100 results about "Nuclear density" patented technology

Method for evaluating cement quality in a cased well. A density log of the well is obtained using, for example, a GammaRay sources and detectors (51). The detector count rates are inverted to provide initial estimates of cement density and thickness (53). Acoustic waveform data are obtained from the well using an acoustic logging tool (52). The acoustic data are inverted (54-56), using the initial estimates of cement density and thickness obtained from the density logs, and an updated density log is inferred. Cement images are obtained from the updated density log, and cement bond quality can be estimated (57).

The invention relates to a method for predicting dynamic risk and vulnerability at fine scale and belongs to the scientific field of global information. The method is mainly characterized in that an optimized Bayesian network is searched from multi-source heterogeneous spatiotemporal data on the basis of a grid format with certain resolution at fine scale; domain knowledge is combined to improve the network; therefore, the uncertain estimation of disaster risk and the vulnerability, namely probability estimation, is carried out. In the method, a nuclear density method is put forward to train a sample according to a sample derivative grid; an optimized discretization method is put forward to discretize continuous variables so as to provide discrete state space input for the network; a simulated annealing optimization algorithm is adopted to search an optimized network structure; and a method of accurate reasoning combined with approximate reasoning to predict the probabilities of risk and the vulnerability is adopted. The method provided by the invention can position the positions of the disaster risk and the vulnerability in real time at the fine spatial scale, estimate the spatial distribution of the risk probability and has important theoretical significance and practical value for improving the effects on the reduction and relief of disaster and building an intelligent public emergency pre-warning system by the state.

The invention discloses an extraction method for dynamic crowd gathering characteristics and belongs to the technical field of intelligent monitoring of computers. According to the extraction method, a nuclear density spatial clustering algorithm is introduced into the 'external characteristics analysis process of crowd gathering', so that extraction and quantitative study and judgment of the crowd gathering characteristics for a mass disturbance are effectively achieved. As the crowd movement mode is recognized by the adoption of the method of crowd barycenter motion tracking, the current situation that at present, mass disturbance early warning is performed only according to crowd density grades, other external characteristics, such as the crowd gathering shape, the crowd movement speed and the crowd growth rate, of crowd gathering fail to be fully considered, and the false alarm rate and the missed alarm rate are high is changed. The extraction method can be applied to other safety-sensitive crowded places, thereby having broad popularization and application prospects.

The invention discloses a method for extracting a significant object based on region significance. The method comprises the following steps: firstly, establishing a significant image with constant scale through by calculating the multiresolution contrast feature of an input image, and dividing the input image into different regions by a non-parameter nuclear density evaluation method; secondly, and then, calculating specific values of region significance of each region assembly and a complementary set thereof; and finally, extracting the significant object through by acquiring the maximum value in the specific values, which comprises the following steps: (1) inputting the an image, and establishing the a significant image with constant scale; (2) inputting the image to realize image segmentation; and (3) extracting the significant image. The method is combined with the region significance, not only can accurately extract a single significant object, but also can extract a plurality of significant objects, so that the extracted significant object can meet the requirement of human vision, and can improve the accuracy of segmentation.

The invention relates to a method for predicting city traffic accidents based on time-space distribution characteristics. The method comprises: first, in combination of the case information and the space information, creating a case space database and performing pretreatment to the data; then, based on surface area statistics, analyzing the traffic accidents' time-space distribution characteristics; using the global and local self-correlation method to realize the analyzing of the aggregate state; based on the case happening point data, analyzing the traffic accidents' time-space distribution characteristics; through the hierarchical clustering analysis, expressing the distribution rule of the cases hierarchically; through the nuclear density estimation method, expressing the continuous changes and accurate gathering center of the traffic accidents' happening distribution; and finally, utilizing the BP neural network prediction algorithm, using the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future. According to the invention, in combination with the time-space distribution and through the utilization of big date excavation BP neural network prediction algorithm and the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future, it is possible to increase the precision, the timeliness and reduce the manual cost.

