317 results about "Goodness of fit" patented technology

A phonopneumograph system for analyzing breath sounds includes a plurality of breath related sensors placed around the respiratory system of a patient for measuring breath related activity and a breath analyzer. The breath analyzer matches the breath sound data produced by the breath related sensors to a plurality of breath sound templates each of which parametrize one type of breath sound and determines the presence of regular and/or adventitious breath sounds only when the breath sound data matches, within predetermined goodness of fit criteria, one or more of the breath sound templates.

A device for generating metadata for a programming event receives data corresponding to a programming event that includes descriptive information and timing information. The device analyzes the data to determine goodness of fit scores for the programming event corresponding to categories of a classification hierarchy. The device then stores metadata for the programming event that includes goodness of fit scores, time data and descriptive data. The stored goodness of fit scores may be a representative subset of all goodness of fit scores for the programming event. Also disclosed are processes performed in a device for generating metadata for programming events. Also disclosed is metadata encoded in a computer readable medium. The metadata includes a programming event identifier, descriptive data describing the programming event, time data enabling determination of a start time and duration of the programming event, and goodness of fit scores for the programming event that are associated with categories of the classification hierarchy.

Metadata for a programming event may be generated through an interactive process. A system may receive data corresponding to a programming event. The data may include descriptive information and timing information. The system may then generate preliminary metadata for the programming event from the received data. The system may provide the preliminary metadata to a system user, and then receive user specified metadata responsive to the preliminary metadata. The preliminary metadata may include one or more of categories of a classification hierarchy, goodness of fit scores associated with the categories, keywords, goodness of fit scores associated with the keywords, and a title. Related embodiments may pertain to a system implementing such methods. Further related embodiments may pertain to methods and systems in which a system user is enabled to approve preliminary metadata provided by the system.

The desirability of programming events may be determined using metadata for programming events that includes goodness of fit scores associated with categories of a classification hierarchy one or more of descriptive data and keyword data. The programming events are ranked in accordance with the viewing preferences of viewers as expressed in one or more viewer profiles. The viewer profiles may each include preference scores associated with categories of the classification hierarchy and may also include one or more keywords. Ranking is performed through category matching and keyword matching using the contents of the metadata and the viewer profiles. The viewer profile keywords may be qualified keywords that are associated with specific categories of the classification hierarchy. The ranking may be performed such that qualified keyword matches generally rank higher than keyword matches, and keyword matches generally rank higher than category matches. In alternative embodiments, scores may be calculated such that the ranges of scores for qualified keyword matches, keyword matches and category matches are overlapping but are generally ordered as previously described. Related embodiments may pertain to systems that implement such methods. Program rankings may be used to generate an alert schedule for providing alerts to viewers regarding programming events.

A methodology for automatic a priori data pattern analysis is provided. Described methods allow consistent and objective determination of outliers; trend; seasonality; and level shifts; and the production of better models and more accurate forecasts. In addition, a two-step way to automatically determine seasonality and locate possible events in the data set is described. Decomposition of data into seasonal, trend and level components; detection of outliers and level-shift events in the time series based on statistical analysis of the time series; detection of seasonality based on statistical analysis of clusters of data, known as cluster-based seasonality analysis, or CBSA; evaluation of the goodness of fit of a model to data, using the existing goodness of fit indicator, R²; and seasonality analysis, using a sequence of cluster-based seasonality analysis (CBSA) and Fourier analysis are described.

A device for providing alerts to a viewer concerning programming events is disclosed. The device determines preferred programming events from among available programming events using programming event metadata and a viewer profile. The metadata includes goodness of fit scores associated with categories of a classification hierarchy and the viewer profile includes preference scores associated with categories of the classification hierarchy. The device provides an alert concerning a preferred programming event to a communication device of the viewer. The alert may be provided by sending data to a set top box, a computer, a pager, an email address, an instant messaging application, or a wireless communication device. The alert may be provided in accordance with contact information contained in the viewer profile. The device may determine a time in advance of the preferred programming event for providing the alert from information in the viewer profile specifying an amount of time in advance for providing alerts, and then provide the alert at the determined time. Related embodiments of the invention may pertain to processes performed in a device for providing alerts to a viewer concerning programming events. Also disclosed is a device for providing programming events to viewers. The device determines a preferred programming event from among available programming events using programming event metadata and one or more viewer profiles. The metadata includes goodness of fit scores associated with categories of a classification hierarchy and the viewer profiles include preference scores associated with categories of the classification hierarchy. The device provides preferred programming events to viewers in accordance with the desirability determinations. The device may notify a viewer of a preferred programming event, and provide the programming event in response to a request from the viewer to receive the programming event. The device may determine the desirability of a programming event by determining categories of the classification hierarchy for which there is a goodness of fit score in the metadata of the programming event and a preference score in one or more of the viewer profiles, and then determining a desirability score for the programming event as a function of the goodness of fit scores and preference scores of the matched categories. That function may be weighted in accordance with the specificities of the matched categories. The device may determine the desirability score for a programming event by determining a score for each matched category that is a function of the goodness of fit score and a preference score of each category in a branch of the hierarchy terminating in the matched category, and then determining a maximum score among the matched category scores. The preference score of each category in a branch of the hierarchy may be weighted in accordance with its ordinal level within the hierarchy. Alternatively, the device may determine a desirability score for a programming event by determining a score for each matched category that is function of the goodness of fit score and a preference score for the matched category in each of the viewer profiles that is weighted in accordance with a priority of the respective viewer profile, and then determining a maximum score among the matched category scores. Alternatively, the device may determine a desirability score for a programming event by determining a score for each matched category that is function of the goodness of fit score, the preference score in each of the viewer profiles for the matched category, and a specificity weight of the matched category, and then determining a maximum score among the matched category scores.

