1158 results about "Iteration process" patented technology

The probability of a computer file being malware is inferred by iteratively propagating domain knowledge among computer files, related clients, and/or related source domains. A graph is generated to include machine nodes representing clients, file nodes representing files residing on the clients, and optionally domain nodes representing source domains hosting the files. The graph also includes edges connecting the machine nodes with the related file nodes, and optionally edges connecting the domain nodes with the related file nodes. Priors and edge potentials are set for the nodes and the edges based on related domain knowledge. The domain knowledge is iteratively propagated and aggregated among the connected nodes through exchanging messages among the connected nodes. The iteration process ends when a stopping criterion is met. The classification and associated marginal probability for each file node are calculated based on the priors, the received messages, and the edge potentials associated with the edges through which the messages were received.

The invention discloses a device and method for executing artificial neural network self-learning operation. The device comprises an instruction storage unit, a controller unit, a data access unit, an interconnection module, a primary operation module and a plurality of secondary operation modules. According to the device and method, the self-learning pre-training of a multilayer neural network can adopt a layer-by-layer training manner; for each layer of network, the self-learning pre-training is completed after multiple operations are iterated until the weight is updated to be smaller than a certain threshold value. Each iteration process can be divided into four stages, calculation is carried out in the first three stages to respectively generate a first-order hidden layer median, a first-order visible layer median and a second-order hidden layer median, and the weight is updated in the last stage by utilizing the medians in the first three stages.

The invention relates to an identification method of cable current-carrying capacity and an identification device; after relative performance parameters of the cable are measured, the temperature endvalue of the conductor is calculated by setting current initial value of the conductor and the temperature initial value of the conductor, and the temperature initial value of a conductor is correctedaccording to the difference of the temperature end value and the temperature initial value of the conductor, until the difference of the two-time conductor temperature is less than a first presettingdeviation range, at the moment, the temperature end value of the conductor is the conductor temperature under the action of the current initial value of the present conductor; the current initial value of the conductor is continuously corrected according to the difference of the temperature end value of the conductor and 90 (the highest insulating working temperature of XLPE), until the absolutedifference of the temperature end value of the conductor and 90 is less than a second deviation range, the current initial value of the conductor, corresponding to the end value of the conductor temperature, is determined into the final cable current-carrying capacity. By initializing the value of the conductor current and temperature and adopting an iteration process which is continuously carriedout, the problem that the obtained result in the existing method is inaccurate, so as to determine the cable current-carrying capacity accurately.

The invention discloses a distributed optimal scheduling method for a multi-energy complementary microgrid containing multiple bodies based on an ADMM in the field of power system microgrid technology. According to the method, operators and users form optimal interaction based on an ADMM framework till supply and demand balance is reached. In the optimal iteration process, the operators and the users can complete optimal scheduling just by exchanging expected power supply capacity and heat supply capacity and actual power supply capacity and heat supply capacity, and therefore the privacy of the operators and the users is greatly protected. Due to efficient energy cascade utilization of cogeneration combined with a heat storage system, demand responses at the user side and renewable energy power generation, the method has the advantages of saving energy, reducing emissions, relieving the pressure of power grids and the like; a comfortable indoor temperature is set, the comfort of the users is considered, and the economical efficiency and the subjective intentions of the users are comprehensively considered in terms of cost. The method is an optimal method with lower cost and higher feasibility for economic operation of the multi-energy complementary microgrid.

The invention discloses a method for estimating pulse noise in an OFDM (Orthogonal Frequency Domain Multiplexing) underwater acoustic communication system. At a receiving end, sparse estimation is performed on pulse noise on an OFDM signal in an underwater acoustic channel transmission process according to a frequency domain signal subjected to redundant Doppler frequency shift compensation, and frequency offset compensation is performed on the frequency domain signal subjected to the redundant Doppler frequency shift compensation with void subcarriers. Under the consideration of mutual interference between the pulse noise and a carrier frequency offset in underwater acoustic communication, compensation of the carrier frequency offset is added in an iteration process while the pulse noise is estimated with all subcarriers and a posteriori distribution under a framework of conventional sparse Bayesian learning, and the frequency domain signal subjected to the redundant Doppler frequency shift compensation and a measurement diagonal matrix for estimating the pulse noise are updated continuously in order to lower influences between the two types of interference. Moreover, the pulse noise is estimated by full utilization of all the subcarriers in the method, so that the spectrum efficiency and the performance of the communication system are improved.

