• ETRI Journal
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• v.43 no.5
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• pp.881-890
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• 2021

Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources. Systolic (S), onset (O), and first derivative peaks (W) are recognized by three different DNN filters. In addition, the boundaries of uninformative regions caused by artifacts are identified by two different filters. The algorithm reliably derives the HR and presents recognition scores for the S, O, and W peaks and artifacts with only a 0.7-s delay. In the evaluation using data from 11 patients obtained from PhysioNet, the algorithm yields 8643 (86.12%) reliable HR measurements from a total of 10 036 heartbeats, including some with uninformative data resulting from arrhythmias and artifacts.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.218-223
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• 2021

Pervasive enhancement and required enforcement of the Internet of Things (IoTs) in a distributed massively multiplayer online architecture have effected in massive growth of Big-Data in terms of server over-load. There have been some previous works to overcome the overloading of server works. However, there are lack of considered methods, which is commonly applicable. Therefore, we propose a combing Sparse Auto-Encoder and Load-Balancing, which is ProGReGA for Big-Data of server loads. In the process of Sparse Auto-Encoder, when it comes to selection of the feature-pattern, the less relevant feature-pattern could be eliminated from Big-Data. In relation to Load-Balancing, the alleviated degradation of ProGReGA can take advantage of the less redundant feature-pattern. That means the most relevant of Big-Data representation can work. In the performance evaluation, we can find that the proposed method have become more approachable and stable.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2005.05a
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• pp.177-181
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• 2005

기계학습에서 커널을 이용한 방법은 그 응용범위가 기계학습의 전반에 걸쳐 다양하게 이용되고 있으며, 그 성능 또한 기존의 방법들을 앞지르고 있다. 이는 기존의 비선형적 접근을 커널을 이용한 고차원 공간에서의 선형적 접근법으로 바꿈으로써 가능하게 되는 것이다. 다양한 분야에 적용되는 많은 커널들이 존재하며 각 커널들은 특별한 분야에 적용되기 쉽도록 다른 형태를 띠고 있기도 하지만, 커널로서 작용하기 위해 양한정 조건(positive definiteness)을 만족해야 한다. 본 연구에서는 DNA 문제에 직접 적용시킬 수 있는 방법으로서의 새로운 커널을 제시한다. 또한 매트로폴리스(Metropolis) 알고리즘을 이용하여 DNA의 hybridization과정을 모사함으로써 새로운 종류의 커널이 양한정(positive definite) 조건을 만족시킬 수 있는 방법을 제시한다. 새로 만들어진 커널이 행렬값을 형성해 나가는 과정을 살펴보면 인간이 예(instance)로부터 개념을 형성해 나가는 과정과 흡사한 양상을 보이는 것을 알 수 있다. 개념을 나타내는 좋은 예로서의 표본(prototype)으로부터 개념이 형성되어 가는 과정은 표본(prototype)이 아닌 예로부터 개념이 형성되는 과정과 다른 양상을 띠는 것과 같은 모양을 보인다.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.625-633
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• 2023

In this paper, we will examine the theoretical background and educational methodological basis of Novel engineering class cases published to date. For this purpose, we selected and investigated 30 Novel engineering-related academic papers and dissertations published from 2016 to 2023. As a result, the theoretical background is Seymour Papert's constructionism and Vygotsky's socio-cultural constructivism, and the educational methodological basis is creative problem-solving learning, problem-based learning, interdisciplinary convergence class, action learning, associating reading and writing education, possibility with integrated curriculum. We hope that this study will solidify the theoretical value of the Nobel Engineering convergence teaching model and serve as an alternative for teachers preparing for future education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1987-1993
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• 2016

The core value of the college of Interdisciplinary & Creative Studies is rely on the supporting system which can develop the learners' practical 'performing' competency. For aiming this, educational process of university constitutes the competencies which can be expected at the local field and develop the competency-oriented extracurricular & its operating system. Defining the creative convergent talent, as 'collaborative creative convergent one, bricoleur' and categorizes core competency named as TX competency. T competency contains humanities and the professionals of each major field needed. X competency consists of creative problem solving, convergent thinking ability, self-oriented learning ability and cooperative leadership. Developing extracurriculum & its operating system learners can be exposured the systemic learning and managing process. They can develop their potential abilities and can accumulate a proper mileage at their learning achievement. This research can be expected as a model of competency-based extracurriculum and its operating system which can develop learners' multiple competency overcoming the limit scope of curriculum.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.53-59
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• 2012

This article reviews the current issues of engineering education: engineering creativity, R&D 3.0 and Education 3.0 and comfirms need of refining quality of engineering education through learner-centered hybrid project learning. this article suggests the Global engineering project(GEP) program as an ideal hybrid project learning model that develop student's creativity and convergence capability. GEP program is learnner-centered interdisciplinary program that whole processes are managed by interdisciplinary students team aiming to global engineers who are globally competent and locally relevant so that they can function effectively in any country by local activity in developing country. The program consist of four main parts: 1)pre activity, 2)local activity from abroad, 3)design and producing prototype and 4)participating in the design contest or academic conference with the products. As a result, students' global competence, teamwork skill and capability of creative problem solving are remarkably improved.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.131-141
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• 2014

UX design e-learning systems in order to establish the reasonable structured modules that are based on interdisciplinary research. E-learning by focusing on technology-related education, information technology and psychology, business administration, etc. has been studied in the field. However, an important part of smart education, one of the students in the field of self-directed research is lacking UX design. UX Design the User Interface and Visual Identity is central to the success of the content of the important research areas in modern smart society. This case study is for smart education digital textbooks considered the characteristics of the study. Presented by the Ministry of Education on the basis of the standard of digital textbook content of the e-learning features, including multi-disciplinary analysis of UX design requires a structured model is proposed. Research data on the smart education and virtual education in UX design being used as the basis for studies forward.

• The Korean Journal of Ecology
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• v.28 no.6
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• pp.405-415
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• 2005

This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.

• The Journal of Korean Association of Computer Education
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• v.9 no.4
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• pp.25-34
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• 2006

Although the e-learning system is being introduced in elementary and secondary education, incorporating it into the regular curriculum is difficult. Other problems include the lack of sessions and connection to regular courses in realistically applying project-based learning in the regular school curriculum. Therefore. this study established, applied, and analyzed the blended learning strategy to resolve such issues. Specifically, a theoretical investigation on the concept of blended learning and IT-oriented, project-based learning was conducted. The theme for learning based on integrated courses was also selected, and the 5-stage project-based teaching and learning strategy, concretized. The concretization strategy involved discussion, role division, and alternative evaluation strategy. The class progressed in formats of on-class online, on-class offline and off-class online. As a result, students experienced inconvenience since the project only lasted for a short period of time. Nonetheless, they responded positively to the effect of project-based learning in general. This study was able to suggest the possibility of applying e learning in regular school curriculum and propose the direction for digital and learner oriented teaching.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.81-91
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• 2024

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.