The invention relates to an image clustering method, which comprises the following steps of: creating a directional graph for a provided image sample set by using a variable bandwidth non-parameter nuclear density evaluation; partitioning the created directional graph into at least two non-intersected sub graphs by using a random walking isoperimetric partition method; and extracting image data in the sub graphs, and classifying the image data in the sub graphs into one category. The image clustering method fully considers the local probability density information of image data distribution, and can effectively cluster the data distributed extremely non-uniformly; and because the non-parameter clustering method is used, the method can process the image data with irregular shape distribution. Moreover, the invention also relates to an image clustering system.

The invention relates to a terminal area prevailing traffic flow recognizing method based on track spectral clusters. The method includes the steps of firstly, analyzing a given airport pavement to obtain flight path data, and conducting dividing to obtain feature track points and normal track points; secondly, setting up a space rectangular coordinate system; thirdly, putting forward an occupation degree concept according to the distance between a track and the center of a space grid, and enabling the occupation degree concept to be used for representing the occupation degree of the track in the space grid; fourthly, setting up an inter-track overall similarity model on the basis of a track partial similarity model; fifthly, constructing an Laplacian similarity matrix, and then analyzing the clusters through the spectral cluster algorithm; sixthly, conducting prevailing traffic flow recognition on the clusters obtained in the fifth step through the nuclear density estimation method; seventhly, displaying the recognition result in a displaying and interaction module. The terminal area prevailing traffic flow recognizing method has the advantages that a prevailing traffic flow track and an abnormal track can be simultaneously obtained through the spectral cluster algorithm, therefore, related personnel are assisted in scientifically and reasonably planning a terminal area and improving airport entering and leaving air lines, and the capacity of the terminal area is improved.

The invention discloses a method for predicting the short-term wind power probability density based on the EWT quantile regression forest. The method comprises the steps of 1) decomposing an originalwind power sequence into a series of mutually different feature empirical modes by using the empirical wavelet transform (EWT); 2) recombining the empirical modes according to a frequency range to form high frequency, intermediate frequency and low frequency components; 3) select an input variable for each component by using the Pearson correlation coefficient; 4) establishing a quantile regression forest prediction model for each component, and obtaining regression prediction results of different quantile points; 5) superposing the prediction results of the components to obtain a wind power prediction value; and 6) obtaining the prediction of the wind power probability density by nuclear density estimation. The method provided by the invention effectively improves the prediction precisionof the wind power, obtains the prediction of the wind power probability density at any moment, and can well solve the wind power prediction problem in a power system.

The invention discloses a method for preparing a kind of thermoplastic resin foam by using ultrasonic technology. The method includes following steps: 1) it put the thermoplastic resin samples into the autoclave, then it access to the high-pressure fluid for constant temperature and pressure. 2) it release the high-pressure fluid and take out the samples form the autoclave, then it puts the samples in the liquid medium of ultrasonic environment for foaming. After that it rapidly quenches the foam samples to form the cell structure of stereotypes to achieve the products. In the invention, the method improves the nuclear density of the thermoplastic resin foam and promotes the process of nucleation and gas nuclear growth.

The invention provides a detecting method and a detecting device for network attack and relates to the technical field of cloud computing. The detecting method for the network attack comprises the following steps: acquiring a current data flow in the network; based on a pre-established malicious act attack signature database, judging whether the behavior of the current data flow is abnormal or not; when the behavior of the current data flow is no, judging whether the behavior of the current data flow is normal or not by using a sliding window genetic algorithm frequent pattern mining model andan abnormal point detection model estimated based on nuclear density; when the behavior of the current data flow is no, extracting behavior characteristics of the current data flow, and adding the behavior characteristics into the malicious act attack signature database. According to the detecting method and the detecting device provided by the invention, by adopting a nested sliding window genetic algorithm frequent pattern mining model, the problems that a frequent mode, based on single-time scanning, of the current data flow is not high in mining accuracy, untimely processing of data is caused by high-speed growth of network data and the accuracy of a conventional intrusion detection technique is reduced due to complexity of a cloud computing environment network can be effectively solved.