The invention discloses a fast decoupled flow calculation method for power systems, which comprises the following steps of: inputting original data and initializing voltage; forming an admittance matrix; forming correction equation coefficient matrixes B' and B'' and performing factor table decomposition; performing P-theta iteration, and correcting a voltage phase angle; performing Q-V iteration, and correcting voltage amplitude; judging whether the iteration is converged; and calculating node power and branch power. The method requires that the P-theta iteration and the Q-V iteration are all converged in the same iteration and the iteration process is finished, so that the algorithm frame is simpler, and the flow is clearer. The sparse matrix technology is not adopted, so the matrix elements are convenient to access and calculate, and the programming is simple; the correction equation coefficient matrixes are stored according to n order, number change of nodes is avoided, and the programming difficulty is reduced; and the calculation amount is reduced through reasonable logic judgment, the calculation speed is obviously improved and the requirement of scientific research can be completely met. The fast decoupled flow calculation method also can process power systems with a plurality of balance nodes.

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.

Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive images and negative images, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative images. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.

The invention provides a three-dimensional multimode medical image automatic registration method based on mutual information and image segmentation. The method comprises the following steps that stepS1, preprocessing is performed by using a threshold method and a mathematical morphology method; step S2, segmentation is performed by using the k-means method; step S3, the optimal registration parameter based on the mutual information is obtained through iteration by using the optimization algorithm; step S4, an original reference image and a floating image are superposed; step S5, the grayscalehistogram of the reference image A through image segmentation preprocessing is calculated, and the pixels having the same grayscale value are arranged in one group; step S6, the registration parameter is initialized, and the initial values of six parameters are set as zero; and step S7, linear interpolation is performed on the floating image B by using the registration parameter to generate the changed floating image, and the zero value is assigned to the pixel points mapped to the floating image outside the reference image in the iteration process. The method is high in accuracy, high in robustness and high in performance.

The invention provides an advanced calculation-based high-dimensional polarization code decoder and a polarization code decoding method. The polarization code decoder comprises a control module used for sending a control signal to a processor module and sending an address signal to a memory unit; the memory unit used for sending input data to the processor module according to the address signal; and the processor module composed of a plurality of node processors. Each node processor is used for conducting the polarization code decoding treatment on K input data sent from the memory unit according to the control signal. The decoding processes of the polarization code decoding treatments at k levels are combined as a one-shot iteration process based on the high-dimensional decoding algorithm, wherein k=log 2K. Meanwhile, all possible output results are calculated based on the advanced calculation at the check nodes of all levels for the subsequent decoding process to choose. Moreover, the decoding results are sent to the memory unit.

The invention discloses a multi-scale porous structure light weight modeling method oriented to 3D printing, and belongs to the field of computer aided design and industrial design manufacture. Undera condition that characteristic constraints and a stress condition are given, through a compact support radial basis function interpolation, a smooth multi-scale porous model is constructed; the multi-scale porous model is applied to light weight modeling, and a feasible solving solution is given; through the 3D printing, an entity experiment model is obtained and is subjected to engineering stress verification; according to engineering verification result analysis, a parameter is corrected to enable the hole change of an optimization model to more approach to practical stress requirements; and through the above loop iteration process, a light weight model which meets stress requirements is obtained. By use of the method, a light weight purpose of the entity model can be truly realized, sothat the light weight design optimization period of the model is greatly shortened, the porous structure obtained by design has the advantages of smoothness, full connectivity, controllability and quasi-self-supporting, and the effectiveness and the manufacturability of light weight can be accurately guaranteed.

Disclosed are an unmanned aerial vehicle (UAV) route planning system and a UAV route planning method based on genetic programming. The method comprises the following steps: initial populations of a tree structure are built through a UAV model module; each individual is decoded and the fitness value of each individual is calculated through a genetic programming algorithm module; selecting and breeding operations are performed between the populations, and an optimal population is obtained through a plurality of iteration processes; and finally, an optimal individual is selected from the optimal population and decoded through a UAV task module to obtain an optimal route of genetic programming. According to the invention, initializing, decoding, selecting and breeding steps are performed by use of the tree structure to optimize the route constantly. The optimization process is quick, the method is visual, the performance of the planned route is improved, the computing time is reduced, the degree of fitness is optimized, and the system and the method are of very high feasibility and robustness.

