139 results about "Adaptive identification" patented technology

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention discloses an industrial robot torque compensation control method based on load adaptive identification. The industrial robot torque compensation control method comprises the steps that according to robot overall stress balance analysis, a robot dynamics model under the load working condition is established in a deduced mode through a Newton-Euler method on the basis of considering friction; the optimal excitation trajectory of robot load identification is solved; load inertial parameters are calculated; and on the basis of load inertia matching, joint driving torque consumed by the load when a robot moves under the load working condition is compensated, and further verification and optimization are conducted through experiments. According to the industrial robot torque compensation control method, the influences of the load on operation performance of the robot are considered, under the premise that no any external sensor is needed, adaptive identification of any load andtorque compensation control are completed through easy operation easy to achieve, the control precision of the robot under the load working condition is effectively improved, and very important significance for processes such as carrying and friction stir welding of the industrial robot under high-speed and large-load movement is achieved.

The invention discloses an on-line identification and control method for a parameter of an alternating current position servo system model. According to the invention, a model reference adaptive identification algorithm based on a Lyapunov stability theory is utilized; and on-line identification is carried out on a rotary inertia J and a viscous damping coefficient B of a controlled object of an alternating current servo system; after convergence is carried out on an identification parameter, an on-line design on a position controller is carried out according to J and B values and the design is automatically switched to position control. According to the invention, an efficiency on an alternating current servo system design can be substantially improved.

In a positioning control device for a two-stage actuator having a coarse-movement actuator and a fine-movement actuator, a notch filter having a cutoff frequency fn is provided to a coarse-movement control loop, and the fine-movement actuator is driven by a sine wave of the frequency fn. At the frequency fn, a head position signal becomes identical with an output signal of the fine-movement actuator, by which the gain of the fine-movement actuator can be estimated by an adaptive identification unit with high accuracy based on the control input to the fine-movement actuator and the head position signal.

The invention discloses a power distribution network intelligent reconstruction method based on multi-information associated utilization, and belongs to the field of distribution automation. The method comprises the following steps: S1, with an incoming circuit breaker as a sign, dividing a distribution network into a plurality of radial independent distribution regions, and numbering the regions in series according to the sequence from small to large; S2, generating M different initial distribution network topologies by using uniform random theory; S3, performing optimizing search of the network topologies by using association characteristics of power flow of the distribution network and the network topology; S4, and comparing M solutions which satisfy a search end condition, and using the network topology whose network loss is minimum as a final distribution network reconstruction scheme. The method obviously improves distribution network reconstruction efficiency through effective association of network topology adaptive identification information and distribution network loss calculation.

The invention provides a multi-natural sensing parameter fusion processing method for recognizing floor where a carrying robot is arranged in an intelligent environment. The method comprises steps of by use of low-cost temperature, humidity and air pressure sensors to search data, and establishing a database; and based on the characteristic that the air pressure is changed along with the height, by use of the big data processing technology, finishing establishing of a prediction model by use of an ELMAN neural network model optimized by the CPSO. According to the invention, adaptive identification of elevator floors where in the carrying robot is arranged in various environments is achieved; a condition of inaccurate floor identification caused by the change of air pressure values at the same height in different environments is effectively solved; and in addition, there is no need to improve the interior or exterior of an elevator, and the method is quite highly universal.

The invention discloses a dynamic test system of a magnetic levitation system. The system mainly comprises an excitation unit, a node selection unit, a tracking band-pass filter unit, an adaptive identification unit and a frequency property extraction unit. The excitation unit is used for generating swept-frequency excitation signals. The node selection unit is used for selecting excitation inputnodes, superposing the swept-frequency excitation signals in a control system, and simultaneously selecting two measurement signal lead-out nodes according to a measured module. The tracking band-pass filter unit is used for filtering lead-out signals. The adaptive identification unit is used for performing Robust adaptive fitting of the processed node lead-out signals by using the swept-frequency excitation signals. The frequency property extraction unit is used for obtaining a frequency property curve of the measured module according to the fitting result. The system disclosed by the invention is integrated in a magnetic levitation control system; observable points are increased; a certain module in the magnetic levitation control system can be subjected to open-loop and closed-loop dynamic test; any equipment is unnecessary to be increased; the cost is saved; and the test efficiency is increased.

