Abstract: The present disclosure provides a method for an accurate well-seismic tie based on stepwise matching and optimization, including: determining target seismic data as a seismic trace near well and performing first data processing on the target seismic data; performing second data processing on the logging information to determine a reflection coefficient sequence; extracting seismic wavelets, convoluting the seismic wavelets with the reflection coefficient sequence, determining a synthetic seismic record of the target calibration object in a time domain, performing third data processing on the synthetic seismic record; determining a correlation between the seismic trace near well and the synthetic seismic record, determining a global time shift between the synthetic seismic record and the seismic trace near well, performing a preliminary alignment on the synthetic seismic record; and calculating a local time shift between the seismic trace near well and the synthetic seismic record and accurately calibrating th

Abstract: A relay includes an electromagnet apparatus, a first transmission part, a second transmission part, a first elastic part, a first movable contact, a second static contact, a second elastic part, a first static contact, and a second movable contact. The first movable contact, the second static contact are both disposed on the first elastic part. The second movable contact and the first static contact are both disposed on the second elastic part. The first movable contact is in contact with or separated from the first static contact, and the second movable contact is in contact with or separated from the second static contact. When the first movable contact is in contact with the first static contact, the second movable contact is in contact with the second static contact, the first movable contact and the first static contact are in parallel with the second movable contact and the second static contact.

Abstract: A power converter includes a housing, a power device, a circuit board, a connection piece, and an inductor apparatus. The power device is installed on the circuit board, and an accommodation cavity is formed in the housing and is configured to accommodate the circuit board and the power device. A through-hole is provided on a surface that is of the housing and that faces the circuit board. The inductor apparatus is completely exposed or partially exposed outside the housing and covers the through-hole. The connection piece extends from the circuit board in the housing to the inductor apparatus, and passes through a surface of the housing to physically connect the inductor apparatus to the housing and electrically connect the power device to the inductor apparatus at the same time.

Abstract: The embodiment of the present disclosure provides an imaging method for a dispersion energy spectrum of surface waves, an electronic device, and a storage medium. The imaging method includes: obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation; processing the first surface wave data to obtain the second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation; and processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result corresponding to a slowness-frequency domain representation.

Abstract: The present disclosure discloses a denoising method and a system for preserving amplitude variation with offset (AVO) features of pre-stack seismic data. The method includes: acquiring seismic wave data at at least one receiving point through a seismic signal acquisition device, and storing the seismic wave data in a memory; in response to ending of an acquisition operation of the seismic signal with the seismic signal acquisition device, determining, based on the seismic wave data in the memory, pre-denoising angle gather data through a processing device; and obtaining and outputting a denoised angle gather signal through inputting the pre-denoising angle gather data to a denoising device and performing denoising processing on the pre-denoising angle gather data based on the denoising device.

Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updated to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.

Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.

Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updated to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.

Abstract: Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.

Abstract: Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.