-
公开(公告)号：KR1020140108396A
公开(公告)日：2014-09-11
申请号：KR1020130020543
申请日：2013-02-26
Applicant: 삼성전자주식회사
IPC: H01S3/0933
CPC classification number: H01J61/025 , G03F7/70016 , G03F7/70175 , H01J61/545 , H01J61/86 , H01J65/042
Abstract: Provided are a light source device and an apparatus for fabricating a semiconductor device using the same. The light source device includes a light emitting lamp; a laser generator for emitting a laser beam to the light emitting lamp; and an optical recycle member for reemitting the laser beam passing through the light emitting lamp to the light emitting lamp. The optical recycle member includes an initial recycled light modulator for changing the phase of the laser beam.
Abstract translation: 提供一种光源装置和使用其的半导体装置的制造装置。 光源装置包括发光灯; 激光发生器，用于向所述发光灯发射激光束; 以及用于将通过发光灯的激光束重新发射到发光灯的光学再循环部件。 光学循环构件包括用于改变激光束的相位的初始再循环光调制器。
-
-
公开(公告)号：KR101877468B1
公开(公告)日：2018-07-12
申请号：KR1020110145437
申请日：2011-12-29
Applicant: 삼성전자주식회사
CPC classification number: H01J65/04
Abstract: 광대역광원장치는고압가스만으로채워져있고구 형상을갖는챔버가구비된다. 상기챔버외부에는레이저방출기가구비된다. 상기챔버와이격되면서상기챔버를둘러싸도록타원경이배치된다. 상기챔버중심부의플라즈마영역으로부터방출되어상기타원경에서반사된광을수집하는로드렌즈가구비된다. 상기챔버내부에고압가스들을이온화시키기위하여제공되는에너지공급부를포함한다. 상기광대역광원장치는챔버내에전극이구비되지않으므로, 생성된광은균일한조도를가지며, 입사각별로광량이균일하다.
-
公开(公告)号：KR1020130104884A
公开(公告)日：2013-09-25
申请号：KR1020120026813
申请日：2012-03-15
Applicant: 삼성전자주식회사 , 사회복지법인 삼성생명공익재단
CPC classification number: A61B5/7264 , A61B5/02007 , A61B5/0205 , A61B5/14546 , A61B5/7275 , A61B8/0891 , A61B8/5223 , G06F19/00 , G06F19/3456 , G16H50/30
Abstract: PURPOSE: A device and a method of predicting the progression of carotid stenosis accurately conduct medicine treatment in advance by predicting a group which is expected to be worse. CONSTITUTION: An input part receives data consisting of many numbers. A cluster designation part designates an included cluster to the data consisting of many numbers. An influence variable value extraction part (220) extracts an influence variable value. A prediction model storage part (230) stores multiple prediction models. A prediction part (240) draws a predicted result by applying the prediction model. [Reference numerals] (200) Input part; (210) Cluster part; (220) Influence variable value extraction part; (230) Prediction model storage part; (240) Prediction part; (AA) Diagnosis data; (BB) Prediction result
Abstract translation: 目的：预测颈动脉狭窄进展的一种手段和方法，通过预测预期更差的一组，提前准确地进行药物治疗。 构成：输入部分接收由多个数字组成的数据。 集群指定部分将包含的集群指定给由许多数字组成的数据。 影响变量值提取部（220）提取影响变量值。 预测模型存储部（230）存储多个预测模型。 预测部（240）通过应用预测模型来绘制预测结果。 （附图标记）（200）输入部; （210）集群部分; （220）影响变量提取部分; （230）预测模型存储部分; （240）预测部分; （AA）诊断数据; （BB）预测结果
-
公开(公告)号：KR1020130104882A
公开(公告)日：2013-09-25
申请号：KR1020120026811
申请日：2012-03-15
Applicant: 삼성전자주식회사 , 사회복지법인 삼성생명공익재단
IPC: G06F19/00
CPC classification number: G06F19/3431 , G06F19/00 , G06Q50/24 , G16H50/30
Abstract: PURPOSE: A coronary calcification value danger prediction apparatus and method are provided to enhance prophylactic potential of a heart disease and a cerebral accident by predicting a group which is predicted to increase a coronary calcification danger degree in the fixed future time point. CONSTITUTION: An input part (100) receives examination data for coronary calcification of an examinee and a cluster specification part (110) designates an affiliation cluster to the examination data for the coronary calcification of the examinee based on the age of the examinee. An influence variable value extracting part (120) stores multiple prediction models which predict a coronary calcification danger degree. A prediction part (140) applies coronary calcification value increasing rate of the examination data and an influence variable value to the prediction model for the affiliation cluster and draws a future coronary calcification danger degree. [Reference numerals] (100) Input part; (110) Cluster specification part; (120) Influence variable value extracting part; (130) Prediction model storage part; (140) Prediction part; (AA) Diagnosis data; (BB) Prediction result
