171 results about "Density model" patented technology

Disclosed herein are a method for tracking object in video in real time in consideration of both color and shape and an apparatus for the same. The apparatus may generate at least one of a correlation filter model and a color probability density model based on an input image, determine an initial position and an initial size of a target to be tracked based on a correlation filter response value calculated by applying the correlation filter model to the input image, calculate a joint probability based on the correlation filter response value and a color object probability that is calculated based on the color probability density model, and update an object model corresponding to the target to be tracked based on a final position and a final size of the target to be tracked, which are determined by applying the joint probability to the initial position and the initial size.

The invention discloses a simulating design system for city expressway interchanges, comprising: data acquisition, model calibration, a data-out module, an evaluation and analysis module connecting with the model calibration module and data-out module respectively, and a data amendment module connecting between the evaluation and analysis module and the model calibration module. Meanwhile a model building and design method using the design system is disclosed too, which comprises: collecting the characteristic parameter of the traffic flow of typical expressway basic sections, interchange triage/confluence area, cutting area and various ramps; building the corresponding database and traffic capacity theoretical analysis model; demarcating and amending speed and density model with determinate service level; building traffic capacity analysis model suiting the traffic flow characteristic of the city expressway interchange. At last, the design of the lectotype of the city expressway interchanges conducted by the design model comprises analyzing and forecasting the traffic, raising a preliminary scheme, calculating the traffic capacity of the interchange, analyzing the suitability, selecting a rational strategy.

The invention discloses an enhanced map learning path planning method for an indoor mobile robot. The method includes the steps that (1) ambient environment information is acquired, and an obstacle probability density model is established; (2) path planning is carried out through a greedy algorithm and an enhanced map learning method; (3) path selection and self-adaptation speed strategy adjustment are conducted on the indoor mobile robot. By the adoption of the enhanced map learning path planning method, a current optimal path can be planned in real time according to the current condition of the indoor mobile robot and the inherent non-holonomic constraint of the robot, meanwhile, the obstacle crossing ability, the target point convergence ability and the planning efficiency of the indoor mobile robot can be considered through the self-adaptation speed strategy adjustment, and therefore the indoor mobile robot can arrive at a specific location safely and effectively.

The invention discloses a simulating design system for city expressway interchanges, comprising: data acquisition, model calibration, a data-out module, an evaluation and analysis module connecting with the model calibration module and data-out module respectively, and a data amendment module connecting between the evaluation and analysis module and the model calibration module. Meanwhile a modelbuilding and design method using the design system is disclosed too, which comprises: collecting the characteristic parameter of the traffic flow of typical expressway basic sections, interchange triage/confluence area, cutting area and various ramps; building the corresponding database and traffic capacity theoretical analysis model; demarcating and amending a speed and density model with determinate service level; building a traffic capacity analysis model suiting the traffic flow characteristic of the city expressway interchanges. Designers can carry out the adaptability analysis of the interchanges for communications using the design model, so as to conduct the design of the lectotype of the city expressway interchanges.

The invention relates to the technical field of oil-and-gas-field geologic modeling, in particular to a shale-gas horizontal-well network crack modeling method based on microseism events. The method includes the steps that 1, data is prepared and imported; 2, a main crack model of shale-gas network cracks is established; 3, in combination with fractured microseism monitoring data, a gridding microseism-event density model and a network crack density model are generated; 4, a branch crack model of the shale-gas network cracks is established; 5, a contact crack model of the shale-gas network cracks is established; 6, the main crack model, the branch crack model and the contact crack model are combined to form a discrete crack model of shale-gas horizontal-well fractured network cracks. According to the shale-gas horizontal-well network crack modeling method based on the microseism events, by establishing the fractured crack models of different levels, the shale-gas horizontal-well complex network crack model is formed, has good fitting effects, and has relatively high practical significance to the development of shale gas.

Based on maximum a posteriori (MAP) estimates, video denoising techniques for frames of noisy video are provided. With the assumptions that noise is similar to or satisfies Gaussian distribution and an a priori conditional density model measurable by bit rate, a MAP estimate of a denoised current frame can be expressed as a rate distortion optimization problem. A constraint minimization problem based on the rate distortion optimization problem is used to vary a lagrangian parameter to optimize the denoising process. The lagrangian parameter is determined as a function of distortion of the noise.

