A Comparative Study on the Effect of Eccentric Viewing Training Using PC and VR Contents

Title : A Comparative Study on the Effect of Eccentric Viewing Training Using PC and VR Contents
Journal : International Journal on Advanced Science, Engineering and Information Technology
Authors : Dokyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.18517/ijaseit.11.2.12832

Abstract
The purpose of this study is to develop PC Eccentric Viewing Training (EVT) and VR EVT Content, to conduct experiments to verify the validity of contents first through non-disabled people, and to compare and analyze VR and PC contents. Both PC and VR contents were produced with UNITY. This content model assumes that reducing inaccurate saccades by eliminating eye movement helps improve reading accuracy. In addition, the two contents are implemented in the same way as VR and PC versions. The content consists of two steps, both PC and VR. The purpose of the content is to improve reading accuracy by improving the fixation stability of Preferred Retinal Locus (PRL) and reducing inaccurate Saccades. The experiment consisted of 12 persons (within maximum visual acuity less than 0.3), and they were assigned to the PC Content group and VR Content group. The experiment was conducted a total of 5 times, except for two weeks, which is the time to adapt PRL. The experimental results showed that the reading accuracy of the VR content group was higher. In addition, When comparing VR contents with PC contents, the group that conducted the training through PC contents showed a decrease in concentration as it progressed to 1-3 steps, and the score distribution also fell overall. In conclusion, the study compared VR and PC contents, and the effectiveness of contents was verified through experiments.

Acknowledgments
This research was financially supported by the “ICT Convergence Smart Rehabilitation Industrial Education Program” through the Ministry of Trade, Industry & Energy (MOTIE). The authors are grateful to Korea Institute for Advancement of Technology (KIAT) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Promotion).

심정지 발생 예측 시스템 및 방법

발명의 명칭 : 심정지 발생 예측 시스템 및 방법
출원번호 : 10-2021-0022359
출원일자 : 2021년 2월 19일
발명자 : 이화민, 길효욱, 조남준, 채민수, 윤건일
특허권자 : 순천향대학교 산학협력단
공개신청 : 무

연구사사
【이 발명을 지원한 국가연구개발사업】
【과제고유번호】 1711104866
【과제번호】 2019M3E5D1A02069073
【부처명】 다부처
【과제관리(전문)기관명】 한국연구재단
【연구사업명】 혁신형의사과학자공동연구사업(R&D)(복지부,
과기정통부)
【연구과제명】 일회용 패치 기반의 환자 모니터링 시스템 개발
【기여율】 1/2
【과제수행기관명】 순천향대학교 산학협력단
【연구기간】 2019.07.01 ~ 2022.12.31

【이 발명을 지원한 국가연구개발사업】
【과제고유번호】 1711093210
【과제번호】 20150004030041001
【부처명】 과학기술정보통신부
【과제관리(전문)기관명】 정보통신기술진흥센터(IITP)
【연구사업명】 대학ICT연구센터육성
【연구과제명】 IoT 보안기술연구
【기여율】 1/2
【과제수행기관명】 순천향대학교 산학협력단
【연구기간】 2015.06.01 ~ 2021.03.31

Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment

Title : Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment
Journal : Lecture Notes in Electrical Engineering book
Authors : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.1007/978-981-15-9343-7_42

Abstract
With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.

Acknowledgments
This research supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2014-1-00720 & IITP-2019-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

심층 신경망을 이용한 부정맥 분류 시스템 및 방법

발명의 명칭 : 심층 신경망을 이용한 부정맥 분류 시스템 및 방법
출원번호 : 10-2018-0158212
출원일자 : 2018년 12월 10일
등록번호 : 10-2199085
등록일자 : 2020년 12월 30일
발명자 : 이화민, 전은광
특허권자 : 순천향대학교 산학협력단
법적상태 : 등록
요약
본 발명은 심층 신경망을 이용한 부정맥 분류 시스템 및 방법을 개시한다. 본 발명의 일 측면에 따른 심층 신경망을 이용한 부정맥 분류 방법은, ECG 신호를 수신하는 단계; 상기 수신한 ECG 신호를 심층 신경망 알고리즘에 적용할 수 있도록 전처리하는 단계; 상기 전처리된 ECG 신호에서 P-QRS-T파의 피크값을 검출하는 단계; 상기 검출된 P-QRS-T파의 피크값을 라벨 데이터와 통합하여 하나의 파일로 구성함으로써 데이터 셋을 구성하는 단계; 및 상기 P-QRS-T파의 피크값 및 데이터 셋에 입력 레이어, 히든 레이어, 출력 레이어를 포함하는 심층 신경망을 적용하여 부정맥을 분류하는 단계;를 포함한다.

