CN108898157A - 基于卷积神经网络的数值型数据的雷达图表示的分类方法 - Google Patents
基于卷积神经网络的数值型数据的雷达图表示的分类方法 Download PDFInfo
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113156391A (zh) * | 2021-04-25 | 2021-07-23 | 电子科技大学 | 一种雷达信号多维特征智能分选方法 |
CN113688931A (zh) * | 2021-09-01 | 2021-11-23 | 什维新智医疗科技(上海)有限公司 | 一种基于深度学习的超声图像筛选方法和装置 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103971129A (zh) * | 2014-05-27 | 2014-08-06 | 浙江大学 | 一种基于学习跨数据域子空间的图像内容识别的分类方法及装置 |
CN105354223A (zh) * | 2015-10-05 | 2016-02-24 | 北京工商大学 | 一种针对非连续层次数据的可视化方法和应用 |
CN105389569A (zh) * | 2015-11-17 | 2016-03-09 | 北京工业大学 | 一种人体姿态估计方法 |
CN106874957A (zh) * | 2017-02-27 | 2017-06-20 | 苏州大学 | 一种滚动轴承故障诊断方法 |
CN107844755A (zh) * | 2017-10-23 | 2018-03-27 | 重庆邮电大学 | 一种结合dae和cnn的脑电信号特征提取与分类方法 |
CN107944412A (zh) * | 2017-12-04 | 2018-04-20 | 国网山东省电力公司电力科学研究院 | 基于多层卷积神经网络的输电线路自动识别***及方法 |
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Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103971129A (zh) * | 2014-05-27 | 2014-08-06 | 浙江大学 | 一种基于学习跨数据域子空间的图像内容识别的分类方法及装置 |
CN105354223A (zh) * | 2015-10-05 | 2016-02-24 | 北京工商大学 | 一种针对非连续层次数据的可视化方法和应用 |
CN105389569A (zh) * | 2015-11-17 | 2016-03-09 | 北京工业大学 | 一种人体姿态估计方法 |
CN106874957A (zh) * | 2017-02-27 | 2017-06-20 | 苏州大学 | 一种滚动轴承故障诊断方法 |
CN107844755A (zh) * | 2017-10-23 | 2018-03-27 | 重庆邮电大学 | 一种结合dae和cnn的脑电信号特征提取与分类方法 |
CN107944412A (zh) * | 2017-12-04 | 2018-04-20 | 国网山东省电力公司电力科学研究院 | 基于多层卷积神经网络的输电线路自动识别***及方法 |
Non-Patent Citations (5)
Title |
---|
FRANCISCO, AT EL.: ""Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process"", 《SCIENCE DIRECT》 * |
JIHEN AMARA,AT EL.: ""CNN based Chart Image Classification"", 《25TH INTERNATIONAL CONFERENCE IN CENTRAL EUROPE ON COMPUTER GRAPHICS, VISUALIZATION AND COMPUTER VISION 2017》 * |
刘文远等: ""基于多维数据雷达图表示的图形分类器研究"", 《计算机工程与应用》 * |
王金甲等: ""雷达图图形特征提取中的特征排序"", 《燕山大学学报》 * |
陆汝华等: ""基于雷达图重心特征提取的轴承故障诊断方法"", 《轴承》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113156391A (zh) * | 2021-04-25 | 2021-07-23 | 电子科技大学 | 一种雷达信号多维特征智能分选方法 |
CN113688931A (zh) * | 2021-09-01 | 2021-11-23 | 什维新智医疗科技(上海)有限公司 | 一种基于深度学习的超声图像筛选方法和装置 |
CN113688931B (zh) * | 2021-09-01 | 2024-03-29 | 什维新智医疗科技(上海)有限公司 | 一种基于深度学习的超声图像筛选方法和装置 |
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