Systems and methods are provided for interfacing multiple layers of optimization for a model of one or more processes in a processing environment to achieve increased or maximized stability in the underlying layer. To improve consistency between the solutions at the different model levels, the lower level of optimization can have extra constraints added to the optimization problem which target variables at their unconstrained values in the upper layer of optimization. The systems and methods can facilitate selection of variables to receive an external target such that stability of the solution is improved or maximized. This can be achieved, at least in part, by identifying variables that provide a reduced or minimized condition number for a sub-matrix in the lower level model when an additional external constraint is applied. The sub-matrix with the reduced or minimized condition number can correspond to a partitioned portion of the gain matrix that corresponds to unconstrained independent variables and constrained dependent variables.

A control system estimates a numerical value range within which a command value can fall from a distribution of second data relating to the command value in a learning data set used to construct a prediction model, and in such a manner that a first acceptable range prescribed by a preset first threshold value with respect to a command value for a subject device is extended, decides a second threshold value with respect to the command value for the subject device on the basis of the estimated numerical value range. Further, in an operational phase, on the basis of an output value from the prediction model, the control system decides a command value for the subject device within a second acceptable range prescribed by the decided second threshold value, and controls an operation of the subject device on the basis of the decided command value.

Implementations described herein generally relate to improving silicon wafer manufacturing. In one implementation, a method includes receiving information describing a defect. The method further includes identifying a critical area of a silicon wafer and determining the probability of the defect occurring in the critical area. The method further includes determining, based on the probability, the likelihood of an open or a short occurring as a result of the defect occurring in the critical area. The method further includes providing, based on the likelihood, predictive information to a manufacturing system. In some embodiments, corrective action may be taken based on the predictive information in order to improve silicon wafer manufacturing.

A learning model generation support apparatus 10 is an apparatus for supporting generation of a learning model to be utilized in odor detection using an odor sensor that reacts to a plurality of types of odors. The learning model generation support apparatus 10 includes a data acquisition unit 11 that acquires sensor data output by the odor sensor under specific measurement conditions and condition data specifying the measurement conditions, and inputs, as training data, the acquired sensor data and condition data to a machine learning engine 31 that generates the learning model, and a condition setting unit 12 that acquires a predictive accuracy output by the machine learning engine in response to input of the training data, and sets new measurement conditions for when the odor sensor newly outputs sensor data as training data, based on the acquired predictive accuracy.

A dense array of sensors positioned in a virtual environment is reduced to a sparse array of sensors in a physical environment, which provides sufficient information to a controller that responds to environmental conditions and parameters in the physical environment in substantially the same manner as it would to the same environmental conditions and parameters in the equivalent virtual environment. Data from a sparse array of virtual sensors is correlated with data from a dense array of virtual sensors and is used for generating control signals for hardware devices that influence a real or virtual interior environment. The correlated data and the control signals are used to train an artificial intelligence based controller that then controls the values of the parameters of the interior environment. A model of the interior environment is created using basic parameters in a computer-aided design application.

The present disclosure provides a method and an apparatus for adjusting a process control prediction model, and a process controller. In an embodiment, the method includes: determining, based on controlled variable data in process control data obtained through real-time monitoring, whether a prediction performance of the process control prediction model is lower than a reference performance; and when the prediction performance is determined to be lower than the reference performance, using manipulated variable data in the process control data monitored to adjust the process control prediction model. By way of the method, a re-test does not need to be executed to re-identify a model so as to eliminate a mismatch of the process control prediction model, thereby eliminating an influence of fluctuation introduced by addition of an excitation signal during the re-testing.

This disclosure describes techniques for identifying and mitigating a voltage loss in a power transmission to a Remote Radio Unit (RRU) associated with Radio Frequency (RF) antennas of a telecommunications network. More particularly, a Supplemental Voltage (SV) controller is described that is configured to monitor and detect a change in voltage that occurs during a power transmission from a primary Direct Current (DC) power source to the RRU and selectively cause a supplemental DC power source to transmit a supplemental voltage to the RF antennas. The SV controller may cause a supplemental DC power source to transmit a supplemental voltage to the RRU based on an empirical data analysis, sensory data analysis, or current environmental metadata. Further, the SV controller may determine whether the primary DC power source has suddenly ceased transmitting power to the RRU, and in doing so, cease transmission of a supplemental voltage to the RRU.

An air conditioning control device includes: an acquisition unit that acquires air conditioning data acquired by an air conditioner, and a start time of the air conditioner predicted by inputting the air conditioning data into a machine learning model; an augmentation unit that generates augmented data by referring to the air conditioning data and the start time acquired by the acquisition unit; and an update unit that updates the machine learning model, by referring to the air conditioning data and the start time acquired by the acquisition unit as well as the augmented data generated by the augmentation unit.

A monitoring apparatus for monitoring a raw material tank monitors the temperature of the raw material tank when the temperature of the raw material tank storing a solid or liquid raw material is raised to a set temperature by a heating unit. The monitoring apparatus includes: a temperature determination unit configured to determine whether the temperature has reached a stable range including the set temperature, and determine whether the temperature has deviated from the stable range; and a setting unit configured to set the set temperature of the heating unit to 0° C. when a predetermined timeout time has elapsed from a time point at which the temperature determination unit determined that the temperature deviated from the stable range.

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.