In the research of automatic classification of epilepsy EEG (Electroencephalogram), the detection model parameters are often set based on artificial experience, and the structure lacks adaptability. The epilepsy EEG signal is used as the research object. After preprocessing the EEG signal, Use CNN (convolutional neural network) for feature extraction, and use the PSO (Particle Swarm Optimization) algorithm to adaptively optimize the CNN model parameters to form a PSO-CNN epilepsy classification model. The algorithm proposed has an accuracy of 94.8% on the epilepsy dataset of the University of Bonn. Compared with traditional detection methods and other deep learning methods, the proposed algorithm achieves a higher accuracy.
In the system of airborne remote sensing measurement, the optical system should meet the requirements of long focal length, large aperture, light weight and wide waveband due to the particular characteristics of usage environment and observation objects. To meet the special requirements of the optical system structure, an off-axis four-mirror reflective optical system is designed based on an off-axis three-mirror reflective optical system from modern optical design theory. The structure and principle of the off-axis four-mirror reflective system are described and the MTF curve is given in this paper. At the same time, according to the working environmental condition of the system, the temperature adaptability of the system is analyzed and the analysis results are given. Based on the results, the system error is analyzed and discussed in detail applying the precision theory. The system error for each optical component is analyzed and allocated. The qualitative analysis for the influence factors of system error is also given.
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