356 results about "System identification" patented technology

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

Techniques for receiving, storing, analyzing, and utilizing raw content are disclosed. The system receives raw text from a user, including a first tag and second tags. The system identifies and removes formatting attributes in the raw text, including whitespace characters, to generate a normalized input. The system stores the normalized input in target dataset(s) and parses the normalized input for the first tag and the second tags. In this case, the first tag corresponds to one or more topics, and the second tags represent a computing system associated with corresponding portions of the normalized input. The system analyses the first tag and the second tags to identify the topics. The system generates one or more topic maps for the target dataset(s) based on the first tag and the one or more second tags. The topic map(s) include one or more references to content items within the target dataset(s).

Techniques for identifying a document layout are disclosed. In one embodiment, attribute data associated with an electronic document is accessed. A machine learning model is then applied to the attribute data. The machine learning model is configured to classify the electronic document based on the attribute data and feature sets of a plurality of document classes. Based on the document class predicted by the machine learning model, the system identifies a layout associated with the document class. The layout specifies layout elements and content types associated with the layout elements. The system extracts and stores information from the electronic document according to its content type.

The invention relates to the technical field of semiconductor material photoelectric detection, and discloses a whole-course calibration method of a microwave photoconductive attenuation tester, which comprises the following steps: steady-state optical calibration: establishing reference mapping from light intensity to absolute carrier concentration; performing sweep frequency perturbation measurement at each concentration working point to obtain multiple groups of complex response data; separating an instrument transfer function from sample differential response sensitivity by adopting a system identification algorithm; an integral differential sensitivity reconstruction curve is converted into an absolute calibration model by using an anchor point; starting closed-loop feedback control to lock a specific phase, and verifying the model; and outputting an absolute calibration model and an instrument transfer function as a static reference and a dynamic operator. According to the invention, the absolute concentration anchor point is established through steady-state optical calibration, integral reconstruction is carried out on the differential response sensitivity decoupled by dynamic frequency sweep measurement, and finally, the anchor point is utilized to determine the integral constant, so that the accuracy and reliability of carrier concentration quantitative analysis are improved.

The invention relates to the field of chip detection, and discloses a high-efficiency chip detection system and method, which are used for realizing long-term performance degradation detection of a chip. Comprising the following steps: synchronously acquiring power pin electric signals and chip surface thermal imaging data to generate an electrothermal coupling characteristic data set in a working state of a to-be-tested chip, and extracting impedance and heat conduction characteristic parameters through impedance spectroscopy analysis and thermal time constant calculation. And analyzing an internal signal transmission path based on impedance characteristic parameters to obtain path characteristic parameters, establishing a mathematical model of dynamic current and working voltage of the chip by combining the two, generating a system identification result, and finally generating a chip detection result and fault positioning information by integrating various parameters. According to the invention, system benchmark testing and environmental interference monitoring and compensation are carried out, and high precision and high robustness of a detection result are realized.

The invention discloses a unified access method and system for peripheral equipment of a swan monk system based on hardware abstract virtualization, and relates to the technical field of peripheral equipment management of an operating system. A structured data set S is constructed by detecting a peripheral equipment access event and collecting equipment description information and physical response data of the peripheral equipment; the method comprises the following steps: carrying out data fusion modeling, obtaining an equipment access qualification index Iconn, evaluating the stable state of equipment, carrying out packaging processing on peripheral equipment in the stable state, then carrying out function stability analysis, obtaining a function stability index Fstab, carrying out coupling modeling on the function stability index Fstab and the equipment access qualification index Iconn, obtaining the equipment access qualification index Iconn, and carrying out data fusion modeling on the equipment access qualification index Iconn. According to the method, the peripheral operation state is evaluated, and the peripheral connection state is regulated, so that the peripheral access compatibility is enhanced, the system identification accuracy and the access success rate are improved, and intelligent management of the peripheral access full life cycle is realized.

