2070results about How to "Verify validity" patented technology

The invention relates to the technology of UAV (unmanned aerial vehicle) control, aims at proposing a UAV locus and attitude cooperative control method and ironing out a defect in the locus planning for each UAV of a conventional UAV cluster, and can effectively reduce the requirements for the positioning precision of the UAV. Therefore, the invention provides a cluster UAV locus and attitude cooperative control method for a safety domain, and the method comprises the following steps: 1, control-oriented four-rotor-wing UAV locus and attitude model building: giving full consideration to the inherent characteristics of a four-rotor-wing UAV and the dynamic factors in flight, and building a locus and attitude mathematic model for the four-rotor-wing UAV; 2, cluster center locus planning: achieving the obstacle avoidance path planning for a cluster UAV central point based on a pseudo-spectral method; 3, safety flight region optimization: completing the optimization of a cluster UAV safetyflight envelope and a cluster UAV optimal formation configuration; 4, distributed cooperative controller design. The method is mainly used in a UAV control occasion.

Embodiments of the present invention are directed to a system, method and apparatus for a data distribution and request authentication system. In embodiments of the present invention, the data authentication system couples a confirmation code with the distribution of data, such as an advertising link, and compares the confirmation code to known data upon a response to the distributed data, for example, linking to a merchant's web site via the advertising link.

The invention discloses an image subtitle generation method and system fusing visual attention and semantic attention. The method comprises the steps of extracting an image feature from each image tobe subjected to subtitle generation through a convolutional neural network to obtain an image feature set; building an LSTM model, and transmitting a previously labeled text description correspondingto each image to be subjected to subtitle generation into the LSTM model to obtain time sequence information; in combination with the image feature set and the time sequence information, generating avisual attention model; in combination with the image feature set, the time sequence information and words of a previous time sequence, generating a semantic attention model; according to the visual attention model and the semantic attention model, generating an automatic balance policy model; according to the image feature set and a text corresponding to the image to be subjected to subtitle generation, building a gLSTM model; according to the gLSTM model and the automatic balance policy model, generating words corresponding to the image to be subjected to subtitle generation by utilizing anMLP (multilayer perceptron) model; and performing serial combination on all the obtained words to generate a subtitle.

The invention relates to a wind power station energy storage capacity control method based on particle swarm optimization. The wind power station energy storage capacity control method includes the steps of taking the interval reference value of the wind power station output power which is adapted to the dispatching cycle of a power grid as a foundation, taking the influence of the wind-abandoning energy of a wind power station and the lost energy of an energy storage system into consideration, taking the lowest costs of the energy storage investment and a wind and power operation system as target functions, establishing a policy model for energy storage capacity optimizing based on a storage battery energy storage system, and then applying the improved particle swarm optimization to solve the functions. By the aid of the wind power station energy storage capacity control method based on the particle swarm optimization, the wind power which is output under effect of the energy storage system can be output smoothly at intervals, so that effective connection between the energy storage system and the existing dispatching operation manner can be realized, and the best economic benefit can be achieved simultaneously.

In response to a validation request that includes second information identifying the certificate authority, key information of the certificate authority at issuance of the public key certificate, and information identifying the public key certificate, if the second information identifying the certificate authority included in the validation request corresponds to the first information identifying the certificate authority included in the authority certificate, and the information identifying the public key certificate included in the validation request does not exist in the revocation information, the validation server creates a validation result indicating that the public key certificate corresponding to the information identifying the public key certificate included in the validation request is valid.

The invention provides an unmanned aerial vehicle trajectory planning method based on EB-RRT. The method comprises the steps that grid partitioning is carried out on a map environment; a node xnearst nearest to a random point in existing nodes is found; an insertion point xnew is calculated according to the step length; if the sum of the distance between a root node to xnew and the Euclidean distance between xnew and the end is not greater than the length of the current optimal path, whether the xnew point is in an obstacle is detected; if not, the surrounding environment information of xnearst is collected, and a new insertion point xnew is randomly sampled in the surrounding free area; xnew is inserted into a tree; the nearby node set of xnew is traversed and found in the corresponding grid, and the path of the nearby nodes is optimized; connection detection is carried out on two trees into which the xnew point is inserted; if not, two trees are exchanged, and random points continue to be sampled; if so, a feasible path is found, and downsampling is carried out; and a Bessel cubic interpolation algorithm is used to optimize the new path. The unmanned aerial vehicle trajectory planning method provided by the invention has the advantages of high convergence speed, good flexibility, high running efficiency and good practicability.

