119 results about "Information Criteria" patented technology

Systems and methods are described that determine which offers for credit-based transactions (if any) to market to a person. Each offer is associated with a set of third party demographic information criteria, a set of lender demographic information criteria, and a set of lender credit data criteria. The set of third party demographic information criteria is applied to a set of offers to determine a first subset of offers. The set of lender demographic information criteria is applied to the first subset of offers to determine a second subset of offers. The set of lender credit data criteria is applied to the second subset of offers to determine a third subset of offers.

Systems and methods for creation, management and delivery of targeted marketing campaigns to a consumer based on a consumer's social check-in activity as identified by a consumer on one or more location based social networks. In one embodiment, a campaign management system receives information relating to a consumer's social check-in activity from various (heterogeneous) location based social networks in a wide variety of formats, and further normalizes such disparate information received into a standardized format. According to an aspect, the system processes the normalized information in conjunction with pre-stored marketing campaigns created by marketers for purposes of identifying consumers who satisfy one or more conditions specific to the pre-stored marketing campaigns. Campaign-related information corresponding to the pre-stored marketing campaigns generally comprise various promotional offers, discounts, coupons, etc., which are delivered to the identified consumers, who initiate some kind of action thereby fulfilling the marketing campaign.

A system and method are described for standardizing disparate personal profile information from different sources into a standardized competency based profile. The standardized competency profile enables equalized and fast candidate evaluation and selection across multiple sources for recruiting, education, training and career management purposes.

The invention relates to a method for sending secondary pre-code based on zero subspace technology in a cognitive radio MIMO (CR MIMO) system of time division duplex (TDD) used in primary user transmission. The method comprises the following steps: firstly, periodically perceiving transmitting signals of primary users and estimating a corresponding covariance matrix through a secondary transmitter; secondly, using the covariance matrix to estimate zero subspaces of channel matrixes from secondary users to all active primary users, and using an information criteria (such as MDL) to estimate the dimension number of the zero subspaces and determining the service conditions of space sub channels; and finally, using the acquired zero subspaces as pre-code matrixes of secondary transmission, wherein the transmission method fully eliminates the interference of the secondary transmission with the primary users. The provided scheme does not need instant channel information of the primary users, and nor the number of the known primary users. Therefore, the provided scheme is easy to be implemented in the practical CR MIMO system.

A topic analyzing method is provided in which the number of main topics in text data which is added in time series and generation and disappearance of topics are identified in real time as needed, and features of main topics are extracted and thereby one can know a change in the content of a topic with a minimum amount of memory and processing time. There is provided a system that detects topics while sequentially reading text data in a situation where the text data is added in time series, including learning means for representing a topic generation model by a mixture distribution model and learning the topic generation model online while more-heavily discounting the older data on the basis of a timestamp of the data; and model selecting means for selecting an optimal topic generation model from among a plurality of candidate topic generation models on the basis of information criteria of the topic generation models, wherein the topics are detected as mixture components of the optimal generation model.

The invention discloses a power system fault diagnosis method comprehensively using electricity amount and timing sequence information. Firstly, power-off areas before and after a fault are compared and analyzed to determine the fault area, and a suspicious element set is formed; secondly, a weighting fuzzy timing sequence Petri net model is built, and SCADA information, electricity amount information of a WAMS and the timing sequence characteristics included in the information are integrated to form the alarm information criterion, place delay restraints and the electricity amount criterion; a Gaussian function is adopted, the confidence coefficient of alarm information is obtained in combination with timing sequence reasoning, the initial confidence coefficient of the Petri net model is calculated through array calculation, and the model is solved; finally, backward reasoning is carried out according to the fault probability of an element, and protection and judgment of maloperation and motion refusal of a disconnector are carried out. The power system fault diagnosis method is high in fault-tolerant capability, can handle the phenomena of protection and maloperation / motion refusal of the disconnector and the phenomenon that an alarm is lost or wrong, and improves the accuracy and reliability of the fault diagnosis result.