연구사사
이 발명을 지원한 국가연구개발사업
과제고유번호 20141007200051001
부처명 과학기술정보통신부
과제관리(전문)기관명 정보통신기술진흥센터(IITP)
연구사업명 대학ICT연구센터육성지원사업
연구과제명 웰니스 삶을 위한 WellTEC 코칭 서비스 및 콘텐츠 개발
기 여 율 1/2
과제수행기관명 순천향대학교
연구기간 2014.06.01 ~ 2018.12.31

이 발명을 지원한 국가연구개발사업
과제고유번호 20150004030041001
부처명 과학기술정보통신부
과제관리(전문)기관명 정보통신기술진흥센터(IITP)
연구사업명 대학ICT연구센터육성 지원사업
연구과제명 IoT보안기술연구
기 여 율 1/2
과제수행기관명 순천향대학교 산학협력단
연구기간 2015.06.01 ~ 2018.12.31

딥러닝 기반 심정지 예측 기술 연구

제목 : 딥러닝 기반 심정지 예측 기술 연구
저자 : 김영민, 채민수, 윤건일, 조유진, 송찬영, 이화민
게재지 : 2020년 한국정보과학회 한국소프트웨어종합학술대회(KSC2020)
장소 : 온라인
교신저자 : 이화민

초록
심정지는 심장이 멈추어 신체 내 혈액이 흐리지 않는 현상이다. 심정지가 발생할 경우 혈액이 흐르지 않아 신체 기능이 손상되며, 초기 대응이 늦을 경우 뇌손상도 일으킬 수 있다. 그렇기 때문에 초기 발견 및 초기 대응이 중요하다. 본 연구팀은 순천향대학교 천안병원 내 병동 환자들의 데이터셋으로 연구를 진행하였다. 2016년부터 2019년까지의 병동환자를 대상으로 연구하였다. LSTM 모델과 결정트리, 로지스틱회귀, 랜덤포레스트를 이용하여 심정지 예측을 하였다. 성능 평가 결과 정밀도는 랜덤포레스트가 가장 높지만, LSTM 모델이 재현율이 가장 높다.

Acknowledgments
이 논문은 2019년도 정부(과학기술정보통신부)의 재원으로 한국연구재단 -현장맞춤형 이공계 인재양성지원사업의 지원을 받아 수행된 연구임(No. 2019H1D8A1105622).
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행되었음 (IITP-2020-2015-0-00403).

Prediction of Cardiac Arrest using LSTM in Hospitalized Patients

Title : Prediction of Cardiac Arrest using LSTM in Hospitalized Patients
Published in : The 4th International Conference on Interdisciplinary research on Computer science, Psychology, and Education (ICICPE’ 2020)
Author : Geonil Yun, Minsu Chae, Hyo-Wook Gil, Nam-Jun Cho, HwaMin Lee
Corresponding author : HwaMin Lee
Location : RAMADA PLAZA JEJU Hotel, Jeju Island, Korea and Online

Abstract
It is necessary to detect unexpected cardiac arrest early in the general ward of the hospital. However, the conventional Track and Trigger System (TTS) has low sensitivity and a high false alarm rate, which may not guarantee the safety of heart attack patients. In addition, false alarms can lead to a waste of movement by medical staff. This can be life-threatening as other critically ill patients lose access to treatment. Therefore, in this paper, we propose an LSTM deep learning network model that predicts heart attack within 24 hours with high sensitivity and low false alarm rate using patient vital signs.

Acknowledgments
This research supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program(IITP-2020-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation) and X-mind Corps program of National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. 2019H1D8A1105622).

Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code

Title : Deep Learning based Cardiac Arrest Prediction using Vital sign and Lab code
Published in : The 12th International Conference on Internet (ICONI 2020)
Author : Minsu Chae, Sangwook Han, Geonil Yun, Hyo-Wook Gil, Min Hong, DoKyeong Lee, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Jeju ShinhwaWorld, Jeju, Korea

Abstract
There are patients in hospitals who are hospitalized for a variety of reasons. We conducted a study on predicting cardiac arrest on patients at Soonchunhyang University Cheonan Hospital. We studied patients from 2016 to 2019. We used deep learning via the LSTM model and the GRU model. We check density of each input feature according to cardiac arrest. We compared only patients with vital signs and lab data. We removed DBP, SBP, BodyTemperature, AST, ALT, WBC, Creatinine, and Bilirubin variable density because there was little difference between those with cardiac arrest and other patients. We experimented with the LSTM model and GRU Model. In this paper, deep learning-based cardiac arrest prediction using vital signs and lab data has high precision. In particular, when using the GRU model, even if the cardiac arrest of 0 to 24 hours for each record is changed, it has a sensitivity of 60%.

Acknowledgments
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2019M3E5D1A02069073) and supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)