The invention discloses an improved function projection method of a complicated dynamic network. establishing a four-dimensional segmented Lorenz-Stenflo chaotic system model with an known parameter, and analyzing characteristic of Liapunov exponent; constructing a response system through utilizing the Lorenz-Stenflo chaotic system as a driving system; designing a projection synchronization controller, and automatically adjusting a feedback coefficient according to error magnitude and state change; and designing an adaptive identification estimator and a parameter updating rule. The improved function projection method is based on fractional order system stability theory combined adaptive control; and a lag improvement projection synchronization method is generated so that the method can realize synchronization between the state of a response node and the state of the driving system at a previous certain time according to a preset desired-dimension function matrix.

The invention provides a smoke detection and fire alarm method based on trajectory identification. The method includes the following steps of 1, video information preprocessing, wherein the video information preprocessing step sequentially includes a dark channel setting stage and an image partitioning stage to increase the thin smoke and environment contrast ratio and a video time information compression stage for transforming three-dimensional images into two-dimensional projection compression images through projection transformation and building a time compression trajectory, 2, smoke detection based on trajectory identification, wherein smoke detection is conducted on the video time compression trajectory obtained in step 1, and the trajectory-identification-based smoke detection step sequentially includes a background image removing stage, a movement trajectory extracting stage and a smoke feature detection stage and 3, smoke source positioning and smoke quantitative calculation, wherein smoke source positioning is conducted on the smoke detected in step 2, and the smoke amount is calculated according to a smoke source. The smoke detection and fire alarm method has the advantages of being easy to calculate, higher in identification accuracy, high in identification speed, good in adaptability and better in identification effect.

The invention discloses an alternating-current servo system speed loop controller parameter self-tuning method. The method includes firstly: identifying rotational inertia of a current system from feedback signals of electric current and feedback signals of rotational speed on the basis of a model reference adaptive identification mechanism; then determining performance indexes according to actual dynamic requirements and output performance of an actuator; finally calculating optimal controller parameters according to estimated values of the rotational inertia of the current system and the performance indexes. The method is simple to implement, less in parameter adjustment, high in universality, good in adaptation for the situation of greater change of working condition (the rotational inertia), capable of meeting application requirements in the high-performance permanent magnet synchronous motor alternating-current servo field and applicable to the situation of dramatic change of the working condition (the rotation inertia).

The invention discloses a fractional order adaptive rapid terminal sliding mode control method of a micro-gyroscope. The method comprises steps of establishing a dimensionless kinetic equation mathematical model and a reference trajectory model of a micro-gyroscope system; and constructing a fractional order-based adaptive rapid terminal sliding mode controller. According to the invention, an objective of real-time tracking of the micro-gyroscope can be achieved; errors can be rapidly converged into zero in the limited time; robustness of the system is enhanced; under the condition that thereis external interference, good performance can be kept; according to a fractional order adaptive rapid terminal sliding mode surface, a fractional order adaptive rule is designed; based on the Lyapunov stability criterion, an adaptive identification method is designed; all kinds of unknown system parameters of the micro-gyroscope are estimated in an online real-time manner; compared with an integer order, adjustable items are added, so control effects and parameter estimation effects are improved; and by use of the adaptive control method, system non-determinacy and parameters of the upper bound of the external disturbance sum are estimated, and automatic tracking of manufacturing errors and external disturbance is achieved.

The invention discloses a multi-target tracking and facial feature information identification method. The method comprises the following steps: firstly, inputting a facial video and converting the facial video into a human face key frame set SF; obtaining a data set SFD through classification and data enhancement SF; extracting SFD features by using an improved miniXaction model to obtain a self-adaptive optimization facial feature recognition model FFsmodel; extracting a key frame sequence in the face data stream FEV, and adaptively aggregating face tracking features to obtain a multi-targetface key frame position set FEC; loading an FFsmodel model and inputting the FFsmodel model into FEC to generate a multi-target facial feature classification result set FECR; and finally, opening an adaptive identification interface to process a terminal request, and storing an obtained processing result set of sign-in and target feature tracking state identification in a multi-target scene in a Web server. The improved multi-target tracking and self-adaptive facial feature recognition technology is combined, the picture feature result label with the highest accuracy can be effectively obtained, and the accuracy and use value of face tracking and facial feature recognition in a multi-target scene are improved.

