Disclosed is an intelligent control system of spunlace production line, which includes a data acquiring module, which is used for acquiring and storing real-time production line data; the production line data includes cotton feeding roller value, real-time moisture value, real-time speed value and real-time gram weight value; the data process module is used for classify and controlling that production line data, and giving the adjustment opinions of the cotton feeding roller parameters; the parameter control module is used for verifying the parameter adjustment opinions and applying the opinions to the control system; the data acquiring module, the data processing module and the parameter control module are connected in sequence.

The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a continuing lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.

A system for predicting a negative pressure of a brake booster of a vehicle includes: a driving information detector detecting driving information related to driving of the vehicle; and a controller determining a negative pressure of an intake manifold based on a pressure of the intake manifold and an atmospheric pressure which is the driving information and including a booster negative pressure predictor predicting the negative pressure of the brake booster by integrating over time a change rate according to a charging rate and a discharging rate of the negative pressure determined using a negative pressure of the brake booster determined in a previous cycle according to a logic for predicting the negative pressure of the brake booster and the negative pressure of the intake manifold of a current cycle and an imitated brake pedal force signal of the current cycle imitating an acceleration of the vehicle.

Embodiments described herein are directed to methods and apparatuses for remotely managing electrical and gas devices. In one scenario, a computer system performs a method for remotely managing electrical devices. The method may include determining that an electrical device has changed state, notifying at least one user that the electrical device has changed state, and either receiving an indication from the user that the electrical device is to be turned off, and turning the electrical device off, or receiving an indication from the user that the electrical device is to be turned on, and turning the electrical device on.

A method for reducing and/or managing energy-related stress in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one intelligent electronic device (IED) in the electrical system to identify and track at least one energy-related transient in the electrical system. An impact of the at least one energy-related transient on equipment in the electrical system is quantified, and one or more transient-related alarms are generated in response to the impact of the at least one energy-related transient being near, within or above a predetermined range of the stress tolerance of the equipment. The transient-related alarms are prioritized based in part on at least one of the stress tolerance of the equipment, the stress associated with one or more transient events, and accumulated energy-related stress on the equipment. One or more actions are taken in the electrical system in response to the transient-related alarms to reduce energy-related stress on the equipment in the electrical system.

A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

Deep Learning is a candidate for advanced process control, but requires a significant amount of process data not normally available from regular plant operation data. Embodiments disclosed herein are directed to solving this issue. One example embodiment is a method for creating a Deep Learning based model predictive controller for an industrial process. The example method includes creating a linear dynamic model of the industrial process, and based on the linear dynamic model, creating a linear model predictive controller to control and perturb the industrial process. The linear model predictive controller is employed in the industrial process and data is collected during execution of the industrial process. The example method further includes training a Deep Learning model of the industrial process based on the data collected using the linear model predictive controller, and based on the Deep Learning model, creating a Deep Learning model predictive controller to control the industrial process.

A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

A computing system is provided for training one or more machine learning models to perform at least a portion of a robotic task of a physical robotic system by monitoring a model-based control algorithm associated with the physical robotic system perform at least a portion of the robotic task. One or more robotic task predictions may be defined, via the one or more machine learning models, based upon, at least in part, the training of the one or more machine learning models. The one or more robotic task predictions may be provided to the model-based control algorithm associated with the physical robotic system. The robotic task may be performed, via the model-based control algorithm associated with the robotic system, on the physical robotic system based upon, at least in part, the one or more robotic task predictions defined by the one or more machine learning models.

A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.