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.

A system and method for designing a progressive lens are provided. 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.

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 invention discloses a method for estimating a 20-kilovolt medium-voltage distribution scheme, which belongs to the technical field of the application of electric industry. In the method, an estimation index system of the 20-kilovolt distribution scheme is established by an analytic hierarchy process (AHP), and a data envelopment analysis (DEA) estimation model with an AHP preference cone is improved, wherein the provided AHP / DEA model is used for solving the weight of an index. By the method, the problem that 9 comparison objects cannot be exceeded in the conventional method is solved and the problem of combining subjectivity and objectivity reasonably in estimation by introducing a relaxation factor is also solved properly.

The invention discloses a method for predicting the relaxation life of a spiral spring on the basis of the stress relaxation theory of a temperature accelerating material. The method comprises the following steps of: testing stress relaxation data of the spiral spring at test temperature and drawing a relaxation curve; drawing a semilogarithmic curve of load loss rate and time and fitting a regression equation; drawing a relation curve of relaxation factors A and B and temperature and fitting a regression equation; and calculating relaxation factors A (actual) and B (actual) of the spiral spring at an actual use temperature and calculating the relaxation life of the spiral spring. Compared with the traditional compression loading and unloading method, the method has the advantages that due to adoption of an RDL10 spring stress relaxation testing machine, errors are reduced and the accuracy is improved; and since a temperature accelerating mode is adopted for predicting the relaxation life of the spiral spring, the predication accuracy is higher, simultaneously the test period is greatly reduced, the efficiency is improved and the cost is lowered.

The invention discloses a water chilling unit fault detection method based on an improved FastICA. The water chilling unit fault detection method introduces relaxation factors on the basis of the traditional FastICA, changes the original iterative manner, and effectively reduces the dependence of the algorithm on initial values. The water chilling unit fault detection method comprises two stages of ''offline modeling'' and ''online detection''. The ''offline modeling'' stage comprises the steps of: firstly, carrying out steady state treatment on a normal data sample; then extracting independent component information of the data by adopting the FastICA algorithm which introduces the relaxation factors; and finally calculating statistics I2 and SPE, and determining control limits by adopting a kernel density estimation method. The ''online detection'' stage comprises the steps of processing newly acquired refrigeration process data according to a model, calculating statistics and comparing the statistics with the control limits to judge whether the refrigeration process operates normally. The water chilling unit fault detection method based on the improved FastICA reduces the dependence of the FastICA algorithm on initial values, and ensures the safety and stability of the water chilling unit in the refrigeration process.

The invention provides a non-stretch cloth simulation oriented mass-spring model constraint method. By adopting the method, deformation repair of a structural spring in a mass-spring model can be realized by correcting the mass point position of the mass-spring model. The mass-spring model constraint method specifically comprises the following steps: in order to accelerate the convergence speed of constraint implementation, giving an optimal relaxation factor based on a topological structure and deformation of the structural spring and the like in the mass-spring model; according to deformation of a single structural spring, calculating components of position correction quantity of mass points at two endpoints along the spring direction; superposing the components of the position correction quantity of the same mass point; multiplying the superposition sum by the optimal relaxation factor, and taking the result as the position correction quantity of the mass point; and finally updating the mass point position so as to realize deformation repair of the structural spring. The mass-spring model constraint method has the beneficial effects that conservation between linear momentum andangular momentum of cloth can be ensured and the non-stretch cloth can be efficiently and stably simulated during the constraint implementation process, thus the method can be widely applied to non-stretch cloth simulation in virtual reality systems for film and video production, digital entertainment and other purposes.

The invention discloses an image de-noising method based on a lattice Boltzmann model. The method comprises the following steps: (1) inputting an initial image I (x,0); (2) setting the initial equilibrium state function Ii<eq>(x,0) of each action direction in a two-dimensional lattice Boltzmann evolution equation; (3) determining the iteration frequency N and the iteration step-length C of the lattice Boltzmann evolution equation; (4) traversing the image to calculate the relaxation factor omega in the lattice Boltzmann evolution equation; (5) calculating the migration process of the lattice Boltzmann model; (6) calculating the action process of the lattice Boltzmann model; (7) updating the equilibrium distribution function as Ii<eq>(x,n); and (8) judging whether achieving the iteration frequency N; if achieving N times, outputting the processed image I (x,N). The method can suppress the noise of the image and effectively protect the edge of the image. The de-noising quality of the image can be enhanced, and iterative calculation of large step-length can be realized, thus enhancing the efficiency of de-noising processing effectively.

