Abstract: An information processing apparatus and method is provided and controls execution of learning processing thereon. Learning data are generated in which readings of a human presence sensor serve as input values, information on receiving or not receiving any operation from an operation panel serves as success/failure flags. The success/failure flag is generated from an assessment result of a current resumption assessment model and the information on receiving or not receiving an operation, and is provided to the learning data. Accordingly, learning processing is performed using the success/failure flags provided in the learning data, thereby efficiently implementing learning.

Abstract: In an embodiment, a system may include one or more processors forming central processing units (CPUs) in the system, a display controller configured to display frames on a display device, a memory controller configured to control a memory, and a power management circuit. The power management circuit may be configured to establish one of a plurality of power states in the system. In a first power state, the display controller and the memory controller are powered on while the CPUs are powered off. The display controller may be configured to read a plurality of prerendered frames from the memory and display the plurality of prerendered frames at times specified for each of the plurality of prerendered frames.

Abstract: A method includes receiving, using at least one processor, first sensor data pertaining to plant-related parameters of each of multiple first plants over time. The method also includes storing the first sensor data in at least one memory. The method further includes identifying, using the at least one processor, an issue affecting at least one of the first plants. The method also includes analyzing, using the at least one processor, at least some of the stored first sensor data to generate a predictive model associated with the issue. The method further includes receiving, using the at least one processor, second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying, using the at least one processor, at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data.

Abstract: Embodiments of this disclosure relate to electronic information, and disclose a smart refrigerator based on big data analysis of a server, including a refrigerator body, a collector, and a communication circuit. The collector is disposed in the refrigerator body and is used to collect human body health parameter information. The communication circuit is disposed in the refrigerator body and is connected to the collector, and is used to: upload human body health parameter information to a server, obtain a diet solution that is returned by the server after the server performs big data analysis on the human body health parameter information, and push the diet solution. This disclosure further discloses a health management method based on a smart refrigerator and a health management system.

Abstract: A method for automatically reducing the dimensionality of a mathematical representation of a controlled application system is provided. The method includes receiving, at a control system, data corresponding to control action and system state variables relating to the controlled application system, fitting a constrained reinforcement learning (CRL) model to the controlled application system based on the data, and automatically identifying a subset of the system state variables by selecting control action variables of interest and identifying system state variables that drive the CRL model to recommend each control action variable of interest.

Abstract: This application provides a power control method based on a user habit and a terminal. The method includes determining, by the terminal, a charge cycle based on a historical charge status, where the charge cycle reflects a charging habit of a user. The method also includes predicting, by the terminal, a charge cycle within a next time period based on the charge cycle. The method further includes determining, by the terminal, a power allocation scheme in the charge cycle within the next time period based on historical power consumption information in one or more charge cycles. The method also includes controlling, by the terminal, use of battery power according to the power allocation scheme.

Abstract: Detecting a thermostat call for heating or cooling based on detecting a heat W or compressor Y signal is activated and energizing a fan signal output and operating a system fan/blower while the W or Y signal is activated and energizing the fan signal and operating the fan for a variable fan-off delay immediately after the W or Y signal is de-activated and de-energizing the fan signal and not operating the fan after the variable fan-off delay when a fan G signal is not activated. Detecting the fan G signal is activated and the W or Y signal is not activated indicating a fan-on duration control selected by a user, and energizing the fan signal and operating the fan while the fan G signal is activated and de-energizing the fan signal and not operating the fan and not providing the variable fan-off delay when the fan G signal is de-activated.

Abstract: The present concepts maintain forward flow and prevent reverse flow of heat-transferring media. A first zone and a second zone are proximate to each other. A first fan moves air through the first zone in a forward direction. A second fan moves air through the second zone in a forward direction. The speed of the first fan is compared with the speed of the second fan. If the first fan speed is slower than the second fan speed, then the first fan speed is adjusted to match the second fan speed. Therefore, reverse airflow in the first zone is prevented.

Abstract: A power grid reactive voltage control method and control system based on two-stage deep reinforcement learning, comprising steps of: building interactive training environment based on Markov decision process, according to a regional power grid simulation model and a reactive voltage optimization model; training a reactive voltage control model offline by using a SAC algorithm, in the interactive training environment based on Markov decision process; deploying the reactive voltage control model to a regional power grid online system; and acquiring operating state information of the regional power grid, updating the reactive voltage control model, and generating an optimal reactive voltage control policy. As compared with the existing power grid optimizing method based on reinforcement learning, the online control training according to the present disclosure has costs and safety hazards greatly reduced, and is more suitable for deployment in an actual power system.

Abstract: A power module includes a circuit board and a load group. The load group is installed on the circuit board. The load group includes a first-stage power conversion circuit, a second-stage power conversion circuit and plural loads. The first-stage power conversion circuit converts an input voltage into a transition voltage. The second-stage power conversion circuit converts the transition voltage into a driving voltage. A rated value of the first input voltage is higher than twice a rated value of the transition voltage. A rated value of the driving voltage is lower than a half of the rated value of the transition voltage. The distance between the input terminal of the second-stage power conversion circuit and the output terminal of the first-stage power conversion circuit is smaller than the distance between the input terminal of the first-stage power conversion circuit and each edge of the circuit board.