The invention discloses a POI (point of interest) Chinese text categorizing method based on a local random word density model. The method includes: judging whether the subject of a text is related to POI or not, and using a modified word concentration, dispersion and frequency method to screen out feature words to build a feature space; performing local area dividing according to the similarity degree of the text and POI category, converting the text into feature vectors in each local area through feature mapping matrixes, and using SVM to perform POI text categorization. The method is good in execution efficiency and categorizing coverage and accuracy. The method is planned to be combined with large knowledge libraries of Hownet so as to capture semantic concepts of low-frequency words and unseen words and further increase the recognition capability of POI text differences, and the problem that good categorization performance cannot be obtained by existing conventional feature evaluation functions and text dimensionality reduction methods is solved.

Disclosed is a reservoir crack modeling method and system. The method comprises steps of: on the basis of a stratum fault model, a stratum tectonic model, and core and/or logging information, dividing multiple layers of single sand body and establishing a 3D distribution model for each layer of single sand body; on the basis of a stratum fracture characteristic and a crack parameter distribution characteristic, acquiring each group of crack planar density distribution intensity of each layer of single sand body; on the basis of the 3D distribution model of each layer of single sand body and the each group of crack planar density distribution intensity of each layer of single sand body and in combination with a crack control factor, establishing each group of crack 3D density model of each layer of single sand body; and on the basis of the crack parameter distribution characteristic, the crack control factor, and the each group of crack 3D density model of each layer of single sand body, acquiring each group of crack 3D discrete model of each layer of single sand body so as to acquire all groups of crack 3D network models of all single sand bodies.

The invention belongs to the technical field of power system dispatching automation, particularly relates to a robust scheduling method considering uncertainty of wind power and load prediction, whichcomprises the following steps of: describing the correlation between input load fluctuation and a prediction error of wind power output by using a correlation coefficient matrix method, and converting random variables with correlation into mutually independent random variable matrixes by using a Cholesky decomposition method; Constructing a probability density model of prediction errors of wind power output and load fluctuation by adopting non-parameter kernel density estimation; Introducing the direct-current power flow model into a power system dispatching model, and establishing an objective function and a constraint condition under the condition of uncertain factors by taking the minimum total dispatching operation cost of the system as an objective function of the model; adopting Benders decomposition method to solve a UC main problem of a robust SCUC problem, a network security check sub-problem of the UC main problem under a basic scene, and a network security check sub-problemunder an uncertain scene of new energy power generation and load.

The invention provides a pre-stack seismic wide angle retrieval method. The pre-stack seismic wide angle retrieval method comprises the following steps: inputting at least three angle stacking seismic data volumes, at least three corresponding wavelets, longitudinal wave speeds, a transverse wave speed and an initial density model; creating a pre-stack retrieval objective function; expanding the taylor series of the pre-stack retrieval objective function; deriving parameters through the matrix form of a Zoeppritz equation to obtain the partial derivatives of a reflection coefficient to the longitudinal wave speeds, the transverse wave speed and the density; according to actual data, decreasing computing amount through matrix partitioning; computing the perturbation of a retrieval parameter; and controlling the convergence of the retrieval objective function through the computed perturbation of the retrieval parameter to obtain a pre-stack retrieval parameter. The pre-stack seismic wide angle retrieval method solves the problems that the approximate conditions of the validity of the Zoeppritz equation are difficult to met and the error is great in the prior art, and has the advantages of wide application range, small error and high retrieval precision.

The invention relates to a detection method for the coal petrography intensity based on multi-wave seismic data, which comprises the following steps that: A, vertical and transverse wave speed models and density models are obtained through multi-wave seismic data and logging data, and initial speed models are built through logging speed models; B, synthetic seismic records are generated, and the total residual error of overlapped data and the synthetic seismic records is obtained through calculation; C, the total residual error of a wave impedance model and a logging external push wave impedance model is obtained through calculation; D, the objective function value is calculated, the vertical and transverse wave speed of each layer is modified when the value is judged not to meet the precision requirement, a new vertical and transverse wave speed model is built, the operation returns to the step A, and the continuous iteration is carried out until the objective function value convergence; and E, after the objective function value convergence, the vertical and transverse wave speed data reaching the logging resolution and the density data obtained through calculation are obtained, slices along a coal bed are extracted, and the firmness factor of the objective coal bed on the whole plane can be obtained through calculation. The problem of judgment of coal and gas projection possibility in the coal bed is solved.

