• The Journal of Korea Robotics Society
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• v.10 no.2
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• pp.79-89
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• 2015

The purpose of this study was to examine the concept of r-learning based on existing studies of r-learning. It also aimed to analyze r-learning environments in an effort to determine prerequisites for the successful entrenchment of r-learning in material(technology and infrastructure), human(young children and teacher) and institutional(law and policy) aspects. This study intended to suggest some of the right directions for the revitalization of r-learning. In conclusion, the position of r-learning and its interrelationship with related systems in the ecosystem of early childhood education should accurately be grasped to accelerate the integration of r-learning into kindergarten education to maximize the effects of the convergence of the two. Intensive efforts should be made from diverse angles to expedite the spread and enrichment of r-learning.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.187-202
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• 2020

Can job training be considered a social right? Who must bear the costs of individual job training? This paper studies these two issues by examining the Korean Individual Learning Accounts (ILA) revised in 2020 and proposes future policy directions. Although there is no explicit legal provision stipulating job training as a lawful right in Korea, such absence does not negate the government's role of providing vulnerable people, etc with necessary training. Korean ILA heavily depends on the Skills Development Scheme under the Employment Insurance System which succeeded the past mandatory training levy system and it becomes harder to maintain principle of insurance because of sizable volume of atypical workers who are not insured. For future policy directions, it is desirable to increase the burden of general budget and self-financing as they are below 30% combined and the coverage of the ILA needs to be steadily expanded to all economically active people. Also, labor-management should step up joint efforts to stimulate the use of already existing policies such as paid training leave and request for reduction of working hours.

• Korean Journal of Child Studies
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• v.36 no.6
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• pp.85-100
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• 2015

This study divided elementary school 6th graders of into a higher academic achievement group (n=19) and a lower academic achievement group (n=19) in order to examine the tendency of left and right hemisphere preferences, characteristics and relationships of learning ability factors by means of EEG. For this purpose, brain waves in performing higher cognitive tasks for 5 min. were measured with a two-channel (Fp1, Fp2) EEG measurement system and hemisphere preference was measured by means of a questionnaire. Our results were as follows. First, hemisphere preference indicated that the higher group showed a left hemisphere tendency and the lower group indicated a right hemisphere tendency. Second, the first learning ability test found that the higher group performed its task rapidly with higher levels of concentration and cognitive strength and lower loading and the lower group conducted its task more slowly with lower levels of concentration and cognitive strength and higher loading. The second test showed that the higher group performed its task rapidly with lower levels of concentration.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.541-550
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• 2009

This research attempted to find out any implications for strategies to design and develop the connections between the activities of the brain function and the fields of English learning (dictation, word level, speaking, word memory, listening). Thus, in developing the brain based learning model for the English education, attempts need to be made to help learners to keep the whole brain toward learning. On this point, this study indicated the significant results for the exclusive brain location and the brainwaves on the each English learning field by the quantitative EEG analysis. The results of this study presented the guidelines for the balanced development of the left brain and the right brain to train the specific site of the brain connected to the English learning fields. In addition, whole brain training model is developed by the quantitative EEG data not by the theoretical learning methods focused on the right brain training.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.111-116
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• 2022

In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network(NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.449-465
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• 2024

This study conducted a systematic literature review using online databases to investigate the effective feedback types that enhance the learning experiences of online students. Feedback is a critical component for learner success. With the expansion of online education, the importance of feedback has become more evident due to the reduced interaction between instructors and learners. Instructors must provide high-quality feedback that motivates learners and supports their educational goals. This involves using automated tools appropriate for the environment and effective feedback strategies to deliver personalized feedback. The literature was gathered through an extensive search process, adhering to predetermined inclusion and exclusion criteria, and included a risk assessment of selected studies, drawing from sources such as Google Scholar, Elsevier, and other Scopus-indexed journals. The review adhered to the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Specific keywords related to the study's focus, including "Online learning," "Improving learning," "Learner performance," "Feedback type," and "Feedback," guided the database searches. The protocol for selecting systematic reviews on learning enhancement involved screening articles published from 2013 to 2021 based on their titles and abstracts according to established criteria. Analyzing and studying data on learning patterns in non-face-to-face educational environments can improve learners' needs and educational effectiveness. Selecting the right types of feedback, taking into account the learners' levels and educational objectives, is crucial for providing effective feedback. A variety of feedback types are essential for the continuous improvement of learners' learning.

• The Journal of Korean Physical Therapy
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• v.21 no.1
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• pp.81-87
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• 2009

Purpose: Human brain was lateralized to dominant or non-dominant hemisphere, and could be reorganized by the processing of the motor learning. We reported four cases which showed the changes of the cortical activation patterns resulting from two weeks of training with the serial reaction time task. Methods: Four right-handed healthy subjects were recruited, who was equally divided to two training conditions (right hand training or left hand training). They were assigned to train the serial reaction time task for two weeks, which should press the corresponding four colored buttons as fast as accurately as possible when visual stimulus was presented. Before and after two weeks of training, reaction time and function magnetic resonance image (fMRI) was acquired during the performance of the same serial reaction time task as the training. Results: The reaction time was significantly decreased in all of subjects after training. Our fMRI result showed that widespread bilateral activation at the pre scanning was shifted toward the focused activation on the contralateral hemisphere with progressive motor learning. However, the bilateral activation was still remained during the performance of the non-dominant hand. Conclusion: These findings showed that the repetitive practice of the serial reaction time task led to increase the movement speed and accuracy, as described by motor learning. Such motor learning induced to change the cortical activation pattern. And, the changed pattern of the cortical activation resulting from motor learning was different each other in accordance with the hand dominance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).