光子学报, 2020, 49 (10): 1015002, 网络出版: 2021-03-10  

基于改进孪生支持向量机的齿廓图像边缘失真分类研究 下载: 563次

Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine
作者单位
1 沈阳工业大学 机械工程学院,沈阳 110870
2 辽宁科技学院 电气与信息工程学院,辽宁 本溪 117004
引用该论文

孙禾, 赵文珍, 赵文辉, 段振云. 基于改进孪生支持向量机的齿廓图像边缘失真分类研究[J]. 光子学报, 2020, 49(10): 1015002.

He SUN, Wen-zhen ZHAO, Wen-hui ZHAO, Zhen-yun DUAN. Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine[J]. ACTA PHOTONICA SINICA, 2020, 49(10): 1015002.

参考文献

[1] 周亮, 王振环, 孙东辰. 现代精密测量技术现状及发展[J]. 仪器仪表学报, 2017, 38(8): 1869-1878.

    ZHOU Liang, WANG Zhen-huan, SUN Dong-chen. Present situation and development of modern precision measurement technology[J]. Chinese Journal of Scientific Instrument, 2017, 38(8): 1869-1878.

[2] 孙禾, 赵文珍, 赵文辉. 基于视觉测量的齿廓图像边缘失真判别算法[J]. 光子学报, 2019, 48(4): 0412003.

    SUN He, ZHAO Wen-zhen, ZHAO Wen-hui. An algorithm for detecting image edge distortion of toothed gear using visual measurement[J]. Acta Photonica Sinica, 2019, 48(4): 0412003.

[3] 裘祖荣, 石照耀, 李岩. 机械制造领域测量技术的发展研究[J]. 机械工程学报, 2010, 46(14): 1-11.

    QIU Zu-rong, SHI Zhao-yao, LI Yan. Research on the development of measurement technology mechanical manufacture[J]. Journal of Mechanical Engineering, 2010, 46(14): 1-11.

[4] WUQing. Research on extended support vector machine algorithm [M]. BeijingScience Press20152-7.吴青. 拓展支持向量机算法研究[M]. 北京科学出版社20152-7.

    WUQing. Research on extended support vector machine algorithm [M]. BeijingScience Press20152-7.吴青. 拓展支持向量机算法研究[M]. 北京科学出版社20152-7.

[5] CAIChun. Support vector machine data disturbance analysis [M]. BeijingTsinghua University Press201933-45.蔡春.支持向量机数据扰动分析[M]. 北京清华大学出版社201933-45.

    CAIChun. Support vector machine data disturbance analysis [M]. BeijingTsinghua University Press201933-45.蔡春.支持向量机数据扰动分析[M]. 北京清华大学出版社201933-45.

[6] 丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1): 1-10.

    DING Shi-fei, QI Bing-juan, TAN Hong-yan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 1-10.

[7] RICHHARIYA B, TANVEER M. A reduced universum twin support vector machine for class imbalance learning[J]. Pattern Recognition, 2020, 102: 107150.

[8] CERVANTES J, GARCIA-LAMONT F, RODRÍGUEZ-MAZAHUA L. A comprehensive survey on support vector machine classification: applications, challenges and trends[J]. Neurocomputing, 2020, 408: 189-215.

[9] 曾柯, 柏林. 齿轮箱故障非线性特征测度及状态TWSVIVI辨识研究[J]. 振动与冲击, 2018, 37(15): 179-198.

    ZENG Ke, BO Lin. Nonlinear characteristic measure of gearbox faults and their category identification with TWSVM[J]. Journal of Vibration and Shock, 2018, 37(15): 179-198.

[10] 李侃, 黄文雄, 黄忠华. 基于支持向量机的多传感器探测目标分类方法[J]. 浙江大学学报(工学版), 2013, 47(1): 15-22.

    LI Kan, HUANU Wen-xiong, HUANU Zhong-hua. Multi- sensor detected object classification method based on support vector machine[J]. Journal of Zhejiang University(Engineering Science), 2013, 47(1): 15-22.

[11] 唐发明, 王仲东, 陈绵云. 支持向量机多类分类算法研究[J]. 控制与决策, 2005, 20(7): 746-754.

    TANG Fa-ming, WANG Zhong-dong, CHEN Mian-yun. On multiclass classification methods for support vector machines[J]. Control and Decision, 2005, 20(7): 746-754.