A reflection full waveform inversion method updates separately a density model and a velocity model of a surveyed subsurface formation. The method includes generating a model-based dataset corresponding to the seismic dataset using a velocity model and a density model to calculate an objective function measuring the difference between the seismic dataset and the model-based dataset. A high-wavenumber component of the objective function's gradient is used to update the density model of the surveyed subsurface formation. The model-based dataset is then regenerated using the velocity model and the updated density model, to calculate an updated objective function. The velocity model of the surveyed subsurface formation is then updated using a low-wavenumber component of the updated objective function's gradient. A structural image of the subsurface formation is generated using the updated velocity model.

The invention relates to a method for processing seismic exploration data in the process of controlling full acoustic wave equation inversion. The method comprises the following steps: acquiring seismic data of a theoretical model to obtain a depth-domain bulk modulus and density initial model; simulating a seismic wave field in an acoustic medium by a pseudo-spectral method; calculating the difference of analog data of the theoretical model and the initial model; giving that delta is equal to 1.0 e-6; calculating error energy E; when E is less than or equal to delta, stopping inversion and outputting the inversion result; if E is more than delta, continuing the following steps; calculating residual error data; calculating the conjugated modification quantity of the bulk modulus and density model; calculating the modified step length to obtain the gradient; modifying the initial model; using the modified model data as a new initial model; carrying out automatic control on the inversion process to make the acoustic wave equation inversion stable, rapid and convergent and improve the inversion convergence speed by nearly five times than a routine method.

The present invention provides a wind power fluctuation probability density modeling method based on nonparametric kernel density estimation. The method comprises the following steps: 1, extracting a fluctuation amount of wind power sample data by wavelet decomposition; 2, establishing a corresponding nonparametric kernel density estimation model based on a fluctuation amount sample, and then aiming at the model bandwidth selection problem, constructing a constrained bandwidth optimization model which uses a goodness-of-fit test as a constraint condition; and 3, solving the optimization model by using a constrained sequence optimization algorithm. According to the present invention, due to adoption of the wavelet decomposition method, a wind power fluctuation component can be more precisely extracted; moreover, a probability characteristic modeling method of the extracted fluctuation component is entirely driven by the sample data without performing prior subjective assumption on the probability density model, so that the method has higher modeling accuracy and applicability; and an improvement strategy aiming at the nonparametric kernel density estimation method also enables modeling accuracy and computing efficiency of the method to be effectively improved.

The invention discloses an off-grid microgrid energy storage optimization configuration method considering demand responses. The demand responses in an off-grid microgrid are divided into an excitation type demand response and a price type demand response, and the output uncertainty of a renewable distributed power supply is taken into account to establish a chance constraint-based microgrid energy storage capacity optimization configuration model. The method comprises three steps that 1, a probability density model of the output of wind and light renewable distributed power supplies is estimated according to the environmental and distributed power supply output historical data information; 2, internal controllable loads of the microgrid are divided into the price demand response and the excitation demand response to conduct active control on the loads; 3, the uncertainties of the renewable distributed power supply and the demand responses are described by means of chance constraint inthe form of probability, an optimization-based scheduling model is established taking the maximization of the reliability of microgrid power supplying and user satisfaction and the minimization of energy storage configuration capacity as targets, and solving is conducted by adopting a Monte Carlo-based genetic algorithm.

The present invention discloses a field robot binocular vision navigation method and system. A baseline is defined at the middle of an image; a density curve is obtained on the baseline by using sector scanning; an angle constraint relation between a sector scanning density model and ridge lines is designed, and the angle constraint relation is used to search other ridge lines; logistic regression is adopted to identify nearest ridge lines, so that a ridge line spacing parameter can be obtained; the elevation map of crop ridges is obtained, and a height limit is added into the elevation map; the enhanced elevation map and a binary image are fused, so that a crop ridge confidence density map can be generated; and a ridge line extraction algorithm is applied to the crop ridge confidence density map so as to extract a navigation parameter. According to the field robot binocular vision navigation method and system of the invention, the enhanced elevation map is adopted to make up the defect of feature point sparseness; the weight of height information is increased; interference information which does not accord with specified height is filtered out; the concept of the confidence density map is adopted; the information of the enhanced elevation map and the binary image are fused; the sector scanning detection is adopted to detect the reference ridge line; a double-peak method is adopted to detect the adjacent ridge lines; logistic theories are used in combination; and therefore, the accuracy of ridge line detection can be improved.

