首页|New Support Vector Machines Findings from Taiyuan University of Technology Repor ted (Interference Fading Suppression for Distributed Acoustic Sensor Using Frequ ency-shifted Delay Loop)
New Support Vector Machines Findings from Taiyuan University of Technology Repor ted (Interference Fading Suppression for Distributed Acoustic Sensor Using Frequ ency-shifted Delay Loop)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning - Support Vector Machines are discussed in a new report. According to news repor ting from Taiyuan, People's Republic of China, by NewsRx journalists, research s tated, "In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (phi-OTDR), a multi-frequency detection method based o n frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up t o five consistent frequency components are generated by periodic frequency-shift ed, amplification, delay and filtering processing in FSDL." Financial supporters for this research include Fundamental Research Program of S hanxi Province, Guiding Funds of Central Government for Supporting the Developme nt of the Local Science and Technology, special fund for Science and Technology Innovation Teams of Shanxi Province. The news correspondents obtained a quote from the research from the Taiyuan Univ ersity of Technology, "The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis an d support vector machine (SVM) are used to extract features and realize the fadi ng classification. With the help of fading label making and restoration, the mul tifrequency signals are intelligently aggregated using rotated-vector-sum (RVS) . Experimental results show that PAA has applicability in data compression of be at signals. After 5-layer wavelet energy spectrum analysis, the fading binary cl assification accuracy can reach 96.77 %, and the fading signals ide ntified after segmentation can be restored to the original data. The fading prob ability based on SVM output labels can be reduced to 1.89 %. The SV M-based classification output labels aggregation shows that the vibration positi oning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodu lation curve is smoother, and the demodulation SNR can be up to 17.63 dB."
TaiyuanPeople's Republic of ChinaAsi aMachine LearningSupport Vector MachinesTaiyuan University of Technology
NETL
NSTL