The invention provides an underactuated autonomous underwater vehicle (AUV) adaptive trajectory tracking control device and a control method. The practical positions and the course angles of an AUV which are acquired to a measurement element (3) and reference positions and reference course angle information which are generated by a reference path generator (1) are converted through a diffeomorphism converter (6) to obtain new state variables, then the new state variables and speed and angular speed information which is acquired by a sensor (5) are transmitted to a parameter estimator (11) anda longitudinal thrust and course changing moment controller (14), and a control command is obtained through resolving to drive an actuating mechanism to adjust the longitudinal thrust and the course changing moment of the AUV. By using the underactuated AUV adaptive trajectory tracking control device, the inertial mass parameters of the AUV and hydrodynamic damping parameters are not required to be known and the goal of arriving at designated positions at designated time according to designated speed is realized. Since the AUV considered in the invention is underactuated, the energy consumption and the manufacturing cost of a system can be reduced, the weight of the system can be reduced and the propulsion efficiency can be improved.

A method and apparatus of X.509 certificate-based mutual authentication and key distribution for a Downloadable Conditional Access System (DCAS) in a digital cable broadcasting network is provided for composing a software-based secure DCAS in various Conditional Access Systems (CASs) based on an embodiment form of Conditional Access (CA) application for CA of digital cable broadcasting.

The invention relates to a simulation platform and a method for power dispatching automation, in particular to a simulation platform and a method for a dispatching automation system. By the aid of the simulation platform and the method, a complex electric power system, a multistage dispatch master station system, a transformer substation, a power plant system, and AGC (automatic generation control) and AVC (automatic voltage control) substation systems are completely simulated, steady state and transient state calculation of the operating condition of the electric power system of simulated automatic control and relay protection devices and so on are carried out, and the operating condition of the electric power system is truly displayed. And therefore, standardized testing environment and testing method can be provided for testing the dispatching automation system and devices, reliability of the system is increased, operating efficiency of the system is improved and safe and stable operation of a power grid is effectively supported.

The invention discloses a method and a system for discovering a network hot topic based on situation assessment. The method comprises the following steps of: acquiring internet forum data in real time; preprocessing corpus of the internet forum data and extracting a candidate word string; forming a characteristic set in a vector form to express each post based on the candidate word string; clustering the posts, acquiring the discovered hot topic according to the clustering result and quantizing; and performing situation assessment on the hot topic generated by clustering and correcting the discovered hot topic according to a situation assessment result. By comprehensively utilizing a topic discovering and tracking algorithm and a situation assessment idea, the organization form of algorithms of selectable and customable processing processes is realized to analyze text information and mine data so as to discover the hot topic. The experiment result verifies the validity and the accuracy of the method and the system and the method and the system have great practical value.

The embodiment of the invention discloses a method and device for detecting network hot topics found based on a maximal clique. The method comprises the following steps of: acquiring data of a network news website, a forum, a blog and a microblog in a real time; carrying out word segmentation, word frequency statistics and other processing on the acquired data to find all hot point word pairs and construct a hot point word pair set; expressing each hot-point word by using a unique serial number; viewing the hot point word pair set as an undirected graph and excavating the undirected graph to obtain all maximal cliques; and transforming each maximal clique into a word combination for expressing one hot point topic. The invention also discloses a device for detecting the network hot topics. According to the embodiment of the invention, the hot point topics in the network can be accurately found in a real time, the detection speed and precision of the hot point topics are improved and higher practical value is obtained.

