Abstract: Military Standard 1553 (MIL-STD-1553) is a military standard that was published by the United States Department of Defense in 1973 for tactical aircraft. Despite advances in communications, many machines retain MIL-STD-1553 buses, since retrofitting the machines with newer communication systems is not always practical. However, newer subsystems for such machines tend to utilize Internet Protocol (IP) for higher bandwidth communications. Accordingly, a device is disclosed that enables IP communications over existing MIL-STD-1553 buses. This enables an existing MIL-STD-1553 bus to span the gap between two separate IP networks (e.g., subsystems) within the machine. In addition, the IP communications may utilize Standard Agreement (STANAG) 7221 to provide higher bandwidth over the existing MIL-STD-1553 buses. For added security, the IP communications may also be encrypted during transport over the MIL-STD-1553 buses.

Abstract: A system can obtain observations from a dataset. The system can generate a set of training partitions based on the observations and generate an ensemble of machine-learning models based on the set of training partitions. The system can then receive new data and detect whether the new data is indicative of the event using the ensemble. In some cases, the system can update the ensemble by providing the new data as input to an unsupervised machine-learning model that is separate from the ensemble of machine-learning models; receiving an output from the unsupervised machine-learning model indicating whether or not the new data is indicative of the event; incorporating a new observation into the dataset indicating whether or not the new data is indicative of the event based on the output from the unsupervised machine-learning model; and updating the ensemble based on the dataset with the new observation.

Abstract: A system can obtain observations from a dataset. The system can generate a set of training partitions based on the observations and generate an ensemble of machine-learning models based on the set of training partitions. The system can then receive new data and detect whether the new data is indicative of the event using the ensemble. In some cases, the system can update the ensemble by providing the new data as input to an unsupervised machine-learning model that is separate from the ensemble of machine-learning models; receiving an output from the unsupervised machine-learning model indicating whether or not the new data is indicative of the event; incorporating a new observation into the dataset indicating whether or not the new data is indicative of the event based on the output from the unsupervised machine-learning model; and updating the ensemble based on the dataset with the new observation.