The invention discloses an overall situation sliding mode control method of a micro gyroscope adaptive neural network. Firstly, a novel adaptive identification method is designed on the basis of an overall situation sliding mode controller and the angular speed of a micro gyroscope and the estimated values of other system parameters are updated online in real time; secondly, the switch gain in a sliding mode control switching item is dynamically adjusted through the adaptive neural network system output to approach to the upper bound of the nondeterminacy of the system and the external disturbance, the switching item of the sliding mode control is converted to continuous neural network output, the buffeting phenomenon in sliding mode control is weakened, and the high adaptive trace ability is achieved. The adaptive algorithm is designed based on the Lyapunov method, the ideal model on the micro gyroscope track and the overall situation asymptotic stability of the system are guaranteed, and the estimated values of an identifier can approach to the respective true value in an asymptotic convergence mode.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention discloses a turntable servo system identification method based on an identification error limitation technology. The method comprises the steps: acquiring a dynamic identification modelof the motor servo system by using a discretization method; acquiring a regression identification model of a servo system according to a discretization method, providing a self-adaptive filter designed based on filtering variable difference information, denoising collected system data, establishing a variable gain attenuation factor to improve the data saturation phenomenon, and designing an intermediate variable and a filtering variable to construct identification error information; applying a constraint condition to the identification error by using a specified performance technology, so theidentification error is limited in a certain interval, and excessive overshoot is avoided; an error conversion technology is adopted to convert a constrained identification error problem into a general identification error design problem, and transient performance realization is ensured based on identification error information and an improved correction gain design parameter adaptive law. Finally, the effectiveness and the usefulness of a turntable servo system identification method based on the identification error limitation technology are verified by utilizing an actual experiment platform.

The invention discloses a digital electric meter reading self-adaptive identification method based on deep learning, comprising the following steps: acquiring a mechanical electric meter, a nixie tubemeter and a non-intelligent electric meter to establish an electric meter image data set, generating a standard data set of a Pascal Voc system, and adopting a Faster-RCNN algorithm to train the generated standard data set to complete the number identification and frame selection work of the electric meter, using the convolutional neural network for predicting category scores of a series of candidate frames on the feature map by using a convolution kernel, and using the category with the highest score as an output result to complete adaptive identification. The method can achieve the adaptivepositioning of digital regions of different types of electric meters, and achieves the recognition of characters in the digital regions through a character recognition algorithm after image preprocessing.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention provides an inverter model prediction control parameter self-adaptive identification method based on a steepest descent method in order to solve the problem that inverter control robustness is affected by parameter mismatch of an LCL type grid-connected inverter filter. According to the method, an error between a state vector prediction value and a sampling value at the current moment is calculated; an objective function is established with the purpose of minimizing the error vector; gradient derivation is conducted on the objective function in the parameter matrix direction based on a steepest descent method; and the minimum value of the objective function is searched, and the parameter matrix is adjusted in real time through an adaptive algorithm, so that the parameter matrix is gradually converged to be close to the true value of the parameter matrix, and therefore, the inductance and capacitance parameters of the filter can be identified online, the filter parametersin the controller can be corrected online, and the robustness of the grid-connected inverter can be improved.

The invention discloses a target vehicle lane changing behavior self-adaptive identification model construction method, which comprises the following steps of 1, establishing a lane changing behaviorinfluence parameter set, and dividing samples to obtain model input; 2, based on the large-scale vehicle trajectory data set, conducting training to obtain a general model for identifying left and right target lane changing behaviors; and 3, establishing an adaptive lane changing behavior recognition model based on Bayesian inference. According to the target vehicle lane changing behavior self-adaptive identification model construction method, the lane changing behaviors of surrounding target vehicles during driving can be effectively identified through setting of the steps 1 to 3 so that information support can be provided for driving behavior decision of the vehicles, and thus driving safety can be enhanced.

