CN113777648A - 一种基于随机编码与神经网络探测器成像的方法及伽马相机 - Google Patents
一种基于随机编码与神经网络探测器成像的方法及伽马相机 Download PDFInfo
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- G01T1/2914—Measurement of spatial distribution of radiation
- G01T1/2921—Static instruments for imaging the distribution of radioactivity in one or two dimensions; Radio-isotope cameras
- G01T1/295—Static instruments for imaging the distribution of radioactivity in one or two dimensions; Radio-isotope cameras using coded aperture devices, e.g. Fresnel zone plates
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115950531A (zh) * | 2023-03-15 | 2023-04-11 | 长春理工大学 | 一种探测器信噪比获取方法及检测装置 |
CN116660969A (zh) * | 2023-07-27 | 2023-08-29 | 四川轻化工大学 | 多时间序列深度神经网络放射源三维定位系统与定位方法 |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105373830A (zh) * | 2015-12-11 | 2016-03-02 | 中国科学院上海高等研究院 | 误差反向传播神经网络的预测方法、系统及服务器 |
CN106846253A (zh) * | 2017-02-14 | 2017-06-13 | 深圳市唯特视科技有限公司 | 一种基于反向传播神经网络的图像超分辨率重建方法 |
CN107229787A (zh) * | 2017-05-24 | 2017-10-03 | 南京航空航天大学 | 一种基于近似系数与深度学习的伽马能谱分析方法 |
CN108566257A (zh) * | 2018-04-27 | 2018-09-21 | 电子科技大学 | 一种基于反向传播神经网络的信号恢复方法 |
CN109031440A (zh) * | 2018-06-04 | 2018-12-18 | 南京航空航天大学 | 一种基于深度学习的伽马放射性成像方法 |
CN110378975A (zh) * | 2019-07-11 | 2019-10-25 | 安徽大学 | 一种基于深度神经网络的压缩编码孔径成像方法及系统 |
CN110502978A (zh) * | 2019-07-11 | 2019-11-26 | 哈尔滨工业大学 | 一种基于bp神经网络模型的激光雷达波形信号分类方法 |
CN112037012A (zh) * | 2020-08-14 | 2020-12-04 | 百维金科(上海)信息科技有限公司 | 一种基于pso-bp神经网络的互联网金融信用评价方法 |
CN112926157A (zh) * | 2021-03-10 | 2021-06-08 | 中国计量大学 | 一种基于神经网络的光栅滤光片结构优化方法 |
-
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- 2021-09-09 CN CN202111056477.8A patent/CN113777648B/zh active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105373830A (zh) * | 2015-12-11 | 2016-03-02 | 中国科学院上海高等研究院 | 误差反向传播神经网络的预测方法、系统及服务器 |
CN106846253A (zh) * | 2017-02-14 | 2017-06-13 | 深圳市唯特视科技有限公司 | 一种基于反向传播神经网络的图像超分辨率重建方法 |
CN107229787A (zh) * | 2017-05-24 | 2017-10-03 | 南京航空航天大学 | 一种基于近似系数与深度学习的伽马能谱分析方法 |
CN108566257A (zh) * | 2018-04-27 | 2018-09-21 | 电子科技大学 | 一种基于反向传播神经网络的信号恢复方法 |
CN109031440A (zh) * | 2018-06-04 | 2018-12-18 | 南京航空航天大学 | 一种基于深度学习的伽马放射性成像方法 |
CN110378975A (zh) * | 2019-07-11 | 2019-10-25 | 安徽大学 | 一种基于深度神经网络的压缩编码孔径成像方法及系统 |
CN110502978A (zh) * | 2019-07-11 | 2019-11-26 | 哈尔滨工业大学 | 一种基于bp神经网络模型的激光雷达波形信号分类方法 |
CN112037012A (zh) * | 2020-08-14 | 2020-12-04 | 百维金科(上海)信息科技有限公司 | 一种基于pso-bp神经网络的互联网金融信用评价方法 |
CN112926157A (zh) * | 2021-03-10 | 2021-06-08 | 中国计量大学 | 一种基于神经网络的光栅滤光片结构优化方法 |
Non-Patent Citations (1)
Title |
---|
汤爱涛等编著: "《计算机在材料工程中的应用》", 重庆大学出版社, pages: 175 - 180 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115950531A (zh) * | 2023-03-15 | 2023-04-11 | 长春理工大学 | 一种探测器信噪比获取方法及检测装置 |
CN116660969A (zh) * | 2023-07-27 | 2023-08-29 | 四川轻化工大学 | 多时间序列深度神经网络放射源三维定位系统与定位方法 |
CN116660969B (zh) * | 2023-07-27 | 2023-10-13 | 四川轻化工大学 | 多时间序列深度神经网络放射源三维定位系统与定位方法 |
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