Abstract: Performing a geo-visual search is disclosed. A query feature vector associated with a query tile is obtained. A lookup is performed at least in part by using a key derived from the query feature vector. A list of candidate feature vectors is obtained based at least in part on the lookup. Based at least in part on a comparison of the query feature vector against at least some of the candidate feature vectors in the obtained list, a tile that is visually similar to the query tile is determined. The determined tile is provided as output.

Abstract: A system can include a bi-directional converter circuit comprising a plurality of switches and a flying capacitor configured to experience a low voltage or a high voltage, and a control module operatively connected to the plurality of switches to control a state of the plurality of switches. The control module can be configured to receive a sense signal indicative of a flying capacitor voltage and to control the one or more switches of the plurality of switches to charge or discharge the flying capacitor to maintain a target voltage or target voltage range.

Abstract: A system can include a bi-directional converter circuit comprising a plurality of switches and a flying capacitor configured to experience a low voltage or a high voltage, and a control module operatively connected to the plurality of switches to control a state of the plurality of switches. The control module can be configured to receive a sense signal indicative of a flying capacitor voltage and to control the one or more switches of the plurality of switches to charge or discharge the flying capacitor to maintain a target voltage or target voltage range.

Abstract: A system can include a bi-directional converter circuit comprising a plurality of switches and at least one flying capacitor. One or more the plurality of switches can experience overvoltage in a fault state. The system can include a control module operatively connected to the plurality of switches to control a state of the plurality of switches. The control module can be configured to receive a sense signal indicative of flying capacitor voltage and to control the one or more switches of the plurality of switches to turn on or remain on to prevent switch overvoltage of the one or more switches if the flying capacitor voltage is outside of a normal range.

Abstract: A cabinet lock for protecting merchandise within a merchandise display cabinet having an inner door and an outer door. The cabinet lock includes a strike plate affixed to the inner door and a lock housing affixed to the outer door such that the cabinet is in a locked configuration that prevents access to the merchandise when the lock housing is operably engaged to the strike plate. A programmable electronic key communicates a security code with the cabinet lock and transfers electrical power to the cabinet lock to operate a lock mechanism between the locked configuration and an unlocked configuration. An indicator is provided for indicating whether the cabinet lock is in the locked configuration or the unlocked configuration. The indicator includes a first segment and a second segment that can be energized to visually indicate the status of the cabinet lock.

Abstract: A cabinet lock for protecting merchandise within a merchandise display cabinet having an inner door and an outer door. The cabinet lock includes a strike plate affixed to the inner door and a lock housing affixed to the outer door such that the cabinet is in a locked configuration that prevents access to the merchandise when the lock housing is operably engaged to the strike plate. A programmable electronic key communicates a security code with the cabinet lock and transfers electrical power to the cabinet lock to operate a lock mechanism between the locked configuration and an unlocked configuration. An indicator is provided for indicating whether the cabinet lock is in the locked configuration or the unlocked configuration. The indicator includes a first segment and a second segment that can be energized to visually indicate the status of the cabinet lock.

Abstract: A cabinet lock for protecting merchandise within a merchandise display cabinet having an inner door and an outer door. The cabinet lock includes a strike plate affixed to the inner door and a lock housing affixed to the outer door such that the cabinet is in a locked configuration that prevents access to the merchandise when the lock housing is operably engaged to the strike plate. A programmable electronic key communicates a security code with the cabinet lock and transfers electrical power to the cabinet lock to operate a lock mechanism between the locked configuration and an unlocked configuration. An indicator is provided for indicating whether the cabinet lock is in the locked configuration or the unlocked configuration. The indicator includes a first segment and a second segment that can be energized to visually indicate the status of the cabinet lock.

Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Detecting an outage in an alternating current (AC) electrical network. One or more time-stamped and location-stamped data packets, each data packet including magnetic sensor data collected by one or more non-contact magnetic sensors in a mobile device in proximity to the AC electrical network are received. Based on the magnetic sensor data, it is determined that an outage exists in the AC electrical network.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

Abstract: An image acquisition system with motion compensation is disclosed. Embodiments of the system include: includes a rectilinear lens assembly, a first 2D-image sensor, and a motion compensation module. The rectilinear lens assembly has an optical axis, an object plane, and a focal plane. During an image acquisition cycle, the rectilinear lens assembly is positioned such that the optical axis is orthogonal to the object plane while the first 2D-image sensor is parallel to the object plane. The motion compensation module can determine a motion vector of an image scene in the object plane. In response the determined motion vector, the rectilinear lens assembly and the first 2D-image sensor relative are translated relative to each other in two dimensions to compensate for the motion of the image scene.

Abstract: Single-die or multi-die packaged modules that incorporate three-dimensional integration of active devices with discrete passive devices to create a package structure that allows active devices (such as, silicon or gallium-arsenide devices) to share the same footprint area as an array of passive surface mount components. In one example, a module includes at least one active device stacked on top of an array of passive surface mount components on a substrate. A conductive or non-conductive adhesive can be used to adhere the active device to the array of passive devices.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.

Abstract: Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.