Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

The disclosure relates to a method for operating a production plant having a plurality of processing stations, wherein the workpieces to be processed can be transported to the various processing stations by means of a transport system, wherein a plurality of computers are provided which are connected for data exchange via a computer network, the production plant being controlled by means of a plurality of control processes. According to the disclosure, during the operation of the production plant an initial decision is taken automatically about which control process is executed on which computer, wherein the initial decision is then implemented automatically.

A computer-implemented method and system for process schedule reconciliation receives a scheduling model and an initial schedule for reconciliation, where the initial schedule includes projected plant data. Current plant data is imported into the system, and dynamic optimization data representing trends in process data at time-varied values for key process and operation parameters are identified. The current plant data and projected plant data is processed using mathematical modeling techniques to identify event boundaries, stream flowrates associated with tanks and process units. The system builds an optimization model applying identified event boundaries, stream flowrates, dynamic optimization data, key scheduling parameters and pre-determined constraints along a period of time that includes priority slots to reconcile the projected plant data of the initial schedule with the current plant data, and then solves the optimization model to develop a reconciled schedule.

A bias estimation apparatus according to an embodiment estimates a bias included in a measured values by each sensor. The bias estimation apparatus includes a reference model builder, a temporary bias generator, a corrected measured value calculator, a similarity calculator, a similarity selector, a score calculator, and an estimated bias determiner. The reference model builder builds a reference model of the measured value packs. The temporary bias generator generates a temporary bias pack. The corrected measured value calculator calculates corrected measured value packs. The similarity calculator calculates a similarity of each corrected measured value pack. The similarity selector selects a part of the similarities according to their values from among the similarities. The score calculator calculates a score based on the selected similarities. The estimated bias determiner determines an estimated bias which is an estimated value of the bias based on the score.

An actuating device for moving an actuator that features an actuating drive for generating an actuating movement for the actuator. The actuating device also includes a transmission device for transmitting the actuating movement from the actuating drive to the actuator. It also features a sensor device for detecting the actuating position of the actuator. The sensor device features a signal receiver and transducer. The signal receiver will be in the actuating drive and the transducer configured as part of the transmission device.

The present disclosure relates to health monitoring and maintenance of mobile platforms such as aircraft. In particular, onboard apparatus and methods and also ground-based apparatus and methods that cooperate in assisting with the maintenance of mobile platforms by facilitating diagnosis of events detected onboard mobile platforms while such mobile platforms are in operation (e.g., transit, flight) are disclosed. In various aspects, the present disclosure discloses apparatus and methods for handling and reporting the detection of events onboard mobile platforms, reporting predefined additional information associated with the event upon request from a ground facility, identifying one or more potential causes for the detected event and determining the occurrence probability for each potential cause identified.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

A method for training a local machine learning model is provided. The method includes receiving a scanning electron microscope (SEM) image of semiconductor features. The method additionally includes determining a location and dimensions of a bounding box within the SEM image. The method yet further includes determining, whether a defect feature exists within the bounding box, based on an unsupervised object detection process. The method also includes, if the defect feature exists within the bounding box, receiving positive rewards. The method also includes, if the defect feature does not exist within the bounding box, receiving negative rewards.