133 results about "A priori probability" patented technology

To appropriately express an erasure position of a code by a small-scale, simple-structured circuit, a soft-output decoding circuit (90) in each element decoder includes a received value and a priori probability information selection circuit (154) to select an input to-be-decoded received value TSR and extrinsic information or interleaved data TEXT, whichever is necessary for soft-output decoding. Based on inner erasure position information IERS supplied from an inner erasure information generating circuit (152), the received value and a priori probability information selection circuit (154) replaces a position where no coded output exists due to puncture or the like with a symbol whose likelihood is “0”. That is, the received value and a priori probability information selection circuit (154) outputs information which assures a probability in which a bit corresponding to a position where there is no coded output is “0” or “1” to be “½”.

The invention relates to a webpage training method and system and a webpage prediction method and system. The webpage training method comprises obtaining a prior probability table of classified keywords according to existing data which are associated with the classified keywords; preprocessing a webpage to be trained to obtain a webpage text to be trained; extracting features in the webpage text to be trained according to the prior probability table to obtain an association relation feature vector representation F1 between the webpage to be trained and a specified category; performing model training on the association relation feature vector representation F1 to obtain a classification result of the webpage to be trained. According to the webpage training method and system and the webpage prediction method and system, category systems which are strong in heterogeneity can be simultaneously processed, the large category systems can be processed through less training data, and the problem of data sparseness is largely solved due to the fact that the browsing and search behavior of a user on the whole Internet not just a website is collected.

The invention discloses an iterative decoding method of an RS product code concatenated convolutional code system, which comprises the following steps of: step S001: providing a convolutional code decoder and an RS product code decoder, and then soft-decision decoding the convolutional code decoder; de-interleaving the bit level soft information transmitted by the convolutional code decoder, and then decoding by the RS product code decoder; and interleaving the bit level external information generated by the RS product code decoder and feeding back to a BCJR decoder as a priori probability; step S002: checking the code word of the RS product code, when the stop condition can not be satisfied after checking, the number of iterations is added with 1, implementing the step S003, and when the stop condition can be satisfied after checking, implementing the step S004; and the decoding method can fully utilize the soft information.

The invention relates to a decision making technique for the events, in order to solve the problem of decision-making simply and rapidly under the condition of a large amount of knowledge. The invention, for each event, can group each condition and become one group for the condition with identical prior probability and identical ' and , or ' relation to form one condition group, the corresponding relation table can be established according to the mapping relation between events, conditions and condition group, then the knowledge base for decision making is established based on the information in the relation table. A decision making application system for the events, which includes a server providing with the knowledge base and a computer connected with the server, can be established based on the knowledge base; the computer may receive one or more conditions inputted by user, the server, for every condition received, can carry out special ergodic process in the knowledge base, and look up out all condition groups with the condition, and the relative decision probability for one or more events can be figured out based on the information such as maximum probability or value of condition group, the satisfied condition number.

A decoder that receives, as input, probability information AMP/C_R×y_t. This probability information is obtained by dividing a channel value obtained by multiplication of received value y_t and a predetermined coefficient AMP by the first additive coefficient C_R for regulating the amplitude of the received value y_t and the probability information 1/C_A×APP_t obtained by multiplying the a priori probability information APP_t by the reciprocal of the second additive coefficient C_A for regulating the amplitude of the a priori probability information APP_t to a soft-output decoding circuit. The soft-output decoding circuit, which may be a large scale intergrated circuit, generates log soft-output C_I×Iλ_t and/or external information 1/C_A×EX_t using additive coefficients for regulating the amplitude of arithmetic operations in the inside of the soft-output decoding circuit.

A probabilistic data signal processor used to determine health of a system is described. Initial probability distribution functions are input to a dynamic state-space model, which iteratively operates on probability distribution functions, such as state and model probability distribution functions, to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled and used as an input to the dynamic state-space model in the iterative algorithm. In various embodiments, the probabilistic data signal processor is used to filter output from any mechanical device using appropriate physical models, which optionally include chemical, electrical, optical, mechanical, or fluid based models. Examples to valve bearings and pipe systems are provided.