[12] 韩顺杰, 赵丁选. 基于SVM的二叉树多类分类算法在工程车辆挡位决策中的应用[J]. 中国公路学报, 2007, 20(5): 122-126.

    HAN Shun-jie, ZHAO Ding-xuan. Application to shift decision for construction vehicle based on SVM binary tree mult-class classification algorithm[J]. China Journal of Highway and Transport, 2007, 20(5): 122-126.

[13] 丁世飞, 张健, 张谢锴. 多分类孪生支持向量机研究进展[J]. 软件学报, 2018, 29(1): 89-108.

    DING Shi-fei, ZHANG Jian, ZHANG Xie-kai. Survey on multi class twin support vector machines[J]. Journal of Software, 2018, 29(1): 89-108.

[14] 李景灿, 丁世飞. 基于人工鱼群算法的孪生支持向量机[J]. 智能系统学报, 2019, 14(6): 1121-1126.

    LI Jing-can, DING Shi-fei. Twin support vector machine based on artificial fish swarm algorithm[J]. CAAI Transactions on Intelligent Systems, 2019, 14(6): 1121-1126.

[15] DINGShi-fei. Twin support vector machine: theory, algorithm and extension [M]. BeijingScience Press201716-40.丁世飞. 孪生支持向量机:理论、算法与拓展[M]. 北京科学出版社201716-40.

    DINGShi-fei. Twin support vector machine: theory, algorithm and extension [M]. BeijingScience Press201716-40.丁世飞. 孪生支持向量机:理论、算法与拓展[M]. 北京科学出版社201716-40.

[16] 王宁, 段振云, 赵文辉. 基于Bertrand曲面模型的边缘检测算法[J]. 光子学报, 2017, 46(10): 1012003.

    WANG Ning, DUAN Zhen-yun, ZHAO Wen-hui. Algorithm of edge detection based on bertrand surface model[J]. Acta Photonica Sinica, 2017, 46(10): 1012003.

[17] 支珊, 赵文珍, 赵文辉. 基于齿轮局部图像的齿距机器视觉测量方法[J]. 仪器仪表学报, 2018, 39(2): 225-231.

    ZHI Shan, ZHAO Wen-zhen, ZHAO Wen-hui. The visual measurement method of pitch machine based on the part of gear image[J]. Chinese Journal of Scientific Instrument, 2018, 39(2): 225-231.

[18] 马笑潇, 黄席樾, 柴毅. 基于SVM的二叉树多类分类算法及其在故障诊断中的应用[J]. 控制与决策, 2003, 18(3): 272-276.

    MA Xiao-xiao, HUANG XI-yue, CHAI Yi. 2PTMC classification algorithm based on support vector machines and its application to fault diagnosis[J]. Control and Decision, 2003, 18(3): 272-276.

[19] ZHIShan. Research on visual measurement method of medium and small module gears and tooth pitch measurement technology [D]. ShenyangShenyang University of Technology201987-89.支珊. 中小模数齿轮视觉测量方法与齿距测量技术研究[D]. 沈阳沈阳工业大学201987-89.

    ZHIShan. Research on visual measurement method of medium and small module gears and tooth pitch measurement technology [D]. ShenyangShenyang University of Technology201987-89.支珊. 中小模数齿轮视觉测量方法与齿距测量技术研究[D]. 沈阳沈阳工业大学201987-89.

[20] 黄春福, 李安, 覃方君. 基于PSO-SVR的光纤陀螺温度误差建模与实时补偿[J]. 光子学报, 2019, 48(12): 1206002.

    HUANG Chun-fu, Ll An, QIN Fang-jun. Temperature error modeling and real-time compensation of fiber optic gyroscope based on PSC-SVR[J]. Acta Photonica Sinica, 2019, 48(12): 1206002.

孙禾, 赵文珍, 赵文辉, 段振云. 基于改进孪生支持向量机的齿廓图像边缘失真分类研究[J]. 光子学报, 2020, 49(10): 1015002. He SUN, Wen-zhen ZHAO, Wen-hui ZHAO, Zhen-yun DUAN. Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine[J]. ACTA PHOTONICA SINICA, 2020, 49(10): 1015002.

引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!