86 results about "Bayesian melding" patented technology

The invention provides an overlapped voice detection method and an overlapped voice detection system. The method comprises the following steps: based on the Bayes information criterion, finding voice fractions which only contain voice of a single speaking person from a plurality of voice fractions in the overlapped voice, and assigning an identical identifier to voice fractions belonging to the same speaking person; randomly selecting the sample data of various voice fractions from the same type of voice fractions and combining the selected sample data so as to obtain various combination results capable of reflecting all voice overlapping possibilities; establishing a single-person speech fraction model and an overlapped voice fraction model on the basis of the obtained single-person voice fractions and the combined multi-person overlapped voice fractions; and finally detecting each voice fraction by using the single-person voice fraction model and the overlapped voice fraction model, and labeling each voice fraction according to the detection result.

The invention discloses a just-in-time learning soft measurement modeling method based on a Bayes Gaussian mixture model, and belongs to the field of complex industrial process modeling and soft measurement. The method is used for a time varying industrial process with nonlinearity and non-Gaussianity; through a strategy of updating localities in real time online, a Bayes information criterion isadopted to determine an optimal Gaussian ingredient number; when new test data comes, a posterior probability that the new test data belongs to each Gaussian ingredient is calcuated, a Mahalanobis distance between the new test data and training data is solved, and the posterior probability and the Mahalanobis distance are blended to serve as a similarity index; and finally, one group of data withthe highest similarity is selected from the original training sample to establish a current GPR (Gaussian Process Regression) model, and model output prediction is carried out to achieve an effect onimproving product quality and lowering production cost.

The invention discloses a hidden-Markov-based Internet network delay forecasting method in the technical field of network delay forecasting. The method comprises the following steps of: acquiring observable status and an observable status sequence according to a historic delay data set and preset delay forecasting precision; clustering the historic delay data set by a K-Means clustering method, computing the error of the historic delay data set under different k values, and confirming an initial value according to the error of the historic delay data set under the different k values; estimating hidden Markov parameters under the different k values, computing a hidden Markov bayes information criterion value under each k value according to the hidden Markov parameters under the different k values, and selecting the k value, which corresponds to the minimum hidden Markov bayes information criterion value, as an optimal hidden status number k-best; and forecasting future delay according to the observable status and the optimal hidden status number k-best. According to the method disclosed by the invention, the rule of the delay data set and the characteristic of the Internet network can be exactly expressed, and the accuracy of forecasting of the future observable status is high.

The invention discloses an expansion target tracking method based on GLMB (Generalized labelled multi-bernoulli) filtering and Gibbs sampling. The expansion target tracking method based on GLMB filtering and Gibbs sampling estimates the target number and the shape of the expansion target, provides a multiple expansion target tracking method under a labelled random finite sets (L-RFS) framework, and mainly includes two aspects: dynamic modeling of multiple expansion targets and tracking estimation of multiple expansion targets. The expansion target tracking method based on GLMB filtering and Gibbs sampling includes the steps: combined with a generalized label multi-bernoulli filter, establishing a measurement limit hybrid model of the expansion targets, by means of Gibbs sampling and Bayesian information criterion, deriving the parameters of the limit hybrid model to learn tracking of the state of the multiple expansion targets, using an equivalent measurement method to replace measurement generated from the expansion targets, and performing ellipse approximating modeling on the shape of the expansion targets to realize estimation of the shape of the expansion targets. The simulation experiment shows that the expansion target tracking method based on GLMB filtering and Gibbs sampling can effectively track the multiple expansion targets, can accurately estimate the state and theshape of the expansion targets, and can obtain the track of the targets.

The invention discloses a method for analyzing fatigue life of an electronic packaging welding spot. According to the method, with the fatigue life prediction problem of a multi-chip assembly welding spot under a thermal cycle load as a starting point, a probability failure physical modeling frame is analyzed and constructed; a strategy of step-by-step implementation of all key steps is elaborated in details and a strategy of carrying out fusion of life data measured by the test by using the Bayesian theory is explained emphatically, and then a Bayesian information update frame is built. Fitting is carried out based on prior distribution of key parameter uncertainty in an obtained model and the obtained welding spot prior life distribution becomes wide and deviation to a practical measurement value occurs; under the framework of the Bayesian theory, fusion with practically measured data of thermal cycling is carried out and thus centralized welding spot posterior life distribution matching the practical situation well is obtained.

