628 results about "Reduced model" patented technology

Computational methods are used to create cardiovascular simulations having desired hemodynamic features. Cardiovascular modeling methods produce descriptions of blood flow and pressure in the heart and vascular networks. Numerical methods optimize and solve nonlinear equations to find parameter values that result in desired hemodynamic characteristics including related flow and pressure at various locations in the cardiovascular system, movements of soft tissues, and changes for different physiological states. The modeling methods employ simplified models to approximate the behavior of more complex models with the goal of to reducing computational expense. The user describes the desired features of the final cardiovascular simulation and provides minimal input, and the system automates the search for the final patient-specific cardiovascular model.

A method and apparatus for determining the number and location of monitoring entities in a distributed system is disclosed. The method comprising the steps of automatically generating a causality mapping model of the dependences between causing events at the nodes of the distributed system and the detectable events associated with a subset of the nodes, the model suitable for representing the execution of at least one system operation, reducing the number of detectable events in the model, wherein the reduced number of detectable events is suitable for substantially representing the execution of the at least one system operation; and placing at least one of the at least one monitoring entities at selected ones of the nodes associated with the detectable events in the reduced model. In another aspect, the processing described herein is in the form of a computer-readable medium suitable for providing instruction to a computer or processing system for executing the processing claimed.

The invention discloses a traffic image multi-type vehicle detection method based on deep study. The traffic image multi-type vehicle detection method based on deep study firstly combines a nerve network characteristic with a region generation algorithm, realizes two processes-region generation and region determination-at the same time through using a nerve network conventional layer, uses a background model to determine a motion area of a discrete series image targeting a specific scene and provides an extra reference basis to region generation, and combines with a vehicle detection result to perform upgrading and correction on the background model according to different conditions. Besides, the traffic image multi-type vehicle detection method also brings forward a network model compression scheme to reduce model parameters and calculation time, brings forward a new detection result optimization means based on grouped error calculation to replace a conventional non-maximum-value inhibition scheme, and improves overall detection accuracy.

A no-reference estimation of video quality in streaming video is provided on a macroblock basis. Compressed video is being deployed in video in streaming and transmission applications. MB-level no-reference objective quality estimation is provided based on machine learning techniques. First the feature vectors are extracted from both the MPEG coded bitstream and the reconstructed video. Various feature extraction scenarios are proposed based on bitstream information, MB prediction error, prediction source and reconstruction intensity. The features are then modeled using both a reduced model polynomial network and a Bayes classifier. The classified features may be used as feature vector used by a client device assess the quality of received video without use of the original video as a reference.

The invention discloses an equipment cluster health state evaluation method based on industrial big data and solves a health state evaluation problem of equipment clusters. Based on the full service period big data environment of the equipment clusters, an entity state information slicing management model is constructed to pre-process data; a condition similarity clustering method is utilized to carry out equipment cluster division, a regression method is utilized to establish a mirror image model, the mirror image model and an entity health state are mutually mapped, and a health state quantification model of the different equipment clusters is acquired; fusion and reconstruction of the health state data of the different equipment clusters are carried out to acquire health degrees of the different pieces of equipment, fitting of an equipment health state degeneration curve is carried out, and residual service life of the equipment is predicted. The method is advantaged in that a big data clustering modeling method is utilized, not only can effectively different evaluation on the equipment cluster health state be carried out, but also model redundancy of cluster modeling can be reduced, the internal structure of the model is simplified, normal operation of the cluster equipment is guaranteed, and use values of the equipment can be mined to a maximum degree.

The invention provides an active-passive collaborative inversion method of forest coverage and effective leaf area index, the method comprising steps: 1) based on the forest parameters in the sample area obtained according to the airborne laser radar, obtained by the Li-Strahler geometrical optics model The percentage of illuminated surface area Kg of each mixed pixel; 2) According to the Kg value of each mixed pixel and the real reflectance of the pixel obtained from the optical remote sensing image, the linear spectral decomposition model is used to obtain the Li-Strahler geometric optics model The reflectance G of the illuminated surface and the average reflectance X0 of other components; 3) The two reflectances G and reflectance X0 obtained in 2) are used to obtain each real pixel on the entire image through the simplified model of Li-Strahler geometric optics The percentage of illuminated surface area Kg', and then get the canopy cover m and effective plant area index PAIe of each pixel. The invention utilizes the combination of the laser radar and the optical remote sensing data to reduce the influence of the mixed pixel and improve the accuracy of the optical remote sensing data inversion of the forest parameters.

