• Journal of Information Technology Applications and Management
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• 제16권4호
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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.

• Steel and Composite Structures
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• 제41권6호
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• 전기학회논문지
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• 제56권7호
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• pp.1324-1330
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• 2007

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

• 한국의류학회지
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• 제47권2호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Geomechanics and Engineering
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• 제31권2호
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Asia Marketing Journal
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• 제22권3호
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• pp.87-105
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• 2020

With the development of Information Technology, customers require promptly higher quality products and services. Companies try to make newly digital marketing strategies, but there are no empirical researches on them. This article empirically presents a new perspective that companies can shape the customer decision journey ahead by coordinating customer experience. In this article, based on Elaborated Likelihood Model (ELM) theory, customer experience consists of the emotional or cognitive experience. We surveyed about 200 subjects (N = 217) in their 20s and 30s based on the International Music Industry Association's Music Listening 2019 report, then analyzed four different models (before personalization-cognitive experience, before personalization-emotional experience, after personalization- cognitive experience, after personalization-emotional experience) by JASP and R Studio. We conducted Structural Equation Model (SEM) and paired t-test. Personalization factors are about recommendation systems in Spotify. The results of survey represent that companies can shape the Customer Decision Journey (CDJ) ahead especially through enhance cognitive experience. It empirically proves Elaborated Likelihood Model (ELM). The conclusion can be drawn that 'pulling' customer experience can be a new marketing strategies in the digital era.

• Asia pacific journal of information systems
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• 제18권3호
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• pp.147-165
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• 2008

IT education can be summarized as persuading the educatee to accept IT. The persuasion is made by delivering the messages for how-to-use and where-to-use to the educatee, which leads formulation of a belief structure for using IT. Therefore, message based persuasion theory, as well as IT acceptance theories such as technology acceptance model(TAM), would play a very important role for explaining IT education. According to elaboration likelihood model(ELM) that has been considered as one of the most influential persuasion theories, people change attitude or perception by two routes, central route and peripheral route. In central route, people would think critically about issue-related arguments in an informational message. In peripheral route, subjects rely on cues regarding the target behavior with less cognitive efforts. Moreover, such persuasion process is not a one-shot program but continuous repetition with feedbacks, which leads to changing a belief structure for using IT. An educatee would get more knowledge and experiences of using IT as following an education program, and be more dependent on a central route than a peripheral route. Such change would reformulate a belief structure which is different from the intial one. The objectives of this study are the following two: First, an identification of the relationship between ELM and belief structures for using IT. Especially, we analyze the effects of message interpretation through both of central and peripheral routes on perceived usefulness which is an important explaining variable in TAM and perceived use control which have perceived ease of use and perceived controllability as sub-dimensions. Second, a longitudinal analysis of the above effects. In other words, change of the relationship between interpretation of message delivered by IT education and beliefs of IT using is analyzed longitudinally. For achievement of our objectives, we suggest a research model, which is constructed as three-layered. While first layer has a dependent variable, use intention, second one has perceived usefulness and perceived use control that has two sub-concepts, perceived ease of use and perceived controllability. Finally, third one is related with two routes in ELM, source credibility and argument quality which are operationalization of peripheral route and central route respectively. By these variables, we suggest five hypotheses. In addition to relationship among variables, we suggest two additional hypotheses, moderation effects of time in the relationships between perceived usefulness and two routes. That is, source credibility's influence on perceived usefulness is decreased as time flows, and argument quality's influence is increased. For validation of it, our research model is tested empirically. With measurements which have been validated in the other studies, we survey students in an Excel class two times for longitudinal analysis. Data Analysis is done by partial least square(PLS), which is known as an appropriate approach for multi-group comparison analysis with a small sized sample as like this study. In result. all hypotheses are statistically supported. One of theoretical contributions in this study is an analysis of IT education based on ELM and TAM which are considered as important theories in psychology and IS theories respectively. A longitudinal analysis by comparison between two surveys based on PLS is also considered as a methodological contribution. In practice, finding the importance of peripheral route in early stage of IT education should be notable.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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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.

• 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.