There are provided an air conditioner automatic control method, an air conditioner automatic control apparatus, a recording medium, and an air conditioner. The air conditioner automatic control method receives occupancy detection data indicating whether a user occupies a room, receives temperature data and humidity data indicating indoor temperature and humidity, predicts future occupancy probability information of the user by using the occupancy detection data, derives an optimal PMV by using the temperature data and the humidity data, and calculates an optimal temperature by using the optimal PMV, and controls the air conditioner based on the occupancy detection data, the future occupancy probability, and the optimal temperature. Accordingly, the air conditioner can be driven at the optimal temperature suitable for the user.

New and/or alternative approaches to engine performance control that can account for the need to robustly monitor performance and/or operation of the physical plant and actuators thereof, while avoiding or limiting performance degradation. Model predictive control (MPC) or other control configuration such as proportional-integral-derivative control may be used to control the system by identifying a performance optimized control solution. In some examples, a modification to the performance optimized solution analysis is made to weight control solutions in favor of robust monitoring conditions. In other examples, the performance optimized solution is post-processed and modified to favor robust monitoring conditions.

According to one embodiment, an information processing apparatus includes: a processor configured to select a first case based on subject data including at least one feature, and acquire a first prediction value that is a value of an objective variable included in the first case; a first estimator configured to estimate frequency data indicating frequencies of observation values of the objective variable, based on a history of observation values of the objective variable; a second estimator configured to estimate first frequency data indicating frequencies of first prediction values, based on a history of first prediction values acquired before the first prediction value is acquired; and a corrector configured to correct the first prediction value acquired by the processor, based on the frequency data and the first frequency data.

Various embodiments of the present disclosure relate to proactive dosing optimization chemical feed units producing an output solution (such as an oxidizing biocide) therefrom. Online sensors generate signals corresponding to directly measured variables for respective process components. Information is selectively retrieved from models relating combinations of input variables to respective industrial process states, wherein various current process states may be indirectly determined based on directly measured variables for respective system components. An output feedback signal is automatically generated corresponding to a detected intervention event based on the indirectly determined process state. A controller may receive the signal and implement, e.g., regulation of oxidizing biocide feed for optimization of end products and/or performance metrics.

A system and method are presented for optimizing choices of control parameters. A method includes collecting demand sequences each associated with a resource managed by a technology platform; processing a demand sequence for a resource to calculate an optimized control parameter (CP) value set to manage an automated process within the technology platform, wherein calculating includes: processing the demand sequence with an advanced bootstrap process to generate a collection of bootstrapped demand sequences; processing the bootstrapped demand sequences with a performance prediction process that models the automated process to predict a performance metric for an initially selected CP value set; identifying a neighborhood of CP value sets that includes neighbors and the initially selected CP value set; predicting the performance metric for each neighbor with the performance prediction process; and identifying from the neighborhood of CP value sets the optimized CP value set that provides a best performance metric.

Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.

Smart mattress topper system and method that uses a smart mattress topper connected to the Internet (IoT), which includes a data processing architecture to collect, classify, storage and analyze the data gathered by the topper, other external devices and the user interface, where the smart mattress topper system, through an Artificial Intelligence (AI) module, and using two main calculated indicators, specifically, the sleep quality and the aggregated recovery, and, when available, other secondary inputs, makes recommendations and when possible, acts over the own topper and other connected external devices in order to continuously improve user sleep quality and recovery level to face in the best optimal state, each day challenges.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

Methods and systems are disclosed for predicting an outcome associated with a driver of a vehicle using a machine learning statistical model. The disclosed techniques include obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Each variable represents data captured from a sensor or a data source. A training dataset for the machine learning model is created by capturing the values of outcome of interest for various values of each input vector for each point in time. The outcome of interest is the predicted by utilizing the machine learning model. In various embodiments, the predicted outcome of interest may be a risk or an energy consumption level associated with the driver.