The invention discloses a hydraulic status optimization scheduling method for a multi-heat source annular centralized heat supply pipe network. The method comprises the steps of (1) giving user flow demands Gsub, n of heating stations; (2) setting an initial iterative point f0 of the frequency of a heat source pump, calculating flow G0 of branches and valve opening x0 of the heating stations, andsetting a step length relaxation factor defined in the specification; (3) calculating a generalized reduced gradient, defined in the specification, of total power consumption WT of the heat source pump under k working conditions, and improving the frequency fk+1 of the heat source pump along the reduced gradient; (4) performing Newton iterative calculation on valve opening xk+1 of the heating stations when the frequency of the heat source pump is fk+1 and the user flow demands are Gsub, n; and (5) calculating total power, defined in the specification, of the heat source pump, and judging whether a condition is met or not. According to the method, the water pump rotating speed and the valve opening of the multi-heat source annular network can be optimized online in real time; the problems of hydraulic disorder and transmission and distribution energy consumption optimization in a scheduling process of the multi-heat source annular network can be solved; the transmission and distributioncapability of the multi-heat source annular network is brought into full play; and the operation of the multi-heat source annular network is more reliable, efficient and energy-saving.

The invention relates to a method for eliminating an EEG signal noise artifact. The method comprises the steps of performing independent component separation on an EEG signal after denoising processing, then selecting an independent component with an artifact, and finally reconstructing the independent component which does not comprise the artifact for obtaining the EEG signal after noise artifactelimination, wherein independent component separation is based on an improved five-order FastICA algorithm. The improvement of the method is characterized in that an improved updating formula is usedfor updating the separated vectors and the relaxation factor of the independent component; and the independent component which comprises the artifact is selected based on a multi-domain adaptive threshold signal selecting method. According to the method for eliminating the EEG signal noise artifact, not only can a plurality of ocular artifacts be automatically identified and eliminated, but alsoa large amount of EEG information can be reserved. Furthermore the method can well improve the signal-to-noise ratio of the EEG signal and reduce the mean square error of the signal.

The invention discloses a fault classification method based on an improved fuzzy support vector machine. The method comprises the steps that sample data of a switch cabinet are acquired, an optimization model is established for the sample data by introducing a relaxation factor, and the minimum radius of the hyper-sphere of the optimization module is calculated; the membership degree of the sample data inside and outside the boundary of the hyper-sphere is calculated by using a segmented half-time normal cloud model with the minimum radius of the hyper-sphere acting as the boundary of the hyper-sphere; the sample data of the switch cabinet, fault classification marks and the membership degree form a sample set of the fault types, and the optimal segmented function of the improved FSVM is acquired by using the sample set of the fault types; and the classifier of the improved FSVM is established by using the optimal segmented function of the improved FSVM, and the sample data pass through the classifier of the improved FSVM in turn so that the fault type of each sample data is acquired. The effect of fault classification of the switch cabinet can be enhanced by the method.

The present invention discloses a symmetry analysis method for a three-dimensional face model. The method comprises: sampling an input face model, and pairing sampling results so as to obtain symmetrical features; and extracting and clustering the obtained symmetrical features, so as to obtain a symmetry relation of the model. According to the method, a PCA constraint is added during feature extraction, which effectively increase the maximum number of initial sampling points when computing resources are limited; by improving the extraction method for the symmetrical feature, the validity of the clustering result is enhanced; and by adding a relaxation factor during feature-grid reassignment, stability of results is ensured and the matching is extended to the whole model. A test is performed on a face model that is acquired at Kinect and reconstructed using KinectFusion, and the validity of the method is verified.

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.

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 a method for determining the leakage pollution range of a hazardous chemical substance, and the method comprises the following steps: s1, determining a simulation scene, and obtaining the meteorological and hydrological information, geographic feature information and leakage source information of a leakage accident generation region; s2, establishing a three-dimensional geometric model of an area near the leakage source, and performing mesh generation; s3, importing the grid into a fluid flow module in finite element analysis software; s4, setting material characteristics and boundary conditions in finite element analysis software according to the information acquired in the step s1, meanwhile, judging whether the leaked substances have phase change or not, and setting an inter-phase mass transfer model according to the phase change type; s5, selecting an appropriate convergence condition and a sub-relaxation factor, calculating a step length, and carrying out numerical calculation; and s6, after calculation is finished, analyzing a result to obtain concentration field distribution, diffusion velocity field distribution and diffusion path data of the leakedsubstances. According to the method, the diffusion ranges of the liquid-phase hazardous chemical substance and the vapor cloud thereof can be determined at the same time, and a hazardous chemical substance concentration distribution cloud chart is provided.