The invention relates to the technical field of semantic processing, and discloses a semantic processing system and method for home elevator intelligent customer service, and the system comprises a data collection module, a semantic processing module, a response generation and optimization module, and a communication and log module. The method comprises the steps of collecting natural language request information input by a user through voice, performing semantic analysis and intention recognition on preprocessed information based on a deep learning model, and synchronously recording an interaction log of a whole link according to an intention recognition result and the emergency degree and priority of optimized response content. In order to solve the problems that a traditional keyword matching system is poor in semantic comprehension ability and prone to misjudgment of user intentions, the context and real intentions of natural language requests can be deeply understood by introducing a semantic analysis model based on deep learning and combining a confidence degree evaluation mechanism, and when the system recognizes the low confidence degree condition, the user intentions can be accurately judged. And multiple rounds of clear conversations can be automatically triggered or interaction channels can be switched.

The invention discloses a mooring type low-altitude monitoring countering unmanned system based on deep learning target recognition. The mooring type low-altitude monitoring countering unmanned system comprises a mooring unmanned aerial vehicle platform module, a multi-source sensing and data fusion module, a deep learning target recognition and classification module, a self-adaptive countering decision and execution module, a dynamic tracking and collaborative aiming module and a ground command and control module. The mooring unmanned aerial vehicle platform module forms a stable air monitoring and countering base point; the multi-source sensing and data fusion module provides high-quality input for system identification; the deep learning target recognition and classification module recognizes and predicts a target behavior through deep learning; the self-adaptive countering decision and execution module realizes precise countering; the dynamic tracking and collaborative aiming module applies counter energy to a dynamic target; the ground command and control module provides a human-computer interaction interface and displays global information. The low-altitude active defense system has the advantages that technologies such as multi-source fusion perception, deep learning AI recognition and self-adaptive precise countering are deeply fused, and the low-altitude active defense system is formed.

Methods and systems for process chamber qualification for maintenance process endpoint detection are provided. Data collected by one or more sensors of a process chamber of a manufacturing system is identified. The identified data is collected during performance of initial maintenance operation(s) of a maintenance process. A current state of the process chamber is determined, based on the identified data, after the performance of the initial maintenance operation(s) based on the identified data. In response to a determination that the current state does not satisfy one or more chamber maintenance criteria, a set of subsequent maintenance operations to be performed to cause the current state of the process chamber to satisfy the criteria is identified. Performance of the set of subsequent maintenance operations is initiated at the process chamber.

The invention discloses a GPS oscillator taming system and method based on PID parameter adaptive adjustment, and the system comprises a forgetting factor dynamic updating module which is used for dynamically updating a forgetting factor with the variance of the frequency error of VC-TCXO as a negative index; the system identification module is used for estimating ARX model parameters of the VC-TCXO by adopting a recursive least square algorithm with the updated forgetting factor; the model parameter mapping module is used for mapping the estimated ARX model parameters to obtain PID control parameters; the incremental PID controller is used for calculating a frequency control word increment based on the frequency error; the digital-to-analog converter is used for calculating according to the frequency control word increment to obtain a frequency control word and converting the frequency control word into analog voltage; and the VC-TCXO is used for obtaining the frequency of the GPS reference signal based on the analog voltage corresponding to the frequency control word so as to complete frequency taming. The method is high in calculation efficiency and robustness, and can adapt to the dynamic change and external disturbance of the controlled object in real time.

The invention discloses a PID parameter optimization system based on offline system identification, and the system comprises a data selection module which is used for screening modeling data in a historical database; the data preprocessing module is used for performing real data complementation and data filtering on the screened modeling data; the model system identification module is used for establishing a controlled object model by using the preprocessed data; and the PID parameter optimization module is used for performing off-line optimization of PID parameters on the controlled object model. The defects in the prior art can be overcome, any on-line closed-loop test is not needed, and the optimal PID parameter is output at a time.

The invention discloses a load self-adaption device based on online system identification. The device comprises a track-Jacobian matrix offline generation module used for obtaining a pseudo-inverse matrix of a track-Jacobian matrix offline; the online excitation and measurement module is used for online controlling the robot to execute excitation motion and measuring to obtain an actual joint response error vector; and the rapid parameter identification module is used for combining the actual joint response error vector with the pseudo-inverse matrix to obtain an inertial parameter of a target load. The track-Jacobian matrix represents a linear sensitivity relation between inertia parameter disturbance of a load at the tail end of the robot and a joint response error. According to the method, the dynamic solving process with high calculation complexity is transferred to the offline stage to be completed, and the deterministic matrix-vector operation only needs to be executed once in the online stage, so that the calculation delay of online identification is greatly reduced on the premise of ensuring the identification precision.

The invention discloses an autonomous underwater vehicle hydrodynamic coefficient solving method based on multi-degree-of-freedom coupling driving, and provides a multi-degree-of-freedom dynamic coupling driving joint simulation strategy aiming at the limitations of low efficiency, step-by-step calculation error accumulation and the like of a traditional hydrodynamic coefficient calculation method. Through deep fusion of a system identification theory and a six-degree-of-freedom rigid body motion equation, a full-degree-of-freedom coupling simulation platform based on dynamic grid self-adaption is developed, transverse fluid power interference under plane constraint motion is decoupled, and synchronous iterative solution of six-degree-of-freedom hydrodynamic parameters of surging, swaying, heaving, pitching, rolling and yawing is achieved. According to the method, six-degree-of-freedom coupling motion numerical simulation is carried out by taking an independently researched and developed cheongfish series AUV (Autonomous Underwater Vehicle) as an example, and all hydrodynamic coefficients can be solved only through one-time example calculation. According to the method, the hydrodynamic coefficient of the autonomous underwater vehicle can be rapidly obtained, and the purpose of accurately controlling the autonomous underwater vehicle is achieved.

The invention relates to the technical field of smart home, in particular to a voice model evaluation method based on dynamic scene modeling and a computer program product, and the method comprises the steps: receiving an input cooking instruction text, and generating a synthetic voice based on the cooking instruction text; and dynamically mixing the synthesized voice with noise extracted from a kitchen noise feature library to construct a test audio simulating a real kitchen environment. According to the voice model evaluation method based on dynamic scene modeling provided by the invention, the high-simulation test audio is generated by utilizing voice synthesis and noise dynamic mixing; the system is accurately positioned through multi-granularity evaluation to identify weak points; and the synthesis and mixing parameters are automatically optimized based on an evaluation result, closed-loop driving of test and optimization is realized, and the recognition accuracy and robustness of the speech recognition model in a real kitchen environment are remarkably improved.

The invention relates to the technical field of industrial image recognition, and discloses an industrial weld X-ray image defect AI recognition system, which comprises a feature acquisition unit for acquiring a to-be-processed target digital grayscale image; the reference field construction unit identifies edge morphological characteristics of a target object to establish a global geometric central axis and generate a second-order smooth reference field; a feature decoupling unit calculates a displacement vector of a local gray distribution mass center relative to a global geometric center axis, performs nonlinear stretching compensation on a second-order smooth reference field local partial derivative matrix by using the displacement vector so as to reconstruct a self-repairing reference field, and extracts a residual feature map through a digital gray image and self-repairing reference field convolution subtraction; according to the method, nonlinear gray jump is balanced by using the endogenous geometric features of the image, accurate stripping of weak features under a complex surface fluctuation working condition is realized, and the recognition accuracy and stability of a system are improved.

Techniques for filter and sort versions of software instances executing on computing devices in a network are disclosed. The system receives an instruction to execute an operation on software instances that satisfy numerical criterion and non-numerical criterion. A standardizing function is applied to the numerical criterion to determine a standardized numerical criterion. The system accesses version number values corresponding to software instances on the computing devices in the network. By applying the standardizing function to numerical version numbers of the version number values, the system determines standardized numerical version numbers for the version number values. The system filters the standardized numerical version numbers based on the standardized numerical criterion. The system identifies software instances with (a) associated standardized numerical version numbers that meet the standardized numerical criterion and (b) non-numerical components that meet the non-numerical criterion. The system executes the operation on the software instances that meet the criteria.

