• Journal of Korean society of Dental Hygiene
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• v.22 no.2
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• pp.81-92
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• 2022

Objectives: The purpose of this study was to evaluate learners' satisfaction with operating Problem-Based Learning (PBL), competency level for dental hygiene, and learners' opinions through a reflection journal by developing a PBL class module and applying it to clinical dental hygiene classes. Methods: The subjects of the study were 31 students in the Clinical Dental Hygiene (oral health management for special patients) course in the second semester of the fourth grade. This study was conducted over the first semester from September to December 2020. The developed PBL learning module was applied for the 15 weeks class, and after all PBL classes were over, the overall satisfaction with the class and the change in student competency level was evaluated. Results: The overall satisfaction of learners with PBL was high, and the level of self-competence also increased compared to before class. In addition, the top three topics (Competencies learned through PBL, humanistic and sociological elements learned through PBL, and obstacles in the PBL) were derived through the reflection journal after PBL learning activities. Conclusions: It was confirmed that the PBL learning module developed in this study is a class that enables students to identify problems and solve them integrally and drives the improvement of humanities and sociological competencies.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.257-267
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• 2018

The purpose of this study was to evaluate the effects of an ESP module program which combines Keller's ARCS model for the $1^{st}$ grade nursing students. Students' learning motivation and academic achievement were compared between an intervention group and a control group and their perception and subjective effects on the module program were identified. Quantitative data showed that the overall level of learning motivation in the intervention group was significantly higher than that in the control group (t=2.391, p=.019). No significant difference was found on the level of academic achievement between two groups (t=0.116, p=.098). Contents analysis on the qualitative data showed that ESP module program was interesting, effective, helpful for understanding clinical settings, and giving confidence and satisfaction. According to the result, it is assumed that the ESP module program which combines ARCS model can be effective in motivating the $1^{st}$ grade nursing students to learn nursing contents and English.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.393-405
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• 2018

Problem-Based Learning (PBL) is a student-centered pedagogy that integrates nursing knowledge, skills, and attitudes into clinical nursing practice. This pilot aims to apply a PBL module on preschoolers' growth and development in the nursing curriculum. This quantitative study was performed to develop a PBL module following Dick and Carye's program development process (planning, development, application, and evaluation phases), and to evaluate its effects using structured questionnaires among sophomore nursing students. These students formed teams of four or five people each and spent 40 minutes participating in the PBL module. Data were analyzed using descriptive statistics, t-tests, and content analysis. Metacognition level increased significantly. There was no significant difference in team efficacy between pre-test and post-test. Post-test learning satisfaction was high. Students reported obtaining knowledge and problem-solving ability with respect to preschoolers' growth and development and were satisfied with teamwork. This finding offers fundamental knowledge concerning the application of a PBL module in nursing curricula.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.568-574
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• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.476-482
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• 2020

In this paper, we propose a block-based modularity architecture design method for distributed machine learning. The proposed architecture is a block-type module structure with various machine learning algorithms. It allows free expansion between block-type modules and allows multiple machine learning algorithms to be organically interlocked according to the situation. The architecture enables open data communication using the metadata query protocol. Also, the architecture makes it easy to implement an application service combining various edge computing devices by designing a communication method suitable for surrounding applications. To confirm the interlocking between the proposed block-type modules, we implemented a hardware-based modularity application system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.447-454
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• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.11-17
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• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.3
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• pp.351-359
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• 2012

This study aims to evaluate the student satisfaction of problem-based learning(PBL) in Korean medical curriculum. A questionnaire was given to clinical clerkship students of Pusan National University School of Korean medicine. These items covered overall evaluation of module & learning environment, tutoring, individual learning, group learning and effectiveness for clinical clerkship in PBL. By most of all respondents, PBL was recognized as a more effective learning method. Most respondents were satisfied with the group learning, individual learning and effectiveness for clinical clerkship. However, satisfaction was lower for tutors and module. The results of this study demonstrated that the students had a high level of satisfaction in PBL. It might be concluded that PBL was successfully implemented into the Korean medical curriculum.

• The Research Journal of the Costume Culture
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• v.29 no.3
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• pp.346-360
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• 2021

Universities have recently introduced problem-based learning (PBL) to various subjects to enhance problem-solving skills (including self-directed learning and small-group learning) required in industry. The PBL module was applied to the personal production process in a draping class. A study was based on a questionnaire after conducting two PBL modules with a group of students. Each PBL module included 'design analysis', 'presentation of flat sketch and draping plan', 'discussion of the plan', 'evaluation of the draping result and correcting the problem', and 'final evaluation of the completed project'. Results showed that satisfaction with the PBL method and its activities was higher than satisfaction with existing teaching methods. In particular, among the various components, the 'design analysis' and 'the presentation step of flat sketch and draping plan' stages were more helpful to students compared to small-group discussion. Moreover, the effects of PBL were observed through student reflection essays, in which students suggested that PBL was very effective in enhancing problem-solving through self-directed and small-group learning. Despite the overall satisfaction with PBL, students expressed some minor difficulties associated with awkwardness with a novel learning method, lack of diverse perspectives among each group, and poor communication skills. Therefore, the study shows that PBL is highly likely to be useful to students when they are solving pattern drafting problems and making samples through self-directed learning and small-group learning.