Abstract: Method and training server for generating a predictive model for the control of an appliance by an environment controller. The predictive model allows a neural network inference engine to infer output(s) based on inputs. The training server receives room characteristic(s), current environmental characteristic value(s), and set point(s) from the environment controller. The training server determines command(s) for controlling the appliance based on the current environmental characteristic value(s), the set point(s) and the room characteristic(s). Each command is executed by the controlled appliance. The training server receives updated environmental characteristic value(s) and determines a reinforcement signal based on the set point(s), the updated environmental characteristic value(s), and a set of rules.

Abstract: A system for controlling an administration of a vasopressor agent comprising: at least one processing unit: and at least one non-transitory computer-readable memory communicatively coupled to the at least one processing unit and comprising computer-readable program instructions executable by the at least one processing unit for: receiving, by the at least one processing unit, a current arterial pressure of a patient: determining, with the at least one processing unit, based on a profile of the patient, a dose parameter to be adjusted as a function of at least the current arterial pressure and a target arterial pressure: and controlling, with the at least one processing unit, the operation of a pump administering the vasopressor agent as a function of the dose parameter: wherein the receiving, determining and controlling are performed continuously by the at least one processing unit.

Abstract: A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two-dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

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: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: Method and computing device using a neural network to determine whether or not to process images of an image flow. A predictive model of the neural network is generated and stored at a computing device. The computing device receives (b) an image of the image flow and executes (c) the neural network, using the predictive model for generating an indication of whether or not to process the image based on input(s) of the neural network, the input(s) comprising the image. The computing device determines (d) whether or not to process the image by an image processing module, based on the indication of whether or not to process the image. The image is processed by the image processing module if the determination is positive and not processed if the determination is negative. Steps (b), (c), (d) are repeated for consecutive images of the image flow.

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: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: Inference server and environment controller for inferring via a neural network one or more commands for controlling an appliance. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller transmits the at least one environmental characteristic, set point and room characteristic to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands for controlling the appliance. The inference is based on the received at least one environmental characteristic value, at least one set point and at least one room characteristic. The inference server transmits the one or more commands to the environment controller, which forwards the one or more commands to the controlled appliance.

Abstract: Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

Abstract: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

Abstract: Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

Abstract: Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

Abstract: Method and computing device for inferring an optimal wireless data transfer rate using a neural network. The method comprises storing a predictive model generated by a neural network training engine in a memory of a computing device. The method comprises determining, by a processing unit of the computing device, parameters of a data transfer through a wireless communication interface of the computing device. The method comprises executing, by the processing unit, a neural network inference engine using the predictive model for inferring an optimal data transfer rate based on the parameters of the data transfer through the wireless communication interface. The method comprises configuring the wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (ECD). The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay.

Abstract: A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two-dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

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: A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

Abstract: Method and computing device using a neural network to determine whether or not to process images of an image flow. A predictive model of the neural network is generated and stored at a computing device. The computing device receives (b) an image of the image flow and executes (c) the neural network, using the predictive model for generating an indication of whether or not to process the image based on input(s) of the neural network, the input(s) comprising the image. The computing device determines (d) whether or not to process the image by an image processing module, based on the indication of whether or not to process the image. The image is processed by the image processing module if the determination is positive and not processed if the determination is negative. Steps (b), (c), (d) are repeated for consecutive images of the image flow.

Abstract: Method and computing device using a neural network to analyze temperature measurements of an infrared sensor. A predictive model of the neural network is stored by the computing device. The computing device receives a two-dimensional (2D) matrix of temperature measurements generated by the IR sensor, and executes the neural network using the predictive model for generating outputs based on inputs. The inputs comprise the 2D matrix of temperature measurements. The outputs comprise a 2D matrix of inferred temperatures. The computing device determines a subset of values of the 2D matrix of temperature measurements, and applies a comparison algorithm to the subset of values of the 2D matrix of temperature measurements and a corresponding subset of values of the 2D matrix of inferred temperatures, to detect an anomaly in the subset of values of the 2D matrix of temperature measurements. A method for training the neural network is also provided.