Abstract: A projector control system and a control method corresponding to same. The system includes a main controller, a dynamic current adjusting module, a color wheel controller, a color wheel rotation speed feedback module, and a light source; the dynamic current adjusting module includes a DLP module, a light source controller, and a power supply module; the color wheel rotation speed feedback module generates a feedback signal; the DLP module receives the feedback signal, and the DLP module receives dynamic brightness information and sends processed dynamic brightness information and a control signal to the light source controller according to the feedback signal; the light source controller controls the power module according to the received processed dynamic brightness information and the control signal sent by the DLP module to turn on a power switch and output a corresponding current value.

Abstract: A projector control system and a control method corresponding to same. The system includes a main controller, a dynamic current adjusting module, a color wheel controller, a color wheel rotation speed feedback module, and a light source; the dynamic current adjusting module includes a DLP module, a light source controller, and a power supply module; the color wheel rotation speed feedback module generates a feedback signal; the DLP module receives the feedback signal, and the DLP module receives dynamic brightness information and sends processed dynamic brightness information and a control signal to the light source controller according to the feedback signal; the light source controller controls the power module according to the received processed dynamic brightness information and the control signal sent by the DLP module to turn on a power switch and output a corresponding current value.

Abstract: A computer-implemented method for predicting received signal strength in a telecommunication network includes receiving, by one or more processors that execute a convolutional neural network, geographic data representing geographic information of a geographic area and antenna and transmit power information of a base station in the geographic area; inputting the geographic data and the antenna and transmit power information into the convolutional neural network; predicting received signal strength using the convolutional neural network that includes a number of convolution layers based on the received geographic data and the antenna and transmit power information, the received signal strength representing signal strength of wireless signals received at different locations in the geographic area; and outputting the predicted received signal strength.

Abstract: A neural network is trained using deep reinforcement learning (DRL) techniques for adjusting cell parameters of a wireless network by generating a plurality of experience tuples, and updating the neural network based on the generated experience tuples. The trained neural network may be used to select actions to adjust the cell parameters. Each experience tuple includes a cell identifier, a first state, a second state, an action applied to the cell that moves the cell from the first state to the second state, a local reward, and a global reward. The neural network is updated based on whether or not each action is acceptable, which is determined based on the global reward and the local reward associated with each action.

Abstract: A computer-implemented method for predicting received signal strength in a telecommunication network includes receiving, by one or more processors that execute a convolutional neural network, geographic data representing geographic information of a geographic area and antenna and trasnmit power information of a base station in the geographic area; inputing the geographic data and the antenna and trasnmit power information into the convolutional neural network; predicting received signal strength using the convolutional neural network that includes a number of convolution layers based on the received geographic data and the antenna and trasnmit power information, the received signal strength representing signal strength of wireless signals received at different locations in the geographic area; and outputting the predicted received signal strength.

Abstract: A neural network is trained using deep reinforcement learning (DRL) techniques for adjusting cell parameters of a wireless network by generating a plurality of experience tuples, and updating the neural network based on the generated experience tuples. The trained neural network may be used to select actions to adjust the cell parameters. Each experience tuple includes a cell identifier, a first state, a second state, an action applied to the cell that moves the cell from the first state to the second state, a local reward, and a global reward. The neural network is updated based on whether or not each action is acceptable, which is determined based on the global reward and the local reward associated with each action.