Use of a neural network to control an adaptive optics system for an astronomical telescope

Decoupling of the position and angular errors in laser pointing with a neural network method

“…In particular, this technique has already been used in many different optical systems. In adaptive optics systems, neural networks have been applied to derive the distorted wavefront from a simultaneous pair of in-focus and out-of-focus images of a reference star [17][18][19] . Breitling et al have used neural networks to predict the angular deviation of a pulse laser from the final four sample positions [20] .…”

An Improved Method of Measuring Wavefront Aberration Based on Image with Machine Learning in Free Space Optical Communication

“…Owing to the high fitting ability and successful application of machine learning in other fields, some research on measuring wavefront aberration with machine learning has been completed. A back propagation (BP) neural network was used to measure wavefront aberration and was verified on the Hubble telescope [8,9,10]. The input to the network is a one-dimensional vector which is composed by all pixels of the point spread functions (PSFs) in the focal and defocus planes.…”

Optimization and kinetic modeling of an enhanced bio-hydrogen fermentation with the addition of synergistic biochar and nickel nanoparticle

“…Apart from that, as the operational parameters are critical to the final hydrogen yield, the modeling and correlations between operational parameters and hydrogen yield become crucial in the designing and sizing of the fermentation system. Due to its excellent abilities such as handling data with high dimensionality, the approximation for arbitrary nonlinear functions, and computational efficiency, the artificial neural network (ANN) system has successfully provided a good representation in biological, chemical, and physical phenomena . Recently, the advanced system that aims to balance the compromise between the accuracy of models and cost‐effectiveness of collecting limited numbers of experimental conditions, of which combines both advantages of ANNs and the response surface methodology (RSM), has been reported .…”

“…Due to its excellent abilities such as handling data with high dimensionality, the approximation for arbitrary nonlinear functions, and computational efficiency, the artificial neural network (ANN) system has successfully provided a good representation in biological, chemical, and physical phenomena. [24][25][26] Recently, the advanced system that aims to balance the compromise between the accuracy of models and cost-effectiveness of collecting limited numbers of experimental conditions, of which combines both advantages of ANNs and the response surface methodology (RSM), has been reported. [27][28][29] Although many works have been done using the RSM as a tool for the optimization of hydrogen dark fermentation, 30,31 the employment of the hybrid ANNs-RSM system and its application in the investigation of the effect of critical operational conditions, ie, BC, metal cofactor Ni 0 , pH, and dosage of microbes upon hydrogen production, to the best of our knowledge, have never been reported before.…”