The invention provides a cable connector fault diagnosing method. The cable connector fault diagnosing method comprises the steps that the temperature of the outer surface of a cable connector and the temperature of a monitoring point (located at the zero potential position of the outer surface of an aluminum protective sleeve or the outer surface of a prefabricated rubber insulating layer) inside the cable connector are monitored in real time; the linear least squares method is used for identifying relevant parameters of cable connector transient state thermal circuit models through the temperatures obtained through real-time monitoring in order to obtain the time constant tau of the first order transient state thermal circuit model and the steady state temperature rise coefficient k of the first order transient state thermal circuit model; and fault diagnose is conducted on the cable connector by the utilization of the identifying result. By means of the cable connector fault diagnosing method, the four common faults of the cable connector can be effectively diagnosed, the defect that in many quality evaluating theories of simplified models, cable heat capacity and the transient state process of cable connector temperature rise are not considered is overcome, the demand for on-line fault monitoring of the cable connector is met, and the stability of system running is improved.

A high-speed platform motion parameter self-tuning method based on model identification and equivalent simplification is provided, comprising: establishing a test of a motion state of a high-speed platform, identifying model parameters, and optimizing motion parameters of an equivalent simplified model; selecting any motion function from a pre-set parameterized curve, setting initial parameters, and driving the high-speed platform to move under the action of a controller and an actuator; collecting dynamic response information of the platform, calculating dynamic characteristic information of the platform such as stiffness, frequency, damping and the like; establishing a dynamic response equivalent simplified model by using the acquired dynamic characteristic information, and performing the optimization constrained by meeting motion precision and targeting at shorter execution time for the motion parameters in the selected parameterized motion function to obtain the optimum parameters. The method of the present invention gives consideration to the dynamic characteristic requirement of the platform and the comprehensive requirement of the parameter identification and optimization on the industrial site, facilitates the implementation of an algorithm in a motion control card, and is suitable for rapidly acquiring the optimum motion parameters of the actual high-speed platform on site.

The method includes applying individual stimuli to different regions of a brain the application of specific stimulus signals to corresponding stimulation elements arranged adjacent to the regions of the brain. The method includes constructing one or more simplified models of the brain, or of one or more sectors of the brain, considering the brain or the sector thereof, as appropriate, as a non-linear coupled oscillating system, and includes determining the stimulus signals so that the latter are suitable for exciting one or more natural vibration modes of the non-linear coupled oscillating system. The system includes stimulation elements (E1, E2 . . . En) arranged adjacent to regions of a brain, and an electronic system in connection with the stimulation elements (E1, E2 . . . En) and intended for applying thereto corresponding stimulus signals and for determining same by applying the proposed method.

The disclosure provides an approach for determining simplified models of humanoid robots. A simplification application linearizes a robot model around a nominal state and performs a singular value decomposition of an inertial term of the model, selecting singular values and corresponding singular vectors to be kept in an inertial term of a simplified model by matching a kinetic energy of the original model to a kinetic energy of the simplified model. Further, a gravitational forces term and a velocity-dependent forces term may be determined by computing active joint torques at sample poses around the nominal pose and solving for the gravitational forces term and the velocity-dependent forces term. A mapping from the simplified model to the original model may be determined using, e.g., numerical optimization.

The invention relates to a distributed MIMO radar target tracking accuracy joint resource optimization method. The method includes the following steps that: an objective function is derived, a resource allocation optimization model containing three variables, namely, array element, power and bandwidth is constructed; the model is simplified, an array element selection problem is converted into a convex optimization problem from a 0-1 problem through convex relaxation; and the optimization model is decomposed into iterative optimization sub-problems of array element selection and power allocation through using cyclic minimization, the problems are solved through using an SPCA (sequential parametric convex approximation) method until the objective function is not optimized any more, final array element selection and power allocation results are obtained, and a final bandwidth allocation result is calculated, and a target position at a next time point is estimated. According to the distributed MIMO radar target tracking accuracy joint resource optimization method of the invention, the accuracy of the target tracking of an MIMO radar can be effectively improved through resource allocation, and the utilization rate of resources can be also improved; and an appropriate array element subset is selected, and power and bandwidth resources are allocated optimally, and therefore, the accuracy of the target tracking can be further improved under a situation that radar system resources are limited.

