Abstract: A method of black ice detection includes receiving image stream data from a mobile device. The mobile device is in a vehicle. At least one of accelerometer data from at least one accelerometer, temperature and humidity data, and on board dash vehicle data is received. A classifier embedded in the mobile device is run to evaluate the received image stream data, and the at least one of the accelerometer data, the temperature and humidity data, and the on board dash vehicle data. It is determined whether black ice is present in the image stream data based on a result generated by the classifier.

Abstract: A method of black ice detection includes receiving image stream data from a mobile device. The mobile device is in a vehicle. At least one of accelerometer data from at least one accelerometer, temperature and humidity data, and on board dash vehicle data is received. A classifier embedded in the mobile device is run to evaluate the received image stream data, and the at least one of the accelerometer data, the temperature and humidity data, and the on board dash vehicle data. It is determined whether black ice is present in the image stream data based on a result generated by the classifier.

Abstract: Methods, apparatus, and articles of manufacture are disclosed. These perform the following: accessing records of previous usage within a billing period of service units for customers, wherein the service units are discrete sizes of services for resource types, wherein each usage of an individual one of the service units has start and stop events, and wherein each resource type has a price fixed as of a date of the previous usage; dividing the billing period into time periods determined using the start and stop events for the usage of all of the service units; using the accessed records and the time periods and based on one or more criteria, assigning resource types to the previous usage in the time periods of the service units by the customers; and determining total charge for a selected customer based on the assignments of the resource types and corresponding prices for the selected customer.

Abstract: A method, system and computer program product are disclosed for identifying false positive indications of high impedance faults in an AC electric power transmission and distribution network. In one embodiment, the method comprises using a procedure to monitor a phase conductor of the network for faults, said procedure generating a fault signal indicating a specified fault in the conductor. In this embodiment, the voltage and current waveform of the electric power conducted through the conductor are monitored. When a phase shift in said waveform is detected over a defined period of time, and said detected phase shift meets one or more given criteria, a correction signal is generated indicating that said fault signal is a false indication of the specified fault. The given criteria may include, for example, that the phase shift is more than a threshold value for a specified period of time.

Abstract: Methods, apparatus, and articles of manufacture are disclosed. These perform the following: accessing records of previous usage within a billing period of service units for customers, wherein the service units are discrete sizes of services for resource types, wherein each usage of an individual one of the service units has start and stop events, and wherein each resource type has a price fixed as of a date of the previous usage; dividing the billing period into time periods determined using the start and stop events for the usage of all of the service units; using the accessed records and the time periods and based on one or more criteria, assigning resource types to the previous usage in the time periods of the service units by the customers; and determining total charge for a selected customer based on the assignments of the resource types and corresponding prices for the selected customer.

Abstract: A method for detecting high impedance faults, including: receiving an input waveform from a circuit; computing a root mean square of the input waveform; fitting a regression line to the root mean squares; computing a deviation between the regression line and the root mean squares; determining whether the deviations are above a threshold; and outputting a value indicating that a fault has occurred in the circuit when the deviation is above the threshold and outputting a value indicating that a fault did not occur in the circuit when the deviation is below the threshold.

Abstract: A method and apparatus detect and localize electric faults in electrical power grids and circuit. High impedance faults are detected by analyzing data from remote sensor units deployed over the network using the algorithms of speech and speaker analysis software. This is accomplished by converting the voltage and/or current waveform readouts from the sensors into a digital form which is then transmitted to a computer located either near the sensors or at an operations center. The digitized data is converted by a dedicated software or software/hardware interface to a format accepted by a reliable and stable software solution, such as speech or speaker recognition software. The speech or speaker recognition software must be “trained” to recognize various signal patterns that either indicate or not the occurrence of a fault. The readout of the speech or speaker recognition software, if indicating a fault, is transmitted to a central processor and displayed to provide information on the most likely type of fault.

Abstract: A method, system and computer program product are disclosed for identifying false positive indications of high impedance faults in an AC electric power transmission and distribution network. In one embodiment, the method comprises using a procedure to monitor a phase conductor of the network for faults, said procedure generating a fault signal indicating a specified fault in the conductor. In this embodiment, the voltage and current waveform of the electric power conducted through the conductor are monitored. When a phase shift in said waveform is detected over a defined period of time, and said detected phase shift meets one or more given criteria, a correction signal is generated indicating that said fault signal is a false indication of the specified fault. The given criteria may include, for example, that the phase shift is more than a threshold value for a specified period of time.

Abstract: A method for detecting high impedance faults, including: receiving an input waveform from a circuit; computing a root mean square of the input waveform; fitting a regression line to the root mean squares; computing a deviation between the regression line and the root mean squares; determining whether the deviations are above a threshold; and outputting a value indicating that a fault has occurred in the circuit when the deviation is above the threshold and outputting a value indicating that a fault did not occur in the circuit when the deviation is below the threshold.

Abstract: A self-propelled, automated, autonomic grid crawler which, when attached to a wire, moves along the wire to sense conditions of the wire. The grid crawler includes a central processing unit (CPU), working memory, such as random access memory (RAM), persistent money, such as read only memory (ROM) and hard drive (HD), sensor electronics, a wireless interface, a location device, such as a global positioning satellite (GPS) receiver, motive power system, and a battery. The sensor electronics serves to sense various types of faults. The CPU analyzes the data from the sensor electronics using pre-analysis and pre-qualification algorithms, which are stored in the HD. Depending on the results of the analysis, the CPU transmits information to the central station, either directly or indirectly via mounted docking devices, that potentially indicates a fault, including the location of the fault as determined by the GPS receiver.

Abstract: A self-propelled, automated, autonomic grid crawler which, when attached to a wire, moves along the wire to sense conditions of the wire. The grid crawler includes a central processing unit (CPU), working memory, such as random access memory (RAM), persistent money, such as read only memory (ROM) and hard drive (HD), sensor electronics, a wireless interface, a location device, such as a global positioning satellite (GPS) receiver, motive power system, and a battery. The sensor electronics serves to sense various types of faults. The CPU analyzes the data from the sensor electronics using pre-analysis and pre-qualification algorithms, which are stored in the HD. Depending on the results of the analysis, the CPU transmits information to the central station, either directly or indirectly via mounted docking devices, that potentially indicates a fault including the location of the fault as determined by the GPS receiver.

Abstract: A method and apparatus detect and localize electric faults in electrical power grids and circuit. High impedance faults are detected by analyzing data from remote sensor units deployed over the network using the algorithms of speech and speaker analysis software. This is accomplished by converting the voltage and/or current waveform readouts from the sensors into a digital form which is then transmitted to a computer located either near the sensors or at an operations center. The digitized data is converted by a dedicated software or software/hardware interface to a format accepted by a reliable and stable software solution, such as speech or speaker recognition software. The speech or speaker recognition software must be “trained” to recognize various signal patterns that either indicate or not the occurrence of a fault. The readout of the speech or speaker recognition software, if indicating a fault, is transmitted to a central processor and displayed to provide information on the most likely type of fault.