Provided is an apparatus configured to support resin molding, including: a prediction unit configured to generate a probability distribution of prediction values of analysis target characteristics of a resin molded body, that correspond to values of a plurality of molding factors of the resin molding; and a display processing unit configured to execute display processing for causing a display apparatus to display the probability distribution of the prediction values of the analysis target characteristics. The prediction unit is configured to calculate a change of the distribution of the prediction values of the analysis target characteristics when a value of at least one of the plurality of molding factors of the resin molding is changed within a predetermined range, and the display processing unit is configured to display the change of the probability distribution of the prediction values of the analysis target characteristics.

A building management system for monitoring and controlling HVAC parameters in a building includes one or more processing circuits configured to initialize a predictive model for predicting temperature, pressure, and humidity within a target area and an adjacent area of the building, receive target area data from a target area sensor array configured to measure temperature, pressure, and humidity of the target area, receive adjacent area data from an adjacent area sensor array configured to measure temperature, pressure, and humidity of the adjacent area, execute the predictive model based on the target area data and the adjacent area data to generate a prediction of future temperature, pressure, and humidity within the target area, and control operation of HVAC equipment to maintain the temperature, pressure, and humidity of the target area within a compliance standard.

A cascaded MPC system includes an upper tier controller and lower tier controller having stored constraints for controlling a process having manipulated variables (MVs), controlled variables (CVs), and conjoint manipulated variable (CMV). The upper tier passes a target value for the CMV to the lower tier which optimizes for determining a local optimal operating point for the MVs, CVs, and CMV, moves towards the target value starting at the CMVs local operating point, and optimizes for identifying of the constraints as selected constraint(s) when the moving is truncated, passes the selected constraint(s) to the upper tier which performs an overall optimization for the process using the selected constraint to generate an optimal value for the CMV that lower tier uses as a new CM V target value for redetermining updated local optimal operating points for the MVs, CVs, and CMV, and for controlling the process utilizing the updated operating points.

A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.

An example dehumidifier system may include one or more of a processor, an input display unit, a temperature and humidity censor configured to acquire current temperature and relative humidity data, an analog-to-digital transducer configured to convert, the acquired current temperature and relative humidity data from analog to digital output, a psychrometric converter module executed by the processor to convert the relative humidity data into ratio/absolute humidity data, a digital-to-analog transducer configured to convert the ratio/absolute humidity data into an analog format, a hysteresis comparator unit configured to compare hysteresis setpoint data received from the input display unit with data received from the temperature and humidity censor, and a ratio/absolute humidity setpoint comparator unit configured to compare a ratio/absolute humidity setpoint data received from the input display unit with the converted ratio/absolute humidity data.

Improvements in computer-based energy asset management technologies are provided. An energy asset management system with a data summarization mechanism can perform computations, for example relating to controlling the assets, which may include electric vehicles (EVs), with fewer computing resources. Further, the system can perform computations on large datasets where such computations would have otherwise been impractical with conventional systems due to the size of the data. A large dataset relating to the energy asset management system is reduced using the summarization mechanism, and a computation model is trained using the reduced dataset. Energy assets in the system may be controlled using the trained computational model. Assets may include EVs, and controlling the EVs may be based on generated predictions relating to charging interactions. The predictions may be based on road traffic information and/or weather related information. Further, the computational model may include an optimizer for scheduling charging interactions of EVs.

Systems, devices, media, and methods are presented for controlling remote equipment in a network. A switch heater control system includes a weather modeling function. The system periodically obtains weather data according to a predetermined time interval. Based on the closest weather data set, the weather modeling function generates a hyperlocal forecast associated with each switch heater location. The system includes an active snowfall mode and a maintenance mode that controls heating based on an estimate of local snow depth, adjusted for wind conditions and passing trains. When the hyperlocal forecast indicates heating is required, the system calculates a melt duration, starts a timer, and transmits a start signal to the switch heater.

A priori geo-referenced data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

Example wireless feedback control systems disclosed herein include a receiver to receive a first measurement of a target system via a first wireless link. Disclosed example systems also include a neural network to predict a value of a state of the target system at a future time relative to a prior time associated with the first measurement, the neural network to predict the value of the state of the target system based on the first measurement and a prior sequence of values of a control signal previously generated to control the target system during a time interval between the prior time and the future time, and the neural network to output the predicted value of the state of the target system to a controller. Disclosed example systems further include a transmitter to transmit a new value of the control signal to the target system via a second wireless link.

An information processing apparatus includes an n-th parameter adjuster and an (n+1)-th parameter adjuster. The n-th parameter adjuster adjusts an n-th parameter set so that an n-th evaluation value set based on the n-th parameter set approaches an n-th target value set. The (n+1)-th parameter adjuster adjusts an (n+1)-th parameter set so that an (n+1)-th evaluation value set based on the (n+1)-th parameter set approaches an (n+1)-th target value set. In addition, the n-th parameter adjuster acquires, based on initial value set or search value set of the n-th parameter set, an n-th actual measured value set or an n-th predicted value set, acquires an (n+1)-th target value set based on the initial value set or the search value set of the n-th parameter set, and searches for the n-th parameter set that optimizes the (n+1)-th target value set under a restriction that the n-th evaluation value set approaches the n-th target value set using the acquired n-th actual measured value set or the n-th predicted value set and the acquired (n+1)-th target value set.