Abstract: A method for controlling a dual fuel engine system includes estimating a total indicated engine load, where the total indicated engine load is based on a sum of a measured engine power and a power loss estimate. The method further includes determining a total fueling amount based on an engine speed and the total indicated engine load, where the total fueling amount includes a gas fueling amount and a diesel fueling amount. The method also includes controlling the dual fuel engine system using the total fueling amount.

Abstract: A method for controlling a dual fuel engine system includes determining a friction power loss amount of an internal combustion engine of the dual fuel engine system, where the friction power loss amount is based on an engine speed of the internal combustion engine and a friction torque estimate. The method also includes determining an accessory power loss amount of a power of the internal combustion engine, where the accessory power loss amount is based on the engine speed and an accessory torque estimate. The method further includes estimating a net engine power amount based on the accessory power loss amount and a brake power amount of the internal combustion engine, estimating an indicated diesel power, and estimating, based on the estimated net engine power, a first indicated engine power and a first gas power.

Abstract: The electronic devices described herein are configured to enhance user experience associated with a network connection when transitioning the network connection between access points. Determinations to scan for available access points and transfer the network connection to an alternative access point are based on connection attributes and/or access point attributes that are compared to scan criteria and transfer criteria. Further, the scan criteria and transfer criteria are updated, or adjusted, according to machine learning techniques such that the determinations to scan for access points and transfer between access points are tuned on a per-device and/or per-user level to fit patterns of use of a particular device and/or user. Over time, the updates to the scan criteria and transfer criteria based on machine learning provide an increasingly consistent, high quality user experience while roaming efficiently between access points.

Abstract: The electronic devices described herein are configured to enhance user experience associated with a network connection when transitioning the network connection between access points. Determinations to scan for available access points and transfer the network connection to an alternative access point are based on connection attributes and/or access point attributes that are compared to scan criteria and transfer criteria. Further, the scan criteria and transfer criteria are updated, or adjusted, according to machine learning techniques such that the determinations to scan for access points and transfer between access points are tuned on a per-device and/or per-user level to fit patterns of use of a particular device and/or user. Over time, the updates to the scan criteria and transfer criteria based on machine learning provide an increasingly consistent, high quality user experience while roaming efficiently between access points.