The invention relates to a multiple transform domain based super-resolution reconstruction method and belongs to the technical field of image super-resolution. The method comprises the following steps: performing nonrelated transform domain analysis on K input low-resolution images separately; according to the sizes of a target super-resolution image and the input low-resolution images, determining a down-sampling matrix D; and reconstructing the super-resolution image by adopting an optimization algorithm for L1-norm minimization in multiple transform domains through the constraint of a cost function min lambda1||W1(z1)||1+lambda2||W2(z2)||2+......+lambdaj||Wj(zj)||1 by z=z1+z2+...+zj and ||DHiMiz-gi||2<=ni (i=1,2,...K). Compared with the prior art, the method solves the problem in selection of a priori probability model in super-resolution reconstruction, namely, multiple transform domain based remote sensing image sparsity expression; and meanwhile, the method solves the problems of irregular geometric distortion and dislocation of low-resolution remote sensing images caused by load platform shake and atmospheric disturbance, and reconstructs the super-resolution image with a good effect.

The invention provides a method for extracting phrases of statistical machine translation, which comprises the following steps of: 1) acquiring a plurality of aligned sentence pair combinations from a bilingual language material from two directions, and calculating the priori probability of the plurality of aligned sentence pair combinations; 2) calculating the alignment probability of word pairsaccording to the sum of the priori probabilities of the word pairs of the plurality of aligned sentence pair combinations, and forming an alignment matrix by using the alignment probability of the word pairs; 3) calculating the frequency of phrase alignment according to the alignment matrix; and 4) calculating the relative frequency and the lexicalization probability of the phrase alignment according to the frequency of the phrase alignment. The method can effectively express all probable aligned phrase combinations, and improves the quality of phrase extraction, thereby being capable of improving the quality of translation which is performed according to the extracted phrases.

The invention discloses an image recognition method and a device, which relate to the technical field of detection and recognition. In order to solve the problem in the prior art that the accuracy ofimage recognition decreases when the distribution of the training set and the test set differs greatly, the image recognition method includes: acquiring a sample image (101); determining a priori probability distribution (102) of each category image occurring in a current scene; and recognizing the sample image according to a priori probability distribution to obtain a recognition result (103). The method is applied in the process of image recognition.

The invention provides a smoothing constraint unscented Kalman filtering method. The smoothing constraint unscented Kalman filtering method comprises the following steps: 1, acquiring an original prior probability density function of a target state at a current target observation moment according to unscented transformation; 2, calculating a mean value and a variance of an original prior probability through numerical expectation; 3, introducing noise constraint information, and calculating the center of the approximate feasible region to obtain corrected prior probability density; 4, seeking aGaussian distribution mean value and a variance meeting the constraint condition through posteriori iterative optimization, and generating a new Gaussian sigma point meeting the constraint condition;and 5, carrying out weighted calculation on the Gaussian sigma points to complete a filtering process. The smoothing constraint unscented Kalman filtering method has the advantages in the aspects ofaccuracy and robustness, and meanwhile, the real-time performance of the smoothing constraint unscented Kalman filtering method is superior to that of a particle filtering algorithm. Correspondingly,the invention further provides a target tracking method.

The invention is a method applicable to oil and gas exploration and exploitation for automatically detecting geological objects belonging to a given type of geological object in a seismic image, on a basis of a priori probabilities of belonging to a type of geological object assigned to each of samples of the image to be interpreted. The image is transformed into seismic attributes applied beforehand, followed by a classification method. For each of the classes, an a posteriori probability of belonging to a type of geological object is determined for each of the samples of the class according to the a priori probabilities, of the class, of belonging, and according to a parameter α describing a confidence in the a priori probabilities of belonging. Based on the class of the sample, the determined a posteriori probability of belonging to a type of geological object is assigned for the samples of the class. The geological objects belonging to the type of geological object are detected based on determined of the a posteriori probabilities of belonging to the type of geological object for each of the samples of the image to be interpreted.

