A method for correcting data sensed by a sensor includes: acquiring the data sensed by the sensor; establishing a surface equation based on an acquired data to performing surface fitting; and correcting the data sensed by the sensor with the surface equation. The method further includes: evaluating the acquired data sensed by the sensor to update fitting parameters of the surface equation so as to obtain an updated surface equation; and correcting the data sensed by the sensor with the updated surface equation. Parameter data sensed by the sensor can be corrected in real time, which reduces the influence of environmental factors on the parameter data sensed by the sensor and improves the accuracy of the data sensed by the sensor.

To provide a performance impact evaluation apparatus and a performance impact evaluation method in which it is possible to more accurately evaluate a performance impact caused by software data/process separation. A performance impact evaluation apparatus 1 evaluates a performance impact caused by separating data 2 used in software into an external DB 4. The performance impact evaluation apparatus 1 includes a log acquisition unit 21 configured to acquire a log for recording an access to the external DB 4 and an evaluation unit 22 configured to evaluate the performance impact by using an evaluation expression considering a software structure in addition to characteristics of the data 2, based on the log acquired by the log acquisition unit 21.

An electronic device includes proximity sensor, a memory and a processor. The proximity sensor is positioned within an enclosure of the electronic proximate to one or more air vents. The proximity sensor generates a sensing signal to detect blockage of the one or more air vents. Based on a response to the sensing signal, the proximity sensor generates an output signal indicating that one or more air vents is blocked. The processor is configured to execute computer-readable instructions stored in memory to receive the output signal from the proximity sensor indicating blockage of the one or more air vents is detected, and, in response to receiving the output signal from the proximity sensor indicating blockage of the one or more air vents is detected, forward, to one of a network device, a user device, and a service provider, a notification of the blockage of the one or more air vents.

The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.

According to at least one aspect, a database system is provided. The database system includes at least one processor configured to receive a restore request to restore a portion of a dataset to a previous state and, responsive to receipt of the restore request, identify at least one snapshot from a plurality of snapshots of at least some data in the dataset to read based on the restore request and write a portion of the data in the identified at least one snapshot to the dataset to restore the portion of the dataset to the previous state.

The present invention generally relates to systems and methods for detecting and/or isolating any causes of defective and/or partially defective IoT device or individual sensor device(s). In embodiments the present invention generally relates to fixing, replacing, and/or troubleshooting IoT devices and/or individual sensor device(s) that are defective and/or partially defective.

A method, computer program product, and computing system for operating an autonomous vehicle; monitoring the operation of a plurality of computing devices within the autonomous vehicle; and in response to detecting the failure of one or more of the plurality of computing devices, switching the autonomous vehicle from a nominal autonomous operational mode to a degraded autonomous operational mode.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

A method is disclosed herein of detecting at least one frozen period in at least one sensor dataset associated with at least one sensor in a technical system. The method includes receiving the at least one sensor dataset in time series and computing run-lengths for the at least one sensor dataset, wherein each of the run-lengths is length of consecutive repetitions of a sensor value in the at least one sensor dataset. The method includes clustering the run-lengths into one of two clusters based on a run frequency, wherein the run frequency is a number of times the run-lengths are repeated in the at least one sensor dataset. Further, the method includes identifying a cluster from the two clusters with lower run frequency and detecting the at least one frozen period in the at least one sensor dataset based on the identified cluster.

A data processing board according to an embodiment of the present disclosure includes a data processing module including at least one data processing board for automatically assigning an identifier according to the voltage value measured in the internal circuit and a communication board for transmitting and receiving signal to/from the data processing board, and a data processing system including the data processing module and a monitor collecting device for selecting and parallel-processing the data received from the data processing module.