The invention discloses an analysis and display method and system for network quality detection, a medium and a product, and relates to the technical field of data processing. In the method, external network detection data and WiFi access quality data reported by probe equipment are received, and the data are aggregated according to a preset time period to form a network quality data group of a sampling period. And the system arranges the data groups according to a time sequence, and identifies an abnormal period in which an external network time delay mean value or a packet loss rate suddenly changes. And for each abnormal period, extracting a change sequence of WiFi signal strength, a working frequency band and an external network performance index in the period and an adjacent period. Based on these sequences, the system identifies quality degraded trigger events and extranet path problem points. And finally, displaying the network quality data of each sampling period in a visual interface, and marking a corresponding trigger event and a problem point at an abnormal period position. By implementing the technical scheme provided by the invention, the analysis and display efficiency of network quality detection can be improved.

The invention discloses an active sonar target identification method based on message passing multi-hypothesis tracking, and belongs to the technical field of underwater target detection. The method comprises the following steps: firstly, processing single-cycle sonar data by using adaptive constant false alarm detection, obtaining observation points, and forming an effective observation set through hierarchical clustering; thirdly, extrapolating the existing target, calculating the association probability of the existing target and the current observation by adopting a message passing method, and generating a plurality of groups of association hypotheses; then, the target state is corrected through Kalman filtering, target rejection and new management are executed, and a high-probability hypothesis is reserved; and finally, estimating the motion speed based on the target multi-period position sequence, and distinguishing a static object from a suspicious motion target according to a speed threshold. Through the multi-hypothesis tracking and probability message passing mechanism, clutter interference and static target influence in a complex underwater environment are effectively overcome, and the stability of target tracking, the accuracy of speed estimation and the overall reliability of system identification are remarkably improved.

The invention provides a multi-algorithm collaborative multi-channel optical module parallel debugging method, system and equipment, and the method comprises the steps: applying an orthogonal test sequence to multiple channels of an optical module in parallel at a system identification stage, synchronously collecting responses, and calculating a channel matrix representing all channel characteristics and mutual crosstalk at one time; in the pre-coding optimization stage, a pre-coding matrix used for actively suppressing crosstalk is calculated based on the channel matrix; in the reinforcement learning optimization stage, each channel is regarded as an independent agent, and a multi-objective collaborative optimization algorithm of reinforcement learning is adopted to search for a globally optimal solution for operation parameters; finally, the optimized operation parameters are written into an optical module register, performance verification is executed, and a debugging report is generated. Through closed-loop cooperation of three algorithms of system identification, precoding optimization and reinforcement learning optimization, efficient and adaptive parallel debugging of the multi-channel optical module is realized, the debugging efficiency and the overall performance of the system are improved, and the dependence on artificial experience is reduced.

A system and method that generates and stores a multivariate time series of the user's conditions and activities, based on the interpretation of the user's natural language input through trained AI including large language models. This time series serves as a comprehensive record of the user's well-being and provides the foundation for the system's ability to identify the factors that most significantly impact the user's desired outcome. By tracking and analyzing the relationships between the various conditions and activities, the system can determine the key drivers of the outcome of interest to the user and provide insights into how to improve and maintain overall well-being.

