基于扩维的卷积网络及脉象识别应用

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 506-507.

基于扩维的卷积网络及脉象识别应用

张宁

中央财经大学中国精算研究院北京100081

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:张宁(1978-),男,博士,副教授,主要研究方向为金融科技、健康量化、精算与长寿风险,E-mail:nzhang@amss.ac.cn(通信作者)。
基金资助:
教育部人文社科项目(16YJCZH148),重点研究基地重大项目(16JJD790060),中国保险学会教保人身险研究基金(jiaobao2017-10),高校学科创新引智计划资助(B17050),厦门产学研协同创新及科技合作项目(3502Z20172012),中央财经大学科研创新团队资助(20170074)资助。

Pulse Condition Recognition Based on Convolutional Neural Network with Dimension Enlarging

ZHANG Ning

China Institute for Actuarial Science,Central University of Finance and Economics,Beijing 100081,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 针对时间尺度变化特征差异较大的非图像多元时间序列,提出了一种卷积神经网络的扩维预处理方法。该方法应用样本统计特征和希尔伯特-黄变换来扩展维度,并加快网络的训练。文中将其用于生理数据分析并进行脉象分类。结果表明,进行扩维能够较大幅度地改善随机梯度算法的效率,同时该卷积网络方法能够较好地捕捉生理信号和脉象的特征关系。

关键词: 经验模型分解, 卷积神经网络, 脉象, 统计特征, 希尔伯特-黄变换

Abstract: A new model of convolutional neural network was promoted for pulse condition recognition.The model is fit for the group including different dimensional data sets.For more effective training process,the sample characters and HHT’s results were considered as a times series.The result shows the expected accuracy rating and training efficiency.The method can also obtain the relations between pulse conditions and several personal biological data.

Key words: Convolutional neural network, Empirical mode decomposition, Hilbert-Huang transform, Pulse condition, Statistical characters

中图分类号:

TP301

张宁. 基于扩维的卷积网络及脉象识别应用[J]. 计算机科学, 2018, 45(6A): 506-507. https://doi.org/

ZHANG Ning. Pulse Condition Recognition Based on Convolutional Neural Network with Dimension Enlarging[J]. Computer Science, 2018, 45(6A): 506-507. https://doi.org/

参考文献

[1]张宁.深度学习改变保险精算定价模式[J].计算机科学,2017,44(3):1-2.
[2]刘琮,许维胜,吴启迪.时空域深度卷积神经网络及其在行为识别上的应用[J].计算机科学,2015,42(7):245-249.
[3]ALTWAIJRY H,TRULLS E,HAYS J,et al.Learning to match aerial images with deep attentive architectures[C]∥Computer Vision and Pattern Recognition.IEEE,2016:3539-3547.
[4]XIA L,et al.Selected by input:Energy efficient structure for rrambased convolutional neural network[C]∥DAC.2016 [5]HILTON G E,SALAKHUTDINOV R R.Reducing the Dimensionality of Data with Neural Network[J].Science,2006,313:504-507.

相关文章 15

[1]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[2]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[3]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[4]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[5]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[6]	金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
[7]	张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036
[8]	戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105
[9]	刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179
[10]	徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085
[11]	孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217
[12]	吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039
[13]	杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148
[14]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236
[15]	杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed