Abstract: The present disclosure discloses a vibration exciter comprising: a housing having a housing cavity, a stator assembly fixed in the housing, and a vibrator assembly housed in the housing cavity and driven to vibrate by the stator assembly; the stator assembly comprises a pole core fixedly attached to the inner side of the housing and a magnet fixedly attached to the side of the pole core that is back from the housing; the vibrator assembly includes a bracket housed in the housing cavity, a coil wound on the outside of the bracket, a counterweight fixed to the bracket, and a column assembly fixed to the end of the bracket and elastically connected to the housing; the bracket is a solid structure. When the coil is wound, it is wrapped around the bracket and column assembly, which is not easily deformed and improves the consistency of the coil shape.

Abstract: A system for load identification in collaboration with a cloud end includes: a smart Internet of Things electricity meter module, used for matching extracted feature quantity data with a first load feature library of the smart Internet of Things electricity meter module, and determining load feature data corresponding to unmatched feature quantity data in the feature quantity data as target load feature data; a use information front-end/acquisition module, used for calling the target load feature data and transmitting the target load feature data to a main station load identification module; and a main station load identification module, used for receiving the target load feature data, performing data direction processing on the target load feature data, determining an optimal matching strategy matching the feature data to be identified, and identifying an optimal matching solution corresponding to the feature quantity data to be identified in a second load feature library.

Abstract: The present invention belongs to the field of gene editing, and particularly relates to a method and means for detecting genome wide random off-target effect of a base editing system in a rapid and high-throughput mode.

Abstract: A system for load identification in collaboration with a cloud end includes: a smart Internet of Things electricity meter module, used for matching extracted feature quantity data with a first load feature library of the smart Internet of Things electricity meter module, and determining load feature data corresponding to unmatched feature quantity data in the feature quantity data as target load feature data; a use information front-end/acquisition module, used for calling the target load feature data and transmitting the target load feature data to a main station load identification module; and a main station load identification module, used for receiving the target load feature data, performing data direction processing on the target load feature data, determining an optimal matching strategy matching the feature data to be identified, and identifying an optimal matching solution corresponding to the feature quantity data to be identified in a second load feature library.

Abstract: Embodiments include systems and methods for automatic timing-sensitive circuit extraction for statistical timing margin analysis of custom designs. A timing-sensitive circuit extractor system can take pre- or post-layout netlists for integrated circuits and can automatically generate a timing-sensitive netlist. For example embodiments can generate a connectivity graph from the netlist and can traverse the graph with constraints defined according to measurement nodes to extract the timing-sensitive circuit. Memory timing checks and corresponding stimuli can generally be pre-defined, and a test-bench generator can generate appropriate parameters, stimuli, etc. Statistical simulations can then be performed to quickly generate results, which can be post-processed to obtain timing margin distributions and to flag out design errors.

Abstract: Embodiments include systems and methods for automatic timing-sensitive circuit extraction for statistical timing margin analysis of custom designs. A timing-sensitive circuit extractor system can take pre- or post-layout netlists for integrated circuits and can automatically generate a timing-sensitive netlist. For example embodiments can generate a connectivity graph from the netlist and can traverse the graph with constraints defined according to measurement nodes to extract the timing-sensitive circuit. Memory timing checks and corresponding stimuli can generally be pre-defined, and a test-bench generator can generate appropriate parameters, stimuli, etc. Statistical simulations can then be performed to quickly generate results, which can be post-processed to obtain timing margin distributions and to flag out design errors.