• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.574-585
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• 2019

The acceleration of digital transformation (DX) has resulted in SNS influencer marketing trends becoming the mainstream of the new market. SNS influencers act as early adopters in the process of new products being accepted. Chinese consumers are most affected by Hallyu, which increases interest in K-beauty products. This study analyzes how K-beauty SNS influencers are related to Chinese consumers. A survey was conducted among Chinese millennial consumers after watching videos provided by K-beauty SNS influencer; subsequently, 456 responses were used for data analysis. As a result, the analysis based on the Elaboration Likelihood Model (ELM) distinguishes the central route and the peripheral route in the process of Chinese consumers accepting new SNS products. The study findings suggested that information quality, credibility, accuracy, and usefulness had significant effects on acceptance intention for new products among central route factors, and similarity, trustworthiness, pleasure, expertise, and attractiveness also had significant effects on the acceptance intention of new product among peripheral route factors. It was found that variables of the central route, rather than those of peripheral route, had stronger effects on the acceptance intention for new products. Consequently, the central route of K-beauty SNS influencer is more important to Chinese consumers' acceptance of new products. It is expected that this study will offer beauty influencer marketing-based cosmetics brands efficient consumer management suggestions.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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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.

• 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.

• Knowledge Management Research
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• v.14 no.4
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• pp.1-17
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• 2013

As online communities proliferate, online news sites have received great attention in news media research. Although most of the online news sites provide contents for free, some have adopted the Pay-What-You-Want (PWYW) model by offering a voluntary payment option to the readers. In this study, we investigate the factors which influence subscribers' voluntary payment behavior on an online news site. Drawing upon both the Stimulus-Organism-Response (SOR) framework and the Elaboration Likelihood Model (ELM), we hypothesize that appreciation has a direct effect on the subscribers' voluntary payment behavior, whereas central factors (positive emotional content, cognitive content) and peripheral factors (news sharing, news article length) of the news articles have indirect impacts on voluntary payment behavior through the enhanced appreciation. Based on an empirical analysis of 172 news articles from the Korean online news site that adopted the PWYW pricing model (i.e., Ohmynews.com), we find that appreciation plays a critical role in voluntary payment behavior and that peripheral factors have significant impacts on appreciation. However, the impacts of central factors on appreciation are not found. By identifying influencing factors of subscribers' voluntary payment behavior on online news sites for the first time, this paper suggests a prospective alternative profit model for online news providers faced with fierce competition.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.217-236
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• 2015

The purpose of this study is to propose the effective location-based IoT service acceptance model by integrating ELM (Elaboration Likelihood Model) with UTAUT (Unified Theory of Acceptance and Use of Technology). The Partial Least Squares was used to analyze the causal relationships with respect to the effects of central route and peripheral route on acceptance intention. The results shows that central route has more significant impacts on perceived usefulness than peripheral route and CFIP (Concern for Information Privacy) weaken the relationship of acceptance intention and perceived usefulness. Our findings indicate some meaningful implications in the acceptance research of IOT services. First, we noted that the easy of use significantly affects the adoption of location-based IoT service. Furthermore, it is important to build the secured mechanism of privacy protection to adopt of location-based IoT service. Second, we tried to attempt the newly integrated approach to technical acceptance using UTAUT's variables and ELM by Petty and Cacioppo (1986). Finally this research empirically analyzed the adoption case of location-based IoT service which is not well-known yet within our country.

• Asia pacific journal of information systems
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• v.24 no.1
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• pp.65-91
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• 2014

Our study adopted ELM (Elaboration Likelihood Model) to measure the impact of central and peripheral cues on e-healthcare website behavior and its consequence on perceived loyalty of users. While most of ELM studies did not elaborate the antecedent of both central and peripheral cues, we measured the antecedents of those information processing routes to clarify how technical and quality factors (i.e. information organization, security concern, and website attractiveness) develop the nature of either central or peripheral route. We found that information organization was the main antecedent of information quality presented on the website. Second, the results revealed that website security has a positive effect on website credibility. Third, we also found that website attractiveness was positively associated with website credibility. Fourth, consistent with elaboration likelihood model, the empirical findings suggested that information quality (central cue) and website credibility (peripheral cue) were strong predictors of behavior intention to use health website. Our findings also suggested that behavior intention to use health website significantly influenced perceived loyalty.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• 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.

• Asian Journal of Innovation and Policy
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• v.4 no.3
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• pp.397-409
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• 2015

Nowadays, crowdfunding presents a promising development. This research focuses on the influence of platform strategy and market response on the success of crowdfunding from the perspective of the elaboration likelihood model (ELM) theory. Detailed product specifications, crowdfunding difficulty coefficient, vivid advertising video such as introduction and music, and recommendations from relevant figures are all used to depict platform strategy. Meanwhile, we use the number of lovers, followers, comments and 1 RMB backers to measure the level of market response. And thus, we model the impact of platform strategy and market response on crowdfunding success with empirical studies based on 400 samples of observed value. We found firstly that there exist significant positive relations between the total amount of funds pledged and detailed product specification, vivid advertising video, recommendations from relevant figures and the number of 1 RMB backers. Secondly, the crowdfunding difficulty of projects affects negatively, and significantly, the total amount of funds pledged. Thirdly, the influence of the number of lovers and followers on funds pledged is not significant.