49 results about "Bayes analysis" patented technology

A system and process for record duplication analysis that relies on a multi-membership Bayesian analysis to determine the probability that records within a data set are matches. The Bayesian calculation may rely on objective data describing the data set as well as subjective assessments of the data set. In addition, a system and process for record duplication analysis may rely on the predetermination of probabilistic patterns, where the system only searches for patterns exceeding a chosen threshold. Work flow may include selecting which fields within each record should be analyzed, normalizing the values within those fields and removing default data, calculating possible patterns and their match probabilities, analyzing record pairs to determine which have patterns exceeding a chosen threshold to determine the presence of duplicates, and merging duplicates, closing transactions reflecting non-duplicates, identifying records having insufficient data to determine the existence or lack of a match, and/or rolling back accidental merges.

Similarity of email message characteristics is used to detect bulk and spam email. A determination of “sameness” for purposes of both bulk and spam classifications can use any number and type of evaluation modules. Each module can include one or more rules, tests, processes, algorithms, or other functionality. For example, one type of module may be a word count of email message text. Another module can use a weighting factor based on groups of multiple words and their perceived meanings. In general, any type of module that performs a similarity analysis can be used. A preferred embodiment of the invention uses statistical analysis, such as Bayesian analysis, to measure the performance of different modules against a known standard, such as human manual matching. Modules that are performing worse than other modules can be valued less than modules having better performance. In this manner, a high degree of reliability can be achieved. To improve performance, if a message is determined to be the same as a previous message, the previous computations and results for that previous message can be re-used. Users can be provided with options to customize or regulate bulk and spam classification and subsequent actions on how to handle the classified email messages.

A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

A system and method of detecting undesired email containing image-based messages employs a statistical analysis process which identifies and assigns probability values to the presence of each of a pre-selected set of text-related characteristics of an email under consideration and to the presence of each of a pre-selected set of image-related characteristics of the email under consideration. The identified characteristics and their associated probability values are used to determine whether the email is undesired. In one embodiment, the identification and assignment of probability values is a Bayesian analysis and, preferably, a Statistical Token Analysis. The system and method can identify undesired emails which contain images having messages, generally in the form of text in the image.

The invention discloses a recommending method and a recommending system. The recommending method includes the following steps of firstly, computing likeness of target users for various attributions of a commodity to be recommended; secondly, integrating likeness of the target users for various attributions of the commodity to obtain the integral likeness of the target users to the commodity; and thirdly, recommending the corresponding commodity to the target users according to the likeness of the target users for the commodity to be recommended. In order to provide more accurate recommendations for new users and users having low purchase quantity, the integral tendency of all users and personal tendency of a single user are comprehensively analyzed by the Bayes method. For optimization of the new users and the users having low purchase quantity, service quality is improved favorably, and searching time of the users is saved.

Systems, methods, and devices are provided for compressing a variety of signals, such as measured signals from a hydrocarbon operation, that may be stored and/or transmitted in compressed form. Segmentation tools and techniques are used to compress the signals. Segmentation techniques include breaking a signal into segments and representing the data samples of the signal as segment boundary points, which may reflect where changes occur in the signal, and segment parameters, which may be utilized to model the segmented data. Embodiments can be used in real-time or in batch modes. New data samples can influence previous segment boundary points and/or segment parameters in some cases. Systems may modify what has already been stored or displayed as a result in revising segmentation information based on analysis utilizing the new data samples. Embodiments may utilize different Bayesian analysis techniques including the use of prior probability distributions and maximum a posteriori analyses.

Provided is a mixing method for a brain-computer interface based on SSVEP and OSP. A subject wears an electrode cap. A SSVEP-OSP mixed paradigm is broadcast in front of the subject by means of a computer screen. The subject stares at any one of simulation units. By a collection system, an electroencephalogram signal generated when the subjects stares at a simulation target is magnified, filtered and subjected to analog-digital conversion by an electroencephalogram acquisition instrument. Digitized electroencephalogram data is inputted into a computer. An electroencephalogram signal feature extraction method based on a typical correlation analysis is adopted for extraction, classification and recognition of features of SSVEP. A support vector machine and naive bayesian algorithm are adopted for extraction and recognition of OSP features. A recognition result is displayed on the screen in order to feed back to the subject. Then neat recognition is carried out. The mixing method for the brain-computer interface based on SSVEP and OSP has following advantages: rate of information transmission of the method for the brain-computer interface is increased based on SSVEP; and the method is easy in operation, few in electrode number and many in target number.

