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제2형 당뇨병 환자에서 우연히 발견된 기종성 방광염 1례
박보민,김윤정,이영태,노정현,권수경,김동준,고경수,이병두,임경호,이순희,박정현 白中央醫療院 2005 仁濟醫學 Vol.26 No.1
Emphysematous cystitis is an uncommon disease in which bacterias produce gas within the bladder wall and surrounding tissue. Patients with diabetes, neurogenic bladder and chronic urinary tract infection are predisposed to the disease. It is usually caused by E.coli and Klebsiella. Severity of illness ranges from an asymtomatic condition to life threatening cystitis. Succesful management depends on early diagnosis with correction of underlying causes, administration of appropriate antibiotics, establishment of adequate bladder drainage and surgical excision of involved tissue when required. We report a case of 52-year-old woman who did not compain of symtoms of cystitis but epigastric pain, nausea, and vomitting. Emphysematous cystitis was revealed on the abdominal X-ray series incidentally. CT scans of the pelvis showed mottled gas bubble within the bladder. After treatment, the symtoms subsided and plain abdominal film showed no evidence of gas shadow in the pelvic cavity.
Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase
Bo-Hyeun Wang,Hyun-Joon Cho 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.2
It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems of fuzzy system reproduction and nonlinear system modeling in order to validate our claim.
Short-term Electrical Load Forecasting Using Neuro-Fuzzy Model with Error Compensation
Bo-Hyeun Wang 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.4
This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.
Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase
Wang, Bo-Hyeun,Cho, Hyun-Joon Korean Institute of Intelligent Systems 2007 한국지능시스템학회논문지 Vol.17 No.2
It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.
Short-term Electrical Load Forecasting Using Neuro-Fuzzy Model with Error Compensation
Wang, Bo-Hyeun Korean Institute of Intelligent Systems 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.4
This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.
Deciphering Diversity Indices for a Better Understanding of Microbial Communities
( Bo-ra Kim ),( Jiwon Shin ),( Robin B. Guevarra ),( Jun Hyung Lee ),( Doo Wan Kim ),( Kuk-hwan Seol ),( Ju-hoon Lee ),( Hyeun Bum Kim ),( Richard E. Isaacson ) 한국미생물생명공학회(구 한국산업미생물학회) 2017 Journal of microbiology and biotechnology Vol.27 No.12
The past decades have been a golden era during which great tasks were accomplished in the field of microbiology, including food microbiology. In the past, culture-dependent methods have been the primary choice to investigate bacterial diversity. However, using culture-independent high-throughput sequencing of 16S rRNA genes has greatly facilitated studies exploring the microbial compositions and dynamics associated with health and diseases. These culture-independent DNA-based studies generate large-scale data sets that describe the microbial composition of a certain niche. Consequently, understanding microbial diversity becomes of greater importance when investigating the composition, function, and dynamics of the microbiota associated with health and diseases. Even though there is no general agreement on which diversity index is the best to use, diversity indices have been used to compare the diversity among samples and between treatments with controls. Tools such as the Shannon- Weaver index and Simpson index can be used to describe population diversity in samples. The purpose of this review is to explain the principles of diversity indices, such as Shannon- Weaver and Simpson, to aid general microbiologists in better understanding bacterial communities. In this review, important questions concerning microbial diversity are addressed. Information from this review should facilitate evidence-based strategies to explore microbial communities.
천연 염색 정보 서비스를 위한 모바일 어플리케이션에 관한 연구
김보경 ( Bo-kyung Kim ),김도현 ( Do-hyeun Kim ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.1
ICT 산업의 패러다임은 인터넷 중심에서 모바일 중심으로 빠르게 변화하고 있다. 이로 인해 모바일 서비스에 대한 관심이 높아지고 범위가 늘어나고 있으며, 새로운 모바일 어플리케이션을 제공하여 제품을 홍보하고 있다. 본 논문에서는 천연 염색 정보를 언제 어디서나 편리하게 제공하기 위한 모바일 어플리케이션을 설계한다. 이 모바일 어플리케이션은 천연염색 관련 자원, 색채, 염색 섬유원단, 제품 중심으로 정보를 제공한다.