Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: A method comprises receiving data associated with a detection of the odor, the data associated with the detection of the odor comprising a location of the detection of the odor and a time of the detection of the odor, retrieving weather data corresponding to the detection of the odor, the weather data including a wind speed and direction in the location of the detection of the odor at the time of the detection of the odor, calculating a location of the source of the odor as a function of the location of the detection of the odor, the time of the location of the odor and the wind speed and direction in the location of the detection of the odor, and outputting to a user on a display a graphical representation of a likely area that includes the location of the source of the odor.

Abstract: A method comprises receiving data associated with a detection of the odor, the data associated with the detection of the odor comprising a location of the detection of the odor and a time of the detection of the odor, retrieving weather data corresponding to the detection of the odor, the weather data including a wind speed and direction in the location of the detection of the odor at the time of the detection of the odor, calculating a location of the source of the odor as a function of the location of the detection of the odor, the time of the location of the odor and the wind speed and direction in the location of the detection of the odor, and outputting to a user on a display a graphical representation of a likely area that includes the location of the source of the odor.

Abstract: Methods, systems, and computer program products for detection of an arbitrarily-shaped source of an abnormal event via use of a hierarchical reconstruction method are provided herein. A computer-implemented method includes detecting an abnormal event based on analysis of sensor data, wherein said analysis of the sensor data comprises comparing the sensor data to a user-defined threshold; generating a query based on the detected abnormal event; processing the query against one or more given data repositories; executing an inverse model using an output generated in relation to said processing to identify a source of the detected abnormal event, wherein the source to comprises an arbitrary shape; and outputting the identified source of the detected abnormal event.

Abstract: A method comprises receiving data associated with a detection of the odor, the data associated with the detection of the odor comprising a location of the detection of the odor and a time of the detection of the odor, retrieving weather data corresponding to the detection of the odor, the weather data including a wind speed and direction in the location of the detection of the odor at the time of the detection of the odor, calculating a location of the source of the odor as a function of the location of the detection of the odor, the time of the location of the odor and the wind speed and direction in the location of the detection of the odor, and outputting to a user on a display a graphical representation of a likely area that includes the location of the source of the odor.

Abstract: A method comprises receiving data associated with a detection of the odor, the data associated with the detection of the odor comprising a location of the detection of the odor and a time of the detection of the odor, retrieving weather data corresponding to the detection of the odor, the weather data including a wind speed and direction in the location of the detection of the odor at the time of the detection of the odor, calculating a location of the source of the odor as a function of the location of the detection of the odor, the time of the location of the odor and the wind speed and direction in the location of the detection of the odor, and outputting to a user on a display a graphical representation of a likely area that includes the location of the source of the odor.