SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.
Abstract. . . . According to the disadvantages that calculation speed of traditional iris localization algorithm is slow and takes up much space. In this paper, we propose a new iris localization algorithm with an improved least squares fitting. Making use of the characteristics that gray value of iris image is difference from peripheral image around the iris, it uses Canny edge detector to detect iris image boundary and looks for a proper threshold to binarize image, and then uses least squares fitting to locate inner boundary and makes up circular arc segment of outer boundary. Finally, the center coordinate and radius of iris boundary can be easy to compute. The experimental results basing on over 150 images from CASIA iris image database have shown that accuracy of our localization method can be achieved as high as 99% and is over 150ms faster than the references mentioned in the paper. Our algorithm also reduces calculation complexity and saves memory space.
In this paper, we design a follow focus system, which can be integrated with a handheld stabilizer. The follow focus system of the handheld stabilizer includes the following steps: establishing communication relationship, signal input, signal transmission, focusing control and adjusting camera parameters. The follow focus system solves the problem that the parameters of the follow focus and the platform need to be adjusted by both hands. It optimizes the problem of wire winding in stabilizer, while it combined with the follow focus wired control. It indirectly shortens the response delay of the wireless follow focus by using the internal wiring of the stabilizer. It achieves not only adjusting the camera parameters, but also stabilizing the follow focus of the real-time camera.
In view of the application of PTZ system in photographic equipment, we design a wireless image transmission system for photographic equipment and mobile equipment. Hi3516A is the main control chip of the system, and the second connection interface is connected with the photographic equipment to collect the relevant data that recorded by the photographic equipment, and the data is encoded and encapsulated by the processing module. The first connection interface adopts 2.4G wireless radio transceiver module, which connects with the mobile device wirelessly to transmit picture data information to the mobile device, and realizes wireless image transmission from the photographic device to the mobile device. The test of wireless picture transmission system shows that the picture transmission system has fast transmission rate, high real-time performance and wide application range.
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