• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.125-130
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• 2016

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.1-8
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• 1998

In this paper, we designed a low power 8bit ELM adder with static CMOS and hybrid logic styles on a chip. The designed 8bit ELM adder with both logic styles was fabricated in a 0.8$\mu\textrm{m}$ single-poly double-metal, LG CMOS process and tested. Hybrid logic style consists of CCPL(Combinative Complementary Pass-transistor Logic), Wang's XOR gate and static CMOS for critical path which determines the speed of ELM adder. As a result of chip test, the ELM adder with hybrid logic style is superior to the one with static CMOS by 9.29% in power consumption, 14.9% in delay time and 22.8% in PDP(Power Delay Product) at 5.0V supply voltage, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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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.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.81-88
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• 2008

It is important to study the energy efficient MAC protocol in sensor networks. We propose a new protocol named as ELM?MAC (Energy efficient Link Management MAC) to increase energy efficiency in sensor networks. ELM-MAC protocol operates, uses, and manages the optimized transmission power level to increase energy efficiency in MAC layer. It includes mechanism that uses the adaptive method in change of surround environment for guarantee of link quality. In this paper we implement ELM-MAC and evaluate its performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.108-116
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• 2009

In this paper, we develop an ELM(Extreme Learning Machine) based short-tenn water demand prediction algorithm which solves overfitting problem of MLP(Multi Layer Perceptron) and has quick training time. To show effectiveness of proposed method, we analyzed time series data collected in A water treatment plant at Chung-Nam province during $2007{\sim}2008$ years and used the selected data for the verification of developed algorithm. According to the experimental results, MLP model showed 5.82[%], but the proposed ELM based model showed 5.61[%] with respect to MAPE, respectively. Also, MLP model needed 7.57s training time, but ELM based model was 0.09s. Therefore, the proposed ELM based short-term water demand prediction model can be used to operate the water treatment plant effectively.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.33-41
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• 1998

We have designed a high speed and low power 16bit-ELM adder with variable-sized cells uitlizing the fact that the logic depth of lower bit position is less than that of the higher bit position int he conventional ELM architecture. As a result of HSPICE simulation with 0.8.mu.m single-poly double-metal LG CMOS process parameter, out 16bit-ELM adder with variable-sized cells shows the reduction of power-delay-product, which is less than that of the conventional 16bit-ELM adder with reference-sized cells by 19.3%. We optimized the desin with various logic styles including static CMOs, pass-transistor logic and Wang's XOR/XNOR gate. Maximum delay path of an ELM adder depends on the implementation method of S cells and their logic style.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.993-1002
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• 2016

Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.164-168
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• 2015

Due to the uncertainty of weather, it is difficult to construct an accurate forecasting model for daily PV power generation. It is very important work to know PV power in next day to manage power system. In this paper, correlation analysis between weather and power generation was carried out and daily PV power forecasting models based on Extreme Learning Machine(ELM) was presented. Performance of district ELM model was compared with single ELM model. The proposed method has been tested using actual data set measured in 2014.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.7
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• pp.951-957
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• 2015

Four rumen fistulated swamp buffaloes were randomly assigned according to a $4{\times}4$ Latin square design to investigate the effects of Eucalyptus (E. Camaldulensis) leaf meal (ELM) supplementation as a rumen enhancer on feed intake and rumen fermentation characteristics. The dietary treatments were as follows: T1 = 0 g ELM/hd/d; T2 = 40 g ELM/hd/d; T3 = 80 g ELM/hd/d; T4 = 120 g ELM/hd/d, respectively. Experimental animals were kept in individual pens and concentrate was offered at 0.3% BW while rice straw was fed ad libitum. The results revealed that voluntary feed intake and digestion coefficients of nutrients were similar among treatments. Ruminal pH, temperature and blood urea nitrogen concentrations were not affected by ELM supplementation; however, ELM supplementation resulted in lower concentration of ruminal ammonia nitrogen. Total volatile fatty acids, propionate concentration increased with the increasing level of EML (p<0.05) while the proportion of acetate was decreased (p<0.05). Methane production was linearly decreased (p<0.05) with the increasing level of ELM supplementation. Protozoa count and proteolytic bacteria population were reduced (p<0.05) while fungal zoospores and total viable bacteria, amylolytic, cellulolytic bacteria were unchanged. In addition, nitrogen utilization and microbial protein synthesis tended to increase by the dietary treatments. Based on the present findings, it is suggested that ELM could modify the rumen fermentation and is potentially used as a rumen enhancer in methane mitigation and rumen fermentation efficiency.