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L자형 슬릿을 이용한 LTE용 마이크로스트립 패치 안테나의 설계
박근철(Guen-Chul Park),강성운(Sung Woon Kang),어용치멕 바야르마(Bayarmaa O),김동환(Dong-Hwan Kim),김갑기(Gab-Gi Kim) 한국정보기술학회 2013 Proceedings of KIIT Conference Vol.2013 No.5
본 논문에서 안테나는 유전율 4.4, 두께는 0.6㎜인 FR-4 기판위에 L자형 슬릿을 포함한 마이크로스트립패치를 삽입하였으며, LTE용 마이크로스트립 패치 안테나를 설계하였다. 제안된 안테나는 기존의 안테나에 비해 소형, 경량화 하였으며 전방향으로 전파가 가능한 방사패턴을 보였으며, 입력대비 반사손실이 -10㏈(VSWR 2:1) 이하로 공진 주파수 대역에서 통신을 가능케 하였다. In this paper was The dielectric constant of 4.4 and thickness of 0.6㎜ FR-4 substrate were implanted, including the L-shaped slit in microstrip patch antenna to design a microstrip patch antenna for LTE. The proposed antenna is compact and lightweight compared to existing antenna and the omnidirectional radiation pattern of propagation was compared to the input return loss than -10㏈(VSWR 2:1) to allow communication from the resonant frequency band.
박근철 ( Geun Chul Park ),이승희 ( Seung Hee Lee ),박창수 ( Chang Soo Park ),김동원 ( Dong Won Kim ),김원택 ( Won Taek Kim ),전계록 ( Gye Rok Jeon ) 한국센서학회 2014 센서학회지 Vol.23 No.1
In this paper, we report a novel algorithm based on phase displacement, which supplements conventional TOF methods for distance measurement using an ultrasonic wave. The proposed algorithm roughly measures the distance between the transmission part and the receiving part by using the initial TOF. Thereafter, the precise distance is determined by measuring the phase displacement value between the synchronizing transmission signal and the signal obtained at the receiving end. A distance measurement experiment using a micrometer was performed to verify the accuracy of the ultrasonic wave sensor system. We found that the mean errors from the one adopting the distance measurement algorithm based on phase displacement varied from a minimum of 0.03 mm to a maximum of 0.09 mm. In addition, the standard deviation varied from a minimum of 0.04 mm to a maximum of 0.07 mm, thus giving a precision of 0.1 mm.
서울·경기지역 폐경 전후 여성의 건강기능성식품 섭취 실태 및 인지도 조사 연구
박근철(Keun-Cheol Park),최윤혜(Yoon-Hye Choi),김우림(Woo-Rim Kim),최예지(Ye-Ji Choi),윤기선(Ki Sun Yoon) 한국식품영양과학회 2014 한국식품영양과학회지 Vol.43 No.7
The purpose of this study was to investigate intake status and recognition of health functional foods by pre- and post-menopausal women in Seoul and Gyeonggi province. Survey questions were administered to 400 women around menopausal period, and data analysis was completed using the SPSS window program. Thirty-three percent of women recognized that they are healthy, and 47.1% of respondents are concerned with maintaining their health. However, respondents showed a low level of knowledge about the definition of menopause and health functional foods. Health concerns of respondents were significantly affected by marital status and level of education (P<0.05). Purchasing and intake of health functional foods was also significantly affected by health concerns and menopause symptoms (P<0.05). Twenty-nine percent of participants had taken health functional foods to prevent disease. However, most of them (85.9%) showed no knowledge of the main components of functional foods. In addition, knowledge associated with menopause and functional foods was affected by the level of education. The respondents" health concern, and thus purchase and intake frequencies of health functional foods, were affected by level of education. ‘Effect of functional foods’ was a top priority when respondents purchased health functional foods. The respondents answered black bean and pomegranate as foods that relieve menopause symptoms. However, they actually showed high intake frequency of black beans than pomegranate due to the high accessibility of black beans. The results of this study show that educational support for dietary guidelines is needed for middle-aged woman to be healthy after menopause.
메트릭 학습 기반 오픈세트 극소수 학습에 대한 분류 성능 향상
박근철(Keunchul Park),정민기(Minki Jeong),김창익(Changick Kim) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.11
To conduct image classification with limited data is a key challenge for image recognition. Although several few-shot learning models already show good performance in closed-set classification, the actual environment for which deep learning is applied is open-set classification rather than closed-set classification. To achieve good open-set classification performance, we apply meta-learning in our model and suggest a layer normalization technique to make class decision boundaries more accurate. Our method shows better classification accuracy and unknown class sample detection capability, compared with previous few-shot learning methods.
클러스터 시스템에서 하드웨어 퍼포먼스 카운터 데이터 수집 방법 및 오버헤드 연구
박근철 ( Guenchul Park ),박찬열 ( Chan-yeol Park ),노승우 ( Seungwoo Rho ),최지은 ( Ji Eun Choi ) 한국정보처리학회 2020 한국정보처리학회 학술대회논문집 Vol.27 No.2
대부분의 최신 마이크로 프로세서에서 사용 가능한 하드웨어 퍼포먼스 카운터는 시스템과 어플리케이션의 상태를 모니터링, 분석 및 최적화하는 다양한 용도로 폭넓게 사용되고 있다. 적은 오버헤드로 시스템의 가장 기본적인 정보를 수집할 수 있기 때문에 다양한 분야에서 활용이 가능하다. 이러한 퍼포먼스 카운터는 리눅스에 내장되어 있는 퍼프 이벤트를 통하여 수집 할 수 있는데 클러스터 시스템에서는 단일 노드에서와는 다른 방법을 사용하여 이벤트를 수집해야 한다. 본 연구에서는 클러스터 시스템에서 하드웨어 퍼포먼스 카운터를 수집하는 방법과 오버헤드에 대하여 연구하여 카운터의 활용을 지원하고자 한다.