The invention provides a moving target detecting and tracing method and a system so as to solve the problem in the existing target detecting and tracing method that a plurality of false detection results easily appears when the scene is complex. When detecting a moving target, the method combines the background difference and the nuclear density estimating difference together and then conducts fusion processing to the image obtained by the two detection methods so as to eliminate the detection error of the two foreground images and finally pick up the moving target. Afterwards, the detected moving target is traced. The method and the system provided by the invention can better detect the variation of images under a complex scene with variational environment, thereby improving the accuracy of the detection. Furthermore, the method can handle the tracing problem of a plurality of moving targets simultaneously.

The invention provides a wind turbine generator system abnormal data recognition method and device. The wind turbine generator system abnormal data recognition method includes: acquiring wind speed data and corresponding power data of a wind turbine generator system; determining power data of the wind turbine generator system, included in each preset power range; determining the wind speed data corresponding to each power range according to the power data included in each power range; using a nuclear density function to fit and determine the probability density of the wind speed data corresponding to each power range; determining a wind speed range of each power range according to the wind speed data corresponding to each power range and the probability density corresponding to the wind speed data; and recognizing abnormal data of the wind turbine generator system according to the wind speed range of each power range. The technical scheme can display a normal operation power band of the wind turbine generator system finally, can provide a data foundation for power characteristic curve modeling of the wind turbine generator system, and provide a support for fan power characteristic evaluation and wind power plant electric quantity loss evaluation.

The invention provides a batch process online fault detection method of a dynamic multi-direction local outlier factor algorithm, relating to a batch process fault detection method. Firstly, three-dimensional data is expanded into two-dimensional in the sliding window of a training sample, and the standard processing is carried out. Then k neighbors of a training set (i) are found in each window, and a local outlier factor algorithm is used to calculate a reachability distance and a local reachability density to obtain an LOF statistical amount. The control limit of the LOF statistical amount at that time is calculated through nuclear density estimation. K neighbors of new time data are founded in the training set, and the LOF statistical amount at that time is calculated by using a local outlier factor algorithm. If the statistical amount exceeds a control limit, the data sample at that time is failed, otherwise, the data sample is normal. If a test indicates that a system is failed, the staff needs to identify a situation timely and eliminate danger. According to the method, the process monitoring can be carried out effectively, and a fault detection effect is improved.

The invention discloses a real-time data abnormal diagnosis method for monitoring operation of a nuclear power unit. The method comprises the following steps: selecting a plurality of important variables for monitoring operation of the nuclear power unit to form a real-time data anomaly diagnosis variable set, establishing an abnormal diagnosis model through the principal component analysis and the independent element analysis technology, determining a control limit used for judging whether an abnormal event occurs in the real-time operation data or not through the nuclear density estimation technology; meanwhile setting an automatic updating mechanism of the model to ensure the precision of the model. The method disclosed by the invention overcomes data model 'ill-condition' problems caused by the characteristics of the actual industrial data such as serious correlation and redundancy, random noise, mode variability, intrinsic nonlinearity and the like, 'false alarm' and 'missed alarm' can be effectively avoided. The method is suitable for real-time monitoring of the nuclear power unit and early warning and diagnosis of the ''abnormal event or accident'', and is beneficial to improving the safety of the nuclear power unit.

The invention discloses a Latin hypercube sampling method probabilistic power flow calculation method based on a normal Copula function. The method is characterized by comprising the following steps: 1) according to the correlation coefficient matrix of a new energy generated output variable, utilizing the normal Copula function to generate a random number matrix which meets the correlation of the new energy generated output variable; 2) utilizing the Latin hypercube sampling method to sample the random number matrix generated in the 1), and establishing a sample matrix of the new energy generated output variable according to the inverse function of the cumulative distribution function of the new energy generated output variable; and 3) taking the sample matrix, which is established in the 2), of the new energy generated output variable as an input quantity to carry out probabilistic power flow calculation, obtaining a discrete result of an output variable, and utilizing nuclear density estimation to fit the discrete result of the output variable to obtain the probability density function of the output variable. Calculation time is shortened while calculation precision is improved.