The invention provides a method for building a software production line platform based on Test Director, Harvest or Subversion, which is characterized in that: by combing the information standards, information flow and information dependency relationship during the process of software research and development; then general defects, modification and configuration management rules (such as flows, tables, fields, roles, authority and protocols) are formulated; unified bases for quality measurement and statistical analysis are established, and a set of measurement models for product quality and process quality is formed. Furthermore, an assistant software research and development synergic management system is also provided for fulfilling support; the operations of service linkage connection, rule mapping and information synchronization are performed for Test Director, Harvest or Subversion. Therefore, the invention has the advantages that: the multi-dimensional data acquisition for demand management, configuration management, alteration management, release management, project management and defect management can be completed, and a software enterprise asset library with a full life cycle is thus formed.

The invention discloses a first-arrival travel time picking method and device for a seismic signal comprising noise, and the method comprises the following steps: carrying out the decomposition of an original seismic signal s(t) comprising noise through a noise adaption complete set EMD (Empirical Mode Decomposition) method, and obtaining a series of finite IMF components from high frequency to low frequency and residual components; screening out a plurality of high-frequency IMF components according to an EIF effective index function, and carrying out the wavelet threshold denoising; carrying out the accumulation and reconstruction of the high-frequency IMF components denoised through wavelet threshold denoising and the high-frequency IMF components which are not denoised, i.e., obtaining the denoised seismic signal x(t); initially determining the first-arrival travel time t0 through an improved sliding time window energy ration method; calculating a local extreme value in a time window comprising the moment t0 through an AIC information criterion method, wherein the time corresponding to the extreme value is the precise first-arrival travel time t. According to the invention, the method can effectively improve the seismic noise inhibition effect and improve the picking precision of the first-arrival travel time, and creates the conditions for the accurate processing and explanation of the subsequent seismic data.

The invention discloses a method for predicting industrial sewage inflow based on an ARIMA model. The method comprises the following steps: analyzing initial time series data to meet a requirement onARIMA model establishment; preprocessing abnormal data by eliminating, filling and the like; removing data noise through moving average filtering; by a unit root testing method, performing ADF testingon the stability of a time series; analyzing and verifying the non-randomness through an autocorrelation coefficient; preliminarily determinating an autoregressive and moving average order of the ARIMA (p, d, q) model, and then performing order determination on the model through combination with an AIC information criterion; optimizing model parameters by a least squares method; finally, testinga residual and evaluating a simulation result to determine a final prediction model. Acquired sewage inflow data are determined, and the obtained prediction model is used for predicting test data, andan output of the model is a prediction result of the sewage inflow. By the method, the model is succinct, the fitting effect of the prediction model is very good, and the precision is high.

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.

A computer implemented method identifying calcification in a patient image data set including blood vessels. The method includes: obtaining an image data set including voxels each having an intensity value; forming the intensity values into an intensity value group covering an intensity range; defining plural intensity thresholds across the intensity range and including its end values; for each intensity threshold, partitioning the intensity values into two sub-groups according to intensity threshold, and calculating an information criterion based on intensity threshold; generating an information criterion measure curve that plots the calculated information criteria against intensity threshold; locating a maximum in the information criterion measure curve and setting the corresponding intensity threshold as a calcification threshold; and partitioning the intensity values into two sub-groups using the calcification threshold, identifying voxels corresponding to intensity values in the sub-group above the calcification threshold as representing calcification in the patient.

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 relates to an IFC data interaction-based engineering information delivery and sharing integrated management system. The information delivery and sharing method comprises the steps of firstly drawing a builder data flow chart; compiling an information delivery demand definition form; establishing information delivery IDM standards; by adopting a programming means and selecting BIM software Revit developed based on a .NET platform, performing programming of secondary development of the Revit by utilizing programming software Visual Studio 2015 through use of a C# programming language; establishing an interface for the Revit; inserting the standards into the interface; creating a self-defined panel; inserting three buttons into the Revit to realize the following three functions of (1) displaying demand definitions according to selected component elements, (2) searching for the demand definitions in a classified manner and (3) viewing the demand definition form; and performing secondary development on the Revit, thereby finishing the whole information delivery process. The information integrated management system comprises an information delivery platform structure which can be divided into a data source, a structure layer, a data layer, a model layer, a delivery layer, a platform layer and an interface layer module. The information standardized delivery of a BIM is realized by utilizing IFC information standards.

