Techniques for analyzing a parameter space are discussed. Techniques may include receiving policy data for evaluating a vehicle controller. The techniques may further include determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events. The adverse events may be associated with a violation of the policy data. The techniques may associate, based on exposure data, parameter bounds of the adverse events and probabilities of the adverse events in a driving environment. A safety metric may be determined based on the Bayesian optimization. The techniques may also include weighting an impact of an adverse event based on the safety metric.

Disclosed is a temperature control method which includes acquiring temperature data of a plurality of temperature detection points in a target environment; calculating, according to the temperature data, an average temperature value of the plurality of temperature detection points and a first temperature difference between the average temperature value and a target temperature value; determining whether an absolute value of the first temperature difference exceeds a first temperature difference threshold; and in response to the absolute value of the first temperature difference exceeding the first temperature difference threshold, controlling the temperature of the target environment by a variable universe fuzzy proportional integral derivative control algorithm.

A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory. The memory stores instructions that cause the processor to receive a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording pseudo-Brownian change in one or more variables in the chaotic environment; determine, based on the first set of sensor readings, an expected range of the one or more variables during a second time window after the first time w window; receive a second set of sensor readings from the one or more remote electronic sensors during the second time window recording change in the one or more variables: determine, based on the second set of sensor readings, that one variable of the one or more variables is not within the expected range; and cause the one or more autonomous agent devices to attempt to mitigate a potential harm indicated by the one variable being outside of the expected range.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management.

A system for detecting an operating state of a work machine (100), comprises at least two sensors (160, 162, 164, 166, 168, 170, 178b, 178c, 178e, 178f, 178g, 172a, 172b, 174, 178a, 178d) for sensing parameters affecting an operation state of the machine (100) and an operating state evaluation circuit (228) having an output for an operating state signal value (232). The operating state evaluation circuit (228determines the operating state signal value (232) based upon fused signals from the sensors and a sensor reliability signal from a weighing function evaluator (240).

Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

The disclosure discloses an inhomogeneous sample equalization method and system for a product assembly process. The method includes the following steps of: A: calculating a similarity among different samples; B: constructing a fuzzy compatibility matrix S for representing the similarity among all the samples, and constructing a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S; C: based on a granular calculating mode, screening out a granule layer with a maximum comprehensive value of an information increment and the similarity among the samples from the fuzzy compatible space X to serve as an optimal granule layer; and D: carrying out equalization processing on the sample of the optimal granule layer.

A machine learning device, which detects an operator, communicates with a database registering information concerning the operator, and learns display of an operation menu based on the information concerning the operator, includes a state observation unit which observes an operation history of the operation menu; and a learning unit which learns the display of the operation menu on the basis of the operation history of the operation menu observed by the state observation unit.

Power management method and system for an unmanned air vehicle, wherein the unmanned air vehicle comprises a plurality of power demanding subsystems and a plurality of power sources. The invention establishes mission oriented fixed parameters. A fuzzy logic power management unit, comprised in the system, automatically calculates and assigns priorities for delivering power to the subsystems. It also automatically calculates and assigns amounts of power delivered to each subsystem and automatically decides which of the power sources to deliver power to which subsystem. The fuzzy logic power management system calculates and assigns the priorities and loads in function of a plurality of internal variables, external variables and the mission oriented fixed parameters.

Methods and apparatus for monitoring remotely located objects with a system including at least one master data collection unit, remote sensor units, and a central data collection server are described. The master unit is configured to monitor any object, mobile or stationary, including monitoring multiple remote sensor units associated with the monitored objects. The master unit may be in a fixed location or attached to a mobile object. The master unit is configured for monitoring objects that enter and leave an area. The master unit may act as a parent controller for one or more child devices including remote sensors or monitors of measurable conditions including environmental conditions, substance identification, product identification, and/or biometric identification. The master unit may discover remote sensor units as they enter or leave the area where the master unit is located. The master unit can be remotely reprogrammed such as with authenticated instructions.