A technique for assessing the viability of several concepts for new/different products, services, or bundles of products and/or services, using discrete choice modeling, or a combination of discrete choice modeling and monadic concept testing. The core of the invention involves one or more of the following: a methodological technique for combining monadic and discrete choice data, a method for gathering monadic and discrete choice data at the same time during a single fielding, a method for gathering specific diagnostic information, a method for using discrete choice modeling to generate specific diagnostic information, a unique web-enabled interface that helps individuals make quick and accurate choices by displaying concepts at low and high resolution at the same time, a unique web-enabled interface that permits gathering choice data on multiple dimensions for each set of concepts shown, methodological innovations permitting hierarchical and/or Bayesian analysis of discrete choice data using data for multiple dimensions within the same model, and methods and apparatus for storing, organizing, and reporting input and output from this system.

The invention provides an analysis method for a pretreatment mechanism of a wood fiber deep eutectic solvent. A big data multivariable analysis method is adopted to reveal deep-level internal relations among key variables of the deep eutectic solvent pretreatment system. Multiple process variables such as physicochemical properties, structural parameters, pretreatment conditions, wood fiber biomass raw material characteristics and pretreatment effect of the deep eutectic solvent are analyzed by adopting methods such as a multiple regression analysis method, a principal component analysis method, a partial least square method, a clustering analysis method, a factor analysis method and Bayesian analysis. Through multivariable analysis, the causal relationship between the key variables in a deep eutectic solvent pretreatment process is obtained, and the pretreatment mechanism is disclosed. The method is simple in step, convenient to operate and high in practicability, provides technical guidance for revealing the deep eutectic solvent pretreatment mechanism, and has a good wood fiber deep eutectic solvent pretreatment application prospect.

The invention discloses a Bayesian analysis-based interpersonal relationship quantization and valuation method. According to the method, an address book, a call record and a short message record are analyzed. After that, according to different weighting factors, the relationship evaluation values between a target person and each of other people are calculated. According to the relationship evaluation values, important troubleshooting people are screened. Therefore, the efficiency of the public security reconnaissance technology is improved.

The invention discloses a three-dimensional face recognition method based on Bayesian multivariate distribution characteristic extraction. The method comprises three-dimensional data preprocessing, characteristic extraction and identification classification. The method has the advantages that the defect of large computation amount in the prior art is overcome; a three-dimensional face depth map is used for identifying, so that the computation amount can be reduced, and the identification efficiency is increased; the problem of insufficient training samples in single sample identification can be solved, and the training samples are added with a partitioning method; a characteristic extraction method based on Bayesian analysis is provided on the basis, so that the obtained characteristics have a minimum intra-class distance and a maximum inter-class distance, namely, optimal separability is realized; moreover, a classification method based on Mahalanobis distance is adopted, so that optimal identification classification is realized. As proved by experiment data, the method disclosed by the invention has a good three-dimensional face recognition result.

The present invention discloses a design method for reducing a cross section of a wind farm output wire. The method comprises the work of preliminary design, reduction coefficient calculation of a cross section and verification. First, based on a design capacity of a wind farm, preliminarily selecting a wire cross section according to economic current density; then, according to environmental characteristics of the wind farm, in combination with a mathematical relationship between a wind speed, along with environmental factors, and a wire current capacity, and by separately using a screening loaded mode and a Bayesian analysis method combining with Markov Chain Monte Carlo, performing effective reduction on the primarily selected wire cross section, so as to determine a wire design cross section of a output wire; and finally, performing verification according to a continuous limit conveying capacity, short circuit thermal stability and a depressurization condition under a heating permitted condition. The method disclosed by the present invention can combine with an actual working condition to perform design and selection on a wind farm output wire, and compared with a current selection method, a cost performance can be further improved when safety is ensured.

The invention relates to a Bayesian analysis based fault locating method of a power distribution network, and belongs to the field of fault diagnosis of the AC power distribution network. When a faultoccurs, feed line terminal equipment uploads over current information and current change rate information, the probability that information received by a main station is correct or wrong is calculated, Bayesian analysis is used by using redundancy of two types of information to solve the probability that each area has the fault, the area with the highest fault occurrence probability is seen as afault area, thus, the fault is located. According to the method, it is not required to change the main station with the automatic power distribution system or transmit waveform information, the errortolerance rate is higher, and the application prospects are wide.