The invention brings forward a frequency domain three-dimensional irregular earthquake data reconstruction method. The method is characterized in that first of all, three-dimensional earthquake data in a time domain is converted to a frequency domain by use of Fourier transform, and then, a projection onto convex set (POCS) algorithm is employed and curvelet transform capable of describing localized features of the earthquake data is introduced; and in an iteration process, a new threshold parameter attenuating according to an index rule is brought forward, and each frequency slice is individually reconstructed by use of a soft threshold operator, such that the iteration frequency is reduced, the reconstruction precision is improved, and the purpose of reconstructing the three-dimensional earthquake data is realized. According to the invention, the new threshold parameter attenuating according to the index rule is brought forward and each frequency slice is reconstructed in individually by use of the soft threshold operator, such that the disadvantage of quite slow convergence speed of a conventional threshold parameter is overcome, the calculation complexity of an algorithm is reduced, the calculation efficiency is substantially improved, and the operation time is reduced.

The invention discloses an image de-noising method based on a sparse self-adapted dictionary, and the method is mainly used for overcoming the defects that over-fitting exists when an existing method is used for training the dictionary, and the self-adaption is insufficient. The realization process comprises the following steps of: (1) obtaining image blocks from images with noises and paralleling the image blocks into vectors to form a training data set; (2) utilizing the training data set to iteratively train the dictionary; in an iteration process, taking the dictionary obtained by iteration as a basic dictionary of the iteration, and after the iteration is finished, obtaining a final dictionary and an encoding coefficient matrix of the training data set on the dictionary; (3) utilizing the dictionary and the encoding coefficient matrix obtained by training to obtain a de-noised data set; and (4) utilizing the de-noised data set to reconstruct a de-noised image. The dictionary trained by the method disclosed by the invention has sparseness and better self-adaptation; the effect of de-noising the image is improved; and the method can be used for de-noising a natural image and a medical CT (Computed Tomography) image.

Disclosed herein is a method and apparatus used to measure the number of time a multi-valued data signal transmitted from either a communication device of subsystem deviates across and into one or more bounded areas or zones as defined by an eye mask that is overlaid onto an eye diagram. The present invention employs an iterative process to accumulate and display mask violation that might result from a data signal transmitted from a target device or communications subsystem that deviates across the boundaries either above or below or into the center of the eye diagram. In addition, the present invention also has the ability to isolate particular threshold voltage-delay points along the boundaries above or below and around the perimeter of the mask polygon of the eye diagram where mask violations have occurred. This provides the ability to supply additional information and feedback about the behavior and performance of the targeted device or subsystem being tested.

The invention relates to a hybrid global optimization method. A particle swarm algorithm is used for solving an optimization problem to obtain one group of current optimal solutions; a particle jumpsout of a local extremum by using a chaotic searching algorithm; and local optimal point searching is accelerated by introducing a sequential quadratic programming algorithm into the each generation ofiteration process of the particle swarm algorithm, so that a global optimal solution to the optimization problem is obtained. According to the invention, the concept of particle swarm fitness variance is introduced and the chaotic search and sequential quadratic programming method are combined. When the particle swarm fitness variance is smaller than a given critical value, the particle is easy to fall into local optimum; and chaotic searching is carried out on the optimal particle, so that the particle jumps out of the local optimum. Moreover, according to the particle evolutionary speed andthe particle aggregation degree, the inertia weight is changed adaptively, so that the motion state of the particle is changed and thus the particle is protected from falling into local optimum. During the each iteration process of the particle, the sequential quadratic programming optimization is introduced, so that the searching of the local optimal point of the particle is accelerated and theoverall searching efficiency of the algorithm is improved.

The invention provides a multiuser detection method based on a serial strategy for an SCMA (Sparse Code Multiple Access) uplink communication system, and belongs to the field of signal detection of wireless communication systems. The method is characterized in that nodes in a conventional SCMA factor graph are divided into J groups (J is a quantity of user nodes); each group comprises one user node and all resource nodes connected with the user node; and all nodes (one user node and all the resource nodes which are connected with the user node) of each group are updated in sequence in each iteration process. According to the multiuser detection method, already-updated node messages are utilized in each iteration process, so that a utilization ratio of the already-updated node messages can be effectively increased. The BER (Bit Error Rate) performance of the method is far superior to the BER performance of a method in the prior art under the condition of less iteration times. The calculation complexity of the method is much lower than the calculation complexity of the method in the prior art under the condition of hardly any loss of the BER performance.

