Abstract: The present invention relates to a split-type fusion sight, comprising a sight main body. An aiming assembly connecting base is arranged at the top of the main body; the aiming assembly connecting base comprises an engagement groove located on the front side, and an engagement base located on the rear side; an engagement block is arranged in the middle of the engagement groove, and a wire arranging base is arranged on the right side of the engagement base; the aiming assembly connecting base is further provided with connecting screw holes. The split-type fusion sight can fuse the low-light-level night vision or thermal imaging lens imaging with human eye observation of a target to assist in observing or determining the target, and then determines the aiming target by means of an aiming light point, thereby performing accurate shooting.

Abstract: An electrical connector is provided, including a plug; a PCB, secured with the plug; a shell assembly, covering the plug and secured therewith, the shell assembly having a plurality of forward extending supporting arms suspending above the plug for forming a plurality of accommodating spaces between the supporting arms and the plug; and an insulating molding layer, covering at least a part of the plug and the whole of the shell assembly and filling the plurality of accommodating space.

Abstract: An electrical connector is provided, including a plug; a PCB, secured with the plug; a shell assembly, covering the plug and secured therewith, the shell assembly having a plurality of forward extending supporting arms suspending above the plug for forming a plurality of accommodating spaces between the supporting arms and the plug; and an insulating molding layer, covering at least a part of the plug and the whole of the shell assembly and filling the plurality of accommodating space.

Abstract: An example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.

Abstract: An example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.