The invention belongs to the technical field of radar system multi-target tracking and discloses a multi-target cognitive tracking method based on a concentrated type MIMO radar under complex environment. The multi-target cognitive tracking method based on the concentrated type MIMO radar comprises the following steps: setting the state (please see the specification for the formula) of the qth target at the moment K and a probability density function of the state; setting a measurement matrix to obtain the conditional probability density of the measurement matrix; calculating a Bayes information matrix (please see the specification for the formula) to obtain the relation a recursive relation between BIMs at the moment K and the moment K+1, and on the premise that the transmitting power is given, calculating the BCRB of a tracking error of the qth target at the moment k+1; establishing a power distribution model and solving the power distribution model, and transmitting a beam with corresponding power to the qth target at the moment K+1 according to a solved result.

The invention provides a service life prediction method of a vertical machining center milling cutter updated by Bayesian information on the basis of a state space model. According to the structural features of the vertical machining center milling cutter, a degeneration signal of the vertical machining center milling cutter is collected, and the obtained signal is processed to obtain a degeneration information characteristic quantity; and according to the obtained degeneration information characteristic quantity of the vertical machining center milling cutter, the state space model used for predicting the service life of the vertical machining center milling cutter is established. On the basis of a Bayesian theory under probability statistics, an established service life prediction model of the milling cutter is subjected to information alternation by sequential Monte-Carlo simulation, above parameters are estimated in real time, and the service life predication method is established. A residual life probability density distribution function of the vertical machining center milling cutter is output according to a failure threshold of the milling cutter to obtain a residual life prediction value. The service life prediction method has the beneficial effects that the work reliability of the cutter is improved through the on-line prediction of the residual life of the cutter, sudden accidents are reduced, and heavy losses and casualties are avoided.

The invention discloses a GNSS base station crustal movement velocity estimation method in consideration of nonlinear change. In consideration of the influence on the base station velocity estimationby the GNSS base station nonlinear change, a random model and like, the latest resolving strategy is adopted to acquire a GNSS base station coordinate time sequence under an ITRF2014 framework, a timesequence model for performing separation on the nonlinear change of the base station coordinate time sequence is established by combining a real physical correction model, abnormal location gross error and step detection and like methods; and a GNSS base station coordinate time sequence background noise model determination technology based on the singular spectrum analysis method is adopted, andan improved Bayes information minimum criterion noise model estimation method is proposed base on above conditions, a GNSS base station crustal movement velocity field estimation method is provided, thereby providing accurate and reliable velocity field data basis for the crustal movement velocity field estimation.

The invention discloses a mechanical system time varying reliability evaluating method based on a dynamic Bayesian network. The mechanical system time varying reliability evaluating method comprises a first stage of determining model basic indexes, a second stage of structuring the structure of the Bayesian network and a third stage of updating a formula and the time varying reliability of a Monte Carlo simulation computer mechanical system according to Bayesian information. The mechanical system time varying reliability evaluating method has the advantages that a knowledge diagrammatic expression method is provided through the Bayesian network, directed diagrammatic expression can be carried out on the cause and effect probability relation between node variables, and the cause and effect probability relation can be used for uncertain knowledge expression, cause and effect reasoning, diagnosis reasoning and the like. The weak link of the reliability of the system can be effectively recognized through reasoning of the Bayesian network; the relation between components in the mechanical system becomes more visual and clear through diagrammatic display, the dynamic Bayesian network technology is applied to evaluation of the time varying reliability of the mechanical system, the multiple states and failure correlation of the mechanical system are analyzed, and a theoretical support is provided for improving the performance and the reliability of the mechanical system.

The invention relates to the online speaking people cluster analysis and particularly relates to the online speaking people cluster analysis based on bayesian information criterion. The method comprises steps of collecting original audio signals and dividing the original audio signals into audio segments with boundaries through the bayesian information criterion, performing audio characteristic extraction on the audio segments, clustering the segments with audio characteristics through the bayesian information criterion, and forming a plurality of clustering groups like the clustering group 1, clustering group 2....clustering group n. An online speaking people cluster analysis system comprises two modules which are an audio signal segmentation module and a segment cluster analysis module, which greatly improve the accuracy of segmentation, guarantee high efficiency of clustering, realize high efficient parallel transcription, segmentation, classification and clustering of signals of the online speaking people on a premise that the audio materials of the original speaking people are not required and realize high efficiency transcription, segmentation, classification and clustering of the signals of online speaker.

