지역 커뮤니티 중심시설로서의 학교건축

최병관 한국교육시설학회 2011 한국교육시설학회 춘계학술발표대회 논문집 Vol.2011 No.4

이차원 블록 추정을 이용한 적응 CFAR 알고리즘

최병관,김환우,이민준 대한전자공학회 2005 電子工學會論文誌-SP (Signal processing) Vol.42 No.1

Adaptive constant false alarm rate(CFAR) algorithm is used for good detection probability as well as constant false alarm rate in clutter background. Especially, filtering technique adaptive to spatial variation is necessary for improving detection quality in non stationary clutter environment which has spatial correlation and large magnitude deviation. In this paper, we propose a two-dimensional block interpolation(TBI) adaptive CFAR algorithm that calculates the node estimate in the fixed two dimensional region and subsequently determines the final estimate for each resolution cell by two- dimensional interpolation. The proposed method is efficient for filtering abnormal ejection by adopting distribution median in fixed region and also has advantage of reducing required memory space by using estimation method which gets final values after calculating the block node values. Through simulations, we show that the proposed method is superior to the traditional adaptive CFAR algorithms which are transversal or recursive in aspect of the detection performance and required memory space. 적응 CFAR(Constant False Alarm Rate) 알고리즘은 클러터 배경 환경에서 일정한 오경보 율을 유지하면서 탐지확률을 높이기 위해 사용된다. 특히 공간 상관관계, 크기 편차가 큰 비 균일한 클러터 환경에서 탐지성능을 향상시키기 위해서는 공간변화에 적응적인 필터링 기법이 요구된다. 본 논문에서는 클러터 배경추정을 위해 이차원적으로 영역을 구분하여 대표 추정 값을 구하고, 보간(interpolation)필터를 이용하여 최종 추정 값을 결정하는 이차원 블록 보간(Two-dimensional Block Interpolation : TBI) 적응 CFAR 알고리즘을 제안한다. 제안한 방법은 부분영역의 히스토그램 분포 중앙값을 영역 추정 값으로 선택함으로 불규칙 간섭신호 제거에 효과적이며, 블록 노드 추정 값을 이용하여 각 셀에 대한 최종 추정 값을 얻는 방식을 취함으로 인해 거리 셀 수가 많고, 고도 빔 수가 많은 시스템에서 클러터 필터링에 필요한 메모리 공간을 줄이는데 이점이 있다. 컴퓨터 모의실험을 통해 기존의 트랜스버설(transversal) 방식, 회귀(recursive)방식의 적응 CFAR 알고리즘과 탐지성능, 필요메모리 측면에서 성능을 비교하여 제안한 방법의 우수성을 확인한다.

모폴로직 함수에 의한 인쇄체 한글 문자 인식 ( Recognition of Printed Korean Characters Via Morphological Functions )

최병관,고광식 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.1

동탄2 신도시 학교시설 복합화

최병관 한국교육시설학회 2017 한국교육시설학회 추계학술발표대회 논문집 Vol.2017 No.11

전직장간막 절제술과 골반 측부 림프절 절제술이 직장암의 국소 재발과 생존율에 미치는 영향

최병관,김형수,서경원,주재균,류성엽,박영규,김형록,김동의,김영진 대한대장항문학회 2007 Annals of Coloproctolgy Vol.23 No.1

Purpose: One of the most common sites of recurrence after a curative resection of rectal cancer is the pelvis, and local control is a major goal of surgical treatment. The advantages of lateral pelvic lymph node dissection are regarded as questionable because lateral pelvic lymph node metastasis does not occur so frequently and because a lateral lymphadenectomy has a negative influence on the postoperative quality of life. The aim of this study was to clarify if lateral pelvic lymph node dissection (LPLD) conferred any benefit. Methods: A total of 769 patients who underwent curative surgery for rectal cancer between 1981 and 2005 at the Department of Surgery, OOO Hospital, were reviewed retrospectively. One hundred ninety-three of these patients underwent a lateral pelvic lymph node dissection, and 576 patients had a total mesorectal excision with high ligation of the IMA. Results: There was no difference in pathological characteristics between the two groups. Patients who underwent a lateral pelvic lymph node dissection had no statistically significant difference in terms of the 5-year survival rate at stage II and III (64% vs 65% at stage II, P=0.391; 49% vs 47% at stage III, P=0.815). Conclusions: A lateral pelvic lymph node dissection has no advantage as part of a standard operation for rectal cancer. A total mesorectal excision alone has good local control and survival compared with a lateral pelvic lymph node dissection.