The invention provides a motor servo system jitter-free sliding mode position control method based on disturbance compensation. According to the method, the nonlinear friction characteristic, external disturbance and other modeling uncertainty of a system are considered, continuous and smooth friction compensation is made for nonlinear friction, and the low-speed servo performance of the motor position servo system is further improved; uncertainty such as non-modeling disturbance is estimated through an extended state observer, feedforward compensation is made when a controller is designed, and the robustness of the actual motor position servo system to external disturbance is improved; jitter and singularity will not be caused to voltage output of the designed terminal sliding mode controller, the controller can guarantee that the state of the system tends to be balanced in a limited time, and the tracking performance of the system is greatly improved; the designed terminal sliding mode controller is simple, has certain robustness to system parameter variation and is more favorable for being applied to engineering practice.

The invention relates to an electric-automobile-contained micro electric network multi-target optimization scheduling method. The method is characterized by comprising steps that, 1), a mode of access of an electric automobile to a micro electrical network is determined, discharging and charging load distribution characteristic superposition of a single electric automobile under different access modes is carried out to obtain discharging and charging load distribution characteristics of the electric automobile; (2), the electric automobile is taken as a micro electric network scheduling object which is added for electric network optimization scheduling, and an micro electric network scheduling model in consideration of large-scale electric automobile access is established according to the discharging and charging load distribution characteristics of the electric automobile; and 3), a particle swarm optimization algorithm based on the automatic recombination mechanism is employed to solve the micro electric network scheduling model in consideration of large-scale electric automobile access, economical efficiency of micro electric network scheduling under various scheduling strategies is compared and analyzed, and thereby the optimum scheduling strategy is obtained. Compared with the prior art, the method further has advantages of comprehensive consideration and effective and feasible performance.

The invention relates to a method for estimating a lithium ion battery state-of-charge (SOC) of a hybrid electric vehicle on line. The method comprises the following steps of: 1, calculating an initiative SOC value of a power battery after the whole vehicle is electrified; 2, calculating an on-line real-time SOC estimated value of the power battery; 3, diagnosing a state of the power battery; and4, self-adaptively correcting the on-line real-time SOC estimated value of the power battery. In the method, an automotive electronic control unit and a time-triggered type controller local area network communication platform are used to realize the functions of performing on-line monitoring, diagnosis and self-adaptive correction on the SOC of a vehicle lithium ion battery, and the battery can be conveniently, quickly and effectively controlled and managed.

The invention relates to an automatic tracking control and online correction system with a welding gun and a control method thereof and belongs to the technical field of robotic arc welding. The automatic tracking and control and online correction system comprises a vision sensing system, an image processing system, a tracking control system and an online correction system, wherein the vision sensing system comprises a CCD (Charge Coupled Device) vision sensor; the image processing system comprises an image preprocessing module and an image processing module; the tracking control system comprises the welding gun, a control system, a driving system, a signal processing module and an arc sensor; the online correction system comprises a laser two-dimensional outline scanning sensor and an error analysis module. According to the automatic tracking control and online correction system, butt-welded seams are predicated, tracked and corrected respectively a prewelding stage, a welding stage and a post-welding stage by virtue of error online fusion of three sensors, namely the CCD vision sensor, the arc sensor and the laser two-dimensional outline scanning sensor, so that the welded seam tracking precision and the fault tolerance of the system are improved, the welding production efficiency and the welding quality are improved, and the automatic tracking control and online correction system has generality.

The invention discloses a hybrid electric vehicle power assembly durability testing frame and a testing method. A dynamometer is connected with an engine and a tooth ring through an ISG motor which is electrically connected with an inverter; a rotation speed sensor which is arranged at the external side of the tooth ring is connected with an instrument controller; a frame control system respectively emits test control signals to a hybrid electric control system, an engine management system and a dynamometer; the hybrid electric control system receives external signals and initiates the requirements of starting or stopping and torque to the engine management system and the motor control system according to a self-control strategy. The frame control system (PUMA) controls the operation of the dynamometer by a pre-arranged speed curve and a torque curve, thus leading the engine to run under the working conditions defined in advance. The distribution of motor torque and engine torque and the auxiliary driving torque of the motor are controlled by the hybrid electric control system according to the rotation speed of the engine and the torque requirement.