The invention provides a character recognition preprocessing binarization method based on adaptive multi-threshold, which comprises the following steps: S1, obtaining binarization standard threshold value of each pixel point in an original image; S2, optimizing the original image to obtain the optimized image; 3, obtaining an integral graph number table of that optimize image; S4, calculating a binarization standard threshold value of each pixel point of the original image; S5: obtaining a relaxation factor Zeta off each pixel point in the original image; S6, obtaining a product of a binary standard threshold value of each pixel point and a relaxation factor to obtain a binary relaxation threshold value of each pixel point; 7, carrying out binarization processing on that original image ofthe binary relaxation threshold; The invention solves the problem of poor recognition effect caused by uneven illumination and uncontrollable environment in the character recognition application of the mobile terminal, reduces the time cost of calculating the relaxation factor, and improves the efficiency and precision of binary preprocessing of the image to be recognized.

The invention relates to a method for numerical simulation of a drop flow tongue. The method comprises the following steps: 1, establishing an overflow weir model, subdividing a structural grid and setting boundary conditions; 2, establishing a drop flow tongue iterative calculation model which comprises establishing a turbulence equation, a two-phase flow model, an initial condition and a relaxation factor; and 3, establishing a numerical simulation result processing model, wherein the numerical simulation result processing model comprises color strip parameter setting, water volume fractionsetting and two-phase display parameter setting. According to the invention, the motion state of the drop tongue can be simulated quickly and accurately, and reference is provided for basic mechanismdevelopment and practical engineering application of the drop tongue diffusion rule.

Disclosed is an SVM-based multivariate quality diagnosis classifier for a manufacturing process. Original data of quality characteristics in the manufacturing process is collected and pre-processed, ahybrid algorithm is applied for conducting process analysis on multivariate quality characteristics of a key procedure, stability and anomalies are judged according to data recorded by a control chart, and a support vector machine method is applied for finding out where a source of the process anomalies is, so that a classification result is more accurate, and relaxation factors are added in an objective function by means of a Lagrangian optimization method. The classifier has the advantages that the process capability coefficient condition is strict, the judgement state is accurate, the algorithm complexity is low, the processing time is short, multivariate quality, misjudgment factors and principal component factors are integrated, the applicability is higher, parameter processing is standardized, data processing is improved, the misjudgment probability is reduced, the problems of data offset and unit inconsistency are solved, and an anomaly diagnosis technology can be achieved.

The invention relates to a non-linear chaos algorithm-based reef reservoir identification method. The non-linear chaos algorithm-based reef reservoir identification method comprises the steps of 1, deducing a non-linear inversion algorithm; 2, introducing a chaos algorithm; and 3, identifying and forecasting reef reservoir according to an inversion result. On the basis of an earthquake non-linearinversion theory, a Lyapunov index in chaos is introduced during the process of deducing an inversion iterative equation again, the resolution and the stability in inversion are equalized by a regularization factor and a relaxation factor, the reef reservoir can be finally and clearly imaged, and an inversion speed result is obtained. By building an inversion forecast mode of different lithologiesand lithology identification quantity version, the reef reservoir forecast is guided, and the purpose of identifying and forecasting the reef reservoir is finally achieved.

The invention discloses an X-ray phase imaging method based on imaging system features. The X-ray phase imaging method comprises the following steps: setting X-ray in-line phase-contrast imaging parameters and exposure parameters of a digital radiography system, placing a tool edge device on an object plane to be imaged, acquiring a tool edge image, acquiring tool edge section curves at different positions in the image, and calculating a two-dimensional point function; placing an imaged object to image the object; calculating an initial value of a relaxation factor according to prior information about object construction and the noise condition of the obtained image; calculating phase information of the object; finely adjusting the relaxation factor according to the contrast of the obtained result until an ideal result is obtained.

The invention provides a road marking accurate extraction method driven by a vehicle-mounted laser radar point cloud. According to the method, the method comprises the following steps: firstly, generating a plane histogram by utilizing point intensity values; segmenting the point cloud data according to the point densities and the point intensity values to obtain grouped data with different pointdensities; threshold segmentation is carried out by using a plane histogram to maximize the between-class variance, an intensity grade critical value at the maximum between-class variance is obtained,and road markings in the data are extracted by using the intensity grade critical value and the relaxation factor; and finally, generating a research and judgment graph of the extraction result by utilizing the point density, and performing density aggregation on the extraction result according to the research and judgment graph to obtain a guest road marking result. The method is good in effectiveness and feasibility, the problem that it is difficult to rapidly and completely extract the road markings from the vehicle-mounted laser radar point cloud data with intensity noise and uneven intensity distribution is solved, and rapid and objective extraction of the road markings in the massive vehicle-mounted laser radar point cloud data is achieved.

