• 대한공업교육학회지
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• v.45 no.1
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• pp.42-62
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• 2020

The Ministry of Education has been quick to apply National Competency Standars (NCS) to industrial sites and educational·training institutions in order to resolve discrepancies between practical affairs in industrial sites and vocational education, training & requirements. Full implementation of NCS-based curriculum in vocational education of vocational high schools has been mandatory since 2018. This research used a region-stratified sample of 350 from teachers in 'machinery' and 'machine·metal' majors in mechanical departments of vocational high schools to investigate the awareness of practical courses for metal machining among the teachers. The research results are as follows. First, a majority of the respondents indicated the availability of turning process, milling process, computer integrated manufacturing and measuring courses in mechanical departments. Second, capabilities required by the industry are considered most in selecting practical courses and competence units. Third, positive changes with the introduction of practical courses in the school education are students' practical skills improvement and satisfaction of industrial requirements. Fourth, negative changes with the introduction of practical courses in the school education are too difficult learning modules used in practical courses for students and students' difficulty in learning because of the difference between equipment in schools and industrial equipment in learning modules. Fifth, teachers' satisfaction with practical courses classes and overall conditions is above the average, and their satisfaction with the level of practical courses and bookbinding or purchase of rearranged textbooks of practical courses is below the average. Therefore, application conditions of above-mentioned representative 4 practical courses should be examined and taken care of for consistent improvement to stabilize NCS-based educational courses in mechanical departments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2349-2356
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• 2008

In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.

• Journal of Physiology & Pathology in Korean Medicine
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• v.33 no.5
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• pp.255-260
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• 2019

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(So-Yang, So-Eum and Tae-Eum) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 32 students participating in Sasang medical practice(12 subjects were So-Yang, 7 subjects were So-Eum, and 13 subjects were Tae-Eum). We analyze subject's clinical practice result reports by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, Bing-xie Pattern Identification, and Visceral Pattern Identification in each Sasang types (So-Yang, So-Eum, Tae-Eum). In the case of Heat-moisture, Tae-Eum showed higher score than So-Eum, but So-Yang showed no difference from the other two Sasang types(p<0.05). And in the case of Food-accumulation, Tae-Eum and So-Yang showed significantly higher score than So-Eum(p<0.05). It is hard to generalize the result because subject of this study was not enough. However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. Therefore use of ODS program in student clinical practice training help to understand the relationship and correlation between different pattern Identification and will help standardization of clinical practice education.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.451-459
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• 2019

Various policies have been implemented for human resources development in preparation for future society. Among the policies, STEAM education, SMART education and SW education are representative examples. In order for these policies to be implemented effectively in the school setting, teachers' positive perception and teaching competency are required. In consideration of the importance of pre-service teacher education, this study analyzed the current status of elementary pre-service teachers' perception and teaching readiness on STEAM education, SMART education and SW education, and sought implications that can be reflected in pre-service teacher education. The results of the study showed that the pre-service teachers' perception on the necessity of each policy was very high, and the understanding level of each policy was relatively high. Compared with this, it was found that pre-service teachers lacked training experience related to each policy, and the level of readiness for teaching was very low. As the most important task to be solved, many pre-service teachers selected the implementation of teacher education and seminars, and the distribution of instructional programs and materials. As the result of analyzing the difference according to pre-service teachers' individual characteristics, differences were found according to the level of their ICT utilization ability. Based on the results of this study, implications to be reflected in pre-service teacher training processes were suggested.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.23-30
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• 2021

This paper proposes a noise-tolerant image classification system using multiple autoencoders. The development of deep learning technology has dramatically improved the performance of image classifiers. However, if the images are contaminated by noise, the performance degrades rapidly. Noise added to the image is inevitably generated in the process of obtaining and transmitting the image. Therefore, in order to use the classifier in a real environment, we have to deal with the noise. On the other hand, the autoencoder is an artificial neural network model that is trained to have similar input and output values. If the input data is similar to the training data, the error between the input data and output data of the autoencoder will be small. However, if the input data is not similar to the training data, the error will be large. The proposed system uses the relationship between the input data and the output data of the autoencoder, and it has two phases to classify the images. In the first phase, the classes with the highest likelihood of classification are selected and subject to the procedure again in the second phase. For the performance analysis of the proposed system, classification accuracy was tested on a Gaussian noise-contaminated MNIST dataset. As a result of the experiment, it was confirmed that the proposed system in the noisy environment has higher accuracy than the CNN-based classification technique.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.449-457
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• 2021