The invention discloses an under-actuated surface vehicle trajectory tracking control system based on a self-adaptive fuzzy observer. A sensor module collects position and course information y of a surface vehicle and outputs the position and course information y to the self-adaptive fuzzy observer; the self-adaptive fuzzy observer receives output of a controller, obtains position and heading estimation, speed estimation, slow varying disturbance force estimation and unknown nonlinear term estimation (please see the expressions in the specification) and respectively transmits the position and heading estimation, the speed estimation, the slow varying disturbance force estimation and the unknown nonlinear term estimation to a trajectory tracking error generator and the controller; a guide system obtains expected speed and accelerated speed information in a hull coordinate system through calculation and respectively transmits the expected speed and accelerated speed information to the trajectory tracking error generator and the controller; the trajectory tracking error generator obtains error information of all states of trajectory tracking and transmits the error information to the controller; the controller calculates a longitudinal thrust and course changing torque control instruction of the current moment and transmits the control instruction to a thrust system, and the thrust system executes the control instruction. The under-actuated surface vehicle trajectory tracking control system can perform fast tracking under the conditions that only the position and the heading angle are measurable and a model has indeterminacy.

The invention provides a method for controlling the position of a self-adaptive robust of a motor servo system based on an extended state observer. The method comprises the steps of 1, establishing a mathematic model of the motor servo system; 2, configuring an adaptive law to estimate the uncertain parameters in the motor servo system; 3, configuring the extended state observer to estimate the uncertainty of the motor servo system; 4, configuring a position controller of the self-adaptive robust of the motor servo system based on the extended state observer; 5, determining relevant parameters and functions in the motor servo system to enable the position output of the motor servo system to accurately and progressively track the expected position command, and enabling the input of the motor servo system to not have the chattering phenomenon. The invention also relates to a system for controlling the position of the self-adaptive robust of the motor servo system based on the extended state observer.

The invention, which belongs to the technical field of vehicle-mounted network, discloses a vehicle-mounted CAN bus network abnormity detection method and system. CAN bus abnormity detection based on a relative entropy is performed on an identifier ID; a sliding window with a fixed message number is employed; messages are paired based on a relationship between a message sensing sequence and a sending number, relative entropies of the paired messages and relative entropies of all IDs and normal distribution are calculated, and whether abnormity occurs is determined based on the two kinds of relative entropies; a replay attack and a denial of service attack are detected; CAN bus network abnormity detection based on a message data domain is performed on a data domain; features, including a constant value feature, a cyclic value feature, and a multi-value feature, of the message data domain are extracted; and a normal message model is established based on the extracted features and the message abnormity is detected. Therefore, the replay attack, the denial of service attack, the tampering attack and the forgery attack can be detected effectively and efficiently; more abnormal information is provided; and thus subsequent protection can be performed well.

The invention discloses a method of deep neural network based on a discriminable region for dish image classification. The method relates to the field of image processing, integrates a significant spectrum pooling operation, and fuses low-level features and high-level features in a network. The method adopts a convolution kernel filling operation, effectively preserves important information on characteristic spectra, and is matched with data dimensions of a full connection layer, so that the full connection layer can utilize a VGG-16 pre-training model at a training state, thereby improving the training efficiency and network convergence speed. Each image to be classified is subjected to normalization processing based on the model which is learned in a constructed database, the image is tested by using a trained convolutional neural network, the classification precision is measured by using Softmax loss, a classification result of the image is obtained, real categories and predicted categories of targets in all test images are compared, and a classification accuracy rate is obtained through calculation. The method is used for testing on a self-established data set CFOOD90, and theeffectiveness and the real-time performance of the method are verified.

The invention relates to an RGB-D image classification method and system. The method comprises the steps of: S1, utilizing a convolution neural network (CNN) to process a source RGB image and a Depth image respectively, and extracting low level characteristics; S2, utilizing a recursion neural network (RNN) to carry out feedback learning on the image low level characteristics, and extracting image middle level characteristics; S3, adopting a block interior constraint dictionary learning method, carrying out characteristic set sparse expression on the image middle level characteristics, and obtaining high level characteristics of the RGB-D images; and S4, inputting the high level characteristics of the RGB-D images into a linear SVM to complete the classified identification of the RGB-D images. According to the invention, automatic characteristic extraction of the images is realized, learning RGB-D image characteristic expressions can effectively distinguish classification of noise data from high similarity images, and the classification precision of the RGB-D images is improved; in addition, the linear SVM is utilized, and the image classification speed is improved.

