38 results about "Relaxation factor" patented technology

A system and method for designing a progressive lens. Mean power is specified at points distributed over the entire surface of the lens and lens height is specified around the edge of the lens. Lens height is determined at the points consistent with the specified mean power and the lens edge height in part by solving a partial differential equation of the elliptic type subject to the lens edge height as a boundary condition. A successive over-relaxation technique may be employed to converge on the solution to the partial differential equation, and an over-relaxation factor may be determined to most efficiently relax the equation.

The rate controller in a digital video encoding system is responsible for allocating a bit budget for video frames to be encoded. The rate controller considers many different factors when determining the frame bit budget. One of the factors considered is the complexity of the frames being compressed. Occasionally there will be a very complex frame that is not representative of the overall video frame sequence. Such a rare complex frame may cause a disproportionate affect on the bit budget allocation. The system of the present invention limits the amount that a very complex frame can change the bit budget allocation. The rate controller of the present invention also includes a relaxation factor. The relaxation factor allows a user to determine if the rate controller should strictly allocate its bit budget or relax its standards such that the rate controller may not be so conservative when allocating bits to frames.

The invention discloses an interruption relaxation alignment method based on multi-point multi-user coordination downlink. The method specially comprises the following steps: (1) completing channel estimation at a sending end by using a corresponding channel estimation technique according to that different performances and transmission modes are required by communication systems to obtain channel matrix parameters; (2) establishing an optimized equation consisting of constraint conditions and a target function, wherein the constraint conditions are formed after relaxed interruption and alignment are carried out on constraint conditions by a relaxation factor lambda; (3) solving the optimized equation; (4) respectively performing premultiplication on a sending signal by various receiving ends with a solved wave beam formation matrix so as to complete pretreatment; and (5) carrying out conjugated transposition premultiplication of the received signal by various receiving ends with a solved interruption elimination matrix so as to complete post-treatment. The method has the advantages that a wave beam formation matrix set and an interruption elimination matrix set which are used for inhibiting multi-user interruption can be rapidly solved, and the pre-treatment and post-treatment are respectively carried out by the sending end and receiving ends so as to inhibit the multi-user interruption.

The invention discloses a fault classification method based on an improved fuzzy support vector machine. The method comprises: obtaining sample data of a switch cabinet, establishing an optimization model for the sample data by introducing a relaxation factor, and calculating the hypersphere of the optimization model The minimum radius of the hypersphere; take the minimum radius of the hypersphere as the boundary of the hypersphere, and use the segmented semi-decreasing normal cloud model to calculate the degree of membership of the sample data inside and outside the boundary of the hypersphere; The degree of membership constitutes a sample set of fault types, and the sample set of the fault types is used to obtain the optimal segmentation function of the improved FSVM; the optimal segmentation function of the improved FSVM is used to establish a classifier of the improved FSVM, and the sample data are sequentially Through the classifier of the improved FSVM, the fault type of each sample data is obtained. This method achieves the effect of improving the fault classification of the switchgear.

The invention provides a method for predicting the mass transfer relaxation factor of subcooled flow boiling heat transfer based on radial basis neural network, which includes trying out the mass transfer relaxation factor under different working conditions through CFD software; collecting flow parameters and Mass transfer relaxation factor; determine the input and output variables of the RBF neural network; train and test the RBF neural network; use the trained RBF neural network to predict the mass transfer relaxation factor under different working conditions, and realize the mass transfer of subcooled flow boiling heat transfer Prediction of relaxation factors. The invention considers the influence of different flow parameters on the mass transfer relaxation factor, tries to gather the best mass transfer relaxation factor under each working condition, and establishes the RBF neural network prediction model based on the flow parameters and the mass transfer relaxation factor. By directly inputting the flow parameters under different working conditions into the RBF neural network prediction model, the mass transfer relaxation factor of the boiling heat transfer in the subcooled flow of the cooling channel can be predicted quickly and accurately.

The invention provides a method for predicting a reservoir by relaxation factor pre-stack seismic inversion. The method comprises the following steps of: A, acquiring pre-stack seismic angle gather data and corresponding logging data in a seismic work area; B, generating an initial model of longitudinal wave velocity, transverse wave velocity and density according to a prior geological layer and the logging data; C, superposing the pre-stack seismic angle gather data to form n superposed seismic angle gathers at different angles; D, extracting corresponding n subwaves according to the n superposed seismic angle gathers; and E, gradually extracting a superposed seismic angle gather, the corresponding subwave and the like from the n superposed seismic angle gathers and the corresponding n subwaves. A relaxation factor generated by the longitudinal wave velocity, the transverse wave velocity and the density is added into the inversion process to predict the reservoir, so that the accuracy of predicting the reservoir is improved, and the advantageous position of oil and gas is more obviously indicated.