The present invention discloses a method for obtaining sensitivity coefficients of an effective multiplication factor to a section under different burnups. The method comprises: 1, performing forward burnup calculation: first, using a subgroup method to calculate an effective self-shielding section of each nuclide, second, using a modular characteristic line method to solve a neutron angle flux density and a neutron conjugate angle flux density, and third, using a chebyshev rational approximation method to calculate a nuclear density of each nuclide; 2, performing conjugate burnup calculation: first, using the chebyshev rational approximation method to calculate an initial conjugate nuclear density of each nuclide, and then calculating a conjugate power, second, using the modular characteristic line method to calculate a generalized neutron transport angle flux and a generalized conjugate neutron transport angle flux, and third, calculating a conjugate initial nuclear density of each nuclide of a next step; and 3, calculating sensitivity coefficients of the section of each nuclide to an effective multiplication factor under different burnups. The method provided by the present invention solves a defect of the existing method that sensitivity coefficients of an effective multiplication factor to a nuclear section under different burnups cannot be accurately and effectively calculated.

The invention relates to a security check system capable of recognizing prohibited articles based on the ray technology and deep learning, and the system is characterized in that the system comprisesa nuclear density meter, a spectrum analyzer, an X-ray security detector, a GPS module, a wireless communication module, a microcontroller, a power module, an alarm module, and a display module; the X-ray security detector stores a collected X-ray transmission image and corresponding image position information in the image storage module, and the function of the microcontroller is to realize the segmentation of an article image and recognize whether a sub-image is an image of a suspicious prohibited item or not, so as to facilitate further identification and confirmation; the wireless communication module realizes the communication of the nuclear density meter, the spectrum analyzer and the X-ray security detector with the microcontroller. The structure chart of a designed security check device consists of a conveyor belt (1), a device housing (2), a ZA150180 channel X-ray machine (3), a DensityPRO (4), a CMS-2S fast spectrum analyzer (5), and an alarm (6). The system achieves the recognition of the suspicious prohibited item through the knowledge of deep learning, and then combines with the nuclear density method and the spectrum imaging technology for further confirming the suspicious prohibited item.

The invention discloses a method suitable for the transportation burnup coupling calculation of a nuclear reactor. The method comprises the following steps that: 1: carrying out transportation calculation on the nucleus concentration of burnup step initiation to obtain a coarse mesh parameter and a microcosmic reaction rate; 2: carrying out burnup calculation by the microcosmic reaction rate and the nucleus concentration to obtain the nucleus concentration estimated by a burnup step end; 3: carrying out the transportation calculation by the estimated nucleus concentration to obtain the coarse mesh parameter and the microcosmic reaction rate; 4: dividing burnup CMFD (Coarse Mesh Finite Difference) substeps in a burnup step, and carrying out linear interpolation on the stored coarse mesh parameter; 5: updating the microcosmic reaction rate of the burnup CMFD substeps by the coarse mesh parameter obtained in the 4 and thin mesh neutron flux in the 3; 6: carrying out the burnup calculation by the microcosmic reaction rate on the burnup CMFD substeps to obtain the accurate nucleus concentration of the burnup step end; and 7: judging whether a burnup step number is consistent with an input value or not to judge whether calculation is finished or not. By use of the method, on a premise that extremely high accuracy is guaranteed, the step length of the burnup calculation is extremely enlarged, and calculation time in the whole service life of the nuclear reactor is shortened.

The invention provides a photovoltaic power generation power seasonal multi-model interval prediction method based on an extreme learning machine and nuclear density estimation, and the method comprises the steps: firstly, analyzing the output power, power deviation, power change rate and other indexes of a photovoltaic power station, and indicating that the photovoltaic power output and fluctuation show obvious seasonal distribution characteristics through a result; establishing a deterministic prediction model of photovoltaic output in different seasons through the neural network of the extreme learning machine; secondly, fitting error distribution of deterministic prediction through a non-parameter kernel density estimation method, and then obtaining a photovoltaic power prediction interval meeting a certain confidence level. According to the method, possible fluctuation ranges of photovoltaic power under different confidence levels can be described, an approach for evaluating the reliability of a prediction interval is provided, and support is provided for risk evaluation and system reliability analysis of the photovoltaic power station.