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.

A system and method for measuring various weld characteristics is presented. The system and method can comprise a means to measure penetration depth of butt welds in thin plates, for example, using laser generated ultrasounds. Superimposed line sources (SLS) can be used to generate narrowband ultrasounds. A signal processing procedure that combines wavenumber-frequency (k-ω) domain filtering and synthetic phase tuning (SPT) is used to reduce the complexity of Lamb wave signals. The reflection coefficients for different wavelengths corresponding to each wave mode can be calculated. Regression analysis that can include stepwise regression and corrected Akaike's information criterion (AIC) can be performed to build prediction models that use the reflection coefficients as predictors.

A method learns a structure of a video, in an unsupervised setting, to detect events in the video consistent with the structure. Sets of features are selected from the video. Based on the selected features, a hierarchical statistical model is updated, and an information gain of the hierarchical statistical model is evaluated. Redundant features are then filtered, and the hierarchical statistical model is updated, based on the filtered features. A Bayesian information criteria is applied to each model and feature set pair, which can then be rank ordered according to the criteria to detect the events in the video.

Techniques for reducing the computational complexity of conventional similarity-based approaches for temporal event clustering of digital photograph collections include one or more approaches to select boundaries based on dynamic programming and the Bayes information criterion. Each method performs competitively with conventional approaches and offer significant computational savings.

Systems and techniques for collecting information as authorized by information providers and sharing the information with information recipients according to criteria specified by the information providers. Information is collected from one or more of a variety of sources and stored in a provider profile, with the provider profile also specifying criteria for sharing the information, including payment required for sharing the information with particular categories of recipients. An exchange system is maintained allowing recipients to request or to otherwise specify needs for particular categories of information and payments to be provided by the information, and needs or requests of recipients for information are matched with criteria specified by providers, with information being transferred or used to provide results for a recipient and payment being transferred from the recipient to a provider or providers when a match between information needs and criteria for sharing information is identified.

Systems and methods are described that determine which offers for credit-based transactions (if any) to market to a person. Each offer is associated with a set of third party demographic information criteria, a set of lender demographic information criteria, and a set of lender credit data criteria. The set of third party demographic information criteria is applied to a set of offers to determine a first subset of offers. The set of lender demographic information criteria is applied to the first subset of offers to determine a second subset of offers. The set of lender credit data criteria is applied to the second subset of offers to determine a third subset of offers.

The invention provides a wireless network flow prediction method based on a local minimax probability machine, which comprises the following steps of: calculating time delay of a time sequence by using a mutual information algorithm; calculating an embedding dimension by using a Cao method; carrying out phase space reconstruction on the time sequence by using a delay coordinate state phase space reconstruction method to obtain a phase space; calculating the number of proximal points of the phase space by using an AICi information criteria; constructing a proximal point set by using a KD-Tree-based proximal point selection algorithm; rejecting pseudo proximal points from the proximal point set by using a Ki strategy to obtain a cut proximal point set for constructing a training characteristic set of the minimax probability machine; regulating parameters of the minimax probability machine; and training a model to obtain a prediction value. The wireless network flow prediction method disclosed by the invention realizes the localization of the prediction model of the minimax probability machine and can carry out wireless network flow prediction more accurately and more timely.