The invention discloses an optimized design method of overall static rigidity of a machine. The method includes the steps of establishing a simple machine model by Pro / E software; importing the simple machine model into SAMCEF software to establish a machine finite element model; parameterizing rigidity features of large structures of the machine to determine design parameters and a design space; selecting test sample points for finite element analysis by a central composite experiment design method; subjecting the test sample points to finite element analysis to obtain machine static rigidity response values of the test sample points; establishing a response surface model for large structure rigidity feature parameters and machine static rigidity; analyzing sensitivities of large structure rigidities to the machine static rigidity so as to determine a static rigidity weak link, and performing optimized design. The optimized design method of overall static rigidity of the machine has the advantages that the sensitivities of the structures of the machine to the machine static rigidity are effectively determined, the weak link restricting the improvement of the machine static rigidity is effectively found, and accordingly important basis for the optimized design of machine structure is provided to guide designers to optimally design the weak large structures of the machine.

The invention discloses a handheld near infrared spectrum detection system and a detection method for the quality of fruits and vegetables, and belongs to the field of quick detection technologies for the quality of foods or agricultural products. The handheld near infrared spectrum detection system and the detection method have the advantages that modulation light paths of a digital micro-mirror device is matched with a single-point detector and other external modules, so that the handheld, low-cost and miniaturized near infrared spectrum detection system for the quality of the fruits and vegetables can be obtained, and high-performance spectrum information can be acquired without expensive linear array detectors; characteristic wave bands are selected at first in the aspect of building fruit and vegetable quality detection models, then wave bands which do not contain information variables and are low in relevancy are removed, then a small quantity of characteristic wavelengths are selected by the id of characteristic wavelength selection processes, internal collinear relations among spectrum data are eliminated, accordingly, model calculation can be reduced, the models can be simplified, and the quality of the models can be improved; the bottleneck problems of high nondestructive detection cost for the quality of fruits and vegetables, carrying inconvenience and poor quality of existing detection models can be solved by the aid of the handheld near infrared spectrum detection system and the detection method.

The invention discloses a three-point support optimization design method of a machine tool bed based on a response surface model. The method comprises the steps that a simplified model of a bed structure is established in CAD (Computer-Aided Design) software; three-point support limiting positions are found; a variable design space and an optimization objective are determined; test sample points for statics analysis are selected from the design space, and subjected to the statics analysis by finite element analysis software; a response value is calculated and extracted; a second-order response surface model, reflecting an input-output relation of structural design, of deformation at guide rails of the bed is established according to the response value; an optimal solution of the response surface model is acquired by a genetic algorithm; based on the established second-order response surface model, three-point support positions when the deformation of the guide rails is minimum are found; a practical model of the bed is subjected to the statics analysis; and the reliability of three-point support optimization design is verified. With the adoption of the method, the deformation of a machine tool is reduced; the machining precision of the machine tool is improved; the three-point support optimization efficiency and the optimization precision are improved; and the problems that the calculation amount is large, the efficiency is low, and an optimal result cannot be ensured easily in the existing optimization technique are solved.

Provided is a pipeline flow-velocity imaging and flow measuring method based on ultrasonic. Ultrasonic probes composed of three piezoelectricity units are processed and used for sending and receiving ultrasonic signals. The ultrasonic probes are respectively arranged on two oblique sections which form 45 degrees with the pipeline axis, measure flight time of fair-current and adverse-current transmission of the ultrasonic on each sound track and calculate average flow velocity of pipeline fluid on each sound track. Uniformly-spaced interpolation is performed to each group of sound tracks, the average flow velocity on each sound track after interpolation is obtained, the flow velocity distribution profile is rebuilt from average flow velocity data on the basis of a filtering back projectionmethod of parallel beam projection, flow velocity points are selected at a uniformly-spaced mode on the basis of a flow velocity distribution profile map obtained by rebuilding, and the flow velocityof each point is simply averaged and then multiplied with the sectional area of the pipeline so that pipeline flow values are obtained. The pipeline flow-velocity imaging and flow measuring method based on the ultrasonic can monitor distribution of flow field in the pipeline under complex flow state and reduces model errors of an ultrasonic flowmeter, and the accurate flow values are obtained finally.