The invention relates to a multi-scene smoke detector based on bidirectional blue light detection and a self-adaptive identification method, which belong to the technical field of fire-fighting fire alarm systems. The method comprises the following steps: S1 a fire detector converts the detected aerosol concentration into an electric signal; S2 amplification filtering and A / D conversion are carried out on the voltage signal through a signal processing circuit; and S3 a digital signal obtained through A / D conversion is analyzed in a microprocessor, signal feature extraction, analysis and identification are achieved, a scene is finally judged, and whether an alarm needs to be given or not is judged. The multi-scene smoke detector comprises two blue light emitting diodes and a blue light receiving tube, and the two blue light emitting diodes and the blue light receiving tube form 70 degrees and 135 degrees in space respectively to form forward scattering and backward scattering; real-time identification, scene judgment and threshold adjustment of fire data can be realized through a bidirectional blue light technology, and a foundation is laid for practical application of fire detection.

The invention provides a non-contact adaptive echo cancellation device and an echo cancellation method. The device comprises an adaptive identification filter (10) and a non-contact measurement device(7), wherein an output end of the non-contact measurement device (7) is connected with an input end of the adaptive identification filter (10), and the non-contact measurement device (7) emits laseror ultrasonic wave to a speaker diaphragm (6), and measures a reflected signal of the laser or the ultrasonic wave so as to acquire and use a signal reflecting vibration information of the speaker diaphragm as a reference signal of the adaptive identification filter (10). When the echo cancellation method is used to cancel echo based on the device, adverse effects of non-linear change of transmission from a speaker driving current signal to a sound signal on the adaptive identification filter can be prevented, and the convergence performance of the filter is improved; and the echo suppressionquantity in adaptive echo cancellation is increased, so that the system performance is improved.

The present invention relates to the field of motor control, especially to a method for identifying a rotational inertia transient state of an induction motor. The method is used for vector control ofan induction motor in an operation condition of a speed sensorless. The current identification method is too large in computing amount and cannot meet the requirement of accurate estimation of the transient state. The method provided by the invention comprises the following steps in order: (1) detecting a three-phase stator voltage and current, and respectively marking the voltage and the currentas a vector us and a vector is; (2) according to a [gamma]-type equivalent circuit, calculating a stator flux and a rotor flux; (3) calculating the electromagnetic torque; (4) calculating a rotationspeed; (5) filtering the calculated rotation speed signals; (6) calculating a load torque; and (7) constructing the adaptive identification of the rotational inertia transient state. The rotational inertia information is extracted in the transient state torque signals, and the method provided by the invention can be employed in the induction motor transient state process; the identification of therotational inertia transient state can be achieved with no need for a speed sensorless to improve the operation dynamic performance of the speed sensorless; and moreover, the computing steps are simple and the process computing amount is small.

The invention provides a non-intrusive load adaptive identification method based on a twin network, and the method comprises the steps: taking a V-I track and active power of a load as to-be-identified load features, discriminating the similarity of the V-I track of the load through the twin network, and obtaining the load number information through the matching with a feature library, thereby achieving the load identification. Wherein the user establishes the mapping between the load number information and the actual type of the electric appliance according to the stored use time and the actual use condition of the day. Through the dynamic construction of the feature library, the method can achieve the accurate recognition of the unknown load. Finally, the validity and universality of the model are verified in a PLAID data set and a COOLL data set.

The invention discloses a method and system for identifying the moment of inertia of a permanent magnet synchronous motor. The method comprises the following steps: obtaining alpha axis current, beta axis current, an alpha axis electromotive force, a beta axis electromotive force and an electromagnetic torque of a servo control system of the permanent magnet synchronous motor; inputting the alpha axis current, the beta axis current, the alpha axis electromotive force and the beta axis electromotive force into a speed observer, and obtaining an output rotor observation speed of the speed observer; and inputting the rotor observation speed and the electromagnetic torque into a model reference adaptive identification algorithm, and outputting the moment of inertia. According to the method disclosed by the invention, the output rotor observation speed of the speed observer and the obtained electromagnetic torque are used as the input of the model reference adaptive identification algorithm to obtain the moment of inertia, the speed observer can eliminate phase hysteresis caused by obtaining the speed through position and improve the identification accuracy, and the model reference adaptive identification algorithm can guarantee good stability for the identification system and improve the user experience.