Abstract translation: 目的：提供冠状动脉钙化危险预测装置和方法，通过预测在固定未来时间点预测会增加冠状动脉钙化危险程度的组，增强心脏病和脑部事故的预防潜力。 输入部分（100）接收被检者的冠状动脉钙化检查数据，集群规范部分（110）根据受检者的年龄，将受试者的冠状动脉钙化检查数据指定为隶属关系。 影响变量值提取部（120）存储预测冠状动脉钙化危险度的多个预测模型。 预测部（140）将检查数据的冠状动脉钙化值增加率和影响变量值应用于归属簇的预测模型，并绘制未来冠状动脉钙化危险度。 （附图标记）（100）输入部; （110）集群规范部分; （120）影响变量值提取部分; （130）预测模型存储部分; （140）预测部分; （AA）诊断数据; （BB）预测结果
-
Patent Agency Ranking
-
公开(公告)号：KR1020130104883A
公开(公告)日：2013-09-25
申请号：KR1020120026812
申请日：2012-03-15
Applicant: 삼성전자주식회사 , 사회복지법인 삼성생명공익재단
CPC classification number: G06N99/005 , G06F19/00 , G16H50/20 , G16H50/30
Abstract: PURPOSE: An apparatus for predicting level change of coronary artery calcium (CAC) and a prediction method thereof are provided to increase prevention possibility of heart disease and stroke, by predicting the population group in which degree of danger of the coronary artery calcium is expected to increase in the future. CONSTITUTION: An input part (100) receives medical examination data about coronary artery calcium of a patient and task information about the medical examination data. A cluster designation part (110) designates a belonging cluster to the medical examination data about the coronary artery calcium of the patient on the ground of the characteristics of the patient. An influence variable value extraction part (120) extracts influence variable value used in collecting influence variables of the belonging cluster from the medical examination data. A prediction model learning part (140) performs machine learning, by applying the influence variable value to a prediction model corresponding to the belonging cluster. A prediction part obtains prediction result, by applying the influence variable value to the prediction model. [Reference numerals] (100) Input part; (110) Cluster designation part; (120) Influence variable value extraction part; (130) Prediction model storage unit; (140) Prediction model learning part; (150) Prediction unit; (AA) Medical examination data and job information; (BB) Prediction result
Abstract translation: 目的：提供一种用于预测冠状动脉钙（CAC）水平变化的装置及其预测方法，通过预测冠状动脉钙危险程度预计为增加心脏病和中风的预防可能性 未来增加 构成：输入部（100）接收关于患者的冠状动脉钙的医学检查数据，以及关于医疗检查数据的任务信息。 群体指定部分（110）基于患者的特征，将关于冠状动脉钙的医学检查数据的属性集合指定到患者的特征。 影响变量提取部（120）从医疗检查数据中提取用于收集所属簇的影响变量的影响变量值。 预测模型学习部（140）通过将影响变量值应用于与属性簇对应的预测模型来进行机器学习。 预测部通过将影响变量值应用于预测模型来获得预测结果。 （附图标记）（100）输入部; （110）集群指定部分; （120）影响变量提取部分; （130）预测模型存储单元; （140）预测模型学习部分; （150）预测单位; （AA）体检资料及工作资料; （BB）预测结果
-
公开(公告)号：KR1020050078034A
公开(公告)日：2005-08-04
申请号：KR1020040006132
申请日：2004-01-30
Applicant: 삼성전자주식회사
IPC: G02F1/13
CPC classification number: G01N21/95 , G01N2021/9513 , G02F1/1309
-
-
公开(公告)号：KR1020130095555A
公开(公告)日：2013-08-28
申请号：KR1020120017092
申请日：2012-02-20
Applicant: 삼성전자주식회사
IPC: G01N21/01
CPC classification number: F21V13/04 , F21V7/0025 , G01N21/8806 , G01N21/9501 , G01N2021/8845
Abstract: PURPOSE: A lighting optical system is provided to improve lighting efficiency using wavelength of broadband. CONSTITUTION: A lighting optical system comprises a first light source, a first ellipsoidal mirror (111a), a first mirror (113a), a second light source, a second ellipsoidal mirror (111b), and a second mirror (113b). The first light source emits the light at first wavelength band. The first ellipsoidal mirror reflects the light emitted from the first light source. The first mirror reflects the light from the first ellipsoidal mirror to condensing lens (116). The second light source emits the light at second wavelength band. The second ellipsoidal mirror emits the light emitted from the second light source. The second mirror is installed between the first mirror and the condensing lens, and reflects the light from the second ellipsoidal mirror to the condensing lens. [Reference numerals] (130) Image formation optical system; (140) CCD camera; (150) Image processing unit; (160) Display unit
Abstract translation: 目的：提供照明光学系统，以提高使用宽带波长的照明效率。 构成：照明光学系统包括第一光源，第一椭圆面镜（111a），第一反射镜（113a），第二光源，第二椭圆面镜（111b）和第二反射镜（113b）。 第一光源以第一波长带发射光。 第一椭圆面镜反射从第一光源发出的光。 第一反射镜将来自第一椭球镜的光反射到聚光透镜（116）。 第二光源以第二波长带发射光。 第二椭圆面镜发射从第二光源发射的光。 第二反射镜安装在第一反射镜和聚光透镜之间，并将来自第二椭圆面镜的光反射到聚光透镜。 （附图标记）（130）图像形成光学系统; （140）CCD摄像头; （150）图像处理单元; （160）显示单元
-
-
-
-
-
-
-
-
-
Search Results
13
records
(took 0.021 seconds)