The invention discloses a speech processing method. The method comprises the following steps: cutting non-speech part in the voice through end point detection to acquire a plurality of first speech fragments; performing Bayes information criterion BIC detection on the plurality of first speech fragments to acquire a speaker transition point; serving the speaker transition point as a break point tobreak the plurality of speech fragments, thereby acquiring a plurality of second speech fragments; extracting speech characteristics of the second speech signal fragments to form characteristic vector, classifying the second speech fragments; and correcting the category of the second speech fragments according to a preset keyword. Therefore, the problem that the algorithm processing effect is non-ideal for the telephone speech recording on the complex service scene by the existing speaker segmentation clustering algorithm can be improved, and an effect of accurately and quickly recognizing the speaker of the speech can be improved.

A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

The invention relates to a shot clustering method based on the spectrum segmentation theory, which comprises the following steps: utilizing, the spectrum segmentation theory for shot clustering; extracting feature vectors of each unspecified shot; calculating similarity between each two categories according to the extracted feature vectors; then constituting each shot cluster as a weighted undirected graph; segmenting each shot category into two shot categories by a using spectrum according to the similarity between each two categories; using Bayesian information criteria to judge whether the segmentation is effective or not, the effectively segmented shot sub-categories are iteratively segmented, the ineffectively segmented shot categories are terminals; finally syncretizing the classification results after the segmentation to get the optimal shot classification number and the classification result. The invention solves the difficult problem that the optimized classification number is difficult to estimate in the clustering algorithm, and improves the recall ratio and the pertinency ratio of the clustering result by utilizing the precise classification spectrum segmentation; the proposed overall fusion operation has a function of correcting the classification errors, thereby effectively avoiding the problem of local optimum relation.

The invention relates to a collaborative detection and power distribution method for target tracking in a multi-radar system. The collaborative detection and power distribution method for target tracking in a multi-radar system includes the steps: establishing a multi-radar system; obtaining a motion model; obtaining an observation model; obtaining a detection model; sending a transmission power distribution result to a transmitter, calculating an effective measurement value of each radar station according to a false alarm rate, calculating the interconnection probability according to the effective measurement values so as to update the target state, wherein the above-mentioned distribution of the transmission power and the selection of the false alarm rate are determined by a final optimization model which is obtained by a Bayesian information matrix obtained by substituting a relaxed information reduction factor with the defined detection model; and minimizing the final optimizationmodel to obtain the optimized transmission power and the optimized false alarm rate. The collaborative detection and power distribution method for target tracking in a multi-radar system is directed to target tracking closed-loop sensing in a multi-radar system, appropriately selects false alarm rate of each radar, for the computing power of the detector according to a fusion center of each tracking frame, and, for the transmitter, correctly distributes the transmission power resources with a predetermined power budget at each tracking frame.

The invention belongs to the technical field of digital audio signal processing and discloses an automatic digital audio tampering point positioning method based on the BIC (Bayesian information criterion). The method comprises the steps as follows: performing active voice detection on a to-be-detected tampering signal to determine a silence fragment in the voice signal; sequentially extracting the Mel-scale frequency cepstral coefficient characteristic of each frame after framing of the silence fragment, and performing long window framing in time sequence; calculating the BIC value of each long-term characteristic frame; taking all crest points in a sequence constituted by BIC values of all long-term characteristic frames as suspicious tamper points, and cutting off the silence fragment front and back with the suspicious tamper points as midpoints; calculating a BIC value sequence of each cut-off window containing suspicious points. Automatic positioning of digital audio tampering points is realized; compared with a traditional tampering detection method, the method has the advantages that the calculated amount is reduced, the omission ratio of the tampering points is reduced, thethreshold selection problem is solved; the method has robustness for the condition of covering of noise with the tampering points.

The invention discloses a feature extraction method for performance degradation evaluation of a rolling bearing. The method comprises the following steps of S1, acquiring vibration signal informationof the rolling bearing; S2, conducting self-adaptive EEMD decomposition on a vibration signal of the rolling bearing; S3, adopting a Bayesian information criterion and a correlation kurtosis method for screening sensitive IMF components, wherein firstly, the Bayesian information criterion is adopted for calculating the number of the sensitive IMF components, secondly, the sensitive components arescreened out according to the values of the correlation kurt (CK), finally, composite spectral analysis is conducted on the sensitive IMF components, and a calculated composite spectral entropy servesas a feature parameter of the performance degradation of the rolling bearing. According to the method, a composite spectral analysis method is adopted for fusing the selected IMF components, the composite spectral entropy is extracted as the degradation feature of the rolling bearing, the sensitivity to the degradation process is high, and the capability of characterizing the degradation processof the rolling bearing by the feature is improved.

