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Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high labor intensity and low efficiency in detecting dark spot eggs, a method of detecting dark spot eggs based on GoogLeNet model is proposed. This method uses Inception convolution module in GoogLeNet model to automatically extract dark spot eggs features and realize the detection. A device for collecting transparent images of eggs was set up in the experiment, and the sample collection experiments were designed to acquire samples. A total of 1200 dark spot eggs images and 8850 normal eggs images were obtained. Selecting 1200 samples of these two kinds for network modeling. The experimental results show that the detection accuracy of dark spotted eggs based on CNN GoogLeNet model is 98.19%. In order to further verify the GoogLeNet model, this paper repeats the above experiments using the VGG16 and VGG19 models of CNN model, and compares the accuracy. To further validate the GoogLeNet model, this paper repeats the above experiments using VGG16 and VGG19 models, and compares the accuracy. The experimental results show that GoogLeNet model has the highest detection accuracy among the three models, which provides a new detection method for egg quality detection. [ABSTRACT FROM AUTHOR]