Abstract: A device for detecting a slot wedge, an air gap and a broken rotor bar is provided, where a sequential circuit generates double concurrent pulses; the sequential circuit is connected to driving power modules; the driving power modules are connected to front-end interface circuits; the front-end interface circuits convert the double concurrent pulses into corresponding magnetic-field pulses; the magnetic-field pulses are transmitted to power supply terminals on adjacent phases of stator windings through impedance matching pins and coupled at a corresponding coil, air gap and squirrel cage rotor to generate single groups of cyclic rotating magnetic potentials; single rotating magnetic potentials are sequentially generated in adjacent slots on each of the phases of the stator windings; rotating electric potentials in magnetic circuits with two symmetrical phases are magnetically coupled to form distributed coupling magnetic field reflected full-cycle waves for reflecting a difference of a corresponding slot wedg

Abstract: A method for fusing map data, includes: obtaining two-dimensional map data and three-dimensional map data to be fused; classifying the three-dimensional map data according to map data types, the map data types including line data, surface data, and traffic body data; performing elevation conversion for each type of three-dimensional map data according to an elevation conversion algorithm preset for each type of three-dimensional map data, to obtain a relative elevation of each type of three-dimensional map data; and fusing the two-dimensional map data and three-dimensional map data to be fused based on the relative elevation of each type of three-dimensional map data, to obtain fused data.

Abstract: The invention discloses a method and a device for constructing a SQL statement based on reinforcement learning, wherein the method includes: initializing an actor-critic network parameter; acquiring a sequence pair of natural language and real SQL statement from a data set; inputting a natural language sequence into an actor network encoder, and inputting a real SQL sequence into a critic network encoder; using an encoded hidden state as an initialized hidden state of a corresponding decoder; gradually predicting, by an actor network decoder, a SQL statement action, and inputting the SQL statement action to a critic network decoder and an environment to obtain a corresponding reward; and using a gradient descent algorithm to update the network parameters, and obtaining a constructing model of the natural language to the SQL statement after repeated iteration training.

Abstract: The present invention discloses a method for detecting abnormity in an unsupervised industrial system based on deep transfer learning. Labeled machine sensor sequence data from a source domain and unlabeled sensor sequence data from a target domain are used in the present invention to train an industrial system abnormal detection model with good generalization ability, and the industrial system abnormal detection model is trained and tested to finally generate a trained industrial system abnormity discrimination model. Using the model, received machine sensor sequence data can be analyzed and whether a machine is abnormal is discriminated.

Abstract: The invention discloses a method and a device for constructing a SQL statement based on reinforcement learning, wherein the method includes: initializing an actor-critic network parameter; acquiring a sequence pair of natural language and real SQL statement from a data set; inputting a natural language sequence into an actor network encoder, and inputting a real SQL sequence into a critic network encoder; using an encoded hidden state as an initialized hidden state of a corresponding decoder; gradually predicting, by an actor network decoder, a SQL statement action, and inputting the SQL statement action to a critic network decoder and an environment to obtain a corresponding reward; and using a gradient descent algorithm to update the network parameters, and obtaining a constructing model of the natural language to the SQL statement after repeated iteration training.

Abstract: The present invention discloses a method for detecting abnormity in an unsupervised industrial system based on deep transfer learning. Labeled machine sensor sequence data from a source domain and unlabeled sensor sequence data from a target domain are used in the present invention to train an industrial system abnormal detection model with good generalization ability, and the industrial system abnormal detection model is trained and tested to finally generate a trained industrial system abnormity discrimination model. Using the model, received machine sensor sequence data can be analyzed and whether a machine is abnormal is discriminated.

Abstract: Provided are pyrrolyl substituted dihydroindol-2-one derivatives represented by formula (I), pharmaceutically acceptable salts or solvates of said derivatives, or solvates of said salts, which are effective tyrosine kinase inhibitors. Also provided are the preparation methods of the above compounds, pharmaceutical compositions containing these compounds, and the use of these compounds in manufacturing drugs useful for the treatment or adjunctive treatment of tumors medicated by tyrosine kinases or proliferation or migration of tumor cells driven by tyrosine kinases in mammals (including human).

Abstract: Provided are pyrrolyl substituted dihydroindol-2-one derivatives represented by formula (I), pharmaceutically acceptable salts or solvates of said derivatives, or solvates of said salts, which are effective tyrosine kinase inhibitors. Also provided are the preparation methods of the above compounds, pharmaceutical compositions containing these compounds, and the use of these compounds in manufacturing drugs useful for the treatment or adjunctive treatment of tumors medicated by tyrosine kinases or proliferation or migration of tumor cells driven by tyrosine kinases in mammals (including human).