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Analysis of Input Filter Interactions in Switching Power Converters
Choi, Byungcho,Kim, Dongsoo,Lee, Donggyu,Choi, Seungwon,Sun, Jian Institute of Electrical and Electronics Engineers 2007 IEEE transactions on power electronics Vol.22 No.2
<P>This paper presents the theoretical and practical details involved with the small-signal analysis of switching power converters under a strong influence of input filter interaction. A boost dc-to-dc converter controlled by voltage-mode control and the same boost converter controlled by current-mode control are used as illustrative examples. The theoretical predictions of this paper are supported by experimental results and computer simulations</P>
Donggyu Choi,Jongwook Jang 한국정보통신학회 2021 2016 INTERNATIONAL CONFERENCE Vol.12 No.1
After surgery, patients find it difficult to use the operating organs or move their bodies. Patients who don"t want a stuffy hospital life want to return to their daily lives as soon as possible, and need a recovery for it. Hospitals are introducing ERAS(Enhanced Recovery After Surgery) or the quick recovery effect of patients. ERAS means that all rehabilitation and pharmacological treatment methods for strengthening recovery before and after surgery are designed in a systematic order to perform treatment. It has been confirmed that the recovery rate varies from person to person depending on symptoms, age and gender, but is faster than the conventional method. However, the current ERAS process requires medical staff to check the patient"s performance for themselves, and there is a significant loss of human resources. Thus, in this paper, a study was conducted on the development of contents that could be checked unattended against the rehabilitation movement of ERAS without the verification process by the medical team.
딥러닝의 다수 입력 이미지 학습 및 추론 효율 향상을 위해 추가적인 처리 프로세스 연구
최동규(Donggyu Choi),김민영(Minyoung Kim),장종욱(Jongwook Jang) 한국정보통신학회 2021 한국정보통신학회 종합학술대회 논문집 Vol.25 No.2
실생활에는 많은 카메라가 활용되고 있으며 단순한 추억을 위한 사진 촬영을 넘어서 문제 상황을 확인하기 위하여 감시, 방범을 위하여 많이 사용되고 있다. 이러한 감시와 방범은 일반적인 형태로 단순한 저장으로만 사용되고 있으며, 다수의 카메라를 활용하는 시스템에서는 추가 기능을 활용하는 것은 하드웨어의 추가적인 사양을 요구하게 된다. 본 논문에서는 일반적인 이미지 처리에서 벗어난 객체 감지 시스템을 수행하는 하나의 하드웨어 또는 서버에서 입력된 여러 개의 이미지 입력 처리하기 위해 이미지 입력 방법과 객체 감지 이후 처리 프로세스를 추가한다. 방법의 수행은 딥러닝을 수행하는 하드웨어의 학습과 추론에 모두 활용해 보며 개선된 이미지 처리 프로세스를 수행할 수 있도록 한다. Many cameras are used in real life, and they are often used for monitoring and crime prevention to check the situation of problems beyond just taking pictures for memories. Such surveillance and prevention are generally used only for simple storage, and in systems utilizing multiple cameras, utilizing additional features would require additional hardware specifications. In this paper, we add image input methods and post-object processing processes to process multiple image inputs from one hardware or server that perform object detection systems that deviate from typical image processing. The performance of the method is utilized in both learning and reasoning of the hardware performing deep learning, and allows improved image processing processes to be performed.