The invention discloses a runoff probability forecasting method, wherein, the method mainly comprises the following steps: adopting a method based on K. Medoids clustering method is used to cluster the training set, and the initialization parameters of HMM are obtained. Using Baum-Welch algorithm to study HMM, the state transition probability matrix of HMM and the probability distribution of observation model are obtained. According to Bayesian Information Criterion (BIC), the model is selected and the number of HMM states suitable for the training set is selected. Finally, the conditional probability distribution function is obtained by Gaussian mixture regression GMR reasoning according to the given forecasting factors as the runoff probability forecast. The probability forecasting method of the invention introduces the concept of runoff hidden state, and can obtain the hidden state transition probability matrix by using hydrology, topography, meteorology and other factors, and obtain effective and reliable future runoff probability forecasting distribution, thereby providing scientific basis for reservoir optimal operation and decision-making.

The invention provides a soil environment quality zoning method and system. The method comprises the following steps: extracting soil environment quality comprehensive characteristics of monitoring points in a target area based on a principal component analysis method; screening out main influence indexes of the soil environment quality by adopting a geographic detector; establishing a series initialization pre-classification scheme, and determining an optimal pre-classification scheme according to a Bayesian information criterion; constructing a Gaussian mixture model according to the optimal pre-classification scheme, and estimating hidden variable parameters representing sample point categories in the Gaussian mixture model through an EM algorithm to obtain initial classification of the monitoring points; and obtaining an initial partition based on the corresponding Thiessen polygon of the monitoring point, and performing final partition on the target area in combination with natural boundary information of the target area. According to the method, on the basis of the comprehensive characteristics of the soil environment quality of the monitoring points, the Gaussian mixture model based on the EM algorithm is constructed, and comprehensive partitioning of the soil environment quality based on the high-dimensional attribute characteristics is achieved.

The invention provides a source number estimation method under the frame of the Bayesian Information Criterion (BIC), and is suitable for large-scale self-adaptive antenna scenes, under generalized asymptotic conditions, namely m and n are infinite, m/n is equal to c belonging to zero to infinity, wherein m and n respectively represent the number of antennas and the number of snapshots, and the reliable detection of the source number is provided under the condition. According to the source number estimation method disclosed by the invention, the prior probability is obtained through the co-calculation of a log-likelihood function and a cost function, and the source number is effectively obtained through maximizing the prior probability. Simulation results prove the superiority and the effectiveness of the source number estimation method disclosed by the invention.

The invention discloses an equipment residual life evaluation method based on continuous Weiner process damage. The method mainly realizes conversion of continuous monitoring data of equipment into performance degradation indexes, evaluates reliability indexes of the equipment in each period based on a continuous Wiener process damage accumulation model in allusion to the fluctuation condition of a degradation physical process, and through overlong and short-term memory network training, full-life monitoring data of continuously operating equipment is converted into a single performance degradation index, and a reliability index and a performance index are fused through Bayesian information fusion to obtain a residual life prediction result of the equipment. The performance degradation index change in the early stage of equipment degradation is not obvious, and the performance degradation index change severely in the late stage of equipment degradation. Therefore, the method of combining continuous Wiener process damage accumulation and network training can greatly reduce the deviation of the residual life prediction of the equipment.

The invention relates to an industrial process fault diagnosis method based on the Bayesian information criterion. The method comprises: collecting normal industrial data and calculating several kindsof detection statistics amounts based on normal data; carrying out fault detection on a to-be-detected sample; expressing a fault isolating task into a combinatorial optimization problem; convertingthe problem into a mixed integer nonlinear programming problem by combining the Bayesian information criterion; on the basis of a forward selection algorithm, simplifying the problem into a mixed integer quadratic programming problem; on the basis of a branch-and-bound algorithm, solving a series of similar mixed integer quadratic programming problem to obtain a fault variable combination causingthe sample fault. The industrial process fault diagnosis method has high universality; and the fault variable can be identified without predetermining a fault direction or a known historical fault data set. When the amplitude of the fault is small, an accurate diagnosis result is obtained. Besides, the combination optimization problem is transformed into the quadratic programming problem with sparse constraints for calculation, so that the computational efficiency is improved substantially.