• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.2
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• pp.371-391
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• 2023

This study examines the impact of live commerce characteristics on customer responses in the ELM perspective. Based on ELM, the central route is composed of information completeness, accuracy, and currency, and the peripheral route is composed of streamer credibility, streamer reputation, social presence, and system quality. An online survey of female customers aged 20 to 49 who have purchased beauty products through live commerce within the past three months was conducted. The results demonstrate that information completeness and information currency exert significant impact on perceived usefulness and enjoyment. Social presence and system quality also exert significant impact on perceived usefulness and enjoyment. Moreover, perceived usefulness and enjoyment had significant impact on behavioral intention. The effect of information completeness on perceived usefulness and enjoyment was much stronger for high product involvement groups. Furthermore, the effect of streamer reputation on perceived enjoyment was much stronger for high product involvement groups. In contrast, the effect of social presence on perceived usefulness and enjoyment was much stronger for low product involvement groups. This study suggests theoretical implications for applying ELM to live commerce and practical implications for differentiated live commerce marketing strategies.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Korean Journal of Environment and Ecology
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• v.26 no.4
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• pp.588-592
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• 2012

To examine the reason of empty samara production of Chinese elm, Ulmus parviflora, twenty two planted trees in Wonju-si were monitered for three years in terms of their flowering, pollinatiom system and samara characteristics. Inflorescences with bisexual flowers of Chinese elm are developed in the leaf axils on the twigs. Dichogamous flowers are varied with protogynuous and protandrous flower, and stamens in some bisexual flowers are developed in seperated time on a inflorescence or a tree. It is revealed newly that the flower of Chinese elm is out-crossed and partially insect(Apis mellifera) pollinated. The ratios of sound samara are significantly differed among years, the heighest values 65.5% were shown in 2009, lowered 42.9% in 2010, and the lowest 37.5% in 2011. This result might be affected by mean daily precipitation and number of rainy days during the flowering date, and lower temperature during the floral initiation stage, especially in 2011. These findings suggest that Chinese elm has self-incompatibility strategy and much pollination failure resulted in a production of much empty samaras. Further researches on the empty-seed production strategies and pollination system of major tree species might be needed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.181-185
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• 2011

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 current harmonics estimation 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 current harmonics for various input voltage.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1548-1555
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• 2017

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.41-58
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• 2009

This research investigated how people are influenced to adopt online review. We applied the Elaboration Likelihood Model (ELM) and the Technology Acceptance Model (TAM) to this study. Our research model highlights the assessment of online review usefulness as a mediator from online review quality to online review adoption. This research predicted online review consensus has a role to bulid up online reviw usefulness. This study also includes vividness and perceived similarity as determinants of online review quality. Survey data reflect user's perceptions of actual online review they read. Results support most of research hypotheses except hypothesis related to moderating effect of user involvement. This research offers a model for understanding online review user's acceptance. Additional theoretical and practical implications are also discussed in the paper.

• Proceedings of the Membrane Society of Korea Conference
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• 1994.10a
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• pp.84-85
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• 1994

본 연구는 수용상에 포함된 중금속이온중 에멀젼 액막법(Emulsion Liquid Membranes, ELM)을 이용하여 납이온을 제거시키기 위한 연구이다. 지금까지 수용액상의 중금속 이온의 제거는 전통적으로 이온 침전법을 사용하여 왔다. 그러나 이 방법은 스럿지 처리문제가 남아 있고 식수로 이용되는 수처리에는 식수기준 만족도 때문데 적합하지 않았다. ELM법에 의한 금속이온 제거처리는 전기도금에 의하여 중금속이온을 회수할 수 있고 고도의 수처리를 가능케하여 최근 많은 관심을 갖고 있다. 본 연구에서는 납 이온 추출제로서 D2EPHA와 Alamine336의 이온교환제를 사용하여, 이 씨스테므이 추출 평형자료를 구하고 pH, 추출제의 농도, 교반속도, 에멀젼비율등에 의한 추출효과등을 검토하였다. 또한 2단계 추출 방법을 사용하여 금속이온추출에서 가장 큰 문제점인 유기상 용액의 leakage를 해결하고자 하였다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. 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.