The invention discloses a flow cytometry data fast analysis method. The flow cytometry data fast analysis method comprises the steps of estimating the number of class groups in flow cytometry data through the nuclear density estimation method to obtain the range of the number of the class groups contained in the data; after the number of the class groups is obtained, automatically clustering the data through the K-means method of an optimized initial clustering center; merging the clustered results through the two-stage linear regression fitting method and screening out the optimal result. According to the flow cytometry data fast analysis method, the result accuracy is high, and the analysis time is much shorter than the time of manual data analysis and other present analysis methods.

The invention discloses a dynamic fault diagnostic method of polymer aggregation in a gas-solid fluidized bed reactor. By using the method, a fault detection variable set formed by a plurality of key variables which have influence on the fault aggregation is selected, a fault diagnostic model by applying a multi-component dynamic principle component analysis theory is established and a control limit for detecting whether faults generate or not by combining with a nuclear density estimation technology is determined. The invention avoids the complicated process mechanism analysis, has the remarkable advantages of directly utilizing the existing measurement variable of DCS, not needing to add any hardware investment or installing any equipment, adding a section of functional module only in a DCS engineers and having convenient installation and use besides the advantages of non-inserting type, safety, environmental protection and accurate sensitivity.

The invention relates to a multi-stage process quality forecast method based on hybrid MPLS. The method includes the following steps: firstly, identifying stages of each batch of acquired data by using a GMM model; , synchronizing different lengths of the same sub-stages of multiple batches of the acquired data to be tracks of equal lengths by using the Dynamic Time Warping (DTW) algorithm in accordance with the smallest similarity and the longest response lasting time; establishing a single MPLS model in a variable expansion manner in the synchronized data; then, based on the Fisher Discriminant Analysis (FDA) method, looking for the best projection vector among respective data sets and minimizing relativity among sub-stage data samples, and introducing the nuclear density method to estimate the probability density distribution of respective sub-stage data in the best projection vector so as to conduct switching of online monitoring stages; and eventually, using the bayes theory to combine MPLS models in respective stages for quality forecast.

One kind plates covers the area of technology the preparation nanometer diamond thin film auxiliary electronics grid hot filament chemistry gas phase sedimentation, in in the hot filament CVD deposition diamond thin film foundation, increases a kind of auxiliary electronics grid, the auxiliary electronics grid in the hot filament deposition initial period, deposits in advance a diamond thin film, then adds on the direct current bias between the electronics grid and the hot filament, the electronics grid for negative, the electronics grid surface diamond thin film is the launch electron forms the direct current electric discharge, cation will shell the electronics grid, the diamond atom and the atomic group which will shell down splashes down to the substrate on, will become the diamond shape nucleus and the growth driving point, The sputtering the diamond high density shape nucleus and quadratic form the nucleus played the key role to the substrate on, the substrate surface deposition obtained the nanometer diamond thin film. The invention can reach to the limit the high shape nuclear density, simultaneously has the very high two speeds in the growth process, can grow obtains a nanometer level the diamond thin film, after the deposition the thin film does not need to grind polishes can achieve a higher smooth finish, satisfies the operation requirements.

The invention discloses a co-location finding method and device considering urban road network constraint. The method includes the steps that a second-order living example proximity relation table is constructed for a target region under a map projection and includes all living example sets different in type and reachable distance values thereof, wherein the living example distance of the living examples and the reachable distance thereof are within a preset distance attenuation threshold value in the target region; the network nuclear density value of each living example under the influence of the other types of living example sets different from the living example in type is obtained through calculation according to the preset distance attenuation threshold value and the second-order living example proximity relation table; the average influence of all living example sets to other types of living example sets is calculated according to the network nuclear density value; the popularity of each candidate co-location mode is calculated according to the average influence, and the popular co-location mode in the candidate co-location modes is determined according to the preset distance attenuation threshold value. By means of the co-location finding method and device, accuracy of the co-location mode in urban facility data mining is improved.

