Abstract: A method for compressing Demura compensation value including: acquiring original compensation values of a target panel; inputting the original compensation values into a spatial domain sampling model, performing a down-sampling to obtain spatial domain sampling values, inputting the original compensation values into a prediction mode overall model, and performing a numerical processing to obtain a plurality of mode characteristic values; performing a data reconstruction on the spatial sampling values and the plurality of mode characteristic values to obtain a reconstructed value set; performing a syntactic encoding on the spatial sampling values and the plurality of mode characteristic values to obtain a syntactic element code set; performing a mode selection according to the reconstructed value set and the syntactic element code set to obtain an optimal prediction mode; and acquiring and outputting a syntactic element code corresponding to the optimal prediction mode to obtain a Demura compensation value com

Abstract: Disclosed is a method and a device for decompressing Demura compensation value based on random-access bit stream. The method includes reading a target compression code corresponding to a target pixel set according to a screen refresh instruction in response to the screen refresh instruction, and decoding the target compression code to obtain Demura compensation values.

Abstract: Provided are a method and a system for optimizing a BMS model. The method includes: creating a BMS model based on test data of a battery; selecting sample vehicles from real driving data of vehicles equipped with this type of battery through active learning approach, and giving a corresponding weight to each of the selected sample vehicles; optimizing the BMS model based on the data of the selected sample vehicles.

Abstract: A method and apparatus for optimizing a battery management system (BMS) are provided. The method includes: obtaining training data, wherein the training data comprises data from a target vehicle and data from auxiliary vehicles, and the auxiliary vehicles are vehicles mounted with a same or similar battery system as the target vehicle; optimizing a BMS related algorithm for the target vehicle by performing transfer learning based on the training data, wherein the BMS related algorithm comprises one or a combination of: a battery model, parameters for the battery model, a battery management algorithm, and parameters for the battery management algorithm.

Abstract: Provided are a method and a system for optimizing a BMS model. The method includes: creating a BMS model based on test data of a battery; selecting sample vehicles from real driving data of vehicles equipped with this type of battery through active learning approach, and giving a corresponding weight to each of the selected sample vehicles; optimizing the BMS model based on the data of the selected sample vehicles.

Abstract: The present invention provides a silicon-based composite negative electrode material, including an inner core, a first shell layer, and a second shell layer, wherein the first shell layer covers the inner core; the second shell layer covers the first shell cover; the inner core includes a carbon-silicon composite material; the first shell layer includes an amorphous carbon layer; and the second shell layer comprises includes a conductive polymer layer. Meanwhile, further disclosed in the present invention are a preparation method for the silicon-based composite negative electrode material and a lithium ion battery including the silicon-based composite negative electrode material. The silicon-based composite negative electrode material provided in the present invention can effectively restrain the volume expansion of the inner core, construct a stable solid-liquid interface, form a stable SEI film, and improve the cycle stability and multiplier performance of the lithium ion battery.

Abstract: Disclosed are a battery charging control method and apparatus, and a battery powered device such as an electric vehicle. The method, for each cell in at least one cell of a multi-cell battery, includes: after the multi-cell battery enters a charging stable state, acquiring actual charging electric quantity required for charging the cell to a target voltage; determining a balance time duration for the cell based on the actual charging electric quantity corresponding to the cell, wherein the balance time duration is a time duration required for a cell balance control circuit matched with the cell to control the cell; and controlling the cell balance control circuit matched with the cell to perform electric quantity adjustment on the cell within the balance time duration. The technical problem of low accuracy of balance control on each cell in the multi-cell battery in the related art is solved.

Abstract: Control methods of sample adaptive offset (SAO) filtering applied to an image processing system with an SAO filter are provided. The method includes the steps of: receiving video signal, wherein the video signal includes at least one group of picture (GOP) and the GOP has multiple frames, each having multiple coding tree units (CTUs); determining whether current frame is an intra-picture frame (I frame); turning on the SAO filter in response to determining that the current frame is the I frame to enable the SAO filter to perform an SAO filtering on the current frame and determining a CTU ratio of the CTUs being not performed with the SAO filtering for the current frame; and selectively turning off the SAO filter based on the CTU ratio, such that the SAO filter does not perform the SAO filtering on subsequent non-I frames in the GOP including the current frame.

Abstract: Control methods of sample adaptive offset (SAO) filtering applied to an image processing system with an SAO filter are provided. The method includes the steps of: receiving video signal, wherein the video signal includes at least one group of picture (GOP) and the GOP has multiple frames, each having multiple coding tree units (CTUs); determining whether current frame is an intra-picture frame (I frame); turning on the SAO filter in response to determining that the current frame is the I frame to enable the SAO filter to perform an SAO filtering on the current frame and determining a CTU ratio of the CTUs being not performed with the SAO filtering for the current frame; and selectively turning off the SAO filter based on the CTU ratio, such that the SAO filter does not perform the SAO filtering on subsequent non-I frames in the GOP including the current frame.

Abstract: The invention relates to a lithium lanthanum titanate composite solid electrolyte material containing silicon in which amorphous Si or an amorphous Si compound exist in a grain boundary between crystal grains, and a method of producing the same, and belongs to a field of a lithium ion battery. According to the invention, the amorphous Si or the amorphous Si compound exist in the grain boundary between the crystal grains of the lithium lanthanum titanate. The amorphous Si or the amorphous Si compound are introduced into the grain boundary by employing a wet chemical method. In the wet chemical method, the inexpensive organosilicon compound is used as an additive, and the organosilicon compound is added into the lithium lanthanum titanate solid electrolyte material.

Abstract: The invention relates to a lithium lanthanum titanate composite solid electrolyte material containing silicon in which amorphous Si or an amorphous Si compound exist in a grain boundary between crystal grains, and a method of producing the same, and belongs to a field of a lithium ion battery. According to the invention, the amorphous Si or the amorphous Si compound exist in the grain boundary between the crystal grains of the lithium lanthanum titanate. The amorphous Si or the amorphous Si compound are introduced into the grain boundary by employing a wet chemical method. In the wet chemical method, the inexpensive organosilicon compound is used as an additive, and the organosilicon compound is added into the lithium lanthanum titanate solid electrolyte material.