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조병동(Byung Dong Cho),오흥국(Heung Kuk Oh),장명국(Myoung Kuk Jang),경태영(Tae Young Kyong),이종민(Jong Min Lee),김용범(Yong Bum Kim),김학양(Hak Yang Kim),박충기(Choong Kee Park),유재영(Jae Young Yoo) 대한소화기학회 1996 대한소화기학회지 Vol.28 No.2
N/A Background/Aims: Extrahepatic bile duct(EHBD) carcinoma is a rare tumor among the population of the world and accounts for less than 2% of cancers found at autopsy and about 10% of all biliary duct cancer. To identify the clinical characteristics associated with prognosis and the survival rate by treatment modalities, we reviewed 120 patients with extrahepatic bile duct carcinoma who were diagnosed at the Kangdong Sacred Heart Hospital of Hallym University from June 1987 to October l994. Methods: According to treatment modalities, these patients were divided into 50 cases of the operation group, 47 cases of the non-operative treatrnent group and 23 cases of the no treatment group. The survival rate of these three groups was analyzed according to Cutler-Ederer methods. Results: The most common symptom and sign were jaundice(85.8%) and hepatomegaly (57.5%). The most common site of tumor was the common hile duct(63.3%), followed by the hepatic duct bifurcation(25.8%), the common hepatic duct(9.2%), the cystic duct(0.8%) and tbe diffuse type(0.8%). The median survival was l0.7 months. The survival rate was 76.7% in 3 months, 68.4% in 6 months, 4l.6% in l year, 14.1% in 2 years, and 3.4% in 5 years. According to the treatment modalities, the median survival was l6.3 months in the operation group, 6.3 months in the non-operative treatment group, and 3.5 rnonths in no treatment group. Conclusions: The operation group had a better survival rate than the other group. We conclude that operation offers the best prognosis. The result of this study suggests that early diagnosis and operation prolong survival in these patients. Further studies of adjuvant chernotberapy and radiotapy wi]l be necessary to improve patients survival. (Korean J Gastroenterol 1996; 28:25] 259)
딥러닝 기반 화력발전 보일러 출구 NOx 농도 선행 예측 모델
조현빈(Hyunbin Jo),강동협(Donghyup Kang),박성민(Seongmin Park),이종욱(Jongwuk Lee),류창국(Kang Y. Huh) 한국연소학회 2022 KOSCOSYMPOSIUM논문집 Vol.2022 No.11
In this study, we developed a deep learning model to forecast the NOx and oxygen concentration, and gas temperature at the boiler exit of a coal-fired power plant. The target boiler is a 500 MWe tangential firing boiler, which is one of 20 units often referred to as standard coal power plant. From the database of the power plant, 73 raw items of operation data with one-minute frequency were collected for a period of approximately 5 months. Through the feature selection procedure, the raw data items were condensed into 19 features which include coal feeder throughput to burners, air flow rate, and burner tilt. The features were then used to establish two types of data segments: segment #1 for current operation status and segment #2 for recent histories measured at the boiler exit. Considering the large fluctuations, the histories of the recent values at the boiler exit values were averaged over 5 min. After evaluating different prediction models with respect to the nature of the data segments, suitable models were applied in the form of ensemble model to forecast the boiler exit values 1 min in advance. When compared to measured data, the prediction quality was sufficiently high with a mean square error of 0.0123 for NOx emission.