The invention relates to the technical field of industrial equipment control, in particular to a non-intrusive screen monitoring image-text dynamic recognition system and method, the system comprises a region configuration module, a visual perception module, an intelligent decision module and a control execution module, and based on the system, the recognition method comprises the steps of determining a to-be-detected screen region in response to a configuration operation of a user; and identifying the preprocessed image through a screen self-capture technology running in a host operating system, outputting an instruction when an identification result is abnormal, and controlling the industrial equipment to start and stop. Equipment state information is obtained through a non-intrusive mode of a visual level, a non-intrusive relay series mechanism of a control level is adopted, and state monitoring and closed-loop control of an old industrial equipment man-machine interface can be achieved on the premise that hardware is not transformed and an internal communication protocol of the equipment is not accessed. The industrial problems of'interface false alarm 'and'out-of-control execution' are solved, and the method has the advantages of high compatibility, low cost and fast deployment.

A control system deploys a virtual safety bubble for autonomous operation of a construction vehicle in a worksite. The control system obtains a work schedule for the construction vehicle to perform one construction action. The control system identifies one or more objects to be interacted with by the construction vehicle while performing the one construction action. The control system deploys a virtual safety bubble for the construction vehicle based on the one construction action. Breach of the virtual safety bubble triggers remedial actions; however, the virtual safety bubble permits breach by the one or more identified objects during the construction action. The control system detects breach of the virtual safety bubble by the one or more identified objects during the construction action. Responsive to the breach of the virtual safety bubble by the one or more identified objects during the construction action, the control system withholds the remedial actions.

A method or system for enhancing vehicle identification accuracy. The system identifies a gap in a training dataset for training a license plate identification model. The gap represents underrepresented visual characteristics in misidentified license plates. A guidance prompt is generated based on the visual characteristics of the misidentified license plate. Condition embeddings are then generated from the guidance prompt, which are used to condition a diffusion model to create synthetic license plate images. The diffusion model is trained to receive a real license plate image, encode it into a vector, apply forward diffusion to add noise, and then apply reverse diffusion to remove the noise, resulting in a denoised vector that represents a synthetic license plate image conditioned by the condition embeddings. The denoised vector is then decoded to produce the synthetic license plate image. The synthetic images are then used to retrain the license plate identification model.

The invention is suitable for the technical field of bio-photonics, and provides a capillary refilling time self-adaptive measurement system and method, and the system comprises a negative pressure adsorption module, a step release module, a modulation module, a detection module, a demodulation module, a signal processing module and a main control unit. Standardized microcirculation stimulation is realized through negative pressure adsorption and step release; adopting a frequency domain modulation and demodulation technology to obtain the phase and the amplitude of the optical signal, and inverting an absolute absorption coefficient and a reduced scattering coefficient by utilizing a frequency domain diffusion equation; and selecting an absolute absorption coefficient under equal absorption point wavelength as a blood volume index, fitting a recovery curve through a system identification model, and calculating to obtain self-adaptive capillary refilling time. According to the collaborative scheme, the problems that in the prior art, stimulation is not standard, the result is greatly influenced by skin color and temperature, and absolute parameter support is lacked are solved, and objective and accurate measurement which is high in repeatability, cross in skin color and resistant to environmental interference is achieved.

The invention belongs to the technical field of mechanical automation control, and particularly relates to a vibration suppression method and system for a five-axis linkage CNC machining center, and the method comprises the following steps: S1, carrying out the system identification of a target machine tool, and obtaining the frequency response characteristics of the target machine tool; s2, virtual cutting simulation is conducted based on the workpiece model and the machining program, and cutting force distribution is obtained; s3, predicting a tremor risk area in the machining process in combination with the frequency response characteristics and the cutting force distribution; s4, optimizing a tool path and a tool posture according to a prediction result so as to avoid tremor; s5, calculating and setting an optimal speed planning parameter under the non-tremor constraint based on the optimized path and the dynamic characteristics of the machine tool; and S6, performing trial processing, collecting actual data, and correcting simulation model parameters to improve subsequent prediction precision. According to the method, the machining program is optimized, cutting simulation is carried out on the machining program, the cutting force distribution condition in the machining process is obtained, tremor prediction is carried out in combination with the workpiece model, and the unreasonable tool posture is optimized.

