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    Robotic Kinematics Learning Guidance Based on Game Engine Simulation
    (2024-01-01)
    Saivichit, Kantinan
    ;
    Aphiratsakun, Narong
    This paper investigates the creation processes of robotic kinematics simulation software for educational guidance purposes using a game engine. The game engine, Unity3D, was chosen due to its accessibility, versatility, and built-in real-time rendering. Three functionalities were implemented to showcase common robotic kinematic concepts: Object Coordinates Simulation, Prismatic Kinematic Simulation, and Modified Denavit-Hartenberg Table Simulation. Each simulation allows users to interact with the concepts and visualize their behavior. The objective is to inform the ease of creating educational robotic simulations with free and accessible tools in details, exploring comparison of simulation to traditional lectures or videos as simulation improve the spatial understanding of robotic concepts better as they can be explicitly visualized, and they allow the student to explore and immerse in the content, increasing classroom engagement. They are also inexpensive and maintenance-free compared to practical labs, making them accessible and cost-effective educational tools.
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    Enhancing Project-Based Blended Learning (PJBBL) Model in Algorithm Design: A Comprehensive Needs Analysis
    (2023-01-01)
    Lu, Tao
    ;
    Morris, John
    ;
    Ratana-Olarn, Thanin
    This research explores the potential of Project-Based Learning (PBL) and the Cloud Education Model in Algorithm Design education. Through a multifaceted approach, the study identifies the specific needs of teachers and students when adopting the project-based learning model. In an experimental study, it used the PJBBL model in the domain of algorithm design education. A pre-test and post-test experimental design were used to reach a conclusion. Twenty teachers and 100 BS students were randomly selected from the college for interviews and questionnaire administration, respectively, whereas 66 students were selected through cluster sampling for experimentation. A pre-test-post-test design was used for the experiment, whereas descriptive statistics besides MANOVA were used to analyze the data. The study results indicate that the PJBBL model has shown promising outcomes in enhancing students' algorithm design and analysis capabilities. Integrating real-world projects and blended learning methods has deeply engaged students, fostering a better understanding of algorithmic concepts and their practical application. Teachers' feedback has shed light on effective pedagogical strategies, enabling them to create a conducive learning environment and support students in their algorithmic learning. The research demonstrates the efficacy of the Project-Based Blended Learning (PJBBL) model in Algorithm Design and Analysis education.
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    Learning Process during the COVID-19 Crisis, Selected Topics in Telecommunication Engineering subject, Department of Electronics and Telecommunication Engineering, RUS, Thailand
    (2021-01-01)
    Archevapanich, Tuanjai
    ;
    Khunthawiwone, Park Poom
    ;
    Sithiyopasakul, Jiran
    ;
    Anuwongpinit, Thanavit
    ;
    Purahong, Boonchana
    This article presents a learning process during the COVID-19 crisis, used in selected topics of telecommunication engineering subject, department of Electronics and Telecommunication Engineering, Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi. The sample consisted of 19 students, enrolled in the selected topics of telecommunication engineering course by selecting a specific sample group. The researcher presented a learning process during the COVID-19 crisis with the orientation of students for the same understanding and then teaching according to the given process. Finally, there are three aspects of satisfaction with learning processs during the COVID-19crisis assessed. The results showed that in all of the three aspects, having a very good level of students' satisfaction with an arithmetic mean of 4.614 and a standard deviation of 0.46 (\overline{X}=4.614, S.D. = 0.46)
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    Extended Extreme Learning Machine: A Novel Framework for Neural Network
    (2018-07-02)
    Jiaramaneepinit, Boonnithi
    ;
    Nuthong, Chaiwat
    Machine learning has been a focus research topic of superior tasks in many real-world applications. One of the famous preferred system is neural network. This approach has been invented for decades but becomes popular recently due to its satisfied results in many applications. The success of applying neural network involves model training which conventionally uses backpropagation method. However, it has many drawbacks. In recent decades, Extreme learning machine (ELM) was first proposed for training single-hidden layer feedforward neural network (SLFN). It optimizes training error by utilizing the whole training dataset with a one-shot calculation. However, for the training in datasets with large number of input features or high-dimensional datasets, original ELM encounters many difficulties. One of them is that the original ELM has no learning process from an input layer. This lead to an incomplete representation of data when it is transferred from one layer to another. Another difficulty involves training instability which causes fluctuation in testing accuracy. This is because networks' input weights are randomly generated. To circumvent these difficulties, the imposing architecture, namely Extended Extreme Learning Machine (X-ELM), is proposed. X-ELM uses ELM as an extension part in order to predict the outputs based on ensemble approach. The proposed framework extends the usage of ELM to apply to more complex network structures, such as networks with multiple hidden layers or networks with multiple computing systems. The proposed framework is applied to vehicles characteristic classifications' datasets. The experimental results show that X-ELM achieves better testing accuracy than of ELM in real-world applications.