IMPLEMENTATION OF ADAPTIVE PRODUCT CUSTOMIZATION PROCESSES IN MASS PRODUCTION

The article presents the results of research and implementation of adaptive calibration systems used in setting up mass-produced products. The proposed method allows you to speed up the tuning of the HF transceiver. In the process of tuning, with the help of a neural network, an adaptive correction of the approximating function of the incident wave sensor is made according to the accumulated data, which makes it possible to reduce the time for tuning the transceiver. The introduction of the mathematical apparatus of neural networks can be applied in the process of mass production for other products.

Algorithm for adaptive correction of the ship's movement along the trajectory in the presence of a sea current

В статье предложен алгоритм адаптивной коррекции движения судна по траектории при наличии морского течения. В основе предлагаемого подхода лежит алгоритм градиента вспомогательных функций. Показаны преимущества предложенного алгоритма адаптивной коррекции движения судна по траектории. Сущность предлагаемого подхода заключается в замене прямолинейного участка маршрута судна, новой виртуальной траекторией и использовании ее во вспомогательной функции и при вычислении соответствующего вектора градиента. Компьютерное моделирование подтвердило эффективность предложенного алгоритма адаптивной коррекции. Адаптивные свойства разработанного алгоритма выражаются в том, что он обеспечивает асимптотически точное движение по участкам запланированной траектории, не используя информации о скорости и направлении морского течения. Предполагается использование предложенного адаптивного алгоритма при разработке систем автоматического управления движением судна по маршруту. The article proposes an algorithm for adaptive correction of the ship's movement along the trajectory in the presence of a sea current. The proposed approach is based on the gradient algorithm of auxiliary functions. The advantages of the proposed algorithm for adaptive correction of the ship's motion along the trajectory are shown. The essence of the proposed approach is to replace the straight-line section of the ship's route with a new virtual trajectory and use it in an auxiliary function and in calculating the corresponding gradient vector. Computer simulation has confirmed the effectiveness of the proposed adaptive correction algorithm. The adaptive properties of the developed algorithm are expressed in the fact that it provides asymptotically accurate movement along sections of the planned trajectory, without using information about the speed and direction of the sea current. It is supposed to use the proposed adaptive algorithm in the development of systems for automatic control of the ship's movement along the route.

Research on a component characteristic adaptive correction method for variable cycle engines

There is inevitably a performance deviation between an engine model and an actual engine that is influenced by unpredictable factors such as the unsuspected environmental conditions and the natural performance degradation in the process of use. Because the engine model precision largely depends on the accuracies of the component maps, it is possible to revise the engine model to determine a better trend for the engine performance from recorded measurements by adjusting the maps. This paper presents a new method for updating the variable geometry component maps of a variable cycle engine (VCE) by using a set of scaling factors estimated with the cubature Kalman filter (CKF). A mapping function is created between the scaling factors and the component characteristic scaling coefficients for the adjustments of the maps. The proposed method is applied to a VCE model according to the VCE benchmark steady-state performance data. The results show that the maximum simulation error of the engine steady-state model decreases from 5.33 to 0.93%, and the CKF-based adaptation method provides a much faster computing rate than the particle swarm optimization (PSO) based adaptation method, which verifies the effectiveness and engineering applicability of the variable geometry characteristic adaptive correction method.

GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.

Deep Learning Assisted Zonal Adaptive Aberration Correction

Deep learning (DL) has been recently applied to adaptive optics (AO) to correct optical aberrations rapidly in biomedical imaging. Here we propose a DL assisted zonal adaptive correction method to perform corrections of high degrees of freedom while maintaining the fast speed. With a trained DL neural network, the pattern on the correction device which is divided into multiple zone phase elements can be directly inferred from the aberration distorted point-spread function image in this method. The inference can be completed in 12.6 ms with the average mean square error 0.88 when 224 zones are used. The results show a good performance on aberrations of different complexities. Since no extra device is required, this method has potentials in deep tissue imaging and large volume imaging.