• Journal of Industrial Convergence
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• v.21 no.8
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• pp.1-12
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• 2023

The purpose of the study is to analyze the impact of blended learning's external classroom formats and internal teaching strategies, which has been implemented in university classes due to COVID-19, on students' academic achievement and learners' perceptions, as well as to provide insights into the desirable direction of online education. The study was conducted during the 1st semester of 2022 at G University, targeting students taking Calculus I. The experimental group consisted of 117 students, while the control group consisted of 707 students. Blended learning, involving a combination of face-to-face classes, online classes, and mixed teaching methods, was implemented, and academic achievement and learner perceptions were assessed. The research findings indicate that compared to solely online classes, adopting a blended learning approach with online classes before the midterm and face-to-face classes afterwards resulted in a decline in academic achievement. The unprepared and simplistic external format of blended learning was found to be ineffective, however, a blended learning model consisting solely of online classes, incorporating a mix of asynchronous and synchronous instruction, demonstrated positive learner perceptions. Additionally, utilizing technology in the teaching strategies yielded positive outcome.

• Journal of Power Electronics
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• v.10 no.5
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• pp.505-517
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• 2010

In this paper, an intelligent sliding-mode speed controller for achieving favorable decoupling control and high precision speed tracking performance of permanent-magnet synchronous motor (PMSM) drives is proposed. The intelligent controller consists of a sliding-mode controller (SMC) in the speed feed-back loop in addition to an on-line trained wavelet-neural-network controller (WNNC) connected in parallel with the SMC to construct a robust wavelet-neural-network controller (RWNNC). The RWNNC combines the merits of a SMC with the robust characteristics and a WNNC, which combines artificial neural networks for their online learning ability and wavelet decomposition for its identification ability. Theoretical analyses of both SMC and WNNC speed controllers are developed. The WNN is utilized to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of a SMC. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode speed controller. An experimental system is established to verify the effectiveness of the proposed control system. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulated and experimental results confirm that the proposed RWNNC grants robust performance and precise response regardless of load disturbances and PMSM parameter uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.326-331
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• 2016

This paper presents a novel robust adaptive AC8B exciter system against synchronous generators for ships. A PID (proportional integral derivative) control framework, which is a part of the AC8B exciter system, is simply composed of nominal and auxiliary control configurations. For selecting these proper parameter values, the former is conventionally chosen based on the experience and knowledge of experts, and the latter is optimally estimated via a neural networks optimization procedure. Additionally, we propose an online parameter learning-based auxiliary control to practically cope with deterioration of control performance owing to uncertainty in electric generator systems. Such a control mechanism ensures the robustness and adaptability of an AC8B exciter to enhance control performance in real-time implementation. We carried out simulation experiments to test the reliability of the proposed robust adaptive AC8B exciter system and prove its superiority through a comparative study in which a conventional PID control-based AC8B exciter system is similarly applied to our simulation experiments under the same simulation scenarios.

• Journal of Information Science Theory and Practice
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• v.7 no.3
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• pp.6-20
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• 2019

Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.245-251
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• 2022

In the context of the pandemic, educational institutions had to ensure an instant transition to remote technological models of communication within the new conditions of the educational environment. The purpose of the academic paper lies in determining the role of the communicative model of educational transformations in the realities of (post) modernity. The research methodology is based on a survey of 120 students from 10 higher educational institutions (HEIs) of Ukraine through an online form regarding the importance of live communication during a pandemic. Results. The communicative model changed significantly during the pandemic - the interaction was mainly due to technologies. The research has identified four communication models of educational transformations under the conditions of the pandemic, depending on learning models. The first traditional model of distance learning involves distance learning; the second model involves contact remote training using remote educational technologies; the third model is blended learning, which combines remote and traditional learning formats, synchronous and asynchronous modes of interaction; the fourth model is traditional contact training. The empirical study of the effectiveness of communication models proves that live communication remains extremely important for learning and understanding of educational materials by students, and technology has provided support for such communication. Along with this, seminars and video lectures with presentations combining live communication and communication technologies are as important as digital learning tools. The most effective teaching method for mastering and memorizing educational material was a live dialogue with a teacher at seminars in ZOOM, followed by individual written assignments on the studied topic.

• Journal of Power Electronics
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• v.13 no.1
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• pp.139-160
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• 2013

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve $L_2$ tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.