机构

• 金属研究所 [1]
• 长春光学精密机械与物... [1]
• 光电技术研究所 [1]
• 合肥物质科学研究院 [1]

采集方式

• OAI收割 [4]

内容类型

• 会议论文 [2]
• 期刊论文 [2]

发表日期

• 2017 [1]
• 2016 [1]
• 2014 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4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 An optimized method to calculate error correction capability of tool influence function in frequency domain 会议论文  OAI收割 作者:  Wang, Jia;  Hou, Xi;  Wan, Yongjian;  Shi, Chunyan   |  收藏  |  浏览/下载：22/0  |  提交时间：2018/12/20 Codes (symbols) - Error correction - Frequency domain analysis - Method of moments - Polishing - Power spectral density - Spectral density   Temperature control characteristics analysis of lead-cooled fast reactor with natural circulation 期刊论文  OAI收割 ANNALS OF NUCLEAR ENERGY, 2016, 卷号: 90, 期号: 无, 页码: 54-61 作者:  Yang, Minghan;  Song, Yong;  Wang, Jianye;  Xu, Peng;  Zhang, Guangyu   |  收藏  |  浏览/下载：17/0  |  提交时间：2018/01/25 Lfr  Natural Circulation  Temperature Control Characteristic  Transfer Function Matrix  Frequency Domain Analysis Method   Model Research of Electric Coal Calorific Value Based on Near Infrared Frequency Domain Self-Adaption Analysis Method 期刊论文  OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 卷号: 34, 期号: 10, 页码: 2792-2798 作者:  Li Zhi;  Wang Shenghao;  Zhao Yong;  Wang Xiangfeng;  Li Yaozheng   |  收藏  |  浏览/下载：15/0  |  提交时间：2021/02/26 SPECTROSCOPY  Near infrared spectra  Fast Fourier transform  Frequency domain self-adaption analysis method  Calorific value of electric coal  Quantitative analysis model   Arc fault signatures detection on aircraft wiring system (EI CONFERENCE) 会议论文  OAI收割 6th World Congress on Intelligent Control and Automation, WCICA 2006, June 21, 2006 - June 23, 2006, Dalian, China Hongkun Z.; Tao C.; Wenjun L. 收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25 In this paper an arc fault detection method Is proposed based on characteristics of the fault current of an electric arc. A localized signal processing method Is developed using wavelet analysis to decompose the differential current signal Into a series of wavelet components  each of which Is a time-domain signal that covers a specific frequency band. Thus  more distinctive signal features that represent arc faults and other fault transient phenomena are extracted. As a result  by quantifying the extracted features  an arc faults are distinguished from phenomena similar to arc and other fault transients using the differences In the quantified features. Simulation studies have demonstrated that the proposed method Is reliable and simple. 2006 IEEE.