A power load data prediction method and device, and a storage medium are disclosed. In an embodiment, the he method comprises: acquiring historical power load data of a one-dimensional time sequence, the historical power load data including values of corresponding time points; mapping the values of corresponding time points to a coordinate system in which a horizontal axis is a set time period, and a vertical axis is time points within the time period, and performing marking at each mapping point by using predetermined pixel values corresponding to the values to obtain a mapping image, wherein different values correspond to different pixel values; and inputting the pixel values of the mapping image to a trained data prediction model, and acquiring a power load data prediction value output by the data prediction model. The method and device and the storage medium can improve the prediction accuracy of the power load data.

A brain functional connectivity correlation value clustering device for clustering subjects having a prescribed attribute on the basis of brain measurement data obtained from a plurality of facilities, wherein a plurality of MRI devices capture resting state fMRI image data of a healthy cohort and a patient cohort; a computing system 300 performs generation of an identifier as ensemble learning of “supervised learning” between harmonized component values of correlation matrixes and disease labels of each of the subjects, selects, during the ensemble learning, features for clustering in accordance with importance from the features specified for generating an identifier for a disease label, and performs multiple co-clustering by “unsupervised learning.”

A pilot protection method includes: obtaining time-domain signals data of target element at a preset sampling frequency; fusing time-domain signals data of multiple first sampling periods to obtain first time-domain signals combination data; based on a machine learning model, determining whether a fault occurs in target element according to the first time-domain signals combination data; when it is determined that a fault occurs in target element according to the first time-domain signals combination data, based on the machine learning model, determining whether a fault occurs in target element in the second sampling period according to the second time-domain signals combination data. The second sampling period is the sampling period after determining a fault occurs; when it is determined that the same type of fault occurs in target element in multiple consecutive second sampling periods, the pilot protection system is controlled to perform the protection action on the target element.

A method of analyzing an application screen is provided. The method includes generating a plurality of links for a plurality of user interface (UI) elements included in the application screen, generating a UI map for each of at least one primitive action, which is a user input for navigating the application screen, based on the plurality of links, and identifying a position of a focus indicating a UI element with which a user is to interact among the plurality of UI elements. The UI map includes a route via which the position of the focus moves between the plurality of UI elements by the at least one primitive action.

A pilot protection method includes: obtaining time-domain signals data of target element at a preset sampling frequency; fusing time-domain signals data of multiple first sampling periods to obtain first time-domain signals combination data; based on a machine learning model, determining whether a fault occurs in target element according to the first time-domain signals combination data; when it is determined that a fault occurs in target element according to the first time-domain signals combination data, based on the machine learning model, determining whether a fault occurs in target element in the second sampling period according to the second time-domain signals combination data. The second sampling period is the sampling period after determining a fault occurs; when it is determined that the same type of fault occurs in target element in multiple consecutive second sampling periods, the pilot protection system is controlled to perform the protection action on the target element.

A power load data prediction method and device, and a storage medium are disclosed. In an embodiment, the he method comprises: acquiring historical power load data of a one-dimensional time sequence, the historical power load data including values of corresponding time points; mapping the values of corresponding time points to a coordinate system in which a horizontal axis is a set time period, and a vertical axis is time points within the time period, and performing marking at each mapping point by using predetermined pixel values corresponding to the values to obtain a mapping image, wherein different values correspond to different pixel values; and inputting the pixel values of the mapping image to a trained data prediction model, and acquiring a power load data prediction value output by the data prediction model. The method and device and the storage medium can improve the prediction accuracy of the power load data.

Systems and methods are provided for simulating operation of an autonomous vehicle control system. Three dimensional multi-sensor data associated with a plurality of real-world drives in a sensor equipped vehicle is accessed. For a particular drive, the three dimensional multi-sensor data is reduced to a time series of two dimensional representations. The time series of two dimensional representations is classified into a sequence of states, where the sequence of states associated with the particular drive and the three dimensional multi-sensor data are stored in a computer-readable medium as a scenario. A query is received that identifies a state criteria, and the scenario is accessed based on the sequence of states matching the state criteria of the query. The three dimensional multi-sensor data of the scenario is provided to an autonomous driving system to simulate behavior of the autonomous driving system when faced with the scenario.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may receive a set of data from a data source. The program may generate a plurality of time series data based on the set of data. The program may determine a subset of the plurality of time series data as anomalies. The program may provide notifications indicating that the subset of the plurality of time series data are anomalies.

An apparatus and method for real-time sensing of properties in industrial manufacturing equipment are described. The sensing system includes first plural sensors mounted within a processing environment of a semiconductor device manufacturing system, wherein each sensor is assigned to a different region to monitor a physical or chemical property of the assigned region of the manufacturing system, and a reader system having componentry configured to simultaneously and wirelessly interrogate the plural sensors. The reader system uses a single high frequency interrogation sequence that includes (1) transmitting a first request pulse signal to the first plural sensors, the first request pulse signal being associated with a first frequency band, and (2) receiving uniquely identifiable response signals from the first plural sensors that provide real-time monitoring of variations in the physical or chemical property at each assigned region of the system.

An abnormality detection apparatus includes an acquisition unit that acquires time-series data of a detection target whose abnormality is detected at a predetermined point in time, a first extraction unit that extracts a feature in a feature quantity direction in a time section before the predetermined point in time from the time-series data, a second extraction unit that extracts a feature in a time direction in the time section from the feature in the feature quantity direction, and a calculation unit that calculates an abnormality score at a predetermined point in time on the basis of the feature in the feature quantity direction and the feature in the time direction, and calculates the degree of contribution in the feature quantity direction and the degree of contribution in the time direction before the predetermined point in time with respect to the abnormality score.