Interference fringe density is an important parameter to be precisely measured and regulated for patterning varied-line-spacing gratings with scanning beam interference lithography. The impacts of the interference fringe density error on grating parameters and exposure performance must be fully analyzed in theory to guide system design and varied-line-spacing grating fabrication. In this paper, a mathematical model of the total exposure dose for varied-line-spacing grating fabrication with scanning beam interference lithography is established. Based on the model, the calculation methods of grating parameters and exposure contrast are presented. The impacts of the fringe density error on grating parameters and exposure performance are analyzed. According to the requirements of grating application and manufacturing process, the error threshold and susceptible parameters are determined. The improvement effect from the step size of stage on error threshold is discussed. The results of a typical grating show that the interference fringe density error leads to the grating performance and exposure contrast degradation. With the fringe density relative error of 0.001, the relative errors of the grating groove density coefficients can be controlled in the order of 10-8 and 10-6, respectively. The ghost line intensity and exposure contrast have higher requirements on fringe density error. Under the same conditions, to obtain a ghost line intensity smaller than 1×10-3 and an exposure contrast better than 0.9, the error threshold should be smaller than 1.7×10-4 and 7×10-4, respectively. A smaller step size of stage can significantly improve the ghost line intensity, and the fringe density error threshold can be relaxed at the expense of production efficiency. The error threshold calculation and error relaxation methods provide a theoretical basis for system design.
Currently, due to the limited amount of data and the difficulty of designing a network, there are few papers on constructing a new convolutional neural network for scene classification using the publicly available datasets of high-resolution remote sensing images. Considering the existing problems, the current scene classification methods of high-resolution remote sensing images are summarized, and the IMFNet model is constructed to classify scenes of high-resolution remote sensing images in this paper. The IMFNet is an end-to-end network, which can learn features from data automatically. The main characteristic of the IMFNet network structure is that the Inception module is used to extract the details of remote sensing images and the multifeature fusion strategy is proposed to ensure the integrity of information. In addition, optimization methods are adopted to improve the classification accuracy. In order to verify the effectiveness of the method proposed in this paper, the two benchmark datasets—the UC Merced dataset and the SIRI-WHU dataset were adopted for experiments. The classification accuracy of the two datasets reaches 92.14% and 90.43%, respectively. Experimental results show that the method proposed has certain advantages over the classification methods based on low-level and middle-level visual features and even some classification methods based on high-level visual features.
A novel approach for evaluating the tracking performance of optoelectronic theodolite is proposed. First, an equivalent
mathematic model of tracking error is established. Then, the equivalent sine signal is inputted to the equivalent model,
and the outputs are sampled. The results of evaluating the tracking performance are obtained based on the statistical
calculation of output produced by equivalent model. Equivalent model using the BP (Backprogration) neural network
structure is identified. The training method of BP neural network adopts the LM (Levenberg-Marquardt) algorithm for
the sake of speeding up training process. The BP neural network is trained and tested by using the training and testing
samples gotten from the simulation model of optoelectronic theodolite tracking system under MATLAB/SIMULINK.
The estimate errors of equivalent model including average error, maximum error and standard error are 2.5872e-006°≈0°,
2.8" and 1.9". The results show that the equivalent model identified based on BP neural network meets the needs of
evaluating the tracking performance of optoelectronic theodolite. The accurate evaluation of tracking performance is
achieved.
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