The invention encompasses methods, including computer-implemented methods, of defining a virtual patient population and mapping the virtual patient population to a population of real patients. The invention utilizes virtual measures from one or more virtual patients, and data representative of multiple real subjects in a sample population, such as data collected from patients in a clinical trial or epidemiological study of a real population. The invention includes evaluating the similarity between the virtual patients and the real subjects, and assigning prevalences to the virtual patients based on the evaluation. The similarity can be assessed using some or all of the virtual measures of the virtual patients and some or all of the data obtained for the real subjects. Any of various goodness-of-fit measures can be used to evaluate the similarity or to help identify prevalences. The virtual patient population is defined as the virtual patients according to their respective prevalences.

An assembly that includes a projector and camera is used with a processor to determine three-dimensional (3D) coordinates of an object surface. The processor fits collected 3D coordinates to a mathematical representation provided for a shape of a surface feature. The processor fits the measured 3D coordinates to the shape and, if the goodness of fit is not acceptable, selects and performs at least one of: changing a pose of the assembly, changing an illumination level of the light source, changing a pattern of the transmitted With the changes in place, another scan is made to obtain 3D coordinates.

A device for determining the desirability of programming events receives metadata for programming events and determines a desirability of each of said programming events with respect to viewing preferences represented in one or more viewer profiles. The metadata includes goodness of fit scores associated with categories of a classification hierarchy, and the viewer profiles include preference scores associated with categories of the classification hierarchy. The device determines a desirability of each of said programming events with respect to viewing preferences represented in the viewer profiles using the metadata. The device may determine the desirability of a programming event by determining categories of the classification hierarchy for which there is a goodness of fit score in the metadata of the programming event and a preference score in one or more of the viewer profiles, and then determining a desirability score for the programming event as a function of the goodness of fit scores and preference scores of the matched categories. That function may be weighted in accordance with the specificities of the matched categories. The device may determine the desirability score for a programming event by determining a score for each matched category that is a function of the goodness of fit score and a preference score of each category in a branch of the hierarchy terminating in the matched category, and then determine a maximum score among the matched category scores. The preference score of each category in a branch of the hierarchy may be weighted in accordance with its ordinal level within the hierarchy. Alternatively, the device may determine a desirability score for a programming event by determining a score for each matched category that is a function of the goodness of fit score and a preference score for the matched category in each of the viewer profiles that is weighted in accordance with a priority of the respective viewer profile, and then determine a maximum score among the matched category scores. Alternatively, the device may determine a desirability score for a programming event by determining a score for each matched category that is function of the goodness of fit score, the preference score in each of the viewer profiles for the matched category, and a specificity weight of the matched category, and then determining a maximum score among the matched category scores. The metadata may include time data that enables determination of a time and duration of a programming event, and the device may determine a schedule of preferred programming events in accordance with desirability scores and times and durations of programming events. The device may alert a viewer of the availability of a programming event in the schedule, or may record programming events in accordance with the schedule. Also disclosed are processes performed in a device for determining the desirability of programming events. Also disclosed is a viewer profile encoded in a computer readable medium. The viewer profile includes a profile identifier and preference scores associated with categories of the classification hierarchy. The viewer profile may further include an activation time, a priority of the viewer profile relative to other viewer profiles, contact information indicating where a programming event alert should be sent for that viewer, or an amount of time in advance of a programming event for providing an alert regarding that programming event to the viewer.

Systems and methods for monitoring operation of a pump, including verifying operation or actions of a pump, are disclosed. A baseline profile for one or more parameters of a pump may be established. An operating profile may then be created by recording one or more values for the same set of parameters during subsequent operation of the pump. A value for a goodness of fit measure comparing the operating profile and baseline profile can be established. If the goodness of fit measure is insufficient, an alarm may be sent or another action taken, for example the pumping system may shut down, etc. An example goodness of fit measure is an R-squared measure.

