• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.13-16
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• 2022

This paper proposes using micro-learning at Saudi universities. It commences with an account of the concept of micro-learning and the difference between micro-learning and electronic learning. Then it touches on the significance, principles, and examples of micro-learning, followed by some micro-learning applications and pitfalls. The paper closes with a proposal for using this learning mode at Saudi universities.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.503-511
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• 1995

A batch-mode algorithm is proposed to increase the speed of learning in the error backpropagation algorithm with variable learning rate and variable momentum parameters in classification problems. The objective function is normalized with respect to the number of patterns and output nodes. Also the gradient of the objective function is normalized in updating the connection weights to increase the effect of its backpropagated error. The learning rate and momentum parameters are determined from a function of the gradient norm and the number of weights. The learning rate depends on the square rott of the gradient norm while the momentum parameters depend on the gradient norm. In the two typical classification problems, simulation results demonstrate the effectiveness of the proposed algorithm.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Steel and Composite Structures
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• v.42 no.5
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• pp.685-697
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• 2022

In this paper, a vibration-based method using the change ratios of modal data and the experience-based learning algorithm is presented for quantifying the position, size, and interface layer of delamination in laminated composites. Three types of objective functions are examined and compared, including the ones using frequency changes only, mode shape changes only, and their combination. A fine three-dimensional FE model with constraint equations is utilized to extract modal data. A series of numerical experiments is carried out on an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam for investigating the influence of the objective function, the number of modal data, the noise level, and the optimization algorithms. Numerical results confirm that the frequency-and-mode-shape-changes-based technique yields excellent results in all the three delamination variables of the composites and the addition of mode shape information greatly improves the accuracy of interface layer prediction. Moreover, the EBL outperforms the other three state-of-the-art optimization algorithms for vibration-based delamination detection of composites. A laboratory test on six CFRP beams validates the frequency-and-mode-shape-changes-based technique and confirms again its superiority for delamination detection of composites.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Engineering Education Research
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• v.15 no.5
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• pp.87-92
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• 2012

Independent study is a major ability of engineering students. In independent study training practice, we need to use different instructional strategies and responds to individual student needs and learning styles. The purpose of this paper is to demonstrate a four-step student independent study training mode we applied to teaching the Biomedical Engineering students in Dali University, China. We developed this teaching mode to fulfill the goals of the first years' undergraduates training and improve the students learning skills. The four-step teaching mode includes both in-class and out-of-class activities. The emphasis is on how to train students to get information from the reading materials, understand the concept, develop critical thinking and eventually become independent learner.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.6
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• pp.1-7
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• 2003

This paper introduces a robust sliding mode repetitive control method for a class of nonlinear system. The sliding mode controller stabilizes the overall system and makes the tracking error converge to some residual set. Also, tile repetitive learning controller makes the tracking error converge to zero. Unlike other methods, the proposed sliding mode controller reduces the chattering effects in the steady state without using high-order sliding manifold approach.

• Communications of Mathematical Education
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• v.30 no.1
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• pp.47-65
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• 2016

The purpose of this paper is to compare the effects on the students' academic achievement and satisfaction level in a Calculus course taught by two different teaching modes, blended learning and face-to-face learning. The comparison analysis was made for Calculus 2 course in H university in South Korea offered in the Spring semester of the year 2015. Calculus 2 is a Calculus course designed for the first year students who plan to choose their majors in Engineering. There were two sections of the Calculus course taught in a blended learning mode and one in a conventional face-to-face learning mode. All these three sections including e-learning parts were taught by the author. We discuss some meaningful differences in the effects by the two different teaching modes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4291-4310
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• 2015

This paper investigates the traffic offloading over unlicensed bands for two-tier multi-mode small cell networks. We formulate this problem as a Stackelberg game and apply a hierarchical learning framework to jointly maximize the utilities of both macro base station (MBS) and small base stations (SBSs). During the learning process, the MBS behaves as a leader and the SBSs are followers. A pricing mechanism is adopt by MBS and the price information is broadcasted to all SBSs by MBS firstly, then each SBS competes with other SBSs and takes its best response strategies to appropriately allocate the traffic load in licensed and unlicensed band in the sequel, taking the traffic flow payment charged by MBS into consideration. Then, we present a hierarchical Q-learning algorithm (HQL) to discover the Stackelberg equilibrium. Additionally, if some extra information can be obtained via feedback, we propose an improved hierarchical Q-learning algorithm (IHQL) to speed up the SBSs' learning process. Last but not the least, the convergence performance of the proposed two algorithms is analyzed. Numerical experiments are presented to validate the proposed schemes and show the effectiveness.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.