Abstract: An apparatus, system, and method are disclosed for determining electrical load and lifestyle characteristics. A record receiving module receives an electrical energy usage record for premises for a predefined time period (“record period”), and receives property characteristics for the premises. The property characteristics include physical characteristics for the premises, environmental characteristics for the premises for the record period, and/or lifestyle characteristics of users of the premises. A load identification module selects a load prediction algorithm to determine if a particular type of electrical load is present at the premises. A comparison module applies the load prediction algorithm to the electrical energy usage record for the premises for at least a portion of the record period (“comparison period”) to determine if the particular type of electrical load is present at the premises. The load prediction algorithm uses the property characteristics of the premises during the comparison period.

Abstract: An apparatus, system, and method are disclosed for calibrating algorithms for determining electrical load and lifestyle characteristics. A model creation module creates an electrical usage model that includes an electrical device usage model for each electrical load assumed to be at simulated premises (an assumed load set). The simulated premises include characteristics from actual premises within an area serviced by an electric utility. A simulation module simulates a number of simulated electrical usages for a number of assumed load sets at the simulated premises. A load prediction module determines if a particular type of load is present within each simulated premises using a load prediction algorithm that includes algorithm parameters. An accuracy module determines an accuracy of the load prediction algorithm and an adjustment module adjusts the algorithm parameters of the load prediction algorithm in response to the determined accuracy.

Abstract: An analytic apparatus and method is provided for diagnosis, prognosis and biomarker discovery using transcriptome data such as mRNA expression levels from microarrays, proteomic data, and metabolomic data. The invention provides for model-based analysis, especially using kernel-based models, and more particularly similarity-based models. Model-derived residuals advantageously provide a unique new tool for insights into disease mechanisms. Localization of models provides for improved model efficacy. The invention is capable of extracting useful information heretofore unavailable by other methods, relating to dynamics in cellular gene regulation, regulatory networks, biological pathways and metabolism.

Abstract: The activity state classification method of the present invention employs a kernel-based modeling technique, and more specifically a set of similarity-based models, which have been created using example data, to process an input observation or set of input observations, each comprising a set of sensor readings or “features” derived there from or other data, to predict the activity state of a person from whom the sensor data was obtained. A model is created for each class of activity. The input data is processed by each model and the resulting predictions are combined to yield a final prediction of which state of activity is represented by the input data.

Abstract: An apparatus, system, and method are disclosed for calibrating algorithms for determining electrical load and lifestyle characteristics. A model creation module creates an electrical usage model that includes an electrical device usage model for each electrical load assumed to be at simulated premises (an assumed load set). The simulated premises include characteristics from actual premises within an area serviced by an electric utility. A simulation module simulates a number of simulated electrical usages for a number of assumed load sets at the simulated premises. A load prediction module determines if a particular type of load is present within each simulated premises using a load prediction algorithm that includes algorithm parameters. An accuracy module determines an accuracy of the load prediction algorithm and an adjustment module adjusts the algorithm parameters of the load prediction algorithm in response to the determined accuracy.

Abstract: An apparatus, system, and method are disclosed for determining electrical load and lifestyle characteristics. A record receiving module receives an electrical energy usage record for premises for a predefined time period (“record period”), and receives property characteristics for the premises. The property characteristics include physical characteristics for the premises, environmental characteristics for the premises for the record period, and/or lifestyle characteristics of users of the premises. A load identification module selects a load prediction algorithm to determine if a particular type of electrical load is present at the premises. A comparison module applies the load prediction algorithm to the electrical energy usage record for the premises for at least a portion of the record period (“comparison period”) to determine if the particular type of electrical load is present at the premises. The load prediction algorithm uses the property characteristics of the premises during the comparison period.

Abstract: An apparatus, system, and method are disclosed for determining a partial class membership of a data record in a class. The apparatus includes a record set acquisition module that receives a set of reference records having the same independent variables and belonging to a known class within a group of classes. An unknown-class record receiving module receives an unknown-class record having same independent variables as reference records. A class identification module creates a class vector for each reference record identifying whether the record is in a class. A weighting module calculates a set of unknown-class record weights for the unknown-class record. A classification module determines a partial class membership for the unknown-class record for each class in the group of classes using the set of unknown-class record weights. Each partial class membership identifies a probability that the unknown-class record belongs to a corresponding class in the group of classes.

Abstract: An analytic apparatus and method is provided for diagnosis, prognosis and biomarker discovery using transcriptome data such as mRNA expression levels from microarrays, proteomic data, and metabolomic data. The invention provides for model-based analysis, especially using kernel-based models, and more particularly similarity-based models. Model-derived residuals advantageously provide a unique new tool for insights into disease mechanisms. Localization of models provides for improved model efficacy. The invention is capable of extracting useful information heretofore unavailable by other methods, relating to dynamics in cellular gene regulation, regulatory networks, biological pathways and metabolism.

Abstract: The activity state classification method of the present invention employs a kernel-based modeling technique, and more specifically a set of similarity-based models, which have been created using example data, to process an input observation or set of input observations, each comprising a set of sensor readings or “features” derived there from or other data, to predict the activity state of a person from whom the sensor data was obtained. A model is created for each class of activity. The input data is processed by each model and the resulting predictions are combined to yield a final prediction of which state of activity is represented by the input data.

Abstract: An apparatus, system, and method are disclosed for determining a partial class membership of a data record in a class. The apparatus includes a record set acquisition module that receives a set of reference records having the same independent variables and belonging to a known class within a group of classes. An unknown-class record receiving module receives an unknown-class record having same independent variables as reference records. A class identification module creates a class vector for each reference record identifying whether the record is in a class. A weighting module calculates a set of unknown-class record weights for the unknown-class record. A classification module determines a partial class membership for the unknown-class record for each class in the group of classes using the set of unknown-class record weights. Each partial class membership identifies a probability that the unknown-class record belongs to a corresponding class in the group of classes.

Abstract: The activity state classification method of the present invention employs a kernel-based modeling technique, and more specifically a set of similarity-based models, which have been created using example data, to process an input observation or set of input observations, each comprising a set of sensor readings or “features” derived there from or other data, to predict the activity state of a person from whom the sensor data was obtained. A model is created for each class of activity. The input data is processed by each model and the resulting predictions are combined to yield a final prediction of which state of activity is represented by the input data.

Abstract: A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

Abstract: A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

Abstract: A process for monitoring a system by comparing learned observations acquired when the system is running in an acceptable state with current observations acquired at periodic intervals thereafter to determine if the process is currently running in an acceptable state. The process enables an operator to determine whether or not a system parameter measurement indicated as outside preset prediction limits is in fact an invalid signal resulting from faulty instrumentation. The process also enables an operator to identify signals which are trending toward malfunction prior to an adverse impact on the overall process.