48 results about "Iterative strategy" patented technology

The invention discloses a BP decoding method and device for a polarization code, and belongs to the technical field of channel coding. A BP decoding algorithm based on an early termination iteration strategy is used for decoding the polarization code, and a judgment condition of early termination of the BP decoding algorithm based on the early termination iteration strategy is that a symbol of the log-likelihood ratio of the leftmost end of a factor graph does not change in continuous two iteration processes. The invention also discloses a BP decoding device for the polarization code. Through the convergence characteristic of the symbol of the log-likelihood ratio, the method and device carry out the judgment of iteration termination of BP coding, can remarkably reduce the number of iteration times under the condition of causing no loss of decoding performance, and are more remarkable in effects under the conditions of medium and high signal-to-noise ratios. The method is simple and easy, is low in calculating complexity, and is simple in hardware implementation.

The invention provides a Split Bregman weight iteration image blind restoration method based on a non-convex higher-order total variation model, and belongs to the technical field of image processing. The method is characterized in that firstly, a non-convex higher-order total variation regularization blind restoration cost function is obtained by introducing image border sparse prior information meeting a hyper-Laplacian model and by combining a high-order filter bank capable of generating piecewise linear solutions; secondly, a weight iteration strategy is provided, a minimization problem of the non-convex higher-order total variation regularization blind restoration cost function is converted into a minimization problem of an approximate convexity cost function with the updated weight; thirdly, the minimization problem of the approximate convexity cost function with the updated weight is converted into a new constraint solving problem through an operator split technology, and the constraint solving problem is converted into a split cost function through the method of adding a penalty term; fourthly, the split cost function is solved through a Split Bregman iteration solving frame. According to the Split Bregman weight iteration image blind restoration method based on the non-convex higher-order total variation model, an image can be restored effectively and rapidly, the shortage that a staircase effect is generated in a traditional total variation regularization blind restoration method is overcome, and meanwhile a better restoration effect on manually degraded images and actually degraded images is achieved.

The invention provides a sparse singular value decomposition scanning radar forward-looking imaging method, which is used for the radar imaging technology field. A radar forward-looking echo signal is analyzed and scanned, and an azimuth echo model of a target scattering coefficient and antenna measuring matrix operation is constructed, and then an azimuth high resolution problem is converted into a matrix inversion problem. A target function is constructed by adopting the sparse singular value decomposition method provided by the invention. The solution of the echo azimuth signal is realized by adopting an iterative strategy. A limitation of conventional TSVD methods of contradiction between resolution improvement and noise suppression is broken through, and sensitivity of an imaging result to noises is reduced, and therefore the azimuth resolution improvement is realized.

The invention discloses a mesh surface curve design method based on distance constraint. The designed curve passes through a given interpolation point and is smooth (in the sense of discreteness) andstrictly located on the mesh surface. The method transforms the complex manifold constraints into distance constraints and describes them as optimization problems together with smooth constraints andinterpolation constraints. The local surface is approximated by the tangent plane, and the distance constraint is relaxed to the distance from the point to the tangent plane. Because the points on thecurve used to calculate the distance are interdependent with their corresponding tangent points, a global-local iterative strategy is adopted and Gauss- Newton idea method is used to control its convergence behavior: in the whole stage, it is relaxed into a convex optimization problem by distance approximation to solve the iterative step size; In the local phase, a robust and efficient projectionmethod is used to map the optimized curves to the surface to update the tangent points. Finally, all the relaxed polygons are mapped to the mesh surface by using the cutting plane method. Compared with the existing methods, this method has many advantages in efficiency, robustness and application range.

The invention discloses a Raman spectrum image demixing method based on nonnegative matrix approximation. Non-pure pixels do not exist in a Raman spectrum image, unknown limitation on ANC, ASC and end elements exists in Raman spectrum demixing, various regularization factors such as sparsity are added in the nonnegative matrix approximation under the condition that the Raman spectrum image meets requirements of an LMM (labor market model), so that mixed pixel components and corresponding abundance thereof can be obtained effectively. Compared with the existing other method, the Raman spectrum image demixing method based on the nonnegative matrix approximation has the following advantages that the number of end elements (substances) is not required to serve as a priori knowledge; an iteration strategy is adopted, and a gain result is not relevant to the number of the end elements; and a graph relation and 1/2 bound norm are used, so that the noise immunity and the stability of an algorithm are improved.

