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Smart Sensor-based Interface Controlin the Mobile CloudEnvironment
Sanghyun Park,Ilmin Kim,Jinsul kim 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.10
In this paper we propose the use of cloud services to delivery next-generation dynamic mobile interface. The improvement is focused on existing soft keyboard application. Through a simple sign-in process, the customized keyboard interfaced defined by user own can by synced, stored on the cloud and ready to be apply on other compatible devices of the same user in anytime. With cloud based interface, through specific username and password, various smart mobile devices are able to receive their custom interface. Also, we use Gyro Sensor and Wi-Fi direct to help the users freely share their layout through the internet.
Artificial Intelligence IoT Architecture Based on Microservices
Yeonggwang Kim,D M BAPPY,Jinsul Kim 한국디지털콘텐츠학회 2021 The Journal of Contents Computing Vol.3 No.1
As Internet of Things (IoT) combined with Artificial Intelligence (AI) continues to develop and evolve, the size and structure of a single application becomes much larger and more complex. This reduces scalability, scalability, and maintenance. In response to these challenges, Microservice architectures have been introduced into AI IoT applications due to their flexibility, lightness, and loose combination. However, the AI IoT framework of existing microservices is very limited in scope because it focuses primarily on specific domains. In this paper, we propose a general microservice system architecture for AI IoT applications. This architecture is highly scalable and highly maintained. Introduce system design and associated microservices and emphasize core services and device communication from service to physical. It supports interoperability and has greater capacity to accommodate heterogeneous objects. The architecture also makes it easy to achieve more application integration such as automation, intelligence, urban disaster response services, and big data.
김영광(Yeonggwang Kim),이지훈(Jihoon Lee),윤준철(Junchul Yoon),김영관(Youngkwan Kim),김진술(Jinsul Kim) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
전력 수요 예측 분야에서는 전력 부하 모니터링 및 수요 예측과 같은 연구를 수행하기 위해 인공지능을 주로 사용한다. 하지만, 단일 컴퓨터로 학습 시 막대한 양의 자원 소모로 과부하가 발생하게 된다. 이러한 문제점을 해결하기 위해 최근에는 분산 컴퓨팅을 기반으로 한 클라우드 형태의 컴퓨팅 자원을 활용하여 연구를 수행하였다. 하지만 메인 서버로부터 거리가 먼 지역과의 정확하고 빠른 데이터 전송을 위해서는 새로운 기술이 필요하다. 그래서 우리는 본 문제를 해결하기 위해 에지 컴퓨팅을 활용하여 전력 수요 예측을 하기 위한 에지 컴퓨팅을 소개하였으며, 에지 컴퓨팅을 적용 시 응답 처리속도를 비교하였다. 그리고 제안하는 에지 컴퓨팅에서 머신러닝 학습을 수행하여 전력 수요 예측 성능을 측정하였다.