2009
DOI: 10.1364/oe.17.024008
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100 W all fiber picosecond MOPA laser

Abstract: A high power picosecond laser is constructed in an all fiber master oscillator power amplifier (MOPA) configuration. The seed source is an ytterbium-doped single mode fiber laser passively mode-locked by a semiconductor saturable absorber mirror (SESAM). It produces 20 mW average power with 13 ps pulse width and 59.8 MHz repetition rate. A direct amplification of this seed source encounters obvious nonlinear effects hence serious spectral broadening at only ten watt power level. To avoid these nonlinear effect… Show more

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Cited by 74 publications
(35 citation statements)
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“…Wavelet transform can decompose the signal into multi-scale components, and selects the appropriate time domain sample step accordance with the different size of scale component to constantly focus on any small details of the processed signal [19]. For developing the model of gyroscope drift character, the multi-resolution theory of wavelet analysis is used to analyze and pretreated the signal of gyroscope drift and the result confirms its effectiveness [20]; aimed to the error fault of gyroscope drift, wavelet analysis is used to extract the error feature vector and then three-layers feedforward BP neural network is adapted to build fault model, finally fuzzy logic judgment makes diagnostic method more simple and quick [21]; Liu Luyuan extracted the tendency of the signal of gyroscope drift by the multi-resolution theory of wavelet analysis, and established the error model of gyroscope to improve the precision of model [22]; because gyroscope's drift output signal is weak and non-stationary random series, wavelet analysis is used to accomplish the hierarchical process of output data in frequency domain and use time series analysis method to build the model of gyroscope drift and finally improved the speed and precision of model [23]; according to the fault of gyroscope drift, a new prediction algorithm based on wavelet support vector machine is put forword for fault prediction and enhances the precision of fault forecasting while ensuring the safety of devices [24]; because of the mutation of the output signal of gyroscope, the signal is decomposed by three-layer wavelet package and extracts its feature to train RBF neural network, the result shows that the method can detect the fault accurately [25].…”
Section: Algorithms Of Fault Diagnosis Based On Wavelet Analysis For mentioning
confidence: 99%
“…Wavelet transform can decompose the signal into multi-scale components, and selects the appropriate time domain sample step accordance with the different size of scale component to constantly focus on any small details of the processed signal [19]. For developing the model of gyroscope drift character, the multi-resolution theory of wavelet analysis is used to analyze and pretreated the signal of gyroscope drift and the result confirms its effectiveness [20]; aimed to the error fault of gyroscope drift, wavelet analysis is used to extract the error feature vector and then three-layers feedforward BP neural network is adapted to build fault model, finally fuzzy logic judgment makes diagnostic method more simple and quick [21]; Liu Luyuan extracted the tendency of the signal of gyroscope drift by the multi-resolution theory of wavelet analysis, and established the error model of gyroscope to improve the precision of model [22]; because gyroscope's drift output signal is weak and non-stationary random series, wavelet analysis is used to accomplish the hierarchical process of output data in frequency domain and use time series analysis method to build the model of gyroscope drift and finally improved the speed and precision of model [23]; according to the fault of gyroscope drift, a new prediction algorithm based on wavelet support vector machine is put forword for fault prediction and enhances the precision of fault forecasting while ensuring the safety of devices [24]; because of the mutation of the output signal of gyroscope, the signal is decomposed by three-layer wavelet package and extracts its feature to train RBF neural network, the result shows that the method can detect the fault accurately [25].…”
Section: Algorithms Of Fault Diagnosis Based On Wavelet Analysis For mentioning
confidence: 99%
“…Recently, high power SC source becomes a new research trend and exhibits strong attraction because of the high spectral power density. The ytterbium fiber (Yb:fiber) laser system has the merits of high efficiency, reliability and compatibility with PCF, rendering them almost ideal candidates for compact sources of SC radiation [5][6][7][8]. H. Chen [7] reported a 35 W high power all fiber SC source based on PCF with picosecond laser, and X. Hu [8] reported a 50W strictly single mode all-fiber SC source spanning from 500 nm to over 1700 nm by using a 5-m-long commercially PCF.…”
Section: Introductionmentioning
confidence: 99%
“…The picosecond seed source produces lower average power at high repetition rate, so it should be amplified by the regenerative or multipass amplifier for the high peak power [9,10], which, however, calls for complex structure and results in thermal optical problem. For this reason, we can use the fiber amplifier, which has the gain per an amplification stage (13 - [12]. However, the fiber-based direct amplification of picosecond pulses is very sensitive to nonlinearity, especially selfphase modulation (SPM) [13].…”
Section: Introductionmentioning
confidence: 99%