Abstract: Adaptive CPU power limit tuning can be performed. A mode detector can employ a machine learning model to detect a mode of operation on a computing device. As the computing device operates in the various modes, a mode boundary tuner of the mode detector can evaluate performance measurements to determine whether the currently defined boundaries between the various modes are optimal for the particular computing device. When the mode boundary tuner determines that a more optimal boundary definition exists, it can dynamically change the boundary to thereby tune the mode selection process on the particular computing device. A power limit setter may also be employed to set the CPU's power limits based on the mode detected by the mode detector. As the computing device operates in a mode, a power limit tuner can evaluate performance measurements and adjust the power limits to thereby tune the mode-specific power limits to the workload that is currently being executed.

Abstract: A power module and a manufacturing method thereof are disclosed. The power module includes a substrate, a power device, a leading component and a molding component. The substrate includes a first side, a second side and a conductive wire. The power device is disposed on the substrate and electrically connected with the conductive wire. The leading component is disposed on the substrate and includes a first horizontal portion and a vertical portion connected with each other. The vertical portion is electrically connected with the conductive wire. The leading component includes a first contact surface and a second contact surface, which are non-coplanar. The molding component is disposed on the substrate and covers at least portion of the substrate and at least portion of the leading component. The first contact surface and the second contact surface are uncovered by the molding component.

Abstract: Methods and apparatus relating to platform power consumption reduction via power state switching are described. In one embodiment, control logic causes a processor to enter a first low power consumption state (e.g., S0ix) instead of a second low power consumption state (e.g., S3) based on whether a threshold time period exists between a first wake event (e.g., corresponding to a first one of one or more awake requests) and a second wake event (e.g., corresponding to a second one of the one or more awake requests). Other embodiments are also claimed and disclosed.

Abstract: A terminal and a method for managing the terminal are provided. The method includes: obtaining usage data of a user; determining whether the terminal is in a predetermined charging state; and performing an operation related to an artificial intelligence based on the usage data of the user, in response to the terminal being in the predetermined charging state. According to the terminal and the method for managing a terminal, the timing for performing the operation related to artificial intelligence can be adaptively selected to achieve a technical effect of improving the stability of the intelligent operation and reducing the power consumption of the terminal.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, where the 3D models of the physical structures are produced so as to facilitate manufacturing of the physical structures using 2.5-axis subtractive manufacturing systems and techniques, include: obtaining a design space for an object to be manufactured, design criteria, and load case(s); iteratively modifying a generatively designed 3D shape of the modeled object, including generating 2D profile representations (corresponding to discrete layers) of an updated version of the 3D shape, extruding the 2D profile representations along the milling direction, and forming a next version of the 3D shape of the modeled object from a combination of the 3D representations produced by the extruding; and providing the generatively designed 3D shape of the modeled object for use in manufacturing the physical structure using a 2.

Abstract: A water heating system can include a water heater having a tank, an inlet line, and an outlet line, where the inlet line provides unheated water to the tank, and where the outlet line draws heated water from the tank. The water heating system can also include multiple sensing devices, where each sensing device of the plurality of sensing devices measures a parameter associated with the tank. The water heating system can further include a controller communicably coupled to the plurality of sensing devices, where the controller determines an amount of heated water in the tank based on measurements made by the plurality of sensing devices.

Abstract: A power estimation method includes acquiring first consumed power information indicating a change in power consumed for a first time period from a first time to a second time within a time period elapsed from the start of the execution of a first job, and second consumed power information indicating a change in power consumed for a second time period from a third time to a fourth time within the time period elapsed from the start of the execution of the first job, the third time being after the second time, generating, from the first consumed power information, a first estimation model for estimating power to be consumed by the job for the first time period, and generating, from the second consumed power information, a second estimation model for estimating power to be consumed by the job for the second time period.

Abstract: A mechanical equipment control system includes a mechanical apparatus, a load ratio detection circuit, and an integration control circuit. The mechanical apparatus includes a motor which is configured to drive the mechanical apparatus. The load ratio detection circuit is configured to detect a load ratio of the motor. The integration control circuit is configured to control the mechanical apparatus based on an operation parameter while keeping the load ratio in an allowable load state.

Abstract: The present disclosure relates to a heating, ventilation, and/or air conditioning (HVAC) system including a controller configured to detect a presence of an occupant in a zone of a plurality of zones. The controller is also configured to determine a priority level of the occupant and control operation of the HVAC system to prioritize supply of conditioned air to the zone of the plurality of zones over remaining zones of the plurality of zones based on the priority level of the occupant.

Abstract: A manipulator learning-control apparatus for controlling a manipulating system that includes an interface configured to receive manipulator state signals of the manipulating system and object state signals with respect to an object to be manipulated by the manipulating system in a workspace, wherein the object state signals are detected by at least one object detector, an output interface configured to transmit initial and updated policy programs to the manipulating system, a memory to store computer-executable programs including a data preprocess program, object state history data, manipulator state history data, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) kernel learning program, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) model learning program, an update-policy program and an initial policy program, and a processor, in connection with the memory, configured to transmit the initial policy program to the manipulating system for initiating a learning process that operates th