The invention discloses a reservoir fracture determining method, which comprises the steps of conducting fracture prediction analysis to actual seismic data through various methods according to types of the actual seismic data to obtain reservoir fracture attribute and distribution; conducting forward modeling by using actual geologic parameters and comparing reservoir fracture distribution by using FMI (Formation MicroScanner Image) data to obtain matching degree and goodness of fit; and according to the matching degree and the goodness of fit, preferably selecting an optimum fracture analysis method from various fracture prediction and analysis methods, and according to the preferably selected fracture analysis method, explaining the actual seismic data to determine reservoir fractures. The reservoir fracture determining method has the advantages that the problem in accurately obtaining reservoir fracture development parameters is solved; the fracture direction and density of fractured reservoirs can be effectively predicted and reliable bases are provided for researches on the fractured reservoirs; and bases are provided and directions are pointed out for work such as oilfield and gas field development plan design of fractured reservoirs, and the exploitation and development risks and costs are decreased.

The invention belongs to the radar target detection technology field and relates to a radar target constant false alarm rate detection method. The radar target constant false alarm rate detection method mainly comprises the following steps: 1) the Weibull background distribution is converted to the Location-Scale distribution through the logarithmic transformation; 2) the orderly truncated (or head cut) treatment is carried out to a background sample to suppress the impact of an interference target, a largest unbiased estimator is adopted for estimating the location and the scale parameters; 3) the normalization of samples in a unit to be tested is carried out by using the estimated parameters; 4) the judgment of whether the background distribution is obeyed or not is carried out by utilizing the Anderson-Darling test, if the background distribution is not obeyed, the target is judged to exist, otherwise, no target is judged to exist. The radar target constant false alarm rate detection method utilizes the difference of the distribution characteristics of the target echo and the background clutter, compared with the traditional detection method based on a self-adaptive threshold, the characteristics of the background distribution and the interference target have little impact on the radar target constant false alarm rate detection method, thereby having very strong adaptability to the non-Gaussian environment and the multi-objective interference environment.

In an electronic design automation technique for optical proximity correction, an optimized mask function that has values other than those allowed for a particular mask type, such as 0 and 1 for a chrome-on-glass binary mask, evolves it to a solution restricted to these values or narrow intervals near them. The technique “regularizes” the solution by mixing in a new cost functional that encourages the mask to assume the desired values. The mixing in may be done over one or more steps or even “quasistatically,” in which the total cost functional and the mask is brought from pure goodness-of-fit to the printed layout for given conditions to pure manufacturability by keeping the total cost functional minimized step-by-step. A goal of this gradual mixing-in is to do thermodynamically optimal work on the mask function to bring it to manufacturable values.

The invention discloses a lithium ion battery full charge storage service life evaluation method. The method comprises steps: a battery sample for evaluation of lithium ion battery full charge states is stored at a plurality of storage temperature values T for appointed sampling time t, a capacity loss rate Q is obtained, and experiment data are generated; a capacity attenuation aged model is established, and values of a model parameter rho and a model parameter a are determined; the selection rationality of the storage temperature scope is determined based on the situation whether the model parameter a meets the Arrhenius formula at each storage temperature value T; a mean value and a standard deviation standard deviation of a model A and a model B are obtained through statistics; the value of the model parameter rho and the mean value of the models A and B are substituted into the model and fitting goodness determination is carried out; after fitting goodness is met, the service life of the lithium ion battery to be evaluated is predicted at a normal temperature and a service life distribution diagram is obtained. The evaluation method has advantages of sufficient service life evaluation principle, high data reliability, high precision, short evaluation time, wide application scope, is simple and practicable, and is easy to implement.

The present method and system provides for the clinical application of neurostimulation and/or neuromodulation to a patient. The method and system includes receipt and acquisition of patient data, processing of that data relative to one or more known data sets, and determination of a good-fit trigger specific treatment protocol. The method and system provides for application of the protocol to the patient, including delivery of neuromodulation and biofeedback. Based thereon, the method and system re-iterates the goodness of fit determination for further treatment to the patient.

A computer-based system and method are provided to determine the minimum number of factors required to account for input data by seeking an approximate minimum complexity model. In an exemplary embodiment, covariance in the daily returns of financial securities is estimated by generating a positive-definite estimate of the elements of a covariance matrix consistent with the input data. Complexity of the covariance matrix is minimized by assuming that the number of independent parameters is likely to be much smaller than the number of elements in the covariance matrix. Each variable is described as a linear combination of independent factors and a part that fluctuates independently. The simplest model for the covariance matrix is selected so that it fits the data to within a specified level as determined by the selected goodness-of-fit criterion.

