71 results about "Spectral density estimation" patented technology

The invention discloses a crowd density estimation method based on video analysis and a pedestrian volume statistical method based on the video analysis. The crowd density estimation method includes the flowing steps of (1) off-line training: manually counting crowd density data, extracting characteristics and conducting training; and (2) on-line estimating: extracting the characteristics and conducting regression prediction by utilizing trained model parameters. The pedestrian volume statistical method includes the step of setting up a robust relationship between a scene and a line-passing number of people by combing the crowd density and a micro-region pedestrian flow speed before a line is passed. Characteristics such as foregrounds, edges and gray scale co-occurrence matrixes are extracted based on a whole area to conduct crowd density estimation, problems of dense crowds, sheltering and the like can be well solved through mixing of the characteristics, and real-time crowd density estimation is achieved. In addition, on the basis of area crowd density estimation, pedestrian volume estimation is conducted through combination of the pedestrian flow speed based on an optical flow, detection and tracking of a large number of individuals under a complex environment are avoided, and two-way pedestrian volume counting of accurate robust under dense crowds is achieved.

The invention relates to a kernel density estimation-based load spectrum compilation method for a transmission shaft of a tracked vehicle. The method comprises the following steps of S1, collecting and preprocessing torque load sample data of the tracked vehicle; and S2, generating a two-dimensional load spectrum through first two-time rain flow counting, mean amplitude extremum inference, second rain flow counting, two-dimensional kernel density estimation, multi-condition synthesis and extrapolation in sequence. According to the method, the two-time rain flow counting is adopted; a first rain flow counting result is used for the mean amplitude extremum inference; a second rain flow counting result is used for the kernel density estimation; mean amplitude distribution can be well fitted; and an actually measured rain flow matrix can be reasonably extrapolated. The load spectrum compiled by adopting the method and the actually measured rain flow matrix have highly