The invention discloses a method for predicating COD load of sewage based on a vector time sequence model. The method comprises the following steps: performing definition of an excavation target in a data mining modeling process; performing data exploration and preprocessing on obtained sewage inlet related data to meet requirements of vector autoregression model modeling; selecting parameters of an maximum likelihood estimation model, selecting information criteria to determine order of the model, and performing model checking through multi-factor blending statics; and further simplifying the model through target parameters, thereby establishing a simple and effective predicating model. The acquired inlet water data determines water inlet amount and inlet water COD, and a sewage COD load attribute is constructed; test data predication is performed through the obtained predicating model, and output of the model is predicated results for a sewage COD load related variable. According to the method disclosed by the invention, the model is simple, multiple variables can be predicated at the same time, predicating precision is relatively high, and needed time is short.

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.

The invention relates to a data processing method for monitoring and prediction in the electrical power system, in particular to an electric load prediction method used for electric power based on factors of temperature and festivals and holidays. The electric load prediction method used for the electric power based on the factors of the temperature and the festivals and holidays comprises the steps of building a model by using historical data, distinguishing electricity utilization data in the festivals and holidays from electricity utilization data in workdays, building a judging dummy variable, building a short-term prediction model of the load by using the temperature and the dummy variable, carrying out a significance testing of independent variables of the model under the alike information criterion (AIC), and finally obtaining a short-term load prediction value by using the model. Due to the fact that the load prediction value is obtained according to the model and by considering the factors of the temperature and the festivals and holidays, prediction effect is good, and prediction precision is high.

The invention discloses a method for unified communication between multiple medical information systems. In accordance with the method of the invention, an isomerous gateway pool comprises multiple servers connected to the clients ports of various medical information systems via network ports. The network ports of a the gateway pool are connected to a network interface module and the information of various medical information systems is transformed by the servers via information analysis modules and information building modules into information conforming to an international electronic medical information standard. The information is then intercommunicated between various medical information systems. By applying the method taught by the invention, the intercommunication of information between various medical information systems is realized, and the problem of Information Island is solved, the work efficiency of medical systems is increased largely, and the information communication between various medical systems is improved, which is beneficial to the patients and the development of the medical profession.

The invention relates to a digitized personnel evaluation system based on a wireless network handheld terminal. The system is composed of a host, a wireless network device and a mobile terminal and iserected simply. A wireless network established by the wireless network device works cooperatively. With corresponding software installed in the host and the mobile terminal, standing inputting of evaluation information of the mobile terminal as well as system maintenance and data storage of the host is realized. The host completes plan designing of an evaluation item, a questionnaire compiling, evaluation issuing and management, and automatic statistic analysis and report generation of evaluation results; and on the basis of account management, real-time evaluation and anonymous evaluation and full-course recording of the operation by the maintenance staff and the like are realized. Personnel evaluation as well as instant analysis of the field report can be completed automatically, safely, accurately, objectively and efficiently with high confidentiality in a standard manner. Besides, the digitized personnel evaluation system also can be applied to scenes of exams, training, voting, research and the like and is suitable for fields like personnel evaluation, thereby providing a new application direction for the information technology and mobile network technology.

The present invention discloses a user attribute analyzing method and device, wherein the method comprises the steps of acquiring the resident information of a mobile terminal in a predetermined geographic area in which the mobile terminal resides within a predetermined time interval; determining whether the resident information reaches a predetermined resident information standard, if yes, determining that a user using the mobile terminal accords with the user attribute of the predetermined geographic area. According to the present invention, an effect of subdividing the users using the mobile terminals is realized.

The invention discloses a method for predicting service lives in consideration of long-range correlation and uncertainty of components, and belongs to the field of prediction and prediction of residual service lives in health management. The method includes steps of reading state monitoring degradation data and initializing model parameters for specified drift structures (such as exponential functions, power functions and linear functions); estimating unknown parameters of models by the aid of maximum likelihood algorithms; carrying out approximation on obtained degradation processes on the basis of weak convergence criteria and computing probability density functions of residual service lives; judging fitting effects and the prediction ability of the models by the aid of AIC (akaike information criteria) and mean-square errors. Compared with existing methods, the method has the advantages of high universality and prediction precision.

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.