Abstract: One or more tangible, non-transitory, computer-readable media storing instructions thereon that, when executed by processing circuitry, are configured to cause the processing circuitry to perform various functions. The functions include determining a state-of-charge (SOC) of a battery during a first interval of time via a voltage look-up algorithm, transitioning from the voltage look-up algorithm to a Coulomb counting algorithm based on battery characteristic data satisfying transition criteria, and determining the SOC of the battery during a second interval of time after the first interval of time via the Coulomb counting algorithm.

Abstract: A system and a method of managing operations in a warehouse is described. The method comprises receiving, from sensors installed at one or more locations in the warehouse, sensor information related to events occurring within the warehouse in a real-time. The events are associated with performance of assets responsible for movement of articles within the warehouse. The sensor information is mapped with one or more assets present in the warehouse, using a process model of the warehouse. A first anomaly in an event is identified based on violation of predefined limits set for the sensor information related to the event. A plurality of scenarios is simulated based on the sensor information, to identify effect of the anomaly on the movement of the articles and to determine one or more recommendations for mitigating the effect of the first anomaly. An operator is notified about the one or more recommendations.

Abstract: Implementations of the subject technology provide systems and methods for detection of a user input with an electronic device, in which the input is received on or using a case for an electronic device. In one or more implementations, the case may be free of user input components such as buttons, switches, or touch sensors.

Abstract: At least some embodiments are directed to an entity classification system that receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: An electronic system can include a charged system and a charging system. The charged system can include a charger and a battery configured to be charged thereby. The charger may be configured to receive power from a charging system that includes a power converter configured to supply power to the charger and a controller configured to control the power converter. The controller may be configured to receive feedback information from the charged system, including one or more voltages. The controller may be further configured to determine an output voltage compensation value for the power converter as a function of the feedback information and to set an output voltage of the power converter as a function of the compensation value. The compensation value and output voltage may be selected to maintain a preselected headroom between a battery charging target voltage of the charger and a voltage supplied to the charger.

Abstract: An electronic system can include a charged system and a charging system. The charged system can include a charger and a battery configured to be charged thereby. The charger may be configured to receive power from a charging system that includes a power converter configured to supply power to the charger and a controller configured to control the power converter. The controller may be configured to receive feedback information from the charged system, including one or more voltages. The controller may be further configured to determine an output voltage compensation value for the power converter as a function of the feedback information and to set an output voltage of the power converter as a function of the compensation value. The compensation value and output voltage may be selected to maintain a preselected headroom between a battery charging target voltage of the charger and a voltage supplied to the charger.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage devices for determining a state of charge of a battery. A temperature, electrical current value, and voltage of the battery of a computing device may be obtained. A charging tier of a multi-tiered charging scheme may be identified based at least in part on the temperature and the voltage. An electrical current rate to be applied to the battery may be obtained using the temperature, the voltage of the battery, and the identified charging tier. A mapping may be obtained based at least in part on whether the battery is being charged during a constant voltage phase or a constant electrical current phase of the charging tier. A state of charge of the battery may be determined using the mapping and either the voltage or the electrical current value. The state of charge may be presented at an output device.

Abstract: Disclosed are various embodiments for generating training data for machine-learning models. A plurality of original records are analyze to identify a probability distribution function (PDF), wherein a sample space of the PDF comprises the plurality of original records. A plurality of new records are generated using the PDF. An augmented dataset that includes the plurality of new records is created. Then, a machine-learning model is trained using the augmented dataset.