Abstract: A contactor and a control method thereof are disclosed, the contactor including: an electromagnetic component; a static contact; a movable component including a connected movable contact, a first elastic part configured to generate an elastic force supporting the movable component and a driving part configured to produce an electromagnetic driving force through a magnetic field generated by the electromagnetic component; a sensor; and a controller configured to: in response to the sensor detecting that the driving part is displaced based on the electric repulsion force generated by the current flowing through the movable contact and the static contact, control the electromagnetic component to adjust the generated magnetic field to reduce the electromagnetic driving force produced by the driving part based on the generated magnetic field in the opposite direction to the electric repulsion force, so that the movable contact is not closed with the static contact again.

Abstract: A motion mechanism, which is installed on a base and capable of translating in a movement direction. The motion mechanism includes: a moving iron core bracket, provided with a moving iron core and including a first connection part; a moving contact bracket, provided with a moving contact and including a second connection part; a guide support mechanism, through which the motion mechanism is supported on the base to enable the motion mechanism to translate in the movement direction; the first connection part and the second connection part are connected so that the moving iron core bracket and the moving contact bracket can be pivotally connected around a pivot axis perpendicular to the movement direction and a gravity direction. A contactor including the abovementioned motion mechanism also is disclosed.

Abstract: A latching module for an electrical device includes: a swing arm movable between an unlocked position and a locked position; a tripper movable between a holding position and a tripped position; a holder, its first portion acts on the tripper and its second portion acts on the swing arm. When the swing arm is in the locked position and the tripper is in the holding position, the holder bears a force from the swing arm to make the holder abut against the tripper to apply a force on the tripper, and the tripper is secured in the holding position, and the holder holds the swing arm in the locked position. When the tripper is in the tripped position, the holder releases the hold on the swing arm and the swing arm moves from the locked position towards the unlocked position. A contactor including the latching module also is disclosed.

Abstract: A contactor, which includes: a housing; a plurality of stationary contact brackets, fixed to the housing, wherein each of the plurality of stationary contact brackets includes a bracket body accommodated in the housing and a wiring terminal extending out of the housing and configured to be connected with an electric wire, the wiring terminal includes a sheet-shaped terminal body and a terminal hole penetrating through a thickness of the terminal body; and an isolation sheet, arranged between adjacent wiring terminals and separates the adjacent wiring terminals. The design of the contactor according to the present disclosure can meet at least four different connection modes of different customers at the same time, thus increasing the application compatibility of the contactor and reducing the design cost and management cost.

Abstract: The present disclosure relates to an arc extinguishing system for contactor and a contactor. The arc extinguishing system includes: an arc extinguishing chamber, accommodating a plurality of arc extinguishing grids, and an extension direction of each of the plurality of arc extinguishing grids being perpendicular to an extension direction of the movable contact; an arc striking piece, electrically connected to the stationary contact and configured to extend toward the arc extinguishing chamber, so that the arc generated upon the movable contact and the stationary contact being separated moves along the arc striking piece toward the arc extinguishing chamber.

Abstract: Dictionary learning method and means for zero-shot recognition can establish the alignment between visual space and semantic space at category layer and image level, so as to realize high-precision zero-shot image recognition. The dictionary learning method includes the following steps: (1) training a cross domain dictionary of a category layer based on a cross domain dictionary learning method; (2) generating semantic attributes of an image based on the cross domain dictionary of the category layer learned in step (1); (3) training a cross domain dictionary of the image layer based on the image semantic attributes generated in step (2); (4) completing a recognition task of invisible category images based on the cross domain dictionary of the image layer learned in step (3).

Abstract: Dictionary learning method and means for zero-shot recognition can establish the alignment between visual space and semantic space at category layer and image level, so as to realize high-precision zero-shot image recognition. The dictionary learning method includes the following steps: (1) training a cross domain dictionary of a category layer based on a cross domain dictionary learning method; (2) generating semantic attributes of an image based on the cross domain dictionary of the category layer learned in step (1); (3) training a cross domain dictionary of the image layer based on the image semantic attributes generated in step (2); (4) completing a recognition task of invisible category images based on the cross domain dictionary of the image layer learned in step (3).

Abstract: 3D reconstruction method based on deep learning includes the following steps: (1) The potential vector constrained in the input image is used to reconstruct the complete 3D shape of the target, and the mapping between the part and the complete 3D shape is learned, then the 3D reconstruction of a single depth image is realized. (2) Learn the intermediate feature representation between the 3D real object and the reconstructed object to obtain the target potential variables in step (1). (3) The voxel floating value predicted in step (1) is transformed into binary value by using the limit learning machine to complete high-precision reconstruction.

Abstract: The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.

Abstract: The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.

Abstract: 3D reconstruction method based on deep learning includes the following steps: (1) The potential vector constrained in the input image is used to reconstruct the complete 3D shape of the target, and the mapping between the part and the complete 3D shape is learned, then the 3D reconstruction of a single depth image is realized. (2) Learn the intermediate feature representation between the 3D real object and the reconstructed object to obtain the target potential variables in step (1). (3) The voxel floating value predicted in step (1) is transformed into binary value by using the limit learning machine to complete high-precision reconstruction.