Ministry of education recently announced to implement AI curriculum in elementary, middle school and highschool from 2025 which will include programing, basic AI principal and AI Ethics, and the media is releasing articles that have reservations on it. This study is focused on analyzing the perspective of elementary teachers - who are going to be in charge of AI education - on the implementation of AI education in elementary schools and the teachers are divided into two groups of 'software-experienced' and 'software-inexperienced' in relation to software training background. The results showed that 100% of the 'software-experienced' teachers agreed on implementing AI education and 80% of 'software-inexperienced' teachers also showed positive perspective on it. Among the reasons that 20% of 'software-inexperienced' teachers had negative perspective on AI education, it was highly rated that existing home economics subject covers fulfilling amount of software education. Both 'software-experienced' and 'software-inexperienced' teachers chose grade 5 and 6 as the most appropriate age for software education and considered one class per a week as the most appropriate amount of AI class. In terms of the subject format, 75% of the 'software-experienced' teachers chose the idea that software education has to be an independent school subject which will include AI education. Also, 54% of the 'software-inexperienced' teachers chose the ideas either AI education should be an independent subject or software education should be an independent subject which will include AI education. The preference of the content of AI education appeared in order of basic AI programing, principles of AI and AI Ethics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.29-35
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• 2022

This paper proposes a fuel cell education framework installed on a Metaverse environment, which is to reduce the burden of education costs and improve the effect of education or learning. This Meta-Fuel cell platform utilizes the Unity 3D Web and enables not only theoretical education but also hands-on training. The platform was designed and developed to accommodate a variety of unit education contents, such as ppt documents, videos, etc. The platform, therdore, integrates ppt and video demonstrations for theoretical education, as well as software content "STACK-Up" for hands-on training. Theoretical education section provides specialized liberal arts knowledge on hydrogen, including renewable energy, hydrogen economy, and fuel cells. The software "STACK-Up" provides a hands-on practice on assembling the stack parts. Stack is the very core component of fuel cells. The Meta-Fuelcell platform improves the limitations of face-to-face education. It provides educators with the opportunities of non-face-to-face education without restrictions such as educational place, time, and occupancy. On the other hand, learners can choose educational themes, order, etc. It provides educators and learners with interesting experiences to be active in the metaverse space. This platform is being applied experimentally to a education project which is to develop advanced manpower in the fuel cell industry. Its improvement is in progress.

• Therapeutic Science for Rehabilitation
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• v.12 no.2
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• pp.69-83
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• 2023

Objective : This study, as a preliminary study, applied an Information & Communication Technology (ICT) home-based program to elderly people aged 65 years or older to confirm the effect of the cognitive enhancement program and to find the possibility of remote rehabilitation. Methods : This study from August to October 2022, three subjects were selected and the intervention was conducted for about 2 months. This intervention was conducted using Korean version of Mini-Mental State Examination, Korean version of Montreal Cognitive Assessment (MoCA-K), Computer Cognitive Senior Assessment System, and the Center for Epidemiologic Studies Depression scale to evaluate cognitive improvement before and after the program. The therapist remotely set the level of cognitive training according to the subject's level through weekly feedback. Results : After the intervention, all subjects showed improved scores in most items of the MoCA-K conducted before and after the intervention. In addition, among the items of Cotras-pro, upper cognition, language ability, attention, visual perception, and memory were improved. Conclusion : Cognitive rehabilitation training using an ICT home-based program not only prevented dementia but also made it habitual. Through this study, it was confirmed that remote rehabilitation for the elderly could be possible.