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).

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.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving operational parameters for one or more automation devices, wherein the one or more automation devices are configured to implement control logic generated based on a decision tree. The operations also include receiving an output by the decision tree based on the operational parameters. Further, the operations include determining the output is an anomalous output based on a constraint associated with the decision tree. Further still, the operations include generating an updated decision tree based on the anomalous output. Even further, the operations include generating updated control logic for the one or more automation devices based on the updated decision tree. Even further, the operations include sending the updated control logic to the one or more automation devices.

Logical boundaries enclosing a physical area are defined. A segment of the logical boundaries is defined as a directional gate, wherein traversing the gate into the physical area is defined as an ingress and traversing the gate out of the physical area is defined as an egress. The directional gate is monitored, and ingresses and egresses are detected. An occupancy count of the physical area is maintained, based on monitoring the gate and detecting ingresses and egresses. One or more conditions are tracked in addition to the occupancy count. Artificial intelligence (AI) processing is applied to the maintained occupancy count and the additional tracked condition(s), in real-time as the monitoring, maintaining and tracking are occurring. One or more responsive actions are automatically taken as a result of applying the AI processing to the maintained occupancy count and the additional tracked condition(s).

A computer-implemented method of controlling power consumption in a multi-processor computing device comprises: determining whether a first processor is operating in a high-power regime or a low-power regime; selecting a first set of control rules that includes a first subset of control rules that apply when the first processor is operating in the high-power regime and a second subset of control rules that apply when the first processor is operating in the low-power regime; determining one or more power settings for the first processor based on the first set of control rules; and causing the first processor to perform one or more operations based on the one or more power settings.

Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.

A system for rule management, predictive maintenance and quality assurance of a process using automatic rule formation comprising a plurality of sensors capable of being attached to at least one machine for measuring at least one information about the process and machine operation. The system comprises a server connected to the sensors over a wireless communication network and running a reconfigurable rule management program for identifying and processing the particular process and machine information related to at least one process received from the plurality of sensors. A controller in communication with the server capable of controlling the process based on a rule set by the rule engine. The rule engine automatically detects the normal process data, classifies the received data based on the dynamic rule formed by the rule engine and finds anomalies in the process or machine operation for predictive maintenance and process quality assurance.