Abstract: Provided is an apparatus for classifying a medical image. The apparatus includes a database configured to store a first image, a generator configured to generate a second image on the basis of a latent vector which is a concatenation of noise information having a certain size and random uniform class labels of a plurality of diseases, a discriminator configured to receive the first image and the second image and attempt to recognize the first image and the second image as a real image and a fake image, and a classifier configured to classify the first image and the second image.

Abstract: One or more processors determine wear for a robotic device. The one or more processors divide a direction of travel of a robotic device into zones. Each zone has an associated counter that counts how many times the robotic device has entered a given zone. The one or more processors update a count for a zone in response to the robotic device entering that zone. The one or more processors determine a level of wear for a component associated with the robotic device. The level of wear is based, at least in part, on a total of counts for the zones.

Abstract: One or more processors determine wear for a robotic device. The one or more processors divide a direction of travel of a robotic device into zones. Each zone has an associated counter that counts how many times the robotic device has entered a given zone. The one or more processors update a count for a zone in response to the robotic device entering that zone. The one or more processors determine a level of wear for a component associated with the robotic device. The level of wear is based, at least in part, on a total of counts for the zones.

Abstract: One or more processors determine wear for a robotic device. The one or more processors divide a direction of travel of a robotic device into zones. Each zone has an associated counter that counts how many times the robotic device has entered a given zone. The one or more processors update a count for a zone in response to the robotic device entering that zone. The one or more processors determine a level of wear for a component associated with the robotic device. The level of wear is based, at least in part, on a total of counts for the zones.

Abstract: In one embodiment, of inventory management for automated data storage libraries, a first subset of data storage cartridges is scanned and extracted from a multi-cartridge deep slot cell and transported to and filled into another multi-cartridge deep slot cell while maintaining tier order of the first subset. A second subset of data storage cartridges is scanned and extracted from the multi-cartridge deep slot cell and returned to its original multi-cartridge deep slot cell while maintaining tier order of the second subset. Other aspects are described herein.

Abstract: In one embodiment, of inventory management for automated data storage libraries, a first subset of data storage cartridges is scanned and extracted from a multi-cartridge deep slot cell and transported to and filled into another multi-cartridge deep slot cell while maintaining tier order of the first subset. A second subset of data storage cartridges is scanned and extracted from the multi-cartridge deep slot cell and returned to its original multi-cartridge deep slot cell while maintaining tier order of the second subset. Other aspects are described herein.

Abstract: The present invention is a method and system for detecting an abnormal event for process units of a Polymers Unit. The method compares the operation of the process units to statistical, engineering or heuristic models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on correlation analysis between variables or simple engineering calculations. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

Abstract: The present invention is a method for detecting an abnormal event for process units of a Delayed Coking Unit. The method compares the operation of the process units to statistical and engineering models. The statistical models are developed by principal components analysis of the normal operation for these units. The engineering models are based statistical and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.