压力抗血栓气动回路气密性检测系统的设计
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摘要
为有效提高抗血栓压力循环装置中气动回路的气密性,本文设计研发了一套新型气动回路气密性检测系统。系统主体硬件设计包括气动回路设计和测控系统设计,系统软件设计提出一种基于支持向量机(SVM)的方法。该方法首先对气动回路进行特征参数提取,然后将提取的特征参数作为支持向量机输入量,并进行训练。训练后的支持向量机可以通过测得的气动回路特征参数来有效识别气动回路类别,即合格与否,并通过人机交互界面将测试结果呈现给用户。实验结果表明,该系统操作简单,能高效完成对待测气动回路气密性能的检测,满足使用需求。
In order to improve the air tightness of the pneumatic circuit in the anti-thrombotic pressure circulation device, a new type of air tightness detection system was designed and developed. The system hardware design includes the design of the pneumatic circuit design and the measurement and control system. The system software design proposes a support vector machine(SVM) method. The method takes the multi-factor input as the input quantity and the single factor air tightness as the output quantity. The test data is processed, and the test results are presented to the user through the man-machine interaction interface. The experimental results show that the system is simple to operate and can effectively detect the airtight performance of the pneumatic circuit to be tested.
In order to improve the air tightness of the pneumatic circuit in the anti-thrombotic pressure circulation device, a new type of air tightness detection system was designed and developed. The system hardware design includes the design of the pneumatic circuit design and the measurement and control system. The system software design proposes a support vector machine(SVM) method. The method takes the multi-factor input as the input quantity and the single factor air tightness as the output quantity. The test data is processed, and the test results are presented to the user through the man-machine interaction interface. The experimental results show that the system is simple to operate and can effectively detect the airtight performance of the pneumatic circuit to be tested.
引文
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[2]胡士琪,唐校光.低分子肝素(LMWH)配合其它措施预防老年人髋部术后深静脉血栓形成[J].中国伤残医学.2011,19(11):9-10.
[3]龙飞,邢武,盛曾慰.气密性检测方法分析[J].液压气动与密封,2000(5):21-23.
[4]陈丹江,万雪梅.气压式泄漏检测装置及应用[J].机电工程技术,2001(1):39-43.
[5]邓建春,赵丹.发动机前后油封气密性检测装置的设计[J].液压气动与密,2005(6):3-5.
[6]陈艳琴.基于SVM的文字商标检测[J].软件,2013,34(1):149-151.
[7]张环,赵亚妮,杨晓波.一种基于SVM的视频检索反馈算法[J].软件,2013,34(3):69-71.
[8]陈海红.多核SVM文本分类研究[J].软件,2015,36(5):7-10.
[9]苏志同,周文龙.基于SVM的心律失常的研究和分析[J].软件,2015,36(9):98-100.
[10]Xiao xiao,Xu Qihua.A Comparative Research on the Classification and Regression Based on SVM and BP[J].The Journal of New Industrialization,2014,4(5):48-53.
[11]李文淳.“五步法”解决控制系统方框图问题[J].教育研究与评论:技术教育.2012(2):42-45.
[12]付敬奇.执行器及其应用[M]:机械工业出版社,2009.
[13]崔静艳,王小杰.气动执行器运行的点检定修与风险防范[J].化工生产与技术.2014,21(5):49-51.
[14]杨清波.传感器与测试技术[M].黑龙江:哈尔滨工程大学出版社,2004.
[15]张金.Lab VIEW程序设计与应用[M].北京:电子工业出版社,2015.
[16]陈守平,董瑞.MATLAB神经网络30个案例分析[M]:北京航空航天大学出版社,2010.
[17]卓金武,李必文.MATLAB在数学建模中的应用[M]:北京航空航天大学出版社,2014.
[18]Vapnic V.Statistical Learning Theory.New York:Wiley Press,1998,11:342-344.