The invention relates to the food field, in particular to a shell food freshness determination system based on a density model and a method of the system. A freshness weigher contains a two-dimensional code for software recognition, a groove for fixing food, a gradienter, a weighing disc for calculating the mass of the shell food and a positioning circle capable of estimating the size of the shell food. Freshness software (an APP, a wechat number, a microblog number and a cloude program) acquires an image through a phone camera under a normal daylight lamp (the illuminance is 100-160 Lux) and performs category recognition, mass recognition and size estimation of the shell food according to the acquired image. An automatic substituting and judging model has the advantage that intelligentilization, feedback real-time transformation and model control parameters can be updated in real time on the networking condition and is mainly used for real-time detection on the freshness of part of the shell food in daily life. Through resolving of the judging model set, a series of judging values and judging conclusions of the shell food freshness are obtained without needing to measure all specific indexes, and information is transmitted in real time. Judging results are judged through an indication interval with a certain confidence degree.

The invention discloses a multi-component seismic data migration imaging method and system. The migration imaging method comprises the following steps: acquiring observation multi-component seismic records; acquiring observation system parameters, longitudinal wave migration speed, transverse wave migration speed, a migration density model and migration parameters of a seismic work area; acquiringa multi-component centrum wave field and a multi-component detection point wave field which correspond to every cannon-shot; carrying out longitudinal and transverse field separation on the multi-component centrum wave fields and the multi-component detection point wave fields; acquiring a gradient profile by using a gradient calculating formula; constructing a declining direction profile corresponding to every cannon-shot; acquiring a multi-component demigration simulated wave field corresponding to every cannon-shot; acquiring multi-component seismic record increment; determining an optimized step length according to the multi-component seismic record increment and the declining direction profile; and determining a migration profile according to the optimized step length and the declining direction profile corresponding to every cannon-shot. By the method or system, a migration profile which can directly reflect PP, PS, SP and SS reflecting coefficient information of an undergroundmedium can be obtained.

The invention relates to an expressway illegal parking detection method based on kernel density estimation and belongs to the field of image processing. The expressway illegal parking detection method comprises the following steps: firstly, carrying out background extraction by adopting a non-parameter kernel density model to obtain a background image; secondly, updating the background image by adopting a gradually-changed updating manner to obtain an updated background image; removing the background image from a currently acquired image to obtain a movement foreground; thirdly, calibrating positions of mass centers of a movable target vehicle; then tracking the target vehicle and measuring the distance between the mass centers; when the distance between the mass centers is gradually reduced, representing that the target vehicle enters a speed reduction process; after the target vehicle enters the speed reduction process, judging a movement state of the target vehicle; when the movement state is a static state, calculating illegal parking time; finally, determining whether the target vehicle is illegally parked or not according to the illegal parking time. The expressway illegal parking detection method provided by the invention can be used for monitoring a monitored scene in real time and alarming in time when the vehicle is illegally parked; the processing speed is rapid and the accuracy of alarming is improved; the expressway illegal parking detection method has the characteristics of good instantaneity, high robustness, high accuracy and the like.

The invention relates to a hypersonic velocity pointed conical appearance heat flux density modeling approach based on functional optimization. The approach comprises the steps of utilizing a hypersonic velocity calorimetric wind tunnel to conduct a ground calorimetric test on n pointed conical models with different shrink ratios of an aircraft; obtaining heat flux density distribution laws of n models with different shrink ratios in the hypersonic velocity calorimetric wind tunnel to obtain heat flux density test values Qwi respectively, wherein 1<=i<=n; adjusting wind tunnel test parameters in the hypersonic velocity calorimetric wind tunnel, and obtaining a first set of heat flux density test values Qwij, wherein j is wind tunnel test times; obtaining heat flux density distribution laws of a first set of aircrafts; totally obtaining heat flux density distribution laws Qwk of k sets of aircrafts; applying a functional optimization algorithm, introducing a wind tunnel quality variable a and a calibration model parameter b, conducting iterative operation on Qwk, solving an optimal spatial alternation, and obtaining pointed conical heat flux density model Qw. The hypersonic velocity pointed conical appearance heat flux density modeling approach based on the functional optimization avoids one-sidedness of a modeling method in the prior art and interference of human experience factors.

