Abstract: The disclosure provides a method and system of improvement in the real-time operation of a terminal station in an industrial facility using a machine learning-based self-adaptive system comprising obtaining real-time operations data and historical data stored in a local database or at a cloud-storage. The data relates to input parameters of the terminal station. The method includes inputting the input parameter to a machine learning configurable module of the machine learning-based self-adaptive system and analyzing the input parameter using dynamic machine learning models and algorithms to identify patterns to each of the input parameters. The method further includes evaluating the identified pattern against the real-time operations data obtained from the terminal station and predicting at least one output parameter based on the input parameter and the identified pattern against the real-time operations. Based on the prediction, adjusting the output parameter of the real-time operations data.

Abstract: The disclosure provides a method and system of improvement in the real-time operation of a terminal station in an industrial facility using a machine learning-based self-adaptive system comprising obtaining real-time operations data and historical data stored in a local database or at a cloud-storage. The data relates to input parameters of the terminal station. The method includes inputting the input parameter to a machine learning configurable module of the machine learning-based self-adaptive system and analyzing the input parameter using dynamic machine learning models and algorithms to identify patterns to each of the input parameters. The method further includes evaluating the identified pattern against the real-time operations data obtained from the terminal station and predicting at least one output parameter based on the input parameter and the identified pattern against the real-time operations. Based on the prediction, adjusting the output parameter of the real-time operations data.

Abstract: A non-volatile memory device comprises an application code sector of sufficient size to store a first copy of an application code and a second copy of the application code; and a boot sector having a boot loader code embodied therein. The boot loader code is configured to cause a processor to check the integrity of both the first and second copies of the application code; if the first copy is corrupted, overwrite the first copy of the application code with the second copy; and if the second copy is corrupted, overwrite the second copy of the application code with the first copy.

Abstract: A microcontroller comprises a random access memory (RAM) device; a non-volatile memory device having a data sector, wherein operation codes are stored as data files in the data sector; and a processor configured to retrieve the operation codes from the data sector, load the retrieved operation codes into the RAM device and run the decrypted operation codes from the RAM device.

Abstract: A non-volatile memory device comprises an application code sector of sufficient size to store a first copy of an application code and a second copy of the application code; and a boot sector having a boot loader code embodied therein. The boot loader code is configured to cause a processor to check the integrity of both the first and second copies of the application code; if the first copy is corrupted, overwrite the first copy of the application code with the second copy; and if the second copy is corrupted, overwrite the second copy of the application code with the first copy.