The invention discloses an SOC estimation method based on a Kalman filtering method, and belongs to the field of power battery management. The method specifically comprises the steps that 1, a power battery model is built, and a precise lithium power battery model is built mainly by determining an equivalent circuit model and based on test data; 2, power battery SOC is estimated; 3, EKF algorithmis adopted to estimate an SOC result and conduct error analysis. The method provided by the invention analyzes the working principle of a lithium ionic battery and builds a power lithium ionic batterydivalent RC equivalent model, identifies the parameter models through laplace transformation and a large number of experiment data, and adopts a matlab platform to estimate the power lithium batterycharge state. The emulation result shows that the model has the good adaptability to the complex working conditions of electric automobiles, not only can the precision of SOC estimation be improved, but also the computing quantity is small, and SOC estimation is easy to achieve.

The invention relates to a test bench and a mounting method and a testing method thereof. The test bench is used for a four-wheel-drive strong-mixing power assembly system test and comprises an engine and controller thereof, a driving motor and controller thereof, speed shifting mechanisms, an entire vehicle controller and dynamometers, wherein the speed shifting mechanisms comprise a first speed shifting mechanism and a second speed shifting mechanism; the dynamometers comprise a front-drive dynamometer and a rear-drive dynamometer; the engine is connected with the front-drive dynamometer through the first speed shifting mechanism; the driving motor is connected with the rear-drive dynamometer through the second speed shifting mechanism; the entire vehicle controller is used for receiving a signal from the outside and transmitting a generation torque request and a driving torque demand to the driving motor controller and the engine controller according to the entire vehicle control strategy; the engine controller is in controlled connection with the engine; and the driving motor controller is in controlled connection with the driving motor.

The invention discloses a rolling bearing health condition evaluation method based on CFOA-MKHSVM, belongs to the technical field of bearing fault evaluation and aims at evaluating rolling bearing performance degradation degree more effectively. The method includes: extracting time domain and frequency domain statistical features of bearing vibration signals and wavelet-packet-based time frequency features; aiming at the problems of nonuniform state data distribution and data heterogeneity of a rolling bearing, adopting a hyper sphere support vector machine for recognition and performing multinuclear convex combination and optimization; in order to eliminate blindness of artificial selection of multiple parameters of a classifier and proneness to selecting into a local optimum problem, combining a fruit fly algorithm with a chaos theory to optimize the multiple parameters; building a chaos optimization fruit fly algorithm-multi-core hyper sphere support vector machine CFOA-MKHSVM model, and putting forward a normalized difference coefficient evaluation index. Experiments for comparing the normalized difference coefficient evaluation index with an SVDD algorithm evaluation index verify effectiveness of the normalized difference coefficient evaluation index, and quantitative evaluation of rolling bearing health state is realized.

The invention discloses an identification method for a horn of a special vehicle based on dynamic time warping (DTW) and hidden markov model (HMM) evidence integration. The identification method for the horn of the special vehicle based on dynamic time warping (DTW) and hidden markov model (HMM) evidence integration comprise a first step of building a vehicle-horn sample library. A second step of preprocessing step. A third step of extracting and dimensionality reduction disposing parameters of vehicle-horn characteristics. A fourth step of identifying the horn of the special vehicle based on the evidence integration, and gaining the DTW identification result and the HMM identification result by respectively adopting DTW algorithm and HMM algorithm. If the DTW identification result and the HMM identification result are consistent, the final identification result is kept consistent with the DTW identification result or the HMM identification result. If the DTW identification result and the HMM identification result are different, the final identification result should be output by an identification decision reasoning with a data set (DS) evidence theory. The identification method for the horn of the special vehicle based on dynamic time warping (DTW) and hidden markov model (HMM) evidence integration adopts integration identification technology and identification rate is high.