• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.4
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• pp.267-275
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• 2004

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller is tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.631-636
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• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.153-158
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• 2006

The effect of solving questions and learning via internet is getting more and more important these days. In this paper we propose an active learning method that makes a database for the information about certificates and practical examinations and accesses it easily. First of all, this method makes it possible to evaluate students individually, improves the motive of learning and gives students a sense of achievement by providing a user-specific question filtering technique using user profile information by weight. And, it elevates the acquisition rate of certificates by advising and managing for certificate-acquisition and it also draw more interest and understanding for future directions. The case using the method of this paper, the examination record of a certificate of qualification is elevated about 10 marks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.339-342
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• 2021

In this paper, we propose a new method for inactive region padding using reinforcement learning. Inactive region is an area that has no information, such as 360 or 3DOF+ vidoes. However, these inactive regions degrade the compression performance in general. To improve the compression performance, simple filtering is applied between active and inactive regions. But it does not fully consider the characteristics of the images. In the proposed method, inactive regions are padded through reinforcement learning that can consider the characteristics of images and the compression process. Experimental results show that the performance is better than the conventional padding method.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.207-213
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• 2022

MBTI(Myer Briggs Type Indicator) is an effective personality type test to intuitively identify and classify people's tendencies. Accordingly, there are active attempts to apply MBTI to the learning area, but research on creating new learning types using MBTI is insufficient. Therefore, this paper examines the factors that affect learning and implements new learning types MY,STI(MY, Study Type Indicator) by applying them to a machine learning algorithm that has these characteristics. Data were collected by conducting a learning type test made with Google Forms on 144 general people, and supervised learning was used during machine learning. As a result, the accuracies of MY,STI were 0.933, 0.866, 0.844, and 0.733 for each learning method, learning motivation, presence or absence of external stimulus, and learning time criteria, respectively.

• Hwankyungkyoyuk
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• v.20 no.3
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• pp.31-44
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• 2007

The solution of the environment problem is the common issue all over the world, for this reason the necessity of the environmental education of school has emphasized. On this a variety method for environmental education is needed, this paper planned and applied the 'ecology & environment' for high school which are based on GBS theory and presented a new model of environment education. GBS(Goal-Based Scenario) is that learners are presented with an end goal that is motivating and challenging. This goal is structured such that, in order to successfully meet it learners are required to build a predetermined core set of skills and knowledge by process mission and scenario. GBS is an active learning environment in which learners are trained in study that have a real-world context. When they are back in real-world they have increased ability to apply what was learned by reflecting on the GBS learning experience. This study was designed on GBS theory and taught a class by using internet Blog. As a result, when carefully reviewing the materials such as final presentation reflect journal, we conclude that the students' awareness of a learning environment is improved and the students seems to try to apply the learning outcome to a real life.

• Fashion, Industry and Education
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• v.14 no.1
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• pp.50-58
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• 2016

Passive learning attitudes and lack of enthusiasm in a retailing math course is quite common and a significant number of students do express their frustrations and struggles by seeking extra help outside the classroom. In order to promote students' active participation in class and to improve their performance and overall satisfaction with the course, a modified team-based learning (TBL) method was implemented in a retailing math course in two consecutive semesters. Implementing TBL into a retailing math course would improve students' accountability for their own learning, increase student interactions and engagement, and develop teamwork and collaboration skills. The scores on the midterm and final tests indicated that students' performance improved especially for the students who scored below 80% on each test when TBL was implemented. Students' reflection on the TBL activities done in class throughout the semester indicated that these TBL activities help them solidify the concepts taught in class better. They were able to realize their own mistakes and other group members who got the question right helped them understand. To maximize the benefit of TBL, it is suggested to implement TBL within the flipped classroom. Further research is called for to evaluate the effect of TBL on long-term knowledge retention among college students.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.481-489
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• 2020

The study examines the role of facilitating conditions and user habits in the use of technology in Online Learning Platform (OLP) in Indonesia. The adoption of online learning, persistence, and learning results in online platforms is essential for ensuring that education technology is implemented and gets as much value as possible. People who use technology and systems will embrace new technologies even more. This quantitative study is based on a survey of 254 respondents, who were active users of the technology, and considers the facilitating conditions and user habits variables. Two research hypotheses were tested using the Partial Least Square-Structural Equation Modeling method. Cronbach's Alpha, path coefficient, AVE, R-square, T-test were applied. The results showed that the factors significantly influence the Online Learning Platform technology behavioral intention. This impact is primarily associated with the availability of the resources required to use OLP technology. The availability of these resources includes supporting infrastructures such as widespread Internet access, easy access to mobile devices, and file sizes that affect access speed. The findings of this study suggest that it is necessary to introduce and increase the availability of resources for using OLP technology, and familiarize people with the technology features.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.