The embodiment of the invention provides a method and device for jointly training a service prediction model by two parties for protecting data privacy. The two parties respectively have a part of feature data. In the model iteration process, the two parties obtain encrypted fragments of the product result of the total feature matrix X and the total parameter matrix W through safety matrix multiplication; the two encrypted fragments are summarized by a second party with the label to obtain an encrypted product result Z; the second party obtains an encrypted error E based on the product resultZ and the encrypted label Y, and carries out secret sharing under homomorphic encryption. Therefore, the two parties respectively obtain error fragments. Then the two parties obtain corresponding gradient fragments through secret sharing and security matrix multiplication based on the error fragments and respective feature matrixes; and then, the first party updates the parameter fragments maintained by the first party by utilizing the gradient fragments of the first party, and the second party updates the parameter fragments maintained by the second party by utilizing the gradient fragments of the second party. Therefore, safe joint training for protecting data privacy is realized.

Disclosed is a hyperspectral data classification method which is combined spectrum and spatial information. The steps comprises (1) reading the hypersectral data, (2) confirming the minimum size of structural element, (3) calculating differentiation between picture elements in neighborhood of each structural element by extended mathematical morphology expansion and corrosion operation, (4) obtaining exponential value of morphology eccentricity by the extended expansion and the corrosion operation of step (3), (5), constantly repeating the above steps with the adding of the size of the structural element to achieve the maximum size of the structural element, (6), constantly updating the exponential value MEI of morphology eccentricity in iteration process via the obtained new value, and generating a final exponential value MEI of morphology eccentricity after the iteration process is finished, (7) realizing the extraction of the data characteristic by the image of the exponential value MEI of morphology eccentricity, namely generating ground object type information, and realizing sophisticated category of the ground object by a minimum-distance classifier. The method is an unsupervised classification method for hyperspectral ground object with strong stability, high reliability and high accuracy.

The invention discloses a heuristic type coarse grain parallel grid task scheduling method which is characterized by comprising the following steps: firstly, a task submitter inputs a to-be-schemed task set, a usable computing resource set, an execution time set of tasks on the computing resource, a maximum number of iteration times, a threshold value delta and an entropy value epsilon; secondly, a task scheduler represents a scheduling problem of allocating resources to execute the tasks to be a standard minimum solving problem under task optimization and constraint conditions; thirdly, a grid task scheduling problem is solved by an iterative process of the heuristic type coarse grain parallel algorithm; and fourthly, after the algorithm is finished, the task scheduling result is output. In the way, the invention provides a new multipoint crossing method and the increasing property of a population optimal solution is maintained by an elitist strategy; during a variation stage, a directed variation method based on task immigration is adopted to prevent degeneration of the population; and the method is high in performance and better than the traditional random algorithm in computing power and convergence rate.

The invention provides a complex network community mining method based on local minimum edges. The complex network community mining method comprises the following steps: obtaining an adjacent matrix A of a complex network; calculating a similarity matrix R; carrying out community detection C to a complex network diagram G; looking up one group of local minimum edges; detecting local topological structures on two end points of each local minimum edge, and confirming and removing the local minimum edges which enable a current community structure to more conform to a community definition; detecting whether a new connected subgraph appears in the network, recalculating a weight of each edge if new connected subgraph does not appear in the network, and judging whether division is reasonable if new connected subgraph appears in the network; and if the division is unreasonable, outputting a result, recalculating the weight of each edge if the division is reasonable, and carrying out a next iteration process. The invention has the characteristics of being high in precision, high in speed and good in universality.

Embodiment of the invention provides parallax generation method, generation unit and three-dimensional video generation method and device. The parallax generation method comprises: calculating the parallax of non-matched pixels according to a first view and a second view, and occlusion pictures of the first and the second views to obtain the parallax pictures of the first and the second views; generating new occlusion pictures of the first and the second views according to the parallax pictures thereof; ending up the iteration if it meets the ending condition of iteration, otherwise, regarding the new occlusion pictures as an occlusion pictures continuous iteration process; obtaining an optimal index according to the occlusion pictures and parallax pictures, and judging whether the iteration ends up according to the optimal index. Embodiment of the invention has implemented to select needed video images from several ones according to current view points, capable of synthesizing two or several video images to generate a three-dimensional video. In the iteration process, it only needs to update values of non-matched pixels so as to reduce the calculation time and speed up the velocity of convergence.

The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.