机构

• 武汉岩土力学研究所 [2]
• 长春光学精密机械与物... [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 期刊论文 [3]
• 会议论文 [2]

发表日期

• 2022 [1]
• 2020 [1]
• 2019 [1]
• 2012 [1]
• 2005 [1]

学科主题

筛选

浏览/检索结果: 共5条，第1-5条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Learning Skill Characteristics From Manipulations 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15 作者:  Zhou, Xiao-Hu;  Xie, Xiao-Liang;  Liu, Shi-Qi   |  收藏  |  浏览/下载：43/0  |  提交时间：2022/06/10 Surgery  Sensors  In vivo  Task analysis  Arteries  Measurement  Sensor phenomena and characterization  Ensemble learning  in vivo porcine studies  percutaneous coronary intervention  skill characteristics  wavelet packet decomposition (WPD)   钙质砂地基含水率对爆破振动特性影响分析 期刊论文  OAI收割 爆破, 2020, 卷号: 37, 期号: 4, 页码: 31 作者:  钟冬望;  杜泉;  孟庆山;  雷学文;  何理   |  收藏  |  浏览/下载：16/0  |  提交时间：2021/05/25 calcareous sand  vibration velocity  Sadovsky formula  watery state  wavelet packet energy analysis  钙质砂  振动速度  萨道夫斯基公式  含水状态  小波包能量分析   A novel bearing fault diagnosis method based on principal component analysis and BP neural network 会议论文  OAI收割 Changsha, China, November 1-3, 2019 作者:  Sun Y(孙越);  Xu AD(徐皑冬);  Wang K(王锴);  Han XJ(韩晓佳);  Guo HF(郭海丰)   |  收藏  |  浏览/下载：18/0  |  提交时间：2020/06/21 Fault diagnosis  rolling bearing  principal component analysis  wavelet packet energy  high dimensional features   Optimization for the Prediction of Waveform and the Estimation of Peak Particle Velocity of Explosive Vibration 期刊论文  OAI收割 DISASTER ADVANCES, 2012, 卷号: 5, 期号: 4, 页码: 502-508 作者:  Chen Shihai;  Zhang Qiuhua;  Yan Yongfeng;  Li Haibo;  Lv Yanxin   |  收藏  |  浏览/下载：18/0  |  提交时间：2018/06/05 Peak Particle Velocity  Waveform Prediction  Modified Sadaovsk Formula  Explosive Vibration  Wavelet Packet Analysis   AE signal processing and DSP implementation based on wavelet packet analysis (EI CONFERENCE) 会议论文  OAI收割 ICMIT 2005: Information Systems and Signal Processing, September 20, 2005 - September 23, 2005, Changchun, China 作者:  Zhao J.;  Wang K. 收藏  |  浏览/下载：12/0  |  提交时间：2013/03/25 To improve the accuracy of AE (Acoustic Emission) testing  the wavelet packet analysis was introduced to process the AE signals. Extraction of the fault characteristic information would be influenced greatly if the faulted AE signal was not effectively denoised. Based on discussing the fast searching algorithm of BWPB (Best Wavelet Packet Basis) adopting Shannon entropy  a new method based on BWPB was presented to denoise the AE signal from the faulted composite plate. Analyzing was performed on the denoised signal and the fault characteristic information was exacted. To improve the real-time performance of the wavelet packet analysis algorithm  it was performed on the DSP (Digital Signal Processing) chip TMS320VC5409. The experimental results show that the algorithm can not only reduce the noise by 10dB but also effectively extract the faulted characteristics information from the AE signal.