제주도의 자연환경에 따른 민가의 배치 특성에 관한 연구

최병관,양택훈,뱍용환 대한건축학회 1991 大韓建築學會論文集 Vol.7 No.4

자율주행자동차 오픈플랫폼 온톨로지 구축을 위한 스마트디바이스 연구

최병관 (사)디지털산업정보학회 2019 디지털산업정보학회논문지 Vol.15 No.3

The 4th Industrial Revolution, intelligent automobile application technology is evolving beyond the limit of the mobile device to a variety of application software and multi-media collective technology with big data-based AI(artificial intelligence) technology. with the recent commercialization of 5G mobile communication service, artificial intelligent automobile technology, which is a fusion of automobile and IT technology, is evolving into more intelligent automobile service technology, and each multimedia platform service and application developed in such distributed environment is being developedAccordingly, application software technology developed with a single system SoC of a portable terminal device through various service technologies is absolutely required. In this paper, smart device design for ontology design of intelligent automobile open platform enables to design intelligent automobile middleware software design technology such as Android based SVC Codec and real time video and graphics processing that is not expressed in single ASIC application software technology as SoC based application designWe have experimented in smart device environment through researches, and newly designed service functions of various terminal devices provided as open platforms and application solutions in SoC environment and applied standardized interface analysis technique and proved this experiment.

육계 불황을 이겨나간 경험담 - 육계 양축가는 적이 많다 싸워 이겨야 한다

최병관 대한양계협회 1973 월간 양계 Vol.5 No.11

인공지능 객체인식에 관한 파라미터 측정 연구

최병관 (사)디지털산업정보학회 2019 디지털산업정보학회논문지 Vol.15 No.3

Artificial intelligence is evolving rapidly in the ICT field, smart convergence media system and content industry through the fourth industrial revolution, and it is evolving very rapidly through Big Data. In this paper, we propose a face recognition method based on object recognition based on object recognition through artificial intelligence. In this method, Were experimented and studied through the object recognition technique of artificial intelligence. In the conventional 3D image field, general research on object recognition has been carried out variously, and researches have been conducted on the side effects of visual fatigue and dizziness through 3D image. However, in this study, we tried to solve the problem caused by the quantitative difference between object recognition and object recognition for human factor algorithm that measure visual fatigue through cognitive function, morphological analysis and object recognition. Especially, The new method of computer interaction is presented and the results are shown through experiments.

인공지능을 이용한 급성 뇌졸중 환자의 재원일수 예측모형 개발

최병관,함승우,김촉환,서정숙,박명화,강성홍 한국디지털정책학회 2018 디지털융복합연구 Vol.16 No.1

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model. 병원 재원일수의 효율적 관리는 병원의 수익과 환자의 진료비 절감을 위해 매우 중요한 요소이다. 이러한 재원일수의 효율적 관리를 위해서는 병원들이 재원일수에 대해서 벤치마킹을 할 수 있도록 지원이 필요하고 재원일수 절감의 구체적인 방향을 제시해 줄 수 있는 재원일수 예측모형의 개발이 필요하다. 본 연구에서는 2013년과 2014년도 퇴원손상환자 자료 중 급성뇌졸중 환자를 추출하여 분석용 자료를 만들고 인공지능을 이용하여 급성뇌졸중 환자의 재원일수 예측모형을 개발하였다. 분석용 자료는 훈련용 60%, 평가용 40%로 분류하였다. 모형개발은 전통적 통계기법인 다중회귀분석기법과 인공지능기법인 대화식 의사결정나무기법, 신경망 기법, 그리고 이들을 모두 통합한 앙상블기법을 이용하였다. 모형평가는 Root ASE(Absolute error) 지표를 이용하였는데, 다중회귀분석은 23.7, 대화식결정나무 23.7, 신경망 분석은 22.7, 앙상블은 22.7로 나타났고 이를 통하여 재원일수 예측모형 개발에 인공지능기법의 유용성이 입증되었다. 앞으로 재원일수 예측모형개발에 인공지능 기법을 보다 효율적으로 활용할 수 있는 방안에 대해서 계속적인 연구가 이루어 질 필요가 있다.