Systems and methods for evaluating cellular voice call quality are disclosed. In some implementations, training data points are received. Each data point includes a call quality value and values of key performance indicators (KPIs) related to call quality. For each KPI, a linear relationship, having a goodness-of-fit value, between the KPI and the call quality value is determined using the multiple data points. Compulsory KPIs are selected based on the goodness-of-fit values. The training data points are separated into clusters based on the compulsory KPI values and the quality values. For each cluster, a mathematical relationship for calculating the call quality within the cluster is determined based on the one or more compulsory KPIs. A module is generated for predicting the call quality value by combining the determined mathematical relationships.

A top-down and bottom-up approach that decomposes product bundles to components, classifies them into different groups corresponding to a component similarity measure, and detects their inherent values. The bundles are reassembled and characterized by several key attributes according to their component inherent values, and classified into segments. A normalized utility model is constructed for each product bundle segment, taking into account the additive effect among different commodity types and product families. The goodness of fit of the top-down and the bottom-up model may be validated. The model may be applied in an RFQ pricing environment.

The invention discloses a method for measuring elemental concentration through laser-induced breakdown spectroscopy based on spectrophotometry, which belongs to the technical field of atomic emission spectrum measurement. The method is characterized in that a laser light is divided into two beams by an optical spectroscope; and then, the two laser light beams are reflected by a reflector, so that the included angle between the incident direction of the two laser light beams and the normal direction of the surface of a sample is 0-90 degrees. Plasmas generated by laser impaction are mainly expanded toward a direction perpendicular to the surface of the sample, therefore, the shielding effect of the plasmas on the laterally incident laser is weak, so that more laser energy can reach the surface of the sample, and the ablation quality can be improved; and by combining a technology of forming aerosols above the surface of the sample to optimize the experimental conditions for plasma generation, the method ensures that the plasmas can be generated more easily and the electronic density is higher. The method disclosed by the invention increases the repeatability of measurement, thereby facilitating the improvement of the goodness of fit and forecast accuracy of a calibration model.

The invention discloses a surface roughness prediction method based on GA-GBRT and a method for optimizing process parameters. The method comprises the steps of: collecting data to construct a data set, and dividing the data set to training set data and test set data, and employing the training set data to perform training of key parameters of a GBRT model; b, performing parameter coding and population initialization: randomly generating a chromosomal sequence for increasing the number of iterations, the maximum depth of the individual regression estimator and the learning rate; c, employing the k-folded cross-validation method to train the GBRT model, and employing the genetic algorithm to calculate the fit goodness fitting value of each individual; d, when the number of cycles does not reach the maximum number of iterations, allowing the population to be selected, crossed and mutated to produce a new generation of populations, and continuously performing training of the GBRT model; and e, repeatedly performing the steps c and d until the number of cycles reaches the maximum evolution algebra or exceeds the maximum number of iterations to obtain the optimal model parameters. The surface roughness prediction method based on GA-GBRT and the method for optimizing process parameters are high in test precision and superior in prediction performance and improves the surface processing precision of the workpiece.

The invention provides a small current grounding system single-phase earth fault direction detecting method utilizing the linearity relation between a line zero sequence current and a voltage derivative and belongs to the field of relay protection of power distribution networks. The linearity relation between upstream and downstream voltages and currents of a fault point is analyzed when a single-phase earth fault occurs in an unearthed and arc suppression coil grounded system, a method for fault direction detection by utilizing the linearity relation between the line zero sequence current andthe voltage derivative at a detection point is provided. The zero-sequence voltage and zero-sequence current at each detection point are acquired, linear fitting is conducted on a zero-sequence current sample value sequence and a corresponding zero-sequence voltage difference value sequence, it is judged whether the detection points having the fit goodness greater than a threshold value and the fitting function slope greater than 0 are located at the downstream of fault points or not, if not, the detection points are located at the upstream of the fault points. The method can be simultaneously suitable for low-resistance and high-resistance earth faults of a small current grounding system, improves the adaptability of a fault direction detection algorithm and has a wide actual applicationvalue.

The invention discloses a short-term life test data-based quick life evaluation method, which comprises the following steps of: (1) selecting proper stress to perform life test on a device to obtain original data; (2) establishing a mathematical model based on data obtained by the life test; (3) performing linear regression analysis on the life test data; (4) determining model parameters by using a least square method principle; (5) performing calculation of test of goodness of fit to judge validity of the model; (6) calculating significance test statistic, and determining whether a regression equation has significance under a given significance level condition; and (7) statistically analyzing a reliability quantitative index of the test device by using a weibull distribution function and the established mathematical model to obtain a life index and life distribution of the device.