은 Moore-Penrose의 일반화된 역행렬 방법을 통하여 구해진다. 실험을 통하여 제안된 알고리즘이 자기부상시스템의 모델링에서 수학적 기법에 비해 우수한 성능을 보임을 알 수 있었다.

• Environmental Engineering Research
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• 제24권3호
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• 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.

• Ocean Systems Engineering
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• 제5권1호
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.189-192
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• 2006

본 논문에서는 ELM(Extreme Learning Machine)을 이용하여 계산속도 뿐만 아니라 성능면에서도 우수한 입력 속성선택 기법을 제안한다. 일반적으로 입력 속성 선택문제는 다양한 속성들의 영향을 고려함으로써 모든 입력속성들을 평가하는데 많은 계산량이 요구되는 단점이 있다. 이러한 문제점을 개선하기 위하여 학습속도가 기존의 신경회로망에 비하여 월등히 우수한 ELM 알고리즘을 적용한다. 입력속성 선택은 ELM으로부터 산출된 출력값을 이용하여 출력 오차에 영향이 큰 속성들 순으로 순위를 결정한 후, 전방향 선택이나 후방향 선택기법을 이용하여 입력속성을 선택한다. 제안된 방법은 다양한 데이터에 적용하여 타당성을 검증한다.

• Steel and Composite Structures
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• 제31권5호
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• pp.427-435
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• 2019

The estimation of moment and rotation in steel rack connections could be significantly helpful parameters for designers and constructors in the initial designing and construction phases. Accordingly, Extreme Learning Machine (ELM) has been optimized to estimate the moment and rotation in steel rack connection based on variable input characteristics as beam depth, column thickness, connector depth, moment and loading. The prediction and estimating of ELM has been juxtaposed with genetic programming (GP) and artificial neural networks (ANNs) methods. Test outcomes have indicated a surpass in accuracy predicting and the capability of generalization in ELM approach than GP or ANN. Therefore, the application of ELM has been basically promised as an alternative way to estimate the moment and rotation of steel rack connection. Further particulars are presented in details in results and discussion.

• 한국음향학회지
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• 제29권2호
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• pp.133-140
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• 2010

연속 심음신호로부터 추출한 새로운 시간영역에서의 특징들을 추가하여 심장질환 분류의 성능을 개선한다. 기존에 사용되고 있는 켑스트럼 영역 특징인 멜주파수 켑스트럼 계수 (MFCC)에 심음 포락선, 심잡음 확률벡터, 심잡음 진폭값 변동으로 구성된 새로운 3종류의 시간영역 특징을 추가한다. 심장 질환 분류 및 검출 실험에서, 시간영역 특징의 분류 정확도에 대한 기여도를 평가하고 순차적 특징선택 방식을 이용하여 시간영역 특징을 선택한다. 선택된 특징들은 다층 퍼셉트론(MLP), support rector machine (SVM), extreme learning machine (ELM)와 같은 신경회로망 패턴 분류기에 대하여 의미있고 일관되게 분류 정확도를 개선함을 보여준다.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.