150 results about "Two-port network" patented technology

A notebook computer storage and charging cart includes a cart having mobile racks arranged in stacks for carrying notebook computers, a power system having electrical power connectors connectable to an external power source and battery charges connected to the electrical power connectors and controllable by a power management device to charge the notebook computers subject to a predetermined charging time, and a network unit having a network connection apparatus installed in the cart and first and second network lines respectively connected between first and second ports network connection apparatus and the notebook computers for the transmission of network signals for real-time online data update and download.

Devices and methods for decoupling two antennas in a compact antenna array and antenna arrays comprising the devices are disclosed. According to an embodiment, the device comprises a first resonator coupled with a source, the source being connected with a first antenna of the two antennas; and a second resonator coupled with the first resonator and a load, the load being connected with a second antenna of the two antennas, wherein the first and second resonators are configured so that a first coupling between the source and the first resonator, a second coupling between the first and second resonators, and a third coupling between the second resonator and the load are satisfied with a constraint that an isolation coefficient in a whole network composed of a first two-port network consisting of the two antennas and a second two-port network consisting of the first and second resonators in parallel approach zero as well as reflection coefficients of each port of the whole network are minimized.

A high-precision, multi-port compatible, relative correction method and apparatus for correcting measurement errors covering an increase in the number of ports of a non-coaxial electronic component, in which a relative correction adapter 31 is provided that is formed of a two-port network connected to each port of a production test fixture 5B adjacent to a measurement apparatus. The relative correction adapter has a characteristic that modifies the electrical characteristics generated by the production test fixture 5B having an electronic component under test mounted thereon into electrical characteristics generated by a standard test fixture 5A having the electronic component under test mounted thereon. An error factor of the relative correction adapter 31 is identified from a standard test fixture measurement value and a production test fixture measurement value of a correction data acquisition specimen 11B. A production test fixture measurement value of the electronic component under test 11A is corrected with the error factor of the relative correction adapter 31 to thereby obtain the standard test fixture measurement value of the electronic component under test 11A which is assumed to be obtained when the electronic component under test 11A.

The invention discloses a winding fault locating method for a power transformer based on fusion of the deep convolutional neural network and visual identification. The method comprises the steps of 1)enabling a power transformer to be equivalent to a two-port network, establishing a winding equivalent circuit, and calculating a transfer function of the winding equivalent circuit; 2) for a circuitwith normal circuit parameters, setting a sine wave excitation source with frequency change at one end of a power transformer winding, and obtaining an amplitude-frequency characteristic curve of each section of winding nodes in the normal state; 3) carrying out sweep frequency response analysis on the circuit in various fault states to extract amplitude-frequency characteristics, and calculatingto obtain the amplitude-frequency characteristics of each section of winding node; 4) establishing a characteristic matrix for the acquired amplitude-frequency characteristics; 5) carrying out frequency sweep response analysis on the to-be-diagnosed power transformer to form a characteristic matrix, (6) converting the characteristic matrix into an image, taking simulation and historical detectiondata as a training set, taking fault types and positions as labels, and inputting the training set and the labels into the deep convolutional neural network for training, and (7) carrying out fault classification and location on the to-be-diagnosed transformer.