A converting line includes a rewinding machine for winding web material into a log. For time points during a time period of the log wind cycle, predictive information representative of a log to be wound is generated and stored in a memory of a control of the rewinding machine. An image capture device is enabled to capture a plurality of images of the log being wound in the winding nest at the plurality of corresponding time points during the time period. Log image capture information is generated based upon the plurality of captured images and corresponding time points during the time period. The log image capture information is compared with the generated predictive log image information to determine a difference in log image. Revised predictive log image information is generated based at least in part upon a difference in the log image capture information relative to predictive log image information.

Methods, apparatuses, and systems for predicting radio altimeter failures are provided. An example method may include determining a first plurality of altitude values associated with a first radio altimeter, determining a second plurality of altitude values associated with a second radio altimeter, calculating a first level feature based at least in part on the first plurality of altitude values and the second plurality of altitude values, and determining a radio altimeter failure indicator based at least in part on the first level feature.

An EMS includes a control unit that controls a power generation facility and a power adjustment unit that adjusts an amount of supply of supply power. The control unit includes: a reverse power flow suppression process determination unit; a priority decision unit that decides a priority for adjusting an amount of power generated for the two power generation devices of the power generation facility on the basis of a predicted degree of fluctuation in load power; and a device control unit that controls the power generation facility such that the amount of power generated is adjusted in descending order of the priority decided by the priority decision unit when the reverse power flow suppression process determination unit determines to reduce the amount of power generated by the power generation facility.

An abnormal consumption detection system is provided with remote valve control for a distributed water infrastructure. The system may comprise an electronically controllable valve, a remote communication transmitter, a remote communication receiver, at least one sensor for measuring water flow information associated with the distributed water infrastructure, and at least one processor. The system may determine from the water flow information obtained from the at least one sensor a potential abnormal consumption associated with the distributed water infrastructure. The system may automatically close a valve, without human intervention, when the potential abnormal consumption is determined. The system may transmit, via the remote communication transmitter to a remote administrator, alert information about the potential abnormal consumption to enable an administrator to decide based on the transmitted information whether to reopen the valve. The system may receive from the administrator via the remote communication receiver a control signal to reopen the valve, despite the information about the potential abnormal consumption, and reopen the valve.

A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

A heating, ventilation, and air conditioning (HVAC) control device configured to generate the machine learning model using the first set of weights and the second set of weights. The machine learning model is configured to output a probability that a user is present at the space based on an input that identifies a day of the week and a time of a day. The device is further configured to determine a probability that a user is present at the space for a predicted occupancy schedule using the machine learning model, to determine an occupancy status based on a determined probability that a user is present at the space, and to set a predicted occupancy status in the predicted occupancy schedule based on a determined occupancy status for each time entry. The device is further configured to output the predicted occupancy schedule.

A heat load predictor predicts a change over time of a heat amount to be processed by air-conditioning equipment, an operation plan creator previously creates an operation plan of the air-conditioning equipment to reduce an evaluation index under a first restrictive condition that a total process heat load be maintained at a same level or within a range of a predetermined amount of difference and a room temperature be maintained within a predetermined comfortable temperature range, and an operation plan corrector corrects the operation plan during controlling the air-conditioning equipment based on the operation plan. If an actually measured temperature differs from a predicted temperature predicted by the operation plan creator at the time of creating the operation plan, the operation plan creator predicts a change over time of the temperature in the room, and corrects the operation plan to minimize the evaluation index under the first restrictive condition.

At least an embodiment may provide a remote server that can predict overheating of an engine. A remote server may receives an engine rotational speed, an engine load factor, and a cooling-water temperature. The remote server calculates a moving average of the engine load factor during a predetermine time, and calculates a correlation between the moving average of the engine load factor and the cooling-water temperature. If the correlation during a predetermined period is in the status of a predetermined difference from the correlation up to the predetermined period, the remote server predicts occurrence of overheating in a swing working vehicle that is used by a user.

A numerical controller controls driving of an axis installed in a machine based on an instruction of a program, and when predictive detection of occurrence of interference between a movable part which moves by the driving of the axis and another object is made, the numerical controller decelerates and stops movement of the movable part. Further, the numerical controller changes a display attribute of information display relating to the axis based on an operation state of the axis which is being driven when predictive detection of occurrence of interference is made, and displays a coordinate value according to the display attribute that is changed by the attribute change processing part.

A system for regulating energy provided to an electricity grid from an energy source, the system includes a converter configured to receive the energy from the source. The converter is configured to dynamically predict real-time maximum reactive power capability as a function of at least one from the group including (i) a direct current link maximum voltage, (ii) an instantaneous grid network voltage, and (iii) a line current. The predicted maximum reactive power capability is configured for optimizing regulation of the energy.