An electronic apparatus and a control method thereof are provided. The electronic apparatus may include an interface; and a processor configured to obtain, via the interface, information related to values, which occur in time series, of a plurality of factors regarding a prediction object, identify, based on the information related to the values of the plurality of factors, at least one factor, from among the plurality of factors, having a time series change of values that corresponds to a time series change of reference values of the prediction object, and output information related to a predicted value of the prediction object based on the time series change of the values of the at least one factor.

In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.

The present disclosure discloses a estimation methods of actuator faults based on bayesian learning, and belongs to the technical field of system detection. According to the present disclosure, an actuator fault is modeled based on a random walking model, and a joint posterior probability distribution of a system state variable and the actuator fault is represented using two mutually independent hypothesis distributions based on a variational Bayesian theory; a system state variable and an actuator fault of a system at a moment k are predicted at a moment k−1; and a predicted system state variable and a predicted actuator fault are iteratively updated at the moment k according to the Bayesian theory to output an estimated value of the system state variable at the moment k as well as a variance of the estimated value and an estimated value of the actuator fault at the moment k as well as a variance of the estimated value. In the present disclosure, by fully using a structure that Bayesian learning is applied to online estimation and decoupling the system state variable of mutually coupled variables and the actuator fault, an actuator fault estimation method for a random system is provided, and can estimate an actuator fault of the random system.

A system includes a plurality of thermostats corresponding to a plurality of HVAC systems that serve a plurality of spaces and a computing system communicable with the plurality of thermostats via a network. The computing system is configured to, for each space of the plurality of spaces, obtain a set of training data relating to thermal behavior of the space, identify a model of thermal behavior of the space based on the set of training data, perform a model predictive control process using the model of thermal behavior of the space to obtain a temperature setpoint for the space, and provide the temperature setpoint to the thermostat corresponding to the HVAC system serving the space. The plurality of thermostats are configured to control the plurality of HVAC systems in accordance with the temperature setpoints.

The present invention discloses an air separation control system and control method comprising: multiple air separation plants for air separation; multiple local controllers, respectively corresponding to the multiple air separation plants; each local controller being located locally at the air separation plant corresponding thereto, and being in communicative connection with the corresponding air separation plant, for the purpose of locally controlling the air separation plant; and a remote optimization controller, in communicative connection with each local controller separately; the remote optimization controller at least comprising a communication module, a prediction module and a control module; the remote optimization controller being able to perform data exchange with and predictive control of the multiple air separation plants simultaneously via the local controllers. The present invention controls multiple air separation plants located at different locations via a remote optimization controller, thus reducing the professional competence requirements placed on operators; during optimization, the relationship between multiple air separation plants can be considered as a whole, so that the air separation plants operate smoothly.

An occupancy tracking device configured to receive sound samples, to identify voices within the sound samples, and to determine a first occupancy level based on the identified voices. The device is further configured to identify user devices connected to an access point and to determine a second occupancy level based on the user devices that are connected to the access point. The device is further configured to measure a signal strength of a network connection with the access point and to determine a third occupancy level based on the signal strength of the network connection with the access point. The device is further configured to determine a predicted occupancy level based on the first occupancy level, the second occupancy level, and the third occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

A method for controlling a plant includes using a neural network modeling technique to calculate a neural network prediction based on plant input data, using a second modeling technique to calculate a second prediction based on the plant input data, and determining whether to use (1) the neural network prediction without the second prediction, (2) the second prediction without the neural network prediction, or (3) both the neural network prediction and the second prediction by comparing a location of the plant input data in a multi-dimensional modeling space to one or more thresholds. The method includes generating a combined prediction using one or both of the neural network prediction and the second prediction in accordance with a result of the determining and controlling the plant using the combined prediction.

A cyber-physical system type machining system includes: a machine tool disposed in a real world and including a machine body and a control device; and a computer device connected to communicate with the control device and including a processor and a memory storing a program for generating, in a virtual world, a virtual machining phenomenon corresponding to an actual machining phenomenon with regard to a workpiece and the machine body. The program, when executed by the processor, causes the computer device to perform: acquiring a command value in synchronization with the control device, the command value for controlling the machine body by the control device; generating a future virtual machining phenomenon, which is the virtual machining phenomenon in a future, based on the command value; and outputting, to the control device, an optimal command value for correcting the command value based on the future virtual machining phenomenon.

In order to provide a method for predicting process deviations in an industrial-method plant, for example a painting plant, by means of which process deviations are predictable simply and reliably, it is proposed according to the invention that the method should comprise the following: automatic generation of a prediction model; prediction of process deviations during operation of the industrial-method plant, using the prediction model.

In a cooperative optimal control system, firstly, two-level models are established to capture the dynamic features of different time-scale performance indices. Secondly, a data-driven assisted model based cooperative optimization algorithm is developed to optimize the two-level models, so that the optimal set-points of dissolved oxygen and nitrate nitrogen can be acquired. Thirdly, a predictive control strategy is designed to trace the obtained optimal set-points of dissolved oxygen and nitrate nitrogen. This proposed cooperative optimal control system can effectively deal with the difficulties of formulating the dynamic features and acquiring the optimal set-points.