1009 results about "Confidence interval" patented technology

The present invention is for a system and method for determining the proximity of a mobile device to a location without the use of a satellite based or other location awareness system, nor a stationary beacon of any kind Instead, the mobile device monitors radio frequency broadcast identification codes from nearby mobile devices, and determines if the set of detected identification codes is sufficiently similar to a weighted set of identification codes attributed to specified location. If the calculation of similarity meets the confidence conditions of the system, notification is made that the customer or visitor has arrived. The invention utilizes a combination of confidence interval computation, machine learning, and fault tolerance mechanisms to optimize the success of correctly detecting that the device is near the relevant location.

A method is disclosed for classifying an input pattern into an associated class through use of a compound classifier. Data pertaining to preselected features present within the input pattern are extracted. A discriminant value for each of a plurality of classes is then determined via a first classification technique. This value reflects the relative likelihood that a class is the associated class. The class with the highest relative likelihood is selected. A confidence value is generated via a second classification technique. This confidence value is reflective of the a posteriori probability that the selected class is the associated class. The selected class is rejected if the determined confidence value is below a predetermined threshold value.

A method and apparatus for interacting with incoming text information, e.g., a user query. The method of the present invention categorizes the incoming text information, and may also provide associated confidence levels with the categorization feature. The categorized information is used to query a database having associated category information with associated threshold values. The confidence levels may also be used when querying the database. The categorized information and corresponding confidence levels are then compared to the threshold value. If the categorized information and corresponding confidence levels equals or exceeds the threshold value then a response is provided by the system and method of the present invention. If the categorized information and corresponding confidence levels does not equal or exceed the threshold value then the system of the present invention will request further information of the user, and the method will repeat until (i) the categorized information and corresponding confidence levels equals or exceeds the threshold value or (ii) the system of the present invention times out.

A computerized system and method for estimating insurance loss reserves and confidence intervals using insurance policy and claim level detail predictive modeling. Predictive models are applied to historical loss, premium and other insurer data, as well as external data, at the level of policy detail to predict ultimate losses and allocated loss adjustment expenses for a group of policies. From the aggregate of such ultimate losses, paid losses to date are subtracted to derive an estimate of loss reserves. Dynamic changes in a group of policies can be detected enabling evaluation of their impact on loss reserves. In addition, confidence intervals around the estimates can be estimated by sampling the policy-by-policy estimates of ultimate losses.

A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.

A method and system is invented for automated continuous monitoring and real-time analysis of body sounds. The system embodies a multi-sensor data acquisition system to measure body sounds continuously. The sound signal processing functions utilize a unique signal separation and noise removal methodology by which authentic body sounds can be extracted from cross-talk signals and in noisy environments, even when signals and noises may have similar frequency components or statistically dependent. This method and system combines traditional noise canceling methods with the unique advantages of rhythmic features in body sounds. By employing a multi-sensor system, the method and system perform cyclic system reconfiguration, time-shared blind identification and adaptive noise cancellation with recursion from cycle to cycle. Since no frequency separation or signal / noise independence is required, this invention can provide a robust and reliable capability of noise reduction, complementing the traditional methods. The invention further includes a novel method by which pattern recognition of groups of key parameters can be used to diagnosis physical conditions associated with body sounds, with confidence intervals on the diagnostic criterion to indicate accuracy of diagnosis.

Methods for monitoring subject compliance with a prescribed treatment regimen are disclosed. In one embodiment, the method comprises measuring a drug level in fluid of a subject and normalizing said measured drug level as a function of one or more parameters associated with the subject. The normalized drug level is compared to a reference value and associated confidence intervals or to a concentration range. The reference value and associated confidence intervals and / or the concentration range may be normalized based on one or more parameters associated with subjects in a reference population.

A system is disclosed for measuring click traffic quality by scoring clicks made on sponsored advertisements. A click score generated by the disclosed system may enable advertisers and publishers to distinguish between legitimate and fraudulent clicks. The disclosed system may filter click data associated with a click made on a sponsored advertisement. The system may generate a click score that may represent the confidence with which the quality of a click may be determined. The system also may generate a confidence interval associated with the click score.

