Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

Abstract: The present relates to a system comprising a housing, a motor assembly, a printed circuit board (PCB) and a shaft. The motor assembly is located inside the housing. The motor assembly comprises a control for alternatively engaging and disengaging a functionality of the motor assembly. The printed circuit board (PCB) is located inside the housing and defines an aperture for receiving the shaft. The shaft extends through the aperture of the PCB. A first end of the shaft is adapted for actuating the control of the motor assembly. A second end of the shaft extends through an opening in the housing and is adapted for receiving an actuator.

Abstract: A system comprising a housing, a printed circuit board and a differential pressure sensor located inside the housing, and first and second male adaptors. The first and second male adaptors respectively extend through first and second openings in the housing. The first male adaptor comprises a proximal end configured to receive a first pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the first pipe to the differential pressure sensor. The second male adaptor comprises a proximal end configured to receive a second pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the second pipe to the differential pressure sensor. The differential pressure sensor is configured to determine a pressure differential between fluid received via the first male connector and fluid received via the second male connector.

Abstract: The present disclosure relates to an environment control device (ECD) and a method using a wireless communication infrastructure for exchanging environmental data. The wireless communication infrastructure comprises a first Wi-Fi hotspot, and at least one of a second Wi-Fi hotspot and a mesh network. The ECD comprises a communication module for exchanging environmental data with at least another device over the wireless communication infrastructure. The communication module is capable of establishing the first Wi-Fi hotspot, associating with the second Wi-Fi hotspot, and communicating over the mesh network. The ECD further comprises a processing module capable of processing environmental data received from the other device via the wireless communication infrastructure, and/or transmitting generated environmental data to the other device via the wireless communication infrastructure. The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay for wired devices.

Abstract: A system comprising a housing, a differential pressure sensor and first and second male adaptors. The differential pressure sensor is located inside the housing. The first male adaptor extends through a first opening in the housing. The first male adaptor comprises a proximal end configured to receive a first pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the first pipe to the differential pressure sensor. The second male adaptor extends through a second opening in the housing. The second male adaptor comprises a proximal end configured to receive a second pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the second pipe to the differential pressure sensor. The differential pressure sensor is configured to determine a pressure differential between fluid received via the first male connector and fluid received via the second male connector.

Abstract: The present relates to a system comprising a housing, a motor assembly, a printed circuit board (PCB) and a shaft. The motor assembly is located inside the housing. The motor assembly comprises a control for alternatively engaging and disengaging a functionality of the motor assembly. The printed circuit board (PCB) is located inside the housing and defines an aperture for receiving the shaft. The shaft extends through the aperture of the PCB. A first end of the shaft is adapted for actuating the control of the motor assembly. A second end of the shaft extends through an opening in the housing and is adapted for receiving an actuator.

Abstract: The present disclosure relates to an environment control device (ECD) and a method using a wireless communication infrastructure for exchanging environmental data. The wireless communication infrastructure comprises a first Wi-Fi hotspot, and at least one of a second Wi-Fi hotspot and a mesh network. The ECD comprises a communication module for exchanging environmental data with at least another device over the wireless communication infrastructure. The communication module is capable of establishing the first Wi-Fi hotspot, associating with the second Wi-Fi hotspot, and communicating over the mesh network. The ECD further comprises a processing module capable of processing environmental data received from the other device via the wireless communication infrastructure, and/or transmitting generated environmental data to the other device via the wireless communication infrastructure. The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay for wired devices.