A design and manufacturing system includes a multi-axis machine tool including a cutting head able to support a plurality of available tools and a part support, the cutting head and part support fully controllable in at least two axes, a design system operable using a computer to generate a 3-D model of a part to be manufactured, and a machine learning model operable using the computer to analyze the part to be manufactured to identify features and develop a manufacturing plan at least partially based on the multi-axis machine tool and the plurality of available tools, the manufacturing plan including a type of tool used for each feature, a feed-rate for each type of tool for each feature, and a speed of the tool for each type of tool for each feature.

A arousal level control apparatus calculates, under a limitation related to a physical quantity that affects an arousal level of a subject, a setting value for controlling the arousal level of the subject by using a physical quantity prediction model for calculating the physical quantity based on a setting value of the physical quantity and an arousal level prediction model for calculating a prediction value of the arousal level based on the calculated physical quantity calculated, and sets the calculated setting value to a control target device that affects the physical quantity.

A system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.

A system and method are provided for operating a power generating asset. Accordingly, at least one external data set indicative of a plurality of variables affecting the performance of the power generating asset is received by the controller. The controller also receives at least one operational data set indicative of the performance of the power generating asset. A plurality of production-assessment models for the power generating asset are generated and trained based on the data sets. A performance prediction is then generated for each of a plurality of model-variable combinations and a control action is implemented based on one of the performance predictions.

A computer-readable medium performs a method for performing a drilling operation in a formation. A plurality of predictive models are determined. Each predictive model of the plurality of predictive models is determined for an interval in the downhole formation, wherein each predictive model of the plurality of predictive models relates one or more drilling parameters of the drilling operation to a plurality of objectives for the drilling operation. A plurality of target objectives is defined. A plurality of outcomes is determined for each of the predictive models of the plurality of predictive models and the plurality of target objectives. An optimization is performed to select an outcome from the plurality of outcomes. The drilling operation is performed using the selected outcome to achieve the plurality of target objectives.

A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.

A method includes: receiving, by a computing device, data from sensors in a manufacturing environment; mapping, by the computing device, the data into a deep learning network; learning, by the computing device, correlations between inputs and outputs of the manufacturing environment using the data; pruning, by the computing device, the deep learning network; predicting, by the computing device and using the pruned network, an output of the pruned network from the inputs of the manufacturing environment; linearizing, by the computing device, the pruned network; optimizing, by the computing device, the output of the linearized pruned network to calculate predicted inputs for the manufacturing environment; and changing, by the computing device, operation inputs in the manufacturing environment to match the predicted inputs.

A disturbance suppression apparatus includes a timing prediction device. The timing prediction device includes a dead time acquisition unit, a disturbance start time acquisition unit, and a timing prediction unit. The dead time acquisition unit performs PID tuning to acquire a dead time. The disturbance start time acquisition unit acquires a disturbance start time representing a period from a time point at which a start signal is input to a time point at which a temperature starts to be affected by a disturbance. The timing prediction unit predicts a disturbance manipulated variable application timing on the basis of the dead time and the disturbance start time.

A method for predicting energy consumption of a vehicle using a statistical model. The method includes (i) predicting a set of future input vectors for the vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously generated at the defined time intervals at a plurality of previous points in time, (ii) predicting a change in the energy level of the vehicle using a processor and the statistical model, and (iii) providing results corresponding to the predicted change in the energy level to an output unit of the vehicle. Each reference input vector comprises a vehicle input vector and a database input vector associated with each point in time of the plurality of previous points in time. The database input vector for each defined time interval may be based on at least one of a plurality of environmental data and information about a road condition.

Devices and methods of allocating distributed energy resources (DERs) to loads connected to a microgrid based on the cost of the DERs are provided. The devices and methods may determine one or more microgrid measurements. The devices and methods may determine one or more real-time electricity prices associated with utility generation sources. The devices and methods may determine one or more forecasts. The devices and methods may determine a cost associated with one or more renewable energy sources within the microgrid. The devices and methods may determine an allocation of the renewable sources to one or more loads in the microgrid.