The invention discloses a three-dimensional super-resolution method based on Bessel film imaging, comprising the following steps: acquiring a three-dimensional image of a sample, wherein the three-dimensional image is obtained by quickly scanning a biological sample by using a piezoelectric ceramic displacement stage to collect fluorescent molecules emitted by stimulation in the sample; performinga radial fluctuation based super-resolution analysis on the three-dimensional image, and generating a first super-resolution image by analyzing a high-order variation of the fluorescent molecule withtime; guided by the first super-resolution image, the three-dimensional image is subjected to three-dimensional Bayesian analysis based on the scintillation and bleaching characteristics of fluorescent molecules to obtain a final super-resolution image. The invention extends the traditional two-dimensional super-resolution radial fluctuation analysis and two-dimensional Bayesian analysis to threedimensions for the first time, and realizes three-dimensional super-resolution; and the invention organically combines two super-resolution algorithms to improve the operation speed of the super-resolution algorithm and the spatial resolution of the super-resolution image.

A method and a printing implementing the method for dynamic printer performance tuning. The method includes the steps of processing a page to be printed by a printer description language interpreter to generate a Display List, selecting tuning parameters from the Display List data, analyzing the tuning parameters by a Bayesian analyzer to obtain a score for each tuning parameter, and based on the score of each tuning parameter, adjusting rendering settings of the tuning parameters for printing the page. The steps of training the Bayesian analyzer includes creating a set of training pages, rendering each page to determine best performance settings for each tuning parameter, grouping the pages based on tuning parameter settings, generating Display List data of each page, rendering the training pages in each group to compile training data that are saved for future printing jobs.

The invention discloses a corrosion pipeline Bayesian degradation analysis method considering a random effect. The method comprises the following steps: 1) describing a corrosion degradation process of a pipeline through an IG process; 2) analyzing each model, a random drift inverse Gaussian model, a random fluctuation inverse Gaussian model and a random drift- fluctuation inverse Gaussian model by using a Bayesian method; 3) checking the applicability of each model by utilizing Bayesian x2 fitting goodness; 4) utilizing each model to simulate and generate random degradation data, and then utilizing a Bayesian analysis method to carry out priori analysis and sample size comprehensive sensitivity analysis; 5) substituting random degradation data generated by simulation in the step 4) into each model, performing parameter estimation through Monte Carlo simulation, and selecting an optimal model; and 6) estimating a relation function of the residual life of the pipeline, the probability density function and the service time by using the optimal model obtained in the step 5). The method considers a random effect to realize high-precision prediction of the corroded pipeline.

In order to guide an electric power enterprise to reserve and optimize a storage space difficultly when facing electric power materials with uncertainty, the invention provides an electric power material warehousing quantity prediction method, and associative prediction is performed on potential materials which are not warehoused yet. The electric power material warehousing quantity prediction method comprises the following steps: firstly, quantifying randomness and warehousing time of the warehousing quantity of the electric power materials; deducing an expression of the expected warehousingquantity of the electric power materials; finding out the number ratio mean value and standard deviation between different electric power materials through historical data analysis, quantifying through a likelihood function, and deriving an update and correction formula of the expected warehousing number of the electric power materials which are not warehoused through a Bayesian analysis method; and carrying out weighted integration on a plurality of formulas to obtain a result which is the result of the invention. The electric power material warehousing quantity prediction method provided bythe invention can effectively improve the precision of electric power material warehousing quantity prediction, reduces the uncertainty of prediction, and guides an electric power enterprise to reasonably configure limited warehouse storage location resources.

The invention discloses a reliability enhancement test quantitative evaluation method based on Bayes analysis, and the method comprises the steps: carrying out enhancement tests under different conditions, respectively employing different estimation methods, setting a certain confidence level, and carrying out staged evaluation, and obtaining a comprehensive MTBF value of the reliability enhancement test. The method overcomes the defect that the required sample size is large or the fault must be corrected in time based on a classical method, solves the problem of small subsample reliability enhancement test evaluation, especially the reliability evaluation of equipment or complete machine level, can be popularized to the field of small subsample reliability evaluation, saves manpower and material resources, accurately obtains the reliability realization level of a product, and has a wide application prospect. And resource allocation decision-making in product development is facilitated.

A method (and non-transitory, computer readable medium comprising computer-readable code) for predicting a financial crisis event (and/or geopolitical risk), positive or negative, comprising receiving from a user a date range and a geographical scope of interest, aggregating prediction data from the date range and geographical scope from one or more of the asset classes currency, bond, commodity, and stock, automatically determining changes over time within the date range of one or more statistical analysis values of the aggregated prediction data selected from mean, median, mode, difference of means, standard deviation, variance, tolerance levels, skewness, kurtosis, inflection points and Bayesian analysis, and automatically reporting to the user a change outside of predetermined expected parameters.