Abstract: Embodiments described herein provide a system for facilitating a training system for a device. During operation, the system determines a system model for the device that can be based on empirical data of the device. The empirical data is obtained based on experiments performed on the device. The system then generates, from the system model, synthetic data that represents behavior of the device under a failure. The system determines uncertainty associated with the synthetic data and, from the uncertainty, determines a set of prediction parameters using an uncertainty quantification model. The system generates training data from the synthetic data based on the set of prediction parameters and learns a set of learned parameters associated with the device by using a machine-learning-based classifier on the training data.

Abstract: Systems and methods described receive a set of experimental data of connection points of an unknown component of a partially known physical system. The systems and methods set feasibility constraints for an untrained model of the unknown component and simulate the partially known system using the untrained model of the unknown component to generate simulated data at the connection points of the unknown component. Systems and methods then optimize the untrained model based on the feasibility constraints and a comparison of the simulated data and the experimental data to generate a trained model of the unknown component.

Abstract: Embodiments described herein provide a system for facilitating a training system for a device. During operation, the system determines a system model for the device that can be based on empirical data of the device. The empirical data is obtained based on experiments performed on the device. The system then generates, from the system model, synthetic data that represents behavior of the device under a failure. The system determines uncertainty associated with the synthetic data and, from the uncertainty, determines a set of prediction parameters using an uncertainty quantification model. The system generates training data from the synthetic data based on the set of prediction parameters and learns a set of learned parameters associated with the device by using a machine-learning-based classifier on the training data.

Abstract: Embodiments described herein provide a system for facilitating comprehensive control data for a device. During operation, the system determines one or more properties of the device that can be applied to empirical data of the device. The empirical data can be obtained based on experiments performed on the device. The system applies the one or more properties to the empirical data to obtain derived data and learns an efficient policy for the device based on both empirical and derived data. The efficient policy indicates one or more operations of the device that can reach a target state from an initial state of the device. The system then determines an operation for the device based on the efficient policy.

Abstract: The presently disclosed embodiment provides an interactive tool for printed image artifact characterization that successfully combines the advantages from both a human visual system and automated image quality characterization. In the interactive tool provided, the user may specify coarse level information, such as the region of interest in a print image, to be examined by the automated image-quality characterization algorithm, which can then employ sophisticated algorithms, e.g. signal processing, feature extraction, estimation and classification, to find precise fine-level descriptors.

Abstract: The presently disclosed embodiment provides an interactive tool for printed image artifact characterization that successfully combines the advantages from both a human visual system and automated image quality characterization. In the interactive tool provided, the user may specify coarse level information, such as the region of interest in a print image, to be examined by the automated image-quality characterization algorithm, which can then employ sophisticated algorithms, e.g. signal processing, feature extraction, estimation and classification, to find precise fine-level descriptors.

Abstract: A system for visualizing a print job includes a print job simulator which applies a print job model to print job image data to generate an image which simulates a rendered image of the print job if the print job were to be printed on a specific marking device. The system also includes a display in communication with the print job simulator for displaying the simulation image. The modified image data displayed on the display enables a customer to see a defect in the print job which would appear if the print job were to be rendered on the specific marking device. The print job model may be derived, at least in part, from information derived from the marking device, such as information derived from image data acquired by scanning a test image which has been rendered by the marking device.

Abstract: An assessment authoring system implemented by an operative set of processor executable instructions configured for execution by at least one processor includes a user interface and an assessment generation component. The user interface communicates user input and the assessment generation component receives the user input from the user interface. The assessment generation component generates an assessment data structure for printing on a multifunction device. The printed assessment includes a marking region adapted to receive a human marking. The system may utilize a model or rubric that can be used to score the human marks when are processed by image processing.

Abstract: A system for visualizing a print job includes a print job simulator which applies a print job model to print job image data to generate an image which simulates a rendered image of the print job if the print job were to be printed on a specific marking device. The system also includes a display in communication with the print job simulator for displaying the simulation image. The modified image data displayed on the display enables a customer to see a defect in the print job which would appear if the print job were to be rendered on the specific marking device. The print job model may be derived, at least in part, from information derived from the marking device, such as information derived from image data acquired by scanning a test image which has been rendered by the marking device.