The invention discloses an MEC random task migration method based on a Bayesian network. The method comprises the following steps of converting an application into a directed graph containing a plurality of sub-tasks; using a probability calculation method of a sub-node in the Bayesian network to calculate a priori probability of a current sub-task migration decision; generating a set of scheduling strategies to minimize the energy consumption of mobile devices according to the probability; using the weak exhaustion algorithm to adjust the generated scheduling strategy so as to obtain the optimal computing task migration strategy. The technical proposal of the invention solves the problem of random task scheduling under the MEC scene.

The invention discloses a defect recognition method based on a power equipment defect recognition learning model. The method is based on multi-layer label labeling and identification; According to thenovel learning model integrating feature extraction, boundary frame regression and classifier technology set, a priori probability is replaced by a clustering algorithm in the model, a training set is generated by training power equipment samples, the type and the defect of the power equipment can be detected in real time, the accuracy and the high efficiency of defect recognition are ensured, and the safe operation of the power equipment is ensured. The automation degree of power equipment state monitoring and defect recognition is improved, a power grid big data analysis and processing system is researched and developed, the power equipment defect recognition technical concept is innovated, and technical development in the field of power systems is promoted. Advanced technologies are applied to the field of power systems, manpower resources and time management cost are saved, and field development is promoted.

The invention discloses a posterior probability distribution calculation method and a system for the hill fire disaster fault of a power network. The method comprises the following steps: the hill fire tripping data of a transmission line under different hill fire disaster conditions are counted; the quantitative relationship between the key factors of transmission line fault and hill fire tripping is analyzed; fault probability distribution functions of transmission lines related to multiple fault probability distribution parameters are constructed; a priori probability distribution of each fault probability distribution parameter is obtained, and random values are extracted from the priori probability distribution repeatedly and independently for verification to train the model; and iterate to the model stationary; the posterior probability distribution of hill fire disaster is obtained by taking several groups of stochastic values as samples after the model is stationary. The invention can quickly obtain the posterior distribution of the hill fire fault probability of the transmission line, which plays an important role in guiding the risk analysis of large-scale mountain fire disaster.

The embodiment of the invention provides a vegetation coverage estimation method and device for surface heterogeneity area and an apparatus. The method comprises: acquiring a priori probability of vegetation coverage and a likelihood probability of vegetation coverage at a predicted time based on a low spatial resolution vegetation coverage product corresponding to each time point of a preset timeseries and high spatial resolution remote sensing data; based on the a priori probability of vegetation coverage and the likelihood probability of vegetation coverage, using a dynamic Bayesian network to obtain a high spatial resolution vegetation coverage estimation result at the prediction time. The embodiment of the invention overcomes the problem of low precision of the estimation result whenthe real surface condition is complex and heterogeneous, can obtain accurate estimation result, and can reflect the dynamic change situation of the vegetation coverage degree, and the texture of theregional vegetation coverage estimation result map is clear, which can reflect the vegetation coverage situation of the surface with strong heterogeneity.

The invention discloses a single-frame image-based high-dynamic range image generation method. According to the method, an edge image block set is found through edge detection, and the total distance of the edge image block set is calculated under a camera response inverse function; a corresponding conditional probability model is constructed; a sampling Gaussian mixture model is constructed according to 201 camera response curves in a DoRF database so as to describe a priori probability model; a maximum posteriori probability model is obtained by using a Bayesian framework; new camera response inverse functions are obtained under a condition that the maximum posteriori probability model is maximum; a Levenberg-Marquardt method is adopted to iteratively solve the new camera response inverse functions to obtain an optimal camera response inverse function; and reconstruction is performed according to the optimal camera response inverse function, so that a high-dynamic range image can be obtained. Image dynamic range expansion is performed in a high-brightness area and a low-brightness area simultaneously, and therefore, the problem of artifacts and details loss at the dark area of the image caused by a method only performing image dynamic range expansion in the high-brightness area can be avoided.

