The present application describes an integrated module. The integrated module includes a microcontroller, an inertial measurement unit (IMU), a low-power accelerometer, and an environmental sensor. A noise target between the IMU and low-power accelerometer is less than a noise target be the IMU and microcontroller. The present application also describes a method of using an integrated module.

A food safety system for food items in cooled environments includes a temperature sensor unit having a temperature sensor, a power supply and a data transmission element. The temperature sensor unit is positioned in a cooler, wherein the cooler has a plurality of predefined food item positions. A control center unit having a computer processor and a memory is adapted to execute a deterministic mode function to predict the core temperature change of such a food item on a predefined food item position in said cooler. The deterministic mode function depends on heat transfer parameters related to the predefined food item position of the cooler used, food specific coefficients related to the kind of food item taken from a group of food types, the environment temperature measured by the temperature sensor, and the predicted current core temperature of the food item.

The prediction system of strip chew collects and stores first data and second data as adaptive model construction data. The first data indicates the occurrence or non-occurrence of the strip chew in an object rolling path and the occurrence point of the strip chew. The second data includes information on a preceding rolling path and attribute information on an object strip. The system constructs an adaptive model using the stored adaptive model construction data, and stores the constructed adaptive model as an adapted model. The system collects prediction data similar to the second data. Then, the system inputs the prediction data to the adapted model to predict the occurrence or non-occurrence of the strip chew in the object rolling path and all or some of the occurrence points of the strip chew before the prediction object strip reaches the object rolling path.

Model predictive control is used to obtain the best performance value for an objective in a dynamic environment. One or more best performance values for a model predictive application are obtained by utilizing asymmetric dynamic behavior that pushes the process to the edges of the operative window. When a setpoint becomes infeasible, a controller slows down when moving away from a specified setpoint. When the infeasibility clears and the controller starts moving back towards the setpoint, the controller follows a tuned speed. When the controller moves in a direction against economic profit, the controller slows down and when moving in the direction of economic profit, the controller follows the tuned dynamic speed. The controller computes the best performance value for each controlled variable and is equal to the setpoint for the controlled variables with a setpoint or the highest profit value between limits for controlled values affected by economic functions.

A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.

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 method for generating a dynamic model of an industrial plant having: a plurality of physical processes that are dependent such that an outcome of at least one first process is fed into at least one second process; a plurality of low-level controllers, each controller acting upon at least one physical process such that at least one process variable of the at least one physical process is controlled to match a set-point of the low-level controller; and a plurality of sensors, each sensor measuring at least one process variable of one of the physical processes, and/or of the plant as a whole, the set-points of the low-level controllers and current values of the process variables measured by the sensors being the inputs of the model, and predicted future values of the process variables that are likely to result from applying the set-points to the low-level controllers being the outputs.

A computer implemented method for improving a similarity search of an industrial component model including obtaining a set of industrial component models, each having associated attributes and a similarity embedding, receiving a similarity request using a given industrial component model as an input, the output of said similarity request being a first subset of industrial component models selected from the set of industrial component models based on the comparison between similarity embeddings and the similarity embedding of the input industrial component model, receiving a second subset of industrial component models from said first subset of industrial component models based on an interchangeability criteria of the input industrial component model with any industrial component model of said second subset of industrial component models, associating a similarity attribute to the input industrial component model, and computing a new set of similarity embeddings.

A method for automatically reducing the dimensionality of a mathematical representation of a controlled application system is provided. The method includes receiving, at a control system, data corresponding to control action and system state variables relating to the controlled application system, fitting a constrained reinforcement learning (CRL) model to the controlled application system based on the data, and automatically identifying a subset of the system state variables by selecting control action variables of interest and identifying system state variables that drive the CRL model to recommend each control action variable of interest. The method also includes automatically performing state space dimensionality reduction of the CRL model using the subset of system state variables, estimating a transition probability matrix for a constrained Markov decision process (CMDP) model of the controlled application system, and formulating the CMDP model as a linear programming (LP) problem using the transition probability matrix and several costs.

An information processing apparatus according to the present invention includes: a prediction value calculation unit configured to, based on a model calculating an objective variable representing a state of a target by using a plurality of explanatory variables, for each of the explanatory variables, calculate a prediction value of the explanatory variable that changes based on a value of the explanatory variable at a predetermined moment; and a change quantity calculation unit configured to, for each of the explanatory variables, calculate a quantity of change of the objective variable with change of the explanatory variable by using the prediction value of the explanatory variable and the model.