The invention relates to a route sector traffic probability density prediction method. The method comprises the following steps of selecting the traffic flow of a route sector in preset time as a sample, and analyzing sample data; and according to sample data analysis, combining model parameter selection to probabilistically predicting a route sector traffic demand, and acquiring a first prediction result. The route sector traffic probability density prediction method is used to predict based on route sector traffic flow historical data which can be obtained in an existing system. Through combining a neural network and a quantile regression method, the several quantiles of the continuous traffic demand data of a certain day in the future are obtained. And then, the continuous conditional quantiles are used to acquire the probability density function and the probability density curve graph of the continuous traffic demands of the certain day in the future through a nuclear density estimation method. A specific point prediction value and a variation interval can be obtained, and the probability of each value of a route sector traffic demand prediction change interval can also be obtained. And the accurate point prediction value of the day is acquired.

The invention provides a three-dimensional module comparison and search method based on nuclear density estimation, which comprises the steps of normalization of three-dimensional model coordinate data, extraction of three-dimensional module characteristics, resampling of the characteristics, nuclear density estimation and three-dimensional model comparison. The normalization of the three-dimensional model coordinate data comprises gridding subdivision, translation normalization of a three-dimensional model and scaling normalization of the three-dimensional model. The resampling of the characteristics is performed by merging adjacent characteristic pairs to reduce the amount of the characteristic pairs from n (n-1) to n. The nuclear density estimation is realized by performing multidimensional density estimation to generate a characteristic distribution function of a three-dimensional model. The three-dimensional model comparison is realized by comparing the similarity of the corresponding characteristic distribution functions of three-dimensional modules by utilizing KL distance to compare and search the three-dimensional models. The nuclear density estimation has greater flexibility and generality for characteristic distribution modeling of three-dimensional models with different types and different shapes. Multidimensional nuclear density estimation can utilize abundant multidimensional shape characteristics, and can better depict the characteristics of the models compared with a method in which data with different dimensionalities are simply combined.

The invention relates to a self-adaptive nuclear density robust state estimation method for a power system. The self-adaptive nuclear density robust state estimation method comprises the following steps: 1) reading network parameters, measured data and standard deviation thereof of the power system; 2) dividing the power system into M observable islands according to the network parameters; 3) performing state estimation on each observable island in sequence: a, establishing a self-adaptive nuclear density robust state estimation mathematical model; and b, dissolving the self-adaptive nuclear density robust state estimation mathematical model by adopting a Newton iteration method to obtain the state estimation results of each observable island. Compared with the prior art, the self-adaptive nuclear density robust state estimation method has the advantages of being good in convergence, good in robust capacity, capable of realizing smooth step-by-step recognition of suspicious data and the like.

The invention discloses an SAR (Synthetic Aperture Radar) ship detection optimization method based on a variation coefficient method, which comprises the steps of (1) acquiring a remote sensing image by using a synthetic aperture radar SAR; (2) performing preprocessing on the remote sensing image acquired in the step (1); (3) performing detection and extraction on a ship target in the remote sensing image processed in the step (2) so as to acquire a potential ship target; and (4) performing optimization on the potential ship target detected in the step (3) by using the variation coefficient method and taking a ship length-width ratio, a nuclear density estimated value and the number of target pixels as judgment factors for the ship confidence. The SAR ship detection optimization method is small in calculation amount, wide in application range and small in man-made interference factor, not only can improve the accuracy of a ship detection result, but also can save a lot of computation time, and can also reduce missed detection for a ship target at the image edge at the same time.

The invention relates to a method for quantifying characteristics of a road network and the effect of the characteristics of the road network on land utilization, and belongs to field of traffic and land utilization planning. The method for researching correlation between roads and land utilization includes the steps that center values of road segments in the road network are quantified through a complex network multi-centricity measurement model, a region is divided into sub regions of different grades according to the center degree through a nuclear density evaluation and reclassification method, landscape indexes of the sub regions are calculated, and eventually correlation coefficients between road centrality and land utilization landscape patterns are obtained respectively through Spearman rank correlation analysis. According to the method, the center characteristics of the road network are taken into account from the geographic space aspect, the structure of the road network is measured, and the characteristics of the road network and interaction between the characteristics of the road network and region land utilization are better quantified compared with traditional road characteristic parameters, and theoretical support is provided for traffic network and land utilization planning.