The invention provides a monopulse antenna angle simulation tracking method, which aims at providing an environment for inspecting and simulating equipment tracking performance at any time to a shooting range monitor system. The method is characterized by comprising the following steps of: establishing an XOY plane measurement coordinate system by taking the center of three axes of an antenna as an original point; constructing a simulation circuit according to an antenna pattern simplified model; drawing four calibration curves of a sum and different signal magnitude difference-deviation angle relationship, sum and difference signal amplitude different-error voltage relationship, deviation angle-error voltage relationship and deviation angle-AGC (automatic gain control) voltage relationship on each angle beyond the main beam of the antenna, and establishing a data mapping table; acquiring the controlled variable of an angle deviation control circuit and simulating to generate a radio frequency tracking signal according to a method of checking a signal amplitude mapping table; and demodulating and extracting an angle error signal by receiving link amplification and down-conversion in an intermediate frequency tracking receiver; and feeding position and pitch angle error voltage to an antenna control unit to complete angle closed-ring tracking.

A method is provided for correcting lightness of an image using a multiscale retinex. Lightness of a subject pixel of an original image is corrected using a convolution operation of a surround function and surround pixels with respect to the subject pixel of the original image, and the convolution operation is performed over a plurality of scales. The surround function has a simplified model such that a value of the surround function is changed in a stepwise way along two axes orthogonal to each other.

The invention discloses a visual implementation method of a BIM model in a three-dimensional large scene, and relates to the field of building information model visualization. The visual implementation method comprises the following steps: firstly, establishing a three-dimensional space division grid according to the dimension of a range of all member models contained in each BIM model; then organizing the member models in each grid block to combine and generate a precise model of a single grid block; recombining and generating a simplified model with consistent shape and appearance according to vein use conditions by using the obtained precise model; and finally, loading and displaying the precise model or the simplified model of the BIM model in the three-dimensional large scene according to a selected display mode and an index number. According to the visual implementation method disclosed by the invention, on the basis of guaranteeing the application of the BIM model, all members of each BIM model are divided according to the three-dimensional space division grid by means of the space grid idea, and the members in the grid are merged to reduce the number of the models of the index and improve the loading efficiency of the BIM model.

The invention relates to a wind power plant voltage automatic control method based on a model predictive control theory, and belongs to the technical field of electric power system wind power plant voltage automatic control. The method includes the steps that various current electric quantity measured values of a system are collected and serve as initial values of various electric quantity predicted values, and an optimized control model composed of an optimized objective function and constraint conditions is established according to the predicted values; the optimized control model is simplified, the simplified optimized control model is solved through an optimization tool, and solution sequences of reactive set values of a draught fan and voltage set values of an SVG in an MPC time window are acquired; first values in the solution sequences serve as control objectives and are issued to the draught fan and the SVG respectively, and therefore wind power plant voltages are automatically controlled. The method can be integrated in the automatic voltage control system for wind power plant field operation, and the system can carry out an optimum control strategy in real time according to the power change of a wind power plant.

Briefly, a method of estimating electrical parameters of an earth formation employs a simplified model of a measurement tool or device in transforming or normalizing data measured by the measurement tool. Electrical parameters of the earth formation, such as conductivity or dielectric constant, for example, may be estimated based on the normalized data.

The invention discloses a method for estimating city expressway traffic states based on mobile detection of smartphones, wherein a city expressway cell transmission model is built firstly, a observing network is built by adopting a smartphone to rapidly detect parameters, then a state space model based on a lighthill-whitham-richards (LWR) traffic flow model is designed, a traffic state and a boundary flux are synchronously estimated by utilizing three-step type recursive filters algorithm, then are coalesced with upstream and downstream subsection boundary flux by adopting a weighted average algorithm, and traffic parameter estimation is upgraded, thereby achieving real-time distributed estimation of city expressway network traffic state. The method for estimating the city expressway traffic states based on the mobile detection of the smartphones can collect average speed information of vehicles on any time and space positions of a loop, enables traffic estimation not to be restrained by the position of a detector, can achieve synchronization estimation of traffic density and boundary flux by designing a state-space model and a three-step recursive filter, and achieves the problem of large range expressway network traffic estimation by being coalesced with subsection boundary flux, reduces model order, and improves efficiency of algorithm.