The invention discloses a text-based key personal name extraction method and system. The method comprises the steps of 1, executing a word segmentation operation on a target text, and extracting target words of which part of speech is a personal name; 2, performing statistics on an occurrence frequency of each target word in the target text, and setting a weight of the target word according to the occurrence frequency; 3, adjusting the weight of the target word according to an occurrence probability of the target word serving as the personal name and recorded in an ambiguous personal name priori probability dictionary; and 4, selecting the target word with large weight as a key personal name. Through the method, the extraction of figures related to specific events, the extraction of the key personal name in the text and the extraction of important spreading users, event development node users, public pointing users and information source users can be realized, and the accuracy and validity of figure extraction can be improved.

Systems and methods of precoded faster than Nyquist (FTN) signalling are provided. In the transmitter, Tomlinson-Harashima Preceding (THP) is applied to produce precoded symbols. The THP is based on inter-symbol interference (ISI) due to using faster than Nyquist (FTN) signalling. An inverse modulo operation is not performed in the receiver. Instead, in the receiver, FTN processing is performed based on a matched filter output by determining log a-posteriori probability ratio LAPPR values computed for an n^th bit b_n of a k^th received symbol and pre-computed a-priori probabilities of an extended constellation for a given pulse shape h(t) and FTN acceleration factor combination.

The invention discloses a model updating method, device and equipment. According to the scheme provided by the embodiment of the invention, the method includes under the condition that a shadow set Sand a target sample are given, respectively calculating prior probabilities alpha of training samples contained in the shadow set; and sampling according to the shadow set S, and training the samplingtraining parameter distribution of the sampling model obtained when the model is trained, calculating the posterior probability P of the target sample z in the shadow set according to the training parameter distribution of the given model and the characteristic value of the target sample; evaluating whether the privacy leakage degree of the trained model for the training sample set is qualified or not according to the difference value of the posterior probability P and the prior probability alpha; and if not, changing the affiliation relationship between the target sample and the training sample set, thereby obtaining a new training sample set and performing model adjustment to avoid privacy data leakage.

The invention provides a joint modulation coding method for deep-space link residual frequency offset. The joint modulation coding method for the deep-space link residual frequency offset is characterized by comprising the following steps, wherein a step A is that low density parity check (LDPC) coding is carried out to information bits at a transmitting terminal, and at the same time, the order of a coding sequence is changed through an intermediate conversion matrix so that the information bits are enabled to be located at positions where phase deviation is small; a step B is that Feher-patented quadrature phase shift keying (FQPSK) modulation is carried out to the coding sequence, and transmitting signals generated through the modulation are transmitted into an additive white gaussian noise (AWGN) channel; a step C is that the transmitted signals are received at a receiving terminal and demodulated by an MAP to obtain a bit-by-bit logarithmic likelihood ratio LLR, and then the obtained LLR is utilized for carrying out compensation and estimation for phase offset and frequency offset to codes; and a step D is that soft information is sent into a decoder eventually, a posteriori probability of the demodulation is used as a priori probability of decoding, and a belief propagation decoding algorithm based on the soft information is adopted to achieve a decoding process. The joint modulation coding method for the deep-space link residual frequency offset effectively assists in frequency offset compensation, and improves the frequency deviation resistant performance of a system.

The invention discloses a method for automatically extracting a flood risk area based on an SAR image.The method comprises the steps: calculating the water body probability of two time phase SAR images before and after a flood event through employing a C wave band backscattering coefficient sigma0, and expressing a flood risk area through employing the increment of the water body probability. The method is a rapid, convenient and nearly real-time flood monitoring method, can effectively extract the newly added submerging area (flood risk area) before and after the rainfall event, and comprises the following steps: acquiring two time phase images before and after a flood event, and forming a sigma0 set; generating a frequency histogram of sigma0, assuming that sigma0 accords with Gaussian mixture distribution, and then filtering outliers; performing unsupervised classification on the downsampled sigma0 set, and calculating a prior probability; carrying out iterative calculation on the downsampled sigma0 set by utilizing an expectation maximization (EM) algorithm and the prior probability, calculating a distribution curve parameter, and calculating a water body probability based on sigma0 before downsampling; and extracting a flood risk area by using the water body probability increment of the two time phase images.