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The double revolving fiber positioning technology is one of the key technologies for the success of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The accuracy of fiber positioning will directly affect the observation efficiency of LAMOST. To achieve higher fiber positioning accuracy, the original open-loop controlled fiber positioning system urgently needs to be upgraded into a closed-loop control system. The fiber detection is the most important part of the closed-loop controlled fiber positioning system. The back-illuminated detection method is usually used to detect the fiber position by directly detecting the light spot generated at the fiber end in the multi-fiber spectral surveys. In this paper, we introduce a new method to measure the fiber position based on the image of the front-illuminated LAMOST focal plane. The front-illuminated image does not require lighting devices inside the spectrograph, and it could reduce the instability and light pollution in the spectrograph end. Our method measures the fiber position by fitting the profile of the fiber pinhole with a 2D Gaussian function. A series of tests show that the relative position measurement precision of the front-illuminated method is about 0.″12, and the method could have the same accuracy as the back-illuminated method once the system bias is calibrated by a simple radial correction function. The required fiber positioning accuracy of LAMOST is 0.″4, and the new method satisfies the requirement of LAMOST fiber detection accuracy and could be used in the closed-loop fiber control system.

Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil–water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems.