49 results about "Structure factor" patented technology

A method for characterizing ultrasound scatterers in a medium comprises receiving ultrasound data representing a region of interest comprising a plurality of scatterers in a medium, the plurality of scatterers including aggregates of the scatterers. The ultrasound data is modeled data using an effective medium theory combined with the structure factor model, the structure factor model defining the spatial organization and concentration of the aggregates. The modeled ultrasound data is compared to theoretical data obtained with the effective medium theory combined with the structure factor model. From the comparison, dimensional data of the aggregates of the scatterers and the volume concentration of scatterers in the medium is determined.

The invention provides a cable type welding wire. The cable type welding wire comprises a center welding wire and n peripheral welding wires arranged around the center welding wire in a spiral rotating manner, wherein the diameter of each of the peripheral welding wires is d, and the neighboring peripheral welding wires are separately arranged in an intersecting manner. The cable type welding wire is characterized in that the lay length T of each of the peripheral welding wires is equal to K*m*d, wherein K is the structure factor of the peripheral welding wires, m is the lay length multiple, and d is the diameter of each of the peripheral welding wires; K is larger than or equal to 1, and less than or equal to 1.5; m is larger than or equal to 20, and less than or equal to 25. According to the cable type welding wire provided by the invention, wire breakage is not liable to generate in the manufacturing process; during welding, the peripheral welding wires are arranged compactly, so that the wire feeding process is more stable, and the welding performance of the cable type welding wire is improved.

According to the invention, an incident X-ray is transmitted in a wide angular sector towards a sample of amorphous material that scatters back the X-rays. The method comprises: a step of storing experimental photonic intensity measures based on the incidence angle; a step of correcting the experimental intensity while taking into account at least the absorption phenomena within the sample based on the penetration length l of the incident wave inside the sample before reflection; a standardisation step of reporting the corrected intensity resulting from the experimental intensity to an electronic intensity according to a standardisation coefficient (Alpha); a step of calculating a discretised function Q.i ( Q ), i being a reduced intensity resulting from the measures of the corrected and standardised experimental intensity and Q being the module of the wave scattering vector proportional to the value sin Theta / Lambda, 2Theta being the scattering angle and Lambda being the length of the transmitted wave, the standardisation constant (Alpha) recursively varying in order to minimise the slope of the affine straight line (42) obtained by linear regression of the values of the function Q.i ( Q ), the reduced intensity values being calculated upon each iteration for a penetration length l, and the sought (41) function Qi ( Q ) corresponding to the minimum slope; and a step of determining the structure factor from the distribution of the radial atomic concentration Rho(r) based on Qi ( Q ).

The invention discloses a face recognition method based on structuralized factor analysis. The problem that the face recognition accuracy rate is low due to the fact that local clustering features and a global distribution structure of face image data cannot be kept in the prior art is mainly solved. The face recognition method comprises the achieving steps of 1 dividing a face image data set; 2 performing clustering analysis on all training sample data; 3 calculating an optimal feature projection matrix through Gibbs sampling; 4 extracting low-dimension features of all test sample data and the training sample data; 5 recognizing a face image. The face recognition method synthesizes the local clustering features and the global distribution structure of the face image data and improves the face recognition accuracy rate.