A method for designing frequency filters, includes (a) setting up a simulation model of a power supply system with frequency filters; (b) determining initial set of filter parameters; (c) calculating the model using the filter parameters in time domain; (d) extracting safety-critical trajectories from the calculated model with underlying interference functions; (e) transforming safety-critical trajectories from time domain into frequency domain; (f) determining transfer functions in s-domain by system identification; (g) extracting pairs of residuals and poles based on transfer functions in s-domain; (h) determining energy loss and voltage stability values based on the residuals and poles; (i) varying the filter parameters; (j) repeating (c)-(h) with the filter parameters varied in step (i), until a termination criterion is reached; (k) after which, selecting, from a set of points with energy loss and voltage stability values determined in step (h), a point lying on a pareto-front of the set of points.

Systems and methods of the present disclosure enable identifying labelling a source signal data signature using a computing system to test candidate chain oracle models by iteratively performing, for each particular number of neural network models in the range of the number of neural network models, a predetermined number of trials, where each trail includes: randomly selecting the particular number of neural network models; utilizing each neural network model of the particular number of neural network models to generate a respective predictive output based on the second input data; utilizing the LR model to generate a trial output based on the respective predictive output, and determining a model trial performance based on: the trial output, the second output data, and at least one machine learning performance metric. A chain oracle model from the candidate chain oracle models is determined based on the machine learning performance metric.

The invention relates to an electrolytic hydrogen safe operation boundary construction and application method and system based on dynamic characteristic identification, and electronic equipment. The method comprises the following steps: acquiring actually measured dynamic response data of an electrochemical device under a dynamic excitation working condition; identifying real dynamic characteristic parameters through a system identification algorithm based on actually measured dynamic response data to replace idealized dynamic setting to construct a high-fidelity security model with a coupling sub-model; a high-fidelity security boundary is obtained based on high-fidelity security model simulation; operating parameters are collected in real time and compared with the high-fidelity safety boundary, and an instantaneous risk component is calculated; when the instantaneous risk component exceeds a safety control threshold, a power limit instruction is generated to perform closed-loop limitation on the input power. According to the method, the limitation of a traditional steady-state model is broken through, the transient out-of-limit risk can be accurately predicted and intervened, and the intrinsic safety of the electrolytic hydrogen system under the fluctuation working condition is greatly improved.

This disclosure relates to a method and system for generating correct answer data for machine learning in a recognition device. A first recognition device that recognizes ambient environment data acquired by a first sensor is fully trained, while a second recognition device that recognizes ambient environment data acquired by a second sensor is untrained. The second sensor and the first sensor are mounted together in a vehicle, and the vehicle is in motion to acquire first ambient environment data from the first sensor and second ambient environment data from the second sensor. The first ambient environment data is then input to the first recognition device to obtain a first recognition result. Furthermore, the first recognition result is projected onto the second ambient environment data by transforming from the coordinate system of the first sensor to the coordinate system of the second sensor. Finally, based on the second ambient environment data projected with the first recognition result, correct answer data for machine learning in the second recognition device is generated.

The invention discloses an SOEC temperature control system based on off-line identification of a heat transfer mechanism model, and the system achieves the decoupling of parameter obtaining and temperature control execution through the mode of obtaining a heat balance equivalent parameter through the off-line identification of a pre-constructed system identification target function, does not need to achieve the online parameter identification, and can achieve the real-time temperature control. The environment temperature is used when the feed-forward power is calculated, so that the capability of inhibiting disturbance generated by the change of the environment temperature can be improved, stable and high-precision temperature control can be ensured, meanwhile, heat balance equivalent parameters are adopted, the temperature dynamic characteristics can be accurately described, known disturbance can be effectively compensated in the feed-forward link, and the stability of the feed-forward power is improved. And the response speed and the robustness are obviously improved. Calculation of the feedforward control signal and the feedback control signal is based on real-time measurement data instead of depending on simulation data or data obtained through an offline experiment, and the control accuracy is improved.