According to one embodiment, a production management device acquires a first short-term plan generated based on a first long-term plan. The first long-term plan is of a plan of production in a prescribed period. The first short-term plan is of a plan of production in a first period. The device acquires first progress data of a progress in a task, and acquires first prediction data by using the first progress data. The first prediction data is of a prediction of a progress in the task. The device revises the first short-term plan based on the first prediction data, and acquires second prediction data by using second progress data. The second progress data is of a progress in the task. The second prediction data is of a prediction of a progress of the production in the prescribed period. The device generates a second long-term plan in the prescribed period.

A device for controlling an operation of a system performing a task is disclosed. The device submits a sequence of control inputs to the system thereby changing states of the system according to the task and receives a feedback signal. The device determines a current control input for controlling the system based on the feedback signal including a current measurement of a current state of the system by solving a polynomial optimization of a polynomial function with a reformulation derived by introducing additional variables reducing a degree of the polynomial function till a target degree subject to constraints on a structure of the additional variables. The device solves a mixed-integer optimization problem to find an optimal solution among all possible encodings of factorizations of the polynomial function that reduces the degree of the polynomial function till the target degree with a minimum number of additional variables.

A method of determining productive capacity parameters includes steps of: obtaining a plurality of parameters of a production line from a memory. By a productive capacity parameters generating system finishing the following steps of: combining parameters of production line to obtain a plurality of parametric combinations so as to generate a plurality of production capacity values; calculating a plurality of stimulation values according to production capacity values and parameters; when at least one stimulation value of stimulation values is greater than to or equals to a preset threshold, setting up at least one stimulation value of stimulation values as at least one related stimulation value; obtaining parameters of at least one target parametric combination of parametric combinations according to at least one related stimulation value; and providing parameters of at least one target parametric combination as productive capacity parameters of production line.

The subject disclosure relates to features that improve safety for autonomous vehicle (AV) driving maneuvers. In some aspects, a process of the disclosed technology includes steps for detecting an unprotected maneuver, navigating an AV into an intersection, and detecting a wheel safety angle relative to the intersection. In some aspects, the process further includes steps for automatically adjusting a wheel angle of the AV based on the wheel safety angle. Systems and machine-readable media are also provided.

A control system for controlling an operation of a machine subject to constraints including equality and inequality constraints on state and control variables of the system iteratively solves an optimal control structured optimization problem (OCP), such that each iteration outputs primal variables and dual variables with respect to the equality constraints and dual variables and slack variables with respect to the inequality constraints. For a current iteration, the system classifies each of the inequality constraints as an active, an inactive or an undecided constraint based on a ratio of a slack variable to a dual variable of the corresponding inequality constraint determined by a previous iteration, finds an approximate solution to the set of relaxed optimality conditions subject to the equality constraints and the active and undecided inequality constraints, and update the primal, dual, and slack variables for each of the equality and inequality constraint.

A simulator models an energy and power system. The simulator allows a user to manipulate digital representations of nodes, which represent one or more components of power and energy assets. The simulator also allows a user to manipulate digital representations of edges, which connect the nodes, to form a power and energy network. A plurality of object classes correspond to the nodes and edges. The object classes comprise class inheritance structures so that constraints of a parent class are retained by one or more child classes. The interface on the simulator allows a user to model a power and energy system by allowing the user to connect the nodes with edges to construct a power and energy system, wherein the object classes for the nodes, when executed by the simulator, implement one or more dynamical models to model the power and energy assets.

Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.

A system for controlling building equipment determines a degradation factor for a first asset of the building equipment by comparing a design curve for the first asset and operational data for the first asset. The design curve includes a plurality of data points that define an operation of the first asset. The system generates an operational curve for the first asset by derating the design curve based on the degradation factor and operates the building equipment based on the operational curve.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for shaping compute load using virtual capacity. In one aspect, a method includes obtaining a load forecast that indicates forecasted future compute load for a cell, obtaining a power model that models a relationship between power usage and computational usage for the cell, obtaining a carbon intensity forecast that indicates a forecast of carbon intensity for a geographic area where the cell is located, determining a virtual capacity for the cell based on the load forecast, the power model, and the carbon intensity forecast, and providing the virtual capacity for the cell to the cell.

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.