The invention discloses a lithium battery equivalent circuit model parameter online robust adaptive identification method. The method comprises the following steps of: establishing an SOC-OCV relation curve according to a battery open-circuit voltage and a state of charge; initializing system parameters, and setting initial values of theta(0) and P(0), an initial SOC state, lambda and a sampling period T; calculating Soc(k) of the k-th step by adopting an ampere-hour integral method, wherein k=1, 2,..., n; according to the collected battery current and terminal voltage observation data, calculating to obtain a matrix by combining an SOC-OCV function relationship; starting an RLsign algorithm, and calculating a1, a2, a3, a4 and a5 according to an iterative formula; further solving parameters R0, R1, R2, C1 and C2 in a battery model; and circulating the steps to carry out online identification on the model parameters so as to realize dynamic adjustment of the battery model parameters. According to the lithium battery equivalent circuit model parameter online robust adaptive identification method, the influence of non-Gaussian noise on parameter identification can be effectively reduced, and the effectiveness of parameter estimation precision is ensured; and the algorithm is stable, the calculation complexity is low, and the steady-state convergence precision is higher than that of other similar traditional algorithms under the non-Gaussian noise condition.

The invention discloses a self-adaptive identification method for particles in an asphalt mixture CT image, aiming to solve the problem of low extraction accuracy of a microstructure of a mixture caused by a black core phenomenon in the center of a test piece in an existing asphalt mixture CT image. The self-adaptive identification method comprises the following steps: 1, determining the radius and circle center position of a test piece in an asphalt mixture CT image; 2, performing gap gray level zeroing processing on the CT image; 3, performing statistical gray distribution on the CT image after the gap gray return-to-zero processing along the radial direction; 4, carrying out the homogenization of the brightness of the image after the statistical gray scale is distributed in the radial direction; 5, performing filtering and noise reduction on the image subjected to the homogenization processing; 6, performing global threshold segmentation on the image subjected to filtering and noisereduction; and 7, performing image morphological processing on the image after global threshold segmentation. The self-adaptive identification method is used for digital image processing and road engineering.

The invention discloses a reliability evaluation method considering influence of a multi-stress coupling effect on product degradation. The method comprises the following steps: 1, establishing a performance parameter degradation model considering product degradation rate-degradation fluctuation correlation; 2, establishing a quantitative influence relationship of a multi-stress coupling effect on a product degradation rate; 3, establishing an accelerated degradation model considering the combined influence of the multi-stress coupling effect on the product degradation rate-degradation fluctuation; 4, deducing a failure distribution function and a reliability function of the product under the multi-stress coupling effect; 5, realizing self-adaptive identification and unknown parameter estimation of the coupling function by using maximum likelihood estimation; and 6, substituting the optimal coupling function obtained by optimization and the unknown parameter estimation value into the failure distribution function and the reliability function to realize reliability evaluation of the product under the multi-stress coupling effect. According to the method, the problem that the multi-stress coupling effect and the influence of the multi-stress coupling effect on the degradation process cannot be accurately and quantitatively described in the product reliability evaluation process is solved.

The invention discloses a plantar reflex zone adaptive identification and treatment method of an intelligent foot biological health information management and hydrotherapy health care decision-making system. The method comprises the following steps: a user puts feet on a water outlet board of a hydrotherapy instrument, and a sensing device arranged on the water outlet board collects response data of the user when the user uses the hydrotherapy instrument; the foot outline of the user is identified and drawn according to the response data, the foot outline is compared with a standard foot outline, and the actual plantar reflex zone positions of the user are calculated out according to the plantar reflex zone positions on the standard foot outline; and hydrotherapy heads corresponding to the actual plantar reflex zone positions of the user are adaptively selected to implement stimulation therapy on the plantar reflex zones of the user. Through the method, the foot outline and the plantar reflex zone positions of a user can be identified automatically, and the hydrotherapy heads in corresponding positions are automatically aligned with the reflex zone positions under control to implement stimulation therapy. The method has a wider range of application. An ideal therapeutic effect is achieved for users of different foot sizes. Family members can share one hydrotherapy instrument.

The invention discloses a high-adaptability identification counting method for multiple fields, which is suitable for counting targets and comprises the following steps of: inputting an to-be-detectedimage; analyzing a to-be-detected region of the to-be-detected image based on gray information and shape information to obtain a target area ROI; extracting array features of a ROI of the target area; judging whether the target is in regular array distribution or not according to the array characteristics, and counting the target according to a judgment result to obtain a counting value of a target. The method can automatically search a target and perform counting operation so as to meet the counting requirements of different fields, does not need repeated development and deployment, is highin adaptability and counting efficiency, can greatly shorten the development period of the counting method, and effectively improves the productivity.