The invention relates to an integration method of multiple prediction results of power load probability density, and belongs to the technical field of power system analysis. According to the method, aplurality of probability density or quantile probability prediction models are obtained through training of a plurality of three-class regression models set by different hyper-parameters, and the output of the quantile prediction models is converted into a probability density model conforming to Gaussian distribution through Gaussian distribution assumption of loads and a least square method. A probability density prediction integration method is adopted, a probability density prediction optimal integration model is constructed based on the trained probability density prediction model and result, the weights of different probability density prediction methods are determined, and therefore the probability loss of the continuous grade of the final integration prediction model is minimum. The method is finally converted into a quadratic programming problem, the global optimal integrated weight is quickly searched by utilizing mature commercial software, the probability density short-termload prediction precision is improved, and the dispatching operation cost of the power system is further reduced.

The invention provides a GNSS multipath signal simulating-and-generating method and a GNSS multipath simulating-and-generating system. The GNSS multipath signal simulating-and-generating method comprises the steps of acquiring a GNSS signal of an urban valley region by means of a high-sampling-rate broadband signal sampler and introducing out intermediate-frequency data, and furthermore calibrating acquisition route information by means of high-precision combined navigation calibration equipment; extracting a multipath signal characteristic parameter in the intermediate-frequency data through a multipath estimation algorithm; estimating three parameter models of the GNSS signal in an urban environment according to the multipath signal characteristic parameter, wherein the three parameter models comprises a multipath signal delay distribution probability density model, a multipath signal energy ebb model and a multipath signal life cycle model; generating the characteristic parameter of the simulated multipath signal based on the three parameter models, and transmitting the characteristic parameter of the simulated multipath signal to a navigation signal simulator, thereby generating a corresponding simulated multipath signal. The GNSS multipath signal simulating-and-generating method and the GNSS multipath signal simulating-and-generating system settle a technical problem of incapability of simulating the multipath signal with a reality characteristic.

The invention discloses an earthquake speed disturbance modeling method for well-free earthquake reversion. The method comprises the following steps: obtaining a longitudinal wave model vp of a deep-water well-free work area, and calculating a transverse wave model vs by taking a background mud stone vp/vs as a constant; according to experience information of the deep-water work area, selecting a constant from a scope of [1.90-2.10] for the vp/vs, selecting a starting point value starting from vp/vs=1.90, carrying out disturbance change according to a mode that a set step length is sequentially increased, forming a vp/vs model array taking the set step length as an interval, and accordingly, obtaining the transverse wave model vs of disturbance corresponding to each progressively increased number; and solving a density model den, a longitudinal wave impedance model zp and a transverse impedance model zs, and taking the multiple models as input of a reversion model for reversion. The method provided by the invention has the following advantages: the operation is simple, the modeling is convenient, the accuracy and the reliability are high, the practicality is high, the modeling is carried out by reliance on a disturbance method of a speed field, a stable reversion result is obtained, and the modeling is realized effectively and rapidly.

The invention discloses an obstacle avoidance pre-judging method based on a speed obstacle model/collision probability density model. The obstacle avoidance pre-judging method comprises the followingsteps: S1, a vehicle-mounted sensing module of a self vehicle monitors the speed and relative position information within the set time period of the self vehicle and an obstacle vehicle in the set range by taking the self vehicle as the center and passes the speed and relative position information back to a vehicle-mounted controller; S2, the vehicle-mounted controller judges whether the self vehicle and the obstacle vehicle simultaneously move at a constant speed or not and establishes a correlation model; S3, the vehicle-mounted controller judges the collision possibility of the self vehicleand the obstacle vehicle; S4, the vehicle-mounted controller gives an alarm prompt and/or controls the self vehicle to have obstacle avoidance operation or directly goes to the step 5; and S5, the steps 1-4 are repeated until the obstacle vehicle does not exist in the set range. According to the obstacle avoidance pre-judging method, the three-dimensional realistic environment is subject to two-dimensional treatment, so that the calculating complexity level is greatly lowered; and based on intelligent driving, the degree of fatigue of a driver is greatly lowered to assist in driving.