A method and apparatus for controlling a process in a system comprising pre-processing of a raw material, processing the pre-processed raw material and acquisition of the result of the processing of the pre-processed raw material, comprising the steps of: capturing input and output variables of the pre-processing; capturing output variables of the processing of the pre-processed raw material; creating a first, second and third process model for at least two different time scales, which describes the effects of adapting the pre-processing of raw material, the effects of adapting the processing of the pre-processed raw material, the effects of adapting the pre-processing of raw material and adapting the processing of pre-processed raw material on the output variables of the processing of pre-processed raw material; wherein the process in the system is controlled using the prediction of the process model which currently provides the best predictions for the process in the system.

Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.

Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.

A ventilation system for a building in some cases includes a main HVAC blower for moving temperature-conditioned air through the building plus a smaller ventilation blower for providing fresh air. A controller regulates the ventilation blower's speed to provide a target ventilation flow rate regardless of changes in the pressure differential between the indoor and outdoor air. To ensure that the target rate is appropriate for a particular building, the target flow rate is determined based on a ventilation setting that reflects a specified number of bedrooms and a specified amount of floor space of the building.

Provided herein is a method for automated control of the gas turbine fuel composition through automated modification of the ratio of fuel gas from multiple sources. The method includes providing first and second fuel sources. The method further includes sensing the operational parameters of a turbine and determining whether the operational parameters are within preset operational limits. The method also adjusting the ration of the first fuel source to the second fuel source, based on whether the operational parameters are within the preset operational limits.

Provided herein is a method for automated control of the gas turbine fuel composition through automated modification of the ratio of fuel gas from multiple sources. The method includes providing first and second fuel sources. The method further includes sensing the operational parameters of a turbine and determining whether the operational parameters are within preset operational limits. The method also adjusting the ration of the first fuel source to the second fuel source, based on whether the operational parameters are within the preset operational limits.

A blast plan control system and method used to control a drill and blast event is disclosed. The system and method customizes results for specific conditions. The system can receive certain inputs, such as conditions of the area to be blasted and the desired rock fragment size, and use these inputs to output a plurality of blast plans characterized by a set of characteristics that achieve the desired fragmentation size. A user can select a blast plan for execution from the plurality of blast plans. When the control system receives a selected blast plan, the control system can generate a work order for the selected blast plan and communicate the work order to operators and/or drilling equipment associated with execution of the drill and blast event. The operators and/or drilling equipment can then prepare for and execute the selected blast plan.

Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

An infrared imaging device includes a plurality of electronic components, a phase change material, and a heat transfer structure. The plurality of electronic components is configured to collect data and have a predetermined temperature parameter. The plurality of electronic components is disposed within the phase change material. The phase change material has a first material phase and a second material phase. The phase change material has a first material phase and a second material phase. The phase change material is configured to absorb heat through changing from the first material phase to the second material phase. The heat transfer structure is disposed within the phase change material. The heat transfer structure is configured to conduct heat within the phase change material. The phase change material and the heat transfer structure are further configured to regulate a temperature of the electronic components below the predetermined temperature parameter.

A cooling system for a datacenter is disclosed. The datacenter cooling system includes a refrigerant cooling loop to extract heat from a secondary cooling loop that is located within the datacenter or to provide supplemental cooling to one or more components of the datacenter that are coupled to the secondary cooling loop.