The invention discloses a method for optimized arrangement of a strain sensor. The method comprises the following steps of building a simplified bridge model, confirming a monitoring cross section needing to be provided with the strain sensor, building a structural multi-scale finite element model on a bridge section including the monitoring cross section, obtaining the position which is located on the monitoring cross section, is stressed adversely and needs to be provided with the strain sensor, building a mesoscopic model of a local detail of a critical element on the monitoring cross section, carrying out hot spot stress analysis, and confirming the spot distribution position of the local detail. By the help of a multi-scale finite element modeling strategy, the method provides an optimized scheme of the strain sensor in a health monitoring system of a bridge, especially a long-span bridge by different levels, can obtain the comprehensive information of structural critical positions with few strain sensors, and is both safe and economical.

Air-blowing or air-breathing compressor cascade experimental system relates to an experimental installation of testing compressor cascade. The present invention solves the problem of prior test equipment can not test inner flowing in air-blowing or air-breathing compressor cascade. The right and left two ends between said top plate (7) and bottom plate (8) are equipped with at least one solid blade (9) separately, three hollow blades (10) are installed between the top plate (7) and the bottom plate (8) in said solid blade (9), said upper Air-blowing / air-breathing interface (11-1) are installed on the top horizontal section of each hollow blades (10), said lower Air-blowing / air-breathing interface (11-2) are installed on the bottom horizontal section of each hollow blades (10), said probe (12) is installed between the top plate (7) and the bottom plate (8). The invention can be considered as the simplified model of carefully analysis of three-dimensional flow field structure and the aerodynamic performance in compressor cascade, it is the effective approach of the searching of compressor performance improvements.

The invention discloses an optimization method for deep learning of edge computing equipment, which comprises the following steps: obtaining the computing power of hardware of the edge computing equipment, determining a model quantization scheme according to the computing power of the hardware of the edge computing equipment, and reducing the model computing precision; carrying model light weightaccording to the model structure, reducing model parameter quantity and calculation quantity, and reducing memory access requirements of the model; performing model branch reduction and matrix decomposition operation according to the model structure and parameters; and deploying the deep neural network application to an edge computing device, carrying out deep reinforcement learning, and carryingout dynamic frequency modulation on a system layer through a DVFS frequency modulation strategy. Deep learning can be deployed on low-power-consumption edge computing equipment, basic availability isachieved, and meanwhile system energy efficiency is optimized.

The invention discloses a multi-feed-in DC recovery process VDCOL control parameter optimization method, comprising steps of establishing an optimized module target function, establishing PQ coupling simplified model to obtain a constraint condition of the optimized model target function, calculating all equations of the PQ coupling simplified model in order to obtain the minimum value Qmax, min of a wattles power peak value consumed by the inverter station after optimization, and determining the optimized VHj, VLj, IHj, and ILj. The invention summarizes the recovery characteristics of the voltage of the convertor station, simplifies the calculation method of active power and wattless power, establishes the PC coupling recovery simplified model, quantitively reflects the changing pattern of the active power and the wattless power after the fault, calculates and analyzes the influence of the VDCOL parameter, and reduces the wattless power requirement of the multi-circuit DC common recovery for the receiving terminal power grid.

Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.

The invention discloses a parameterized reduced model based three-dimensional electromagnetic field simulation method of a periodic structure. Geometric parameters, needing optimization, of the periodic structure are set to be variables, finite-element eigen analysis of the microwave-tube periodic structure is performed after a group of data is fetched in a geometric parameter variation range for modeling, and then all high-frequency characteristic curves of the microwave-tube periodic structure in variation ranges of multiple geometric parameters such as pitch of a helix slow-wave structure, internal diameter of a helix and clamping rod width can be acquired rapidly and accurately, so that quick optimized simulation of the microwave-tube periodic structure is realized.