6results about How to "Address sensitivity" patented technology

The invention relates to the technical field of intelligent evaluation of garment flatness, and particularly discloses an automatic evaluation method of a non-ironing suit seam flatness grade, and the method comprises the steps: obtaining image data and physical parameter data of a seam region in response to a detection trigger signal of the seam region of a non-ironing suit; and performing key scale screening based on texture contribution degree on the image data, calculating a relevance index between feature distribution and flatness grade probability distribution under different texture scales, and determining the texture scale of which the relevance index meets a preset contribution degree condition as a target key scale. According to the method, the entropy screening mechanism based on the texture contribution degree is introduced, so that the key scale which is most effective for flatness representation can be locked from the multi-source data in a self-adaptive manner, and the refinement and pertinence of feature extraction are realized.

The invention provides a surgical instrument registration method and system based on an augmented reality technology, and relates to the technical field of intelligent medical systems. The method comprises the steps that based on a plurality of sensor modules, target RGB images and target space depth information of an operation area are collected from different visual angles; generating a surgical area three-dimensional model according to the target RGB image and the target space depth information; determining a target instrument type corresponding to a to-be-registered target surgical instrument in the surgical area according to the instrument identification model; loading a target standard virtual component matched with the target equipment type from a virtual component database; and dynamically superposing the target standard virtual component to the three-dimensional model of the surgical area according to the real-time tracking data of the device. Therefore, the system can still accurately and stably complete registration of the surgical instruments in a complex and changeable surgical environment, and provides a solid technical guarantee for modern informatization surgery.

The invention relates to a permanent magnet synchronous motor model prediction control method and system, belongs to the technical field of permanent magnet synchronous motor driving, and solves the problem that parameter robustness and dynamic performance cannot be considered when parameter mismatch occurs in permanent magnet synchronous motor model prediction control in the prior art. Comprising the steps of obtaining sampling values of stator current, stator voltage and electrical angular velocity at a current sampling moment and two previous moments and a direct current bus voltage at the current sampling moment, further updating incremental current prediction errors and time sequence sampling data of incremental voltage, and further obtaining a first sample vector and a second sample vector at the current sampling moment; and then a small-batch stochastic gradient descent method is adopted to obtain an identification value of a parameter mismatch coefficient at the current sampling moment, then stator current prediction values of different voltage vectors at the next moment are obtained, then an optimal voltage vector is obtained, and permanent magnet synchronous motor model prediction control at the next moment is carried out based on the optimal voltage vector.

The invention discloses a vehicle dynamics parameter estimation method based on machine learning, which mainly obtains important parameters of vehicle dynamics involved in simulation test software through analysis, provides a prediction method for unknown parameters in the important parameters, and provides a solution for the condition that some parameters are difficult to obtain in engineering. Specifically, a grouping orthogonal test method is adopted, the test of a simulation software platform is taken as the basis, the importance of vehicle dynamics parameters is judged, a full-connection neural network is adopted to predict important unknown parameters of vehicle dynamics, and a real vehicle test result is compared with a simulation test to verify the prediction accuracy. The method focuses on analyzing and predicting unknown parameters in the vehicle dynamic parameters according to the characteristics that the types of the vehicle dynamic parameters are various and the influence of each parameter on the vehicle dynamic response is different, effectively solves the problem of sensitivity of the vehicle dynamic parameters with the automatic driving function, and particularly reliably estimates the unknown vehicle dynamic sensitive parameters.

The present invention provides a millimeter wave large-scale MIMO channel estimation method based on one-bit quantized reception. First, the sparsity of the millimeter wave channel in the virtual angle domain is utilized to convert the channel estimation problem of a large-scale MIMO system with a one-bit analog-to-digital converter into a framework of sparse signal recovery. A one-bit analog-to-digital converter is adopted at the base station end, which reduces the cost and energy consumption of the large-scale MIMO system. Then, a sparse Bayesian learning (SBL) method is used to achieve the estimation of the sparse channel in the angle domain by solving the second-class maximum likelihood problem. Compared with the existing one-bit channel estimation method that does not consider the sparsity of the angle domain channel, the channel estimation accuracy is greatly improved. The SBL method can realize the automatic selection of a sparse promotion prior model, and compared with the existing expectation maximization basis pursuit denoising method of manually selecting the hyperparameters of the prior model, the sensitivity problem of the estimation performance to the hyperparameter selection is avoided, thereby achieving the improvement of the estimation performance.

This invention provides a lithium lanthanum zirconium oxide composite solid electrolyte, its preparation method, and its application, relating to the field of solid electrolyte technology. The lithium lanthanum zirconium oxide composite solid electrolyte comprises, from the inside out, a lithium lanthanum zirconium oxide core-shell particle layer, a chlorine-doped sulforaphane-germanium oxide electrolyte layer, and a PEO-LiTFSI-based flexible polymer electrolyte layer. The chlorine-doped sulforaphane-germanium oxide electrolyte in the chlorine-doped sulforaphane-germanium oxide electrolyte layer has the chemical formula Li. 7‑x PS 6‑x Cl x 0<x≤2, the room temperature ionic conductivity of the lithium lanthanum zirconium oxy-based composite solid electrolyte is 1.5 mS / cm, and the conductivity retention rate after 24 hours of air exposure is >90%.