• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.29-34
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• 2012

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop a load modeling method based on Extreme Learning Machine(ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate active and reactive powers than conventional methods.

• Journal of the Korean Wood Science and Technology
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• v.38 no.5
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• pp.385-390
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• 2010

This study was carried out to manufacture very thin natural elm veneer overlaid charcoal board for enhancing aesthetic value of charcoal board for the indoor application, and to use the advantageous properties of the charcoal as a building material for solving the sick house problem. The thin elm veneer had 26.9% opening ratio. The experiment results showed that the spreading area and the nonvolatile content of adhesive did not affect the gas adsoprtion of fancy veneer overlaid charcoal board. The natural thin elm veneer overlaid charcoal board enhanced not only the aesthetic beauty but also showed the same gas adsorption by the charcoal board.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.228-233
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• 2012

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for monitoring of power transformer by FCM(Fuzzy c-means) and ELM(Extreme Learning Machine). The proposed technique make it possible to diagnosis the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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• pp.204-209
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• 2016

It is difficult to establish accurate modeling of PV power system because of various uncertainty. However, it is important work to modeling of PV for fault diagnosis. This paper proposes modeling and fault diagnosis method using correlation analysis and ELM(Extreme Learning Machine). Rather than using total data, we select optimal time interval with higher corelation between PV power and solar irradiation. Also, we use average value during 60 minute to avoid rapid variation of PV power. To show the effectiveness of the proposed method, we performed various experiments by dataset.

• Earthquakes and Structures
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• v.22 no.4
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• pp.401-419
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• 2022

A novel hybrid extreme machine learning-multiverse optimizer (ELM-MVO) framework is proposed to predict the liquefaction phenomenon in seismically excited tunnel lining inside the sand lens. The MVO is applied to optimize the input weights and biases of the ELM algorithm to improve its efficiency. The tunnel located inside the liquefied sand lens is also evaluated under various near- and far-field earthquakes. The results demonstrate the superiority of the proposed method to predict the liquefaction event against the conventional extreme machine learning (ELM) and artificial neural network (ANN) algorithms. The outcomes also indicate that the possibility of liquefaction in sand lenses under far-field seismic excitations is much less than the near-field excitations, even with a small magnitude. Hence, tunnels designed in geographical areas where seismic excitations are more likely to be generated in the near area should be specially prepared. The sand lens around the tunnel also has larger settlements due to liquefaction.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.5
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• pp.38-47
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• 1999

We have designed a 16bit adder which reduces the power consumption at each level of architecture, logic and transistor. The conventional ELM adder has a major disadvantage which makes glitch in the G cell when the particular input bit patterns are applied, because of the block carry generation signal computed by the input bit pattern. Thus, we propose a low power adder architecture which can automatically transfer each block carry generation signal to the G cell of the last level to avoid glitches for particular input bit patterns at the architecture level. We also use a combination of logic styles which is suitable for low power consumption with static CMOS and low power XOR gate at the logic level. Futhermore, The variable-sized cells are used for reduction of power consumption according to the logic depth of the bit propagation at the transistor level. As a result of HSPICE simulation with $0.6\mu\textrm{m}$ single-poly triple-metal LG CMOS standard process parameter, the proposed adder is superior to the conventional ELM architecture with fixed-sized cell and fully static CMOS by 23.6% in power consumption, 22.6% in power-delay-product, respectively.

• Water for future
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• v.28 no.4
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• pp.125-136
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• 1995

ELM, a characteristic line based method, was applied to advection-dispersion equation, and the results obtained were compared with those of Eulerian schemes(Stone-Brian and QUICKEST). The calculation methods consisted of Lagrangian interpolation scheme and cubic spline interpolation scheme for the advection calculation, and the Crank-Nicholson scheme for the dispersion calculation. The results of numerical methods were as follows: (1) for Gaussian hill: ELM, using Lagrangian interpolation scheme, gave the most accurate computational result, ELM, using cubic spline interpolation scheme, and QUICKEST scheme gave numerical damping for Peclet number 50. Stone-Brian scheme gave phase shift introduced in the numerical solution for Peclet number 10 and 50. (2) for advanced front: All schemes gave accurate computational results for Peclet number 1 and 4. ELM, Lagrangian interpolation scheme, and Stone,Brian scheme gave dissipation error and ELM, using cubic spline interpolation scheme, and QUICKEST scheme gave numerical oscillation for Peclet number 50.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.565-579
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• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.