The invention belongs to the technical field of communication and relates to a method for selecting a relaxation factor of a massive MIMO detector. The method is extended to obtain a relaxation factoronly related to the number of transmitting and receiving antennae according to approximation of characteristic values of a filter matrix on the basis of an existing optimal factor under a certain condition. The relaxation factor is easy to calculate. A simulation result shows that, compared with performance achieved by a successive over-relaxation (SOR) method and symmetric successive over-relaxation (SSOR) method based on an existing relaxation factor, performance achieved by the SOR and SSOR methods based on the relaxation factor obtained by the method is greatly improved. Performance gainsof the SOR and SSOR methods based on the relaxation factor are more remarkable when an NB / NU ratio is relatively small, and are still obvious in case of high NB / NU ratio. Particularly in case of lowNB / NU ratio, the SOR and SSOR methods based on the existing relaxation factor cannot be converged, but performance of an MMSE algorithm is achieved by the methods based on the relaxation factor afterfour iterations.

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 large-scale MIMO detection method and device based on an adaptive damping Jacobi iteration, which can overcome the problem that because of large-scale application of MIMO technology in 5G communication system, excessive computational complexity of minimum mean square error(MMSE) detection brings great technical difficulty to specific hardware implementation and circuit system design. The method provided by the invention uses a damping Jacobi iterative method to approximate a large matrix inverse operation. At the same time, a damping factor is automatically updated during an iterative process and that a relaxation factor used in each iteration is optimal is ensured as much as possible, so as to achieve faster convergence effect. The invention also discloses hardware architecture of the adaptive damping Jacobi detection method, mainly comprising a pre-processing module, a residual vector calculation module and an adaptive update module. According to the invention, by improving traditional Jacobi algorithm, faster convergence effect is realized and validity of the Jacobi iterative method is improved. Thus computational complexity of large-scale MIMO detection and complexity of hardware implementation is reduced.

The invention provides a non-stretch cloth simulation oriented mass-spring model constraint method. By adopting the method, deformation repair of a structural spring in a mass-spring model can be realized by correcting the mass point position of the mass-spring model. The mass-spring model constraint method specifically comprises the following steps: in order to accelerate the convergence speed of constraint implementation, giving an optimal relaxation factor based on a topological structure and deformation of the structural spring and the like in the mass-spring model; according to deformation of a single structural spring, calculating components of position correction quantity of mass points at two endpoints along the spring direction; superposing the components of the position correction quantity of the same mass point; multiplying the superposition sum by the optimal relaxation factor, and taking the result as the position correction quantity of the mass point; and finally updating the mass point position so as to realize deformation repair of the structural spring. The mass-spring model constraint method has the beneficial effects that conservation between linear momentum andangular momentum of cloth can be ensured and the non-stretch cloth can be efficiently and stably simulated during the constraint implementation process, thus the method can be widely applied to non-stretch cloth simulation in virtual reality systems for film and video production, digital entertainment and other purposes.

The invention discloses a compressed Landweber detection method and a structure based on a large-scale MIMO, which comprises the following steps: (1) calculating a matrix R=IM[omega]HHH according to areceiving channel matrix H and a receiving signal vector y, where IM is the identity matrix, [omega] is the relaxation factor, and HH is the conjugate transpose of H; (2) setting initial value s (0)= [omega] HHy and iteration times k=0; (3) calculating according to the formula s (k+1) = s (k) + [omega] R 2ks (k), where s (k) denotes the value of s at the kth iteration and R2k denotes the 2k th power of the matrix; (4) k=k+1, and returning to step (3) for iteration, as that estimate of the transmit signal (img file = 'DDA0001806251210000011. TIF' wi= '58' he= '51' / ), s (k+1) at the end of thepreset iteration is reached and the complexity of the present invention is low.

The invention provides a service combination-oriented global QoS (Quality of Service) constraint decomposition method for constraint intensity perception. A measurement method of user constraint strength is defined, a relaxation factor self-adaptive adjustment method based on a fuzzy inference rule is designed, and a QoS constraint decomposition model of constraint strength perception for servicecombination is constructed. By using the model, in a constraint decomposition stage, when the user constraint strength is relatively weak, candidate services of each task can be effectively eliminated, so that the size of a solution space when a global optimization method is adopted is reduced; when the user constraint strength is high, a certain number of candidate services can be reserved for each task, so that the probability that a feasible combination scheme can be found during service combination is increased.

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.