Abstract: A machine learning-based method for designing a high-strength high-toughness steel, including: (S1) obtaining data and filling in missing parts to form a data set; (S2) selecting feature data in the data set to form a standard data set; (S3) constructing two machine learning models of the high-strength high-toughness steel; (S4) completing training after the two models are evaluated to be qualified; (S5) finding frontier points, drawing a Pareto front, and distinguishing a known region and a feature space; (S6) in the feature space, setting a step for the feature data, drawing a grid space, and performing multiple training predictions on each grid point by using the models, to obtain predicted Gaussian distributions of two objectives; and (S7) searching for an expected improvement point through an efficient global optimization algorithm, and obtaining design parameter values of corresponding features.

Abstract: The invention belongs to the technical field of harmonic contribution evaluation, and particularly relates to a system for tracing harmonic sources based on a cloud-edge-terminal collaboration and a method thereof, comprising: terminal module, collecting and transmitting real-time harmonic data to edge module; edge module, comprising plurality of edge computing servers deployed for the transformer substations and the transformer districts by units, realizing hierarchical control between edge computing servers according to voltage levels, and transmitting harmonic sources tracing task through hierarchical interactions between the edge computing servers; the edge computing server receiving harmonic sources tracing service request performs the tracing task, and feeds tracing result back to central module; and, central module, comprising cloud server receiving harmonic sources tracing service request initiated by user, determining edge computing server offloaded by the request, storing final tracing result from e

Abstract: The present invention discloses a method and an apparatus for monitoring an elevator door-opening operation fault. The method includes: acquiring elevator control signals from a serial interface between an elevator controller and a control box by using a preset data acquisition device, wherein the elevator control signals include an elevator operation signal, an elevator door control signal, an elevator internal call signal and an elevator floor signal; monitoring whether there is an elevator door fault in the elevator based on the elevator operation signal, the elevator door control signal and the elevator floor signal; when there is the elevator door fault in the elevator and the elevator internal call signal is in a preset level state, determining that there is the elevator door-opening operation fault in the elevator. The present invention is lower in use cost and may be easily popularized and applied.

Abstract: The invention belongs to the technical field of harmonic contribution evaluation, and particularly relates to a system for tracing harmonic sources based on a cloud-edge-terminal collaboration and a method thereof, comprising: terminal module, collecting and transmitting real-time harmonic data to edge module; edge module, comprising plurality of edge computing servers deployed for the transformer substations and the transformer districts by units, realizing hierarchical control between edge computing servers according to voltage levels, and transmitting harmonic sources tracing task through hierarchical interactions between the edge computing servers; the edge computing server receiving harmonic sources tracing service request performs the tracing task, and feeds tracing result back to central module; and, central module, comprising cloud server receiving harmonic sources tracing service request initiated by user, determining edge computing server offloaded by the request, storing final tracing result from e

Abstract: The present disclosure discloses a method and an apparatus for determining a leveling fault of an elevator. The method includes: acquiring a leveling signal and a target floor signal of an elevator, and determining whether the elevator is at a target floor according to the target floor signal; when yes, calculating a normal change sequence of signals of a leveling sensor of the elevator from a starting floor to the target floor; acquiring a real-time change sequence of the signals of the leveling sensor from the starting floor to the target floor; comparing whether the normal change sequence is the same as the real-time change sequence; and when the normal change sequence is the same as the real-time change sequence, determining that the elevator is normally leveled, and when the normal change sequence is different from the real-time change sequence, determining that the elevator has a leveling fault.

Abstract: A reverse blocking gallium nitride (GaN) high electron mobility transistor includes, sequentially stacked from bottom to top, a substrate, a nucleation layer, a buffer layer, a barrier layer, a dielectric layer. The buffer layer and the barrier layer form a heterojunction structure. The barrier layer is provided with at least two p-GaN structures. The barrier layer is provided with a source metal at one end and a drain metal at the other end, source metal forms ohmic contact and drain metal forms Schottky contact with AlGaN barrier, respectively. In forward conduction, the two-dimensional electron gas below the spaced p-GaN structure connected to the drain metal is conductive, and a turn-on voltage of the device is low. During reverse blocking, the two-dimensional electron gas at the spaced p-GaN structure is rapidly depleted under reverse bias, to form a depletion region, so that the blocking capability of the device is improved.

Abstract: A machine learning-based method for designing a high-strength high-toughness steel, including: (S1) obtaining data and filling in missing parts to form a data set; (S2) selecting feature data in the data set to form a standard data set; (S3) constructing two machine learning models of the high-strength high-toughness steel; (S4) completing training after the two models are evaluated to be qualified; (S5) finding frontier points, drawing a Pareto front, and distinguishing a known region and a feature space; (S6) in the feature space, setting a step for the feature data, drawing a grid space, and performing multiple training predictions on each grid point by using the models, to obtain predicted Gaussian distributions of two objectives; and (S7) searching for an expected improvement point through an efficient global optimization algorithm, and obtaining design parameter values of corresponding features.

Abstract: The present invention discloses a method and an apparatus for monitoring an elevator door-opening operation fault. The method includes: acquiring elevator control signals from a serial interface between an elevator controller and a control box by using a preset data acquisition device, wherein the elevator control signals include an elevator operation signal, an elevator door control signal, an elevator internal call signal and an elevator floor signal; monitoring whether there is an elevator door fault in the elevator based on the elevator operation signal, the elevator door control signal and the elevator floor signal; when there is the elevator door fault in the elevator and the elevator internal call signal is in a preset level state, determining that there is the elevator door-opening operation fault in the elevator. The present invention is lower in use cost and may be easily popularized and applied.

Abstract: A bio-enzyme sensor capable of super-hydrophobic solid-liquid-gas three-phase coexistence and a method for preparing the same. The bio-enzyme sensor comprises, from bottom to top, a base material with super-hydrophobic surface, a catalytic material having the function of catalyzing hydrogen peroxide, and bio-enzyme capable of reacting with a substance to be tested to generate hydrogen peroxide. A sufficient amount of oxygen can be provided for enzymatic reaction by forming a state of solid-liquid-gas three-phase coexistence on the surface of the super-hydrophobic material.

Abstract: The present disclosure provides a lift running speed measurement method and system. Record a qth start time of a lift when the lift starts on a start floor, and record a qth stop time and a qth floor number when the lift stops running on a qth destination floor. Compute a qth time difference between the qth stop time and the qth start time. If the q is less than the statistic times Q, record q=q+1 and return to carry out the step of starting the lift on the start floor; if the q is greater than or equal to the statistic times Q, respectively compute a difference between a time difference of each value from 2nd to Qth and a 1st time difference, and a difference between a floor number of each value from 2nd to Qth and a 1st floor number.

Abstract: A bio-enzyme sensor capable of super-hydrophobic solid-liquid-gas three-phase coexistence and a method for preparing the same. The bio-enzyme sensor comprises, from bottom to top, a base material with super-hydrophobic surface, a catalytic material having the function of catalyzing hydrogen peroxide, and bio-enzyme capable of reacting with a substance to be tested to generate hydrogen peroxide. A sufficient amount of oxygen can be provided for enzymatic reaction by forming a state of solid-liquid-gas three-phase coexistence on the surface of the super-hydrophobic material.