An electronic device is provided. The electronic device includes a switch configured to select a mode of the electronic device, a multiply and accumulate (MAC) array configured to include a plurality of MAC units, and at least one processor configured to include a zero weight skip unit for confirming a first weight having a value of ‘0’ among weights related with input data, and for forwarding a second weight not having a value of ‘0’ among the weights, to the MAC array. The at least one processor is configured to acquire the input data, acquire the weights, select the mode of the electronic device by using the switch, in response to a first mode of the electronic device being selected, perform convolution operations between the input data and the second weight forwarded to the MAC array through the zero weight skip unit, and, in response to a second mode of the electronic device being selected, perform convolution operations between the input data and the weights forwarded to the MAC array.

Training and/or using a recurrent neural network model for visual servoing of an end effector of a robot. In visual servoing, the model can be utilized to generate, at each of a plurality of time steps, an action prediction that represents a prediction of how the end effector should be moved to cause the end effector to move toward a target object. The model can be viewpoint invariant in that it can be utilized across a variety of robots having vision components at a variety of viewpoints and/or can be utilized for a single robot even when a viewpoint, of a vision component of the robot, is drastically altered. Moreover, the model can be trained based on a large quantity of simulated data that is based on simulator(s) performing simulated episode(s) in view of the model. One or more portions of the model can be further trained based on a relatively smaller quantity of real training data.

A method of controlling a vehicle or robot. The method includes the following steps: determining a first control sequence, determining a second control sequence for controlling the vehicle or robot depending on the first control sequence, a current state of the vehicle or robot, and on a model characterizing a dynamic behavior of the vehicle or robot, controlling the vehicle or robot depending on the second control sequence, wherein the determining of the first control sequence is performed depending on a first candidate control sequence and a second candidate control sequence.

A machine learning method includes: a first learning step which is performed in a phase before a neural network is installed in a mobile robot and in which a stationary first obstacle is placed in a set space and the first obstacle is placed at different positions using simulation so that the neural network repeatedly learns a path from a starting point to the destination which avoids the first obstacle; and a second learning step which is performed in a phase after the neural network is installed in the mobile robot and in which, when the mobile robot recognizes a second obstacle that operates around the mobile robot in a space where the mobile robot moves, the neural network repeatedly learns a path to the destination which avoids the second obstacle every time the mobile robot recognizes the second obstacle.

A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

An information handling system includes a chassis having multiples sleds and an embedded controller. The embedded controller retrieves relative impedances for all of the sleds, and calculates a maximum available airflow for the first sled based the relative impedances of all other sleds. A baseboard management controller (BMC) of a first sled requests a boot operation for the first sled. The BMC collects configuration information for the first sled, and determines an airflow impedance of the first sled based on the configuration information. The BMC provides the airflow impedance and a power allocation request to the embedded controller. The BMC compares the maximum available airflow to a minimum airflow requirement for the first sled. If the maximum available airflow is less than the minimum airflow requirement, the BMC implements power limits for processors in the first sled to prevent overheating of components within the first sled.

The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

In various aspects, a first set of attributes associated with a target location are determined. The target location is a location that one or more items are to be delivered to by an unmanned aerial vehicle (UAV). The first set of attributes are compared with a second set of attributes. The second set of attributes indicate attributes of each UAV of a plurality of UAVs. Based on the comparing, a UAV, of the plurality of UAVs, is recommended to deliver the one or more items to the target location.

A food class of a food item is recognized. A target doneness score is defined for the food item based on the food class and a desired doneness level. A recurrent model is utilized to determine a current doneness score of the food item according to sensor data captured of the food item. The current doneness score and the target doneness score are utilized to control a heating system to cook the food item.

A system for sorting seeds based on their resistance to a stress is disclosed. Batches of purified seeds sorted using the system are also disclosed.