The invention discloses an analysis method for a structure response interval comprising interval parameters based on a hyper-volume iterative strategy. According to the method, a structure response variable is taken as a target function, the maximum/minimum value of the response is solved through an optimization algorithm, and therefore an upper/lower boundary of the response is determined. The adopted optimization algorithm is a global optimization algorithm based on the hyper-volume iterative strategy, a strategy of Simpson integral is adopted to self-adaptively arrange integral points within a range of uncertain parameters, the corresponding maximum/minimum value of the target function in the integral points is founded, then an integral point corresponding to the maximum/minimum value of the target function is taken as a starting point, the global maximum/minimum value is obtained on the basis of a Newton method, and namely the upper/lower boundary of the uncertain parameters which is propagated in a nonlinear system. According to the analysis method for the structure response interval comprising interval parameters based on the hyper-volume iterative strategy, under an uncertain input condition, the interval response propagation boundary of a structure can be determined accurately.

The invention discloses an adaptive selection method for an adaptive region in real-time target matching and locating, and belongs to the technical field of computer image identification. The method comprises the steps of performing preprocessing and threshold segmentation on a real-time image; building a potential function by utilizing a spatial position relationship between a landmark and a target; by utilizing a local region stability index and a local region intensity index, performing first layer screening under the constraint of a spatial position potential function; performing non-maximum suppression on obtained candidate matching regions, and removing the regions with the relatively high overlapping degree; then performing correlation surface feature index screening and sorting; and finally selecting an optimal adaptive region through a potential function iterative strategy. Under the condition of insufficient matchable region quantity in target matching identifying and locating processes, the adaptive region which is abundant in information amount, is stable and reliable and is free of a repeat mode is selected in real time adaptively.

The invention relates to a detection method and system applied to Flash intelligent analysis and detection, an intelligent terminal and a computer readable storage medium. The detection method comprises the steps: S1, acquiring a Column total set, a Page total set and a Block total set, and presetting a bad Column total set, a bad Page total set, an error threshold value and an initial bad Block template; S2, alternately acquiring bad Page elements and bad Column elements in sequence from the Block total set based on an error threshold value, and alternately updating a bad Page total set and a bad Column total set in sequence; S3, on the basis of all the bad Column total sets corresponding to different error thresholds and all the bad Page total sets corresponding to different error thresholds, updating the error thresholds according to a bad template iteration strategy, obtaining a final Column total set from all the bad Column total sets, and obtaining a final Page total set from all the bad Page total sets; S4, obtaining a final bad Block template. The method has the advantages that the influence of the bad Page on follow-up operation of selecting bad Column elements is reduced, generation of false bad Column is reduced, and then the effective capacity of Nand Flash after analysis and detection is improved.

The invention discloses a process early warning method and system for analysis and estimation of a full measurement point coupling structure. The invention provides a full-measurement-point synchronous estimation and monitoring model based on analysis of a coupling structure between measurement points and measurement point estimation errors, and aims to solve the problems that an original monitoring method based on working condition estimation is incomplete in monitoring and insufficient in modeling of a coupling relation between the measurement points. On the basis that full measurement points are regarded as a full measurement point diagram, a multi-kernel graph convolutional layer is provided and applied to a full measurement point synchronous estimation and early warning model, and synchronous working condition estimation and monitoring of full sensor measurement points are achieved by explicitly modeling the coupling relation between the measurement points. According to the method, a self-iteration strategy based on feature approximation is designed for the provided model, so that the problem that estimation values of part of measuring points are abnormal due to strong coupling between the measuring points when a system is abnormal in a traditional method is solved, and the method has important significance on accurate monitoring of the industrial process.

The invention discloses an input vector-oriented RTL-level circuit reliability calculation method. Based on the principle that the modules can be calculated, an integrity linked list corresponding toeach module is constructed for each module through a depth-first search algorithm, so that an input source of each module can be obtained from an upper-layer sub integrity linked list of the module. The smooth calculation of each module is guaranteed. Based on a recursion principle, the reliability of the circuit module is analyzed by means of an SCA method; according to the constructed integritylinked list, an iterative strategy is utilized to realize reliability calculation of the RTL-level circuit; for the extracted modules, a recursive algorithm is used for detection, when it is found that all composition units of some sub-modules are basic gates, an SCA method is called to carry out calculation to guarantee precision, and when a calculation result is fed back to the top-layer linkedlist, the next module is called to carry out iterative calculation until the end of the top-layer linked list is reached. The method is suitable for the calculation of a super-large-scale integrated circuit and the use of a parallel strategy, and is high in calculation precision.

