• Journal of Engineering Education Research
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• v.22 no.6
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• pp.12-20
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• 2019

The purpose of this study is to development of a teaching-learning model for active learning in engineering education. For this, the adequacy between educational objectives and active learning activities is verified and furthermore an "active learning teaching-learning model" is suggested. This suggested teaching-learning model is expected to supplement weakness of traditional lecture-type teaching-learning activity. Based on the literature review, first, the representative activities of active learning were derived. there are twenty active learning activities, which compose of five of individual learning activity, five of pair-learning activity and five of group-learning activity, and five of alternative- learning activity. In addition, a survey on adequacy between designed active learning activities and learning outcomes were conducted to ten educational experts. Lawshe's content validity calculation method was applied to analyze the validity of this study. Second, five teaching-learning principles, such as thinking, interaction, expression, reflection, and evaluation were derived to develop an "active learning teaching-learning model" which supplements lecture-type classes and then the "TIERA teaching-learning model" which consists of five stages was designed. Finally, based on the survey on educational experts, adequate active learning activities were proposed to apply in each stage of the "TIERA teaching-learning model" and as a result the TIERA model's active learning activities were developed. The result of this study shows that some activities of active learning are appropriate to induce high cognitive learning skills from the learners even in traditional lecture-type classrooms and therefore this study suggests meaningful direction to new paradigm of teaching-learning for engineering education. This study also suggests that instructors of engineering education can turn their traditional teaching-learning activities into dynamic learning activities by utilizing "active learning teaching-learning model".

• ETRI Journal
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• v.32 no.5
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• pp.766-773
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• 2010

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call "the small example problem" and "the asymmetric distribution problem." This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2650-2662
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• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• The Korean Journal of Emergency Medical Services
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• v.23 no.2
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• pp.61-73
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• 2019

Purpose: This study aimed to introduce active learning methods, including flipped, case-based, and team-based learning in an electrocardiography (ECG) course and to investigate outcomes and satisfaction with these methods. Methods: To identify the learning effect of active learning, pre-and post-academic self-efficacy was compared between the experimental and control groups. In the experimental group, pre-and post-knowledge and clinical performance regarding ECG were also assessed. In addition, class satisfaction was investigated after application of active learning methods in the experimental group. Data were collected from 84 paramedic students and analyzed using SPSS 22.0 (IBM, Armonk, NY, USA). Results: The experimental group showed significant improvement in post-academic self-efficacy and knowledge. The experimental group also showed high clinical performance (9.83 out of 10 in ECG checking ability and 9.63 out of 10 in ECG reading ability). The mean satisfaction score was 4.23 out of 5 (responses based on a Likert scale) in the experimental group. Conclusion: Active learning in an ECG course was found to be highly effective and satisfactory. Furthermore, paramedic students can enhance their accountability and judgement with team-based learning through free engagement in discussion.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.295-303
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• 2022

This study compares two cases in which flipped learning is applied in the active learning classroom (ALC) and fixed classroom of advanced engineering education. To this end, the difference in pre-learning, academic achievement, and class satisfaction between ALC and fixed classroom flipped learning were compared. The results revealed that students in ALC flipped learning watched more video lectures for pre-learning than those in the fixed classroom flipped learning and achieved higher scores on final tests, though they obtained lower points on midterm exam. In addition, examination of class satisfaction with questions about class factors, instructor factors, and overall satisfaction revealed that ALC flipped learning showed higher satisfaction in all factors than the fixed classroom flipped learning. This case study suggests that the ALC environment, a learning space built to facilitate learner-centered activities, is more effective for flipped learning that requires active interaction in the classroom.

• Journal of Korean Association for Spatial Structures
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• v.21 no.4
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• pp.73-80
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• 2021

A mid-story isolation system was proposed for seismic response reduction of high-rise buildings and presented good control performance. Control performance of a mid-story isolation system was enhanced by introducing semi-active control devices into isolation systems. Seismic response reduction capacity of a semi-active mid-story isolation system mainly depends on effect of control algorithm. AI(Artificial Intelligence)-based control algorithm was developed for control of a semi-active mid-story isolation system in this study. For this research, an practical structure of Shiodome Sumitomo building in Japan which has a mid-story isolation system was used as an example structure. An MR (magnetorheological) damper was used to make a semi-active mid-story isolation system in example model. In numerical simulation, seismic response prediction model was generated by one of supervised learning model, i.e. an RNN (Recurrent Neural Network). Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm The numerical simulation results presented that the DQN algorithm can effectively control a semi-active mid-story isolation system resulting in successful reduction of seismic responses.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.165-171
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• 2017

It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.4
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• pp.577-586
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• 2014

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.