Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.

Performance optimization of power plants is one of the major challenges. Several machine learning based techniques are available which are used for optimization of the power plants. Coal selection and blending is critical to ensuring optimum operation of thermal power plants. The present disclosure provides a system and method for optimum coal selection for the power plant and power plant optimization. The system mainly comprises two components. First, a coal usage advisory module providing coal usage and blending ratio advice to the operators based on the available coal. The optimization is with respect to the entire power plant operation including its components. And second, a performance optimization advisory module provides operation instruction for boiler, SCR, APH and other power plant equipment based on the implemented coal blend in real-time.

A state prediction system includes a controller. The controller is configured to acquire a current state value indicating a current state of a targeted individual in a group. The controller is configured to acquire a state propagation amount indicating a state amount propagated from another person in the group to the targeted individual by communication between the targeted individual and the other person. The controller is configured to predict a future state value indicating a future state of the targeted individual from the acquired current state value of the targeted individual and the acquired state propagation amount.

Systems and methods for control optimization of building subsystems are disclosed. In some embodiments, a system comprises at least one processor; and memory storing instructions executable by the at least one processor, the instructions when executed cause the system to: obtain building load information, the building load information related to one or more power consuming subsystems of the building; determine a future building load based on the building load information; and adjust one or more setpoint tolerances for the one or more subsystems based on the determined future building load.

Systems and methods described herein can involve executing a functional generator configured to generate multivariate continuous sensor curves from training with arbitrary multivariate sensor data with irregular timestamps received from one or more apparatuses; executing a functional discriminator to discriminate the generated multivariate continuous sensor curve from the arbitrary multivariate sensor data; and for the functional discriminator discriminating the generated multivariate continuous sensor curve from the arbitrary multivariate sensor data with irregular timestamps, providing feedback to the functional generator to retrain the functional generator.

A cooling control system and method for a motor vehicle comprising: a server unit and N client units, wherein N is greater than or equal to 1, the server unit being in data connection with the N client units via a wireless network, the N client units configured to be arranged on N motor vehicles respectively, each client unit configured to perform real-time collection and storage of calculation input data on the corresponding motor vehicle for evaluating a temperature of a unit requiring cooling on the motor vehicle, perform real-time collection and storage of temperature data of the unit requiring cooling, predict, using the collected calculation input data, temperature data at a future time of the unit requiring cooling based on a predictive mathematical model determined by the server unit (200), and enable the selective cooling in advance of the unit requiring cooling based on the predicted temperature data.

A predictive control method and system are provided for controlling a device or system. The control method and system involves receiving a setpoint signal as input; and performing closed loop control of the device or system by outputting a control signal according to the setpoint signal and a response model of the device or system using a predictive control algorithm. The predictive control algorithm is configured to implement control according to a polynomial representation for regulation, sensitivity and tracking and further implement non-linearity or time delay compensation using the response model. The closed loop control is tunable using an adjustable single parameter for accelerating or decelerating the closed loop control relative to an open loop control scenario.

A material synthesis apparatus includes: at least one device configured to synthesize a material; a user interface configured to obtain information on a target product; and a processor, wherein the processor is configured to: determine synthesis conditions for preparing the target product using a pretrained synthesis prediction model; calculate a first synthesis method for preparing the target product based on the synthesis conditions; and control the at least one device based on the first synthesis method.

A processor may receive a submission of a command. The processor may analyze the command for at least one commonality with a previous command and predict a predicted reason for the submission of the command based on historical learning. The processor may integrate the predicted reason into a corpus specific to a user, wherein the corpus includes user preference data, and wherein the processor predicts one or more orders of the user using the corpus.

Methods and systems for controlling a system such as an engine having an airflow system. A model predictive control calculation is configured in an off-line mode, having a linear part and a non-linear part. In an on-line mode, the linear part of the MPC and/or a Hessian matrix used with the MPC is modified responsive to special modes or other operating changes or conditions. The online mode is configured to respond to changing modes or conditions without requiring recalculation of the MPC. Certain changes of conditions and modes are used to modify feedforward, while others modify responsiveness.