The invention provides an edge gateway, and an edge gateway dynamic policy service implementation method, device and system. The edge gateway comprises a policy module, a digital signature module, a pull module and a drive registration module. A script vm is embedded in the strategy module, and when a new strategy is updated, the strategy module is directly executed; the digital signature module is used for recording the md5 signature of the service subfile; the pull module is used for sending md5 signatures of all files to a strategy verification service by the edge gateway when an updated strategy exists, calculating sub-files needing to be updated, and pushing the strategy needing to be updated; and the drive registration module is used for interacting with the device based on the updated new strategy and the edge gateway. According to the method, the updating speed is high, the error-tolerant rate of the weak network environment is high, full-amount updating is not needed through dynamic strategy issuing, the updating size is small, and only necessary parts are updated; fast iteration is supported; the strategy verification service is simple, control and data separation can be achieved only through a control end, and the problem of compatibility with multiple versions is not needed.

The invention provides a conjugate gradient large-scale MIMO detection method based on deep learning for signal detection for a large-scale MIMO system in which the number of transmitting antennas is greater than that of receiving antennas. According to the method, proper trainable parameters are selected, a CG iterative algorithm with the trainable parameters is expanded into a deep neural network, and optimal parameters of each layer are found through network training. And through an initial value iteration strategy based on characteristic value estimation, the convergence of the algorithm is improved. According to the method, rapid convergence during parameter estimation can be ensured. In addition, the number of trainable parameters is only related to the number of network layers and is not related to the number of antennas. The characteristics ensure the rapid and stable training process of the method and the reasonable expandability of a large-scale system. According to the invention, a lower bit error rate is achieved with lower complexity.

The invention discloses a sectional calibration method for a successive approximation analog-to-digital converter. The successive approximation analog-to-digital converter for realizing the method comprises two N-bit SARADCs which are independent from each other and have deviation and two groups of digital calibration modules. Redundancy is introduced into two independent N-bit SARADC capacitor array structures, and back-end processing is carried out segment by segment through a digital method by using the redundancy, so that the ADC precision is improved. The digital calibration module realizes an LMS algorithm through a digital circuit so as to achieve a calibration purpose. The calibration algorithm is continuously carried out in the background, and normal work of the foreground is not affected. Compared with the existing calibration design, the segmented calibration algorithm uses two groups of different iteration steps, the configuration of two different iteration strategies is more flexible to adjust, and the calibration precision and speed can be better considered.

The invention discloses a method applicable to screen capture of various non-rectangular sections, and the method comprises the following steps: constructing a deep confidence multi-shape screenshot model based on a least square support vector machine principle; performing optimization training on the multi-shape screenshot model by using an optimal iterative strategy, and importing the trained multi-shape screenshot model into a screenshot mode; selecting the screenshot mode at a position to be subjected to screenshot, and triggering the multi-shape screenshot model to run a touch point received on a detection screen; when the number of the touch points is greater than a set threshold value, determining a screenshot range and a screenshot shape according to the number of the touch pointsand the positions of the touch points on the screen so as to carry out screenshot. According to the method, the number of the touch points can be accurately obtained under various scenes so as to carry out operation of screenshot of various shapes, the real-time performance and applicability of screenshot can be guaranteed while complex scene conditions are met, and the wide applicability and userexperience of screenshot are improved.

The invention discloses a semi-supervised multi-label learning method based on SPUM data. The method comprises the following steps of: considering single positive example unlabeled multi-label data (SPUM) data in which only one label in a plurality of labels of data is definitely labeled as positive and the other labels are in an unlabeled state, and solving a globally optimal solution through information transmission when the unlabeled data and the SPUM data are dispersed in different data nodes. According to the method, a real-time label prediction loss function based on positive class number constraint is adopted, so that the algorithm can learn a large amount of information by constraining the positive class number and alternately optimizing label prediction and model output in a scene that only a single label of partial data is labeled as a positive example, and the cost of a labeling task is greatly reduced. In a distributed network in which resources such as communication bandwidth are limited, an iterative strategy based on event triggering is adopted, so that the gradient information amount is allowed to be transmitted after reaching a certain degree, and the network burden is greatly reduced while the performance is kept.