A vehicle-grid integration management system determines use of a power grid by an electric vehicle in a dual multi-part rate structure including a grid account portion associated with a relationship between the electric vehicle and the power grid, a group account portion associated with a relationship between the vehicle group and the electric vehicle and/or the power grid, a consumption portion associated with a volume of electricity drawn from the power grid by the electric vehicle over a time period, a supply portion associated with a volume of electricity delivered to the power grid by the electric vehicle over the time period, a demand portion associated with an upper threshold of electricity drawn from the power grid by the electric vehicle over the time period, and a capacity portion associated with an upper threshold of electricity delivered to the power grid by the electric vehicle over the time period.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

A control method based on an adaptive neural network model for dissolved oxygen of an aeration system includes: obtaining related water quality monitoring data of a sewage treatment plant, and performing data preprocessing on the related water quality monitoring data; performing principal component analysis on the preprocessed related water quality monitoring data and a dissolved oxygen concentration of the aeration system through a principal component analysis method, and determining a water quality parameter with a highest rate of contribution to a principal component; taking the water quality parameter with the highest rate of contribution to the principal component, and predicting a dissolved oxygen concentration of the aeration system; and optimizing a dissolved oxygen predictive value obtained by means of the adaptive neural network model to obtain an optimal regulation value, and performing online regulation on a fuzzy control system of the adaptive neural network model.

Provided is an electronic device for controlling surface heart and a method of controlling the electronic device. The electronic device includes a speaker, a temperature sensor, a memory, and a processor electrically coupled to the speaker, the temperature sensor, and the memory. The processor obtains first temperature information based on impedance information measured in a coil included in the speaker; obtains second temperature information measured by the temperature sensor, the second temperature information based on a heat source disposed adjacent to the speaker; predicts a surface temperature of a surface area of the electronic device, opposite to an internal area in which the speaker is disposed, based on the first temperature information and the second temperature information using a nonlinear approximation function; and controls an audio signal input to the speaker based on the predicted surface temperature.

Methods and apparatus to control roll-forming processes are disclosed. A disclosed example roll-forming apparatus includes an inlet portion to receive material, an outlet portion from which the material exits the roll-forming apparatus, a plurality of rollers extending between the inlet and outlet portions, a sensor to measure at least one dimension of the material as the material moves through the roll-forming apparatus, the material measured by the sensor between the inlet and outlet portions, and material adjuster circuitry to adjust roll-forming of the material by moving at least one of the plurality of rollers based on the at least one dimension.

In some examples, an electrical power system includes a power source and a load modulator configured to receive power from the power source and to deliver power to a load zone. The electrical power system also includes a controller configured to determine a software-controlled power flow limit for the load zone. The controller is further configured to receive information indicating the power delivered to the load zone and to cause the power delivered to the load zone to remain below the software-controlled power flow limit.

The present invention relates to a control system for a movement reconstruction and/or restoration system for a patient, comprising a movement model generation module to generate movement model data information, an analysis module receiving and processing data provided at least by the movement model generation module, wherein the control system is configured and arranged to prepare and provide on the basis of data received by the movement model generation module and the analysis module a movement model describing the movement of a patient and providing, on the basis of the movement model, stimulation data for movement reconstruction and/or restoration.