Medical guidelines are generated based on the history of the medical records for a large patient population, which includes creating a patient trajectory graph from the records including nodes and edges by automatically clustering patients based on relevant the patients' features included in their medical records. The nodes are scored based on the time patients remain with the nodes and desirability of any associated outcomes, resulting in edge scores derived from the scores of the edge-connected nodes. Top ranked interventions obtained from the edge scores that evaluates whether a transition from one node to another is better or worse are included in the generated medical guidelines. Additionally, effect sizes and confidence intervals of medical treatments for a pre-defined patient population are estimated by using the patients' medical records and dividing the population in an exposed and non-exposed group. Estimates are based on match choices between exposed and non-exposed patients.

A method for converging on a route through a radio frequency (RF) communications network, that includes multiple nodes, includes (a) identifying all RF links between each node in the RF communications network; (b) determining a connectivity confidence interval for each RF link by: (aa) developing a calculated signal to noise ratio using a radio frequency communication link propagation loss model; (bb) determining a threshold signal to noise ratio based on a predetermined RF packet completion rate; (cc) determining a standard deviation value based on a signal strength and a noise level at a signal receiving node; (dd) calculating a Z number, associated with a normal distribution table which is based on the threshold signal to noise ratio minus the calculated signal to noise ratio with that result being divided by the standard deviation value; and (ee) assigning a CCI probability value based on said Z number. The method further comprises (c) setting a predetermined connectivity confidence interval minimum; (d) comparing each connectivity confidence interval for each RF link to the predetermined connectivity confidence interval minimum to determine which are greater than or equal to the predetermined connectivity confidence interval; (e) assembling each RF link corresponding to each connectivity confidence interval that is greater than or equal to the predetermined connectivity confidence interval together to identify various routes through the RF network; (f) calculating a resultant connectivity confidence interval for each route by multiplying together each connectivity confidence interval for each RF link of each particular route; and (g) comparing each resultant connectivity confidence interval to identify the route with the greatest resultant connectivity confidence interval. An apparatus for carrying out such a method is also presented.

The invention provides a neural network and Q learning combined estimation method under non-complete information, and the method comprises the steps: 1, converting the non-complete information into a partly observable Markov decision-making model; 2, converting the non-complete information gaming into complete information gaming through the Monte Carlo sampling technology; 3, calculating Q learning delay return value through a Q learning algorithm based on former n steps, an algorithm combining a neural network and the Q learning and an algorithm UCT based on an upper limit confidence interval; 4, carrying out the fusion of the Q value obtained at a former step, and obtaining a final result. According to the technical scheme of the invention, the method can be used in various types of non-complete information gaming, such as the Chinese poker and Texas Hold'em poker, and improves the gaming level of an intelligent agent. Compared with the conventional related research, the method greatly improves the precision.

The invention discloses a network traffic anomaly detection method and a detection device. The detection device comprises a data selection unit, a distribution analysis unit, an observation information entropy acquisition unit, a prediction unit, a confidence interval acquisition unit and an anomaly judging unit, wherein the data selection unit is used for selecting network index data to be detected and establishing an attribute record; the distribution analysis unit anomaly inspects the distribution situation of each attribute of the attribute record in connection initiated by and to each host computer in a network; the observation information entropy acquisition unit is used for acquiring observation information entropy according to the distribution situation of the attributes when a time interval reaches a set time threshold; the prediction unit predicts the information entropy of the network index data of the next time interval according to the observation information entropy; the confidence interval acquisition unit acquires a confidence interval needed by anomaly judgment according to the observation information entropy and the prediction information; and the anomaly judging unit analyzes the distribution of the observation information entropy in the confidence interval and determines whether network traffic is anomalous or not according to an analysis result. Through the method and the device, problems of not strong operability and relatively poorer flexibility in network traffic anomaly detection in the prior art are solved.

In one example, a method includes receiving a first group of indications associated with a first group of modalities and a second group of indications associated with a second group of modalities. The method also includes determining a confidence value for at least one modality of the first or second groups of modalities based at least in part on an indication associated with the at least one modality. The confidence value may indicate a likelihood that a first user associated with the first remote computing device is within a physical presence of a second user associated with the second remote computing device. The method may also include, performing an operation to indicate that the first user associated with the first remote computing device is within a physical presence of the second user associated with the second remote computing device.

Systems, methods, and computer program products for system online availability estimation. A method according to one embodiment can include a step for providing an availability model of a system. The method can also include a step for receiving behavior data of the system. In addition, the method can include estimating a plurality of parameters for the availability model based on the behavior data. The method can also include determining individual confidence intervals for each of the parameters. Further, the method can include determining an overall confidence interval for the system based on the individual distributions of the estimated parameters. The method can also include determining control actions based on the estimated overall availability or inferred parameter values.

