Abstract: Provided herein are methods and systems for a hard drive failure prediction system to determine a predicted hard drive failure within a time-in-advance determined time period. The system includes a machine-learning model, such as an XGBoost model, trained using historical hard drive operational data from a data center. The historical hard drive operation data corresponds to SMART attributes. Statistical analysis is performed to determine a set of SMART attributes most indicative of hard drive failure. The model is trained using the historical hard drive operational data corresponding to the set of SMART attributes. The accuracy results of the model are used to determine a time-in-advance value that balances accuracy with failure lead time. A signal length of operational data for a particular hard drive is provided as input to the trained model and an output indicates if the hard drive will fail within the time period of the time-in-advance value.

Abstract: In example embodiments, there is provided a dynamic dispatcher simulator for an electrical power system. The dynamic dispatcher simulator can be operable to receive an input from a user identity during a run time, wherein the input represents a disturbance event indicative of a simulated disturbance to an electrical power system. Based on the input, a signal can be transmitted to a transient simulation engine component. The transient simulation engine component can output simulated phasor measurement unit data representative of the disturbance event to a wide area monitoring system that facilitates a display of alarms in response to the receipt of the simulated phasor measurement unit data. The dynamic dispatcher training simulator can also receive another input from the user during the run time, the second input being representative of a simulated condition related to the electrical power system.

Abstract: In example embodiments, there is provided a dynamic dispatcher simulator for an electrical power system. The dynamic dispatcher simulator can be operable to receive an input from a user identity during a run time, wherein the input represents a disturbance event indicative of a simulated disturbance to an electrical power system. Based on the input, a signal can be transmitted to a transient simulation engine component. The transient simulation engine component can output simulated phasor measurement unit data representative of the disturbance event to a wide area monitoring system that facilitates a display of alarms in response to the receipt of the simulated phasor measurement unit data. The dynamic dispatcher training simulator can also receive another input from the user during the run time, the second input being representative of a simulated condition related to the electrical power system.

Abstract: A method and a cellular wireless access data offload system (CWADOS) implemented in a base station of a cellular wireless communication network, for offloading data from a first cellular wireless communication interface (FCWCI) to a second wireless communication interface (SWCI) without impacting cellular wireless protocols of the cellular wireless communication network are provided. The CWADOS transmits wireless connection identification information to a user device for establishing the SWCI between the user device and a wireless access point, determines the FCWCI or the SWCI for transmitting downlink data packets from a core network to the user device and for transmitting uplink data packets from the user device to the core network based on switching criteria, forwards the downlink data packets to the user device via the FCWCI or the SWCI, and forwards the uplink data packets received from the user device via the FCWCI or the SWCI to the core network.