Abstract: A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.

Abstract: A system and method for determining vehicle component conditions is provided. A predictive model is built for a vehicle component and values are mapped for a feature of the vehicle component using the predictive model. A threshold is applied to the mapped values. An occurrence of a fault of the vehicle component is predicted when one or more of the mapped values exceeds the threshold and an extended optimal interval during which the fault is predicted to occur is identified.

Abstract: A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.

Abstract: A method for determining vehicle component conditions via performance correlation is provided. A list of doors for maintenance on a transport vehicle is maintained. Measurements for one of the doors based on an inspection of that door are maintained. A determination is made as to whether maintenance is required for the door based on the measurements and a maintenance status is assigned to the door. The door measurements are compared to measurements for other doors of the transportation vehicle. Those other doors with measurements similar to the door are identified and the maintenance status of the door is assigned to the other doors identified.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A method for determining vehicle component conditions via performance correlation is provided. A list of doors for maintenance on a transport vehicle is maintained. Measurements for one of the doors based on an inspection of that door are maintained. A determination is made as to whether maintenance is required for the door based on the measurements and a maintenance status is assigned to the door. The door measurements are compared to measurements for other doors of the transportation vehicle. Those other doors with measurements similar to the door are identified and the maintenance status of the door is assigned to the other doors identified.

Abstract: A system and method for determining vehicle component conditions are provided. A function that maps features to door conditions is stored. Motor current measurements are obtained for a door over a predetermined time period. The features of the motor current measurements are determined based on the stored function. The features are then analyzed by mapping the features to one or more door conditions via a predetermined function. At least one condition of the door is determined based on the analyzed features.

Abstract: A system and method for determining vehicle component conditions are provided. A function that maps features to door conditions is stored. Motor current measurements are obtained for a door over a predetermined time period. The features of the motor current measurements are determined based on the stored function. The features are then analyzed by mapping the features to one or more door conditions via a predetermined function. At least one condition of the door is determined based on the analyzed features.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: Described herein is a printing system including a plurality of print processing modules which can selectively transfer print media there between during printing. The system further including a controller that can predict the impending unhealthy state of at least one module and redirect one of an unprocessed job and a partially processed job preemptively from an impending unhealthy module to selectively one of a healthy module and another unhealthy module, with suitable characteristics, to process unprocessed portions of the job.

Abstract: A printing system includes a plurality of print media processing modules which transfer print media therebetween during printing and a fault management agent associated with each of the modules for acquiring fault-related data from the respective processing module. A fault management system is in communication with the fault management agents and receives fault-related data from the fault management agents. The fault management system processes the fault related data to identify faults in the system, such as when a first of the processing modules is a cause of fault-related data acquired in a second of the processing modules. When a fault is identified, a reconfiguration agent may reconfigure the printing system to mitigate an impact of at least one of the identified faults.

Abstract: A method for generating service rules corresponding to business data is disclosed. A plurality of business related data is gathered from various sources. The data is combined using a subjective logic technique. The data is then evaluated for temporal patterns. Finally a set of service rules corresponding to the combined business data are developed.

Abstract: A method and system for translating documents with the use of a multifunctional printer machine, including capturing an image of a document; determining regions of the document captured that include original text; performing optical character recognition of the regions of the document captured that include the original text; specifying a source language corresponding to the original text; specifying one or more target languages corresponding to translated text; performing language translation of the original text into translated text; selecting one or more page layout templates having multiple pre-designated areas for receiving the original text and the translated text; and outputting one or more printouts in accordance with the one or more page layout templates selected, including at least an area designating (i) the original text in the source language and (ii) the translated text in the one or more target languages.

Abstract: The disclosure describes a method and system monitoring a set of loans and identifying loans in the set that that are likely to default before an upcoming date. The system uses a set of data about loans that are in a default status and loans that are in a non-default status to train a set of loan models. The loan models include at least one model for a defaulted loan and at least one model for a non-defaulted loan. After the loan models are created, the system monitors active loans and classifies each active loan in accordance with one of the loan models. Based on the loan model to which the active loan is classified, the processor will determine a probability of default over a prospective time period for the active loan and issue an alert when a loan's probability of default exceeds a threshold.

Abstract: A method and system for automatically determining an optimal re-training interval for a fault diagnoser based on online monitoring of the performance of a classifier are disclosed. The classifier generates a soft measure of membership in association with a class based on a training data. The output of the classifier can be utilized to assign a label to new data and then the members associated with each class can be clustered into one or more core members and potential outliers. A statistical measure can be utilized to determine if the distribution of the outliers is sufficiently different than the core members after enough outliers have been accumulated. If the outliers are different with respect to the core members, then the diagnoser can be re-trained; otherwise, the output of the classifier can be fed to the fault diagnoser.

Abstract: A method for generating service rules corresponding to business data is disclosed. A plurality of business related data is gathered from various sources. The data is combined using a subjective logic technique. The data is then evaluated for temporal patterns. Finally a set of service rules corresponding to the combined business data are developed.