A system and method for detecting click fraud where data is received corresponding to a tracking of movement of a cursor on a web page. The movement of the cursor is associated with at least one vector. The at least one vector represents at least a portion of the cursor movement. A confidence level useable in the determination of click fraud is determined. The confidence level is responsive to analysis of the at least one vector representing at least a portion of the cursor movement.

The invention discloses a dynamic confirming method for performance index value normal fluctuating range, comprising following steps: obtaining a historical value of the network performance index, and dynamically obtaining the latest historical sample data; processing pre-treatment to obtain a normal sample data; processing phase space re-construction to obtain a training sample data; processing training modeling to take residual error white noise as condition of selecting the optimal supporting vector machine model; using the optimal supporting vector machine model to predict the data on the to-be predicted time point, and calculating the confidence interval of the predicted value to obtain the performance index value normal fluctuating range; detecting whether the optimal supporting vector machine model is suitable for the next prediction of the to-be predicted time point; if the model is not suitable, processing re-training. The invention also discloses a device for the dynamic confirmation performance index value normal fluctuating range. The invention significantly enhances the precision of the performance dynamic early warning, and reduces wrong report and missing report of the performance warning.

The invention discloses a wind power short-term prediction method based on a long-term and short-term memory network, comprising a long-term and short-term memory neural network training algorithm, ashort-term wind power prediction error distribution algorithm and a wind turbine generator power short-term prediction model design. A long-term and short-term memory network algorithm (LSTM)-based wind pow prediction model is established based on the depth learning network, and the Gaussian mixture model (GMM) is used to analyze the error distribution characteristics of the short-term wind powerprediction. The invention can obtain different confidence intervals of two units through the GMM model. It is proved that LSTM method has higher precision and faster convergence rate, and GMM method has practical application value for wind power dispatching.

Methods and systems for estimating fetal weight at birth and risk of macrosomia are disclosed. Information regarding maternal characteristics and ultrasound measurements are obtained for the fetus. A computer program selects the most accurate equation from a hierarchy of birth weight / risk of macrosomia estimation equations based on the available information. Program outputs include estimated birth weight, risk of macrosomia, and confidence intervals.

Evaluating an electronic document in connection with a search. An external source provides data for use in evaluating an electronic document retrieved by a search engine. A first confidence level of the electronic document is determined based on the externally provided data. The first confidence level indicates a likelihood that the electronic document is undesirable. A second confidence level of the electronic document is determined based on attributes of the electronic document. The second confidence level indicates a likelihood that the electronic document is unsatisfactory with respect to a search. A rating for the electronic document generated as a function of the determined first confidence level and the determined second confidence level is used to categorize the electronic document as unsatisfactory in connection with a received search request.

A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

The invention discloses a sampling GPR method of continuous anomaly detection in a collecting data flow of an environment sensor, and belongs to the technical field of data monitoring of environment sensors. The sampling GPR method of the continuous anomaly detection in the collecting data flow of the environment sensor is used for solving the problem that anomaly detection can not be conducted in real time, wherein the problem is caused by the fact that data calculation amount is large in data flow anomaly detection of a traditional environment sensor. The sampling GPR method of the continuous anomaly detection in the collecting data flow of the environment sensor is based on a prediction-model method, a prediction model is built through historical data, the mean value and the confidence interval of current data are obtained, a current data value is compared with the confidence interval, and the current data value is regarded as exceptional data if the current data value exceeds the confidence interval. According to the sampling GPR method of the continuous anomaly detection in the collecting data flow of the environment sensor, less historical data are needed, algorithm operation efficiency is improved, and input training data are not required to be provided with category tags. The sampling GPR method of the continuous anomaly detection in the collecting data flow of the environment sensor can detect an exceptional situation in a self-adaptive mode according to real-time arrival data, and is applied to continuous exceptional data detection in collecting data flow of the environment sensor.

The invention discloses a Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries, relates to a method for predicting the SOH of the lithium batteries, belongs to the fields of electrochemistry and analytic chemistry and aims at the problem that the traditional lithium batteries are bad in health condition prediction adaptability. The method provided by the invention is realized according to the following steps of: I. drawing a relation curve of the SOH of a lithium battery and a charge-discharge period; II, selecting a covariance function according to a degenerated curve with a regeneration phenomenon and a constraint condition; III, carrying out iteration according to a conjugate gradient method, then determining the optimal value of a hyper-parameter and bringing initial value thereof into prior distribution; IV, obtaining posterior distribution according to the prior part; V, obtaining the mean value and variance of predicted output f' without Gaussian white noise; and VI, together bringing the practically predicted SOH of the battery and the predicted SOH obtained in the step V into training data y to obtain the f', then determining the prediction confidence interval and predicting the SOH of the lithium battery. The method provided by the invention is used for detecting lithium batteries.

The invention discloses a method for accurately estimating wind power prediction error intervals. The method comprises the following steps: firstly, obtaining historical wind power data of a wind power plant; secondly, calculating wind power predication errors of all prediction points of the wind power plant, and establishing a wind power predication error distribution model; thirdly, establishing an error probability density function according to the distribution of the predication errors; fourthly, obtaining a confidence interval, meeting a certain confidence level, of the predication errors according to a given wind power predication value; and fifthly, calculating the shortest confidence interval through a Lagrange multiplier algorithm. On the basis of point predication, a probability density function of wind power prediction errors is obtained through interval prediction, and the confidence interval under a certain confidence level is calculated by a probability theory. In this way, the reliability of the interval to contain a wind power point predication value is determined, and the precision of wind power interval prediction is effectively improved.

In a method of analyzing patient physiological data, the data is subjected to principal component analysis and compared to a model physiological data principal component analysis. The comparison is used to identify correlations present in the morphology of the patient physiological data. The present invention further includes determining a confidence interval for the detection of a morphological feature and utilizing this confidence interval for improving the quality of the detection of morphological features of the patient physiological data, including automated morphological feature identification.

The invention discloses a method for network flow anomaly detection, comprising the following steps: acquiring statistical information on bit rate and flow rate of the network flow in a sliding window, and calculating a mean value of the bit rate and the flow rate; building a flow model in accordance with the mean value of the bit rate and the flow rate; determining the normal confidence interval of the network flow in accordance with the flow model; and when the network flow does not meet the normal confidence interval, judging that the network flow is abnormal network flow. The embodiment of the invention provides the method and system for network flow anomaly detection; and by means of the method and the system, the network flow can be detected more simply and more exactly from the standpoint of correlation of the bit rate and flow rate of the network.

The invention discloses a multi-source data fusion method in a clustering wireless sensor network, which comprises the following specific contents: a distributive data fusion structure is adopted; at all cluster-head nodes, an evidence set is preprocessed according to reliability degree of the member nodes in the cluster; based on the consistent intensity and the value of primitive supporting degree of the evidence, the evidence conflicts are distributed, the evidence combination sequence is optimized, the rules of conflicting evidence combination are established to synthesize all evidences; in connection with the evidence combination results, the value of the fine confidence interval of the primitive proposition is obtained by utilizing the uncertainty measure and the property supporting degree of the set; and then an evidence decision model is constructed based on the priority sequence of the fine confidence interval, and the final diagnosis is made. The method can improve the identifying accuracy ratio of the detected goal by the clustering wireless sensor network, and simultaneously and effectively reduce the transmitting volume of redundant data in the network and satisfy the application demands of the clustering wireless sensor network in the fields such as pipe leakage diagnosis, target tracking, environment detecting and the like.

The invention discloses a power system abnormal data identifying and correcting method based on time series analysis. The power system abnormal data identifying and correcting method includes data preprocessing, time series modeling, abnormal data identifying and abnormal data correcting. Data preprocessing includes the step of identifying and correcting missing data in data to be detected and data suddenly changing to be zero. Time series modeling comprises the steps of conducting time series analyzing on the preprocessed data to be detected and establishing a model according to the time series, and a difference autoregression moving average model is used for modeling the data to be detected. According to abnormal data identifying, the fitting residual series of the established difference autoregression moving average model is analyzed, an error confidence interval is set, and abnormal data are identified. According to abnormal data correcting, a neural network method is used for establishing a prediction model for correcting the abnormal data, the data value of the moment when the abnormal data exist is predicted, and the abnormal data are corrected. The power system abnormal data identifying and correcting method is easy to implement and high in accuracy.