96results about How to "Responsiveness" patented technology

The invention relates to the automatic driving vehicle performance test technology field and especially relates to a static obstacle response performance test method of an automatic driving vehicle and a test field. In the test method, the test field comprises a road having at least two lanes and a static obstacle. A vehicle to be detected and the static obstacle are arranged in a same lane. A vehicle lane changing operability distance is arranged between the static obstacle and the vehicle to be detected. The test field is similar to an actual driving environment so that a test result can accurately reflect response performance of the vehicle to be detected to the static obstacle. A response of the vehicle to be detected is limited to be a condition that the vehicle runs towards the static obstacle and changes the lane so as to avoid the static obstacle. Evaluation of static obstacle response performance of the vehicle is standardized and the test result is authoritative and reliable. The test field provided in the invention can make the test result accurately reflect the response performance of the vehicle to be detected to the static obstacle and can be served as a standard scene.

A semiconductor device with low parasitic capacitance is provided. The semiconductor device includes a first oxide insulator, an oxide semiconductor, a second oxide insulator, a gate insulating layer, a gate electrode layer, source and drain electrode layers and an insulating layer. The oxide semiconductor includes first to fifth regions. The first region overlaps with the source electrode layer. The second region overlaps with the drain electrode layer. The third region overlaps with the gate electrode layer. The fourth region is between the first region and the third region. The fifth region is between the second region and the third region. The fourth region and the fifth region each contain an element N (N is hydrogen, nitrogen, helium, neon, argon, krypton, or xenon). A top surface of the insulating layer is positioned at a lower level than top surfaces of the source and drain electrode layers.

The invention discloses a force-induced response dynamic polymer. The polymer chain skeleton contains at least one force-sensitive group and at least one boron-free dynamic covalent bond, optionally contains at least one boron-containing dynamic covalent bond, and also optionally contains at least one supramolecular interaction; and under the action of mechanical force, the force-sensitive group in the dynamic polymer is subjected to chemical and / or physical change to realize force-induced response. According to the dynamic polymer, the force-sensitive group with a force-induced response characteristic, the boron-free dynamic covalent bonds with different dynamic properties, the optional boron-containing dynamic covalent bond and the supramolecular interaction are introduced, so that the force-induced response dynamic polymer material with a wide controllable range, a rich structure and various properties can be prepared. The force-induced response dynamic polymer can be widely appliedas a functional coating with a force-induced response function, a self-repairing material, an energy storage device material, an energy absorbing material, a photoelectric device material, a mechanical probe and the like.

The invention discloses an experimental method for simulating the stress corrosion of a metal material under working conditions, belongs to the technical field of metal corrosion simulation, and solves the problem in the prior art that a sample device cannot truly simulate solution scouring in the working environment. The experimental method comprises the following steps: firstly, designing related experimental parameters according to the actual working environment; then respectively connecting a test piece to an upper loading part and a lower loading part through pins, mounting respectively corresponding nuts, adhering a strain gage to the center of the test piece, connecting the stress gage to a strain meter through a wire, loading load onto the test piece through the upper loading part and the lower loading part; finally, after loading a certain stress, taking down the strain gage, suspending a loading mechanism on a circular tray through a high-temperature-resistant cotton thread, placing the loading mechanism in a vessel body, and starting to do an experiment and record related data according to the conventional method. The experimental method can be used for testing related properties of the metal test piece which is corroded at high temperature and high pressure, such as the corrosion rate and the tensile property.

The invention provides a by-pass valve actuator of a turbocharger, which comprises an actuator case (1), a membrane sheet (2), an inner piston case (3), an inner piston (4), an actuating push rod (5), an inner piston upper spring (6) and an inner piston lower spring (7), wherein the inner piston (4) and the inner piston upper spring (6) are arranged in the inner piston case (3), the inner piston (4), the inner piston upper spring (6) and the inner piston lower spring (7) are all sheathed and arranged on the actuating push rod (5), the inner piston (4) is positioned between the inner piston upper spring (6) and the inner piston lower spring (7), and the elasticity of the inner piston upper spring (6) is smaller than that of the inner piston lower spring (7). The by-pass valve actuator has good control precision on the pressure, and has sensitive responsivity, and in addition, the structure is simple and reliable.

The invention discloses a deep learning characteristic generalization method based on a latent variable model. The method is carried out according to the following steps that: S1: on the basis of thelatent variable model, establishing one pair of bidirectional parametric mapping between data space and latent variable space, and combining with a weighted relationship to construct a characteristicgeneralization layer; S2: embedding the characteristic generalization layer into a deep neural network, and dividing the network into three parts, including a DNN (Deep Neural Network)-1, the characteristic generalization layer and a DNN-2; S3: determining the optimization objective of the model, and defining a target function; and S4: lowering characteristic pattern data complexity, and establishing a multi-branch parallel forward propagation structure. The method is favorable for improving the generalization ability of a deep network model, and an overfitting phenomenon during small-scale data training can be lightened. Compared with other methods which use a generative model to enhance data, the method disclosed by the invention simplifies network complexity and improves training efficiency.

A semiconductor device is formed in such a manner that a first insulator, a first oxide semiconductor, and a first conductor are formed; the first conductor is processed to form a second conductor; the first oxide semiconductor is processed to form a second oxide semiconductor; a second insulator is formed over the second conductor; a third insulator is formed over the second insulator; a fourth insulator is formed over the third insulator; the fourth insulator, the third insulator, the second insulator, and the second conductor are selectively processed to partly expose the second oxide semiconductor; a fifth insulator is formed over the second oxide semiconductor and the fourth insulator; and a third conductor is formed over the fifth insulator and then chemical mechanical polishing treatment is performed to expose a top surface of the fourth insulator.

A semiconductor device includes a first insulating layer over a substrate, a first metal oxide layer over the first insulating layer, an oxide semiconductor layer over the first metal oxide layer, a second metal oxide layer over the oxide semiconductor layer, a gate insulating layer over the second metal oxide layer, a second insulating layer over the second metal oxide layer, and a gate electrode layer over the gate insulating layer. The gate insulating layer includes a region in contact with a side surface of the gate electrode layer. The second insulating layer includes a region in contact with the gate insulating layer. The oxide semiconductor layer includes first to third regions. The first region includes a region overlapping with the gate electrode layer. The second region, which is between the first and third regions, includes a region overlapping with the gate insulating layer or the second insulating layer. The second and third regions each include a region containing an element N (N is phosphorus, argon, or xenon).

A semiconductor device includes a first oxide insulating layer over a first insulating layer, an oxide semiconductor layer over the first oxide insulating layer, a source electrode layer and a drain electrode layer over the oxide semiconductor layer, a second insulating layer over the source electrode layer and the drain electrode layer, a second oxide insulating layer over the oxide semiconductor layer, a gate insulating layer over the second oxide insulating layer, a gate electrode layer over the gate insulating layer, and a third insulating layer over the second insulating layer, the second oxide insulating layer, the gate insulating layer, and the gate electrode layer. A side surface portion of the second insulating layer is in contact with the second oxide insulating layer. The gate electrode layer includes a first region and a second region. The first region has a width larger than that of the second region.

The invention discloses a hybrid electric vehicle self-adaptive PID dynamic control method for improved grey prediction, and the method comprises the following steps: (1) building an improved grey prediction model GM (1, 1); (2) establishing a self-adaptive PID control model; (3) establishing an HEV power driving model; (4) taking a vehicle speed requirement of a typical working condition (NEDC new European driving working condition) as an input; and based on Matlab / Simulink, respectively establishing an adaptive PID closed-loop control HEV driving simulation model based on improved grey prediction and an HEV driving simulation model based on conventional PID closed-loop control, and respectively carrying out comparative simulation analysis on the two models. According to the method, the HEV actual vehicle speed is effectively predicted by establishing the improved grey prediction model, meanwhile, the self-adaptive PID controller with parameters capable of being automatically corrected is established, real-time optimization control is conducted on the HEV power system, the vehicle speed is made to rise rapidly and stably, and the aim of saving energy is truly achieved.

The invention provides a preparation method of DNA base pair cross-linked supermolecular hydrogel. Adenine and thymine are respectively modified to obtain a double bond connected base monomer, water serves as a solvent, the water and hydrophilic monomer acrylamide are subjected to thermal initiation by an initiator potassium persulfate, a high-molecular polymer with a three-dimensional network structure is formed through free radical copolymerization, and the polymer is subjected to the hydrogen-bond interaction of complementary pairing of DNA base pairs to obtain the DNA base pair cross-linked supermolecular hydrogel. According to the preparation method, the base pairs contained in a living body are introduced to polyacrylamide hydrogel, not only terminal connection is adopted, and the base pairs are inlayed into macromolecular chain segments, so that a large number of base pair hydrogen-bond interactions exist among chains, the mechanical properties are affected and stronger and more sensitive response is produced to salt and pH. The influence of the organic solvent is eliminated, and the DNA base pair cross-linked supermolecular hydrogel has good biocompatibility, viscosity and pH and salt responsiveness and can be used in the fields of gene transfer, gene therapy, drug controlled release, tissue engineering and the like.

A method for communicating data using a “burst protocol” includes enabling power to transmitters and receivers when needed to balance power consumption with latency and unscheduled communication. A transmitter transmits a plurality of packets indicative of substantially the same payload. Power then may be disabled to the transmitter. A first predetermined time is determined to enable a receiver. A second predetermined time is determined to disable the receiver. Power then may be enabled to the receiver based on the first predetermined time. The payload is output in response to receiving one or more of the plurality of packets. Power to the receiver is disabled based on the second predetermined time.