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We demonstrate an all-fiber narrow-linewidth amplifier employing a bidirectional pump scheme and cascaded white-noise-source phase-modulated seed laser. The stimulated Raman scattering effect in the amplifier is investigated by substituting different types of seed lasers. The influence of pump distributions and seed injection power on mode instability (MI) in the amplifier is also experimentally investigated. As a result, a 3 dB linewidth of 0.175 nm and a beam quality of M 2 ≈ 1.5 are obtained at the output of ~3 kW, without observation of MI and stimulated Brillouin scattering effect. With the further increase of pump power, MI occurs as the output exceeds 3.17 kW, along with beam quality degradation. Optical efficiency decreases to 71.5% at the ultimate output of 3.5 kW. Therefore MI becomes the main limitation to further power scaling.

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The self-pulsing phenomenon in kilowatt level narrow-linewidth fiber amplifiers with white noise source (WNS) phase-modulation is observed experimentally. It possesses the obvious threshold of the pump power and prevents the narrow-linewidth fiber lasers from further power scaling. The experimental study shows that known explanations are not applicable here and indicates that occurrence of self-pulsing is closely related to Stimulated Brillouin Scattering (SBS) process. The theoretical discussion reveals that the spikes in the modulated spectrum are the critical factor that SBS threshold is lower than the theoretical estimation. The 1+1 dimensional SBS model analysis predicts that self-pulsing originates from forward second order Stokes pulses, which is in good qualitative agreement with the experimental data.

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown to be (a) bounded from below by the smallest error variance in a variable from the analyzed set and (b) bounded from above by the largest error variance in a variable from that set. In the case of a unidimensional set of analyzed measures, it is pointed out that the reliability and criterion validity of any principal component are bounded from above by these respective coefficients of the optimal linear combination with maximal reliability and criterion validity (for a criterion unrelated to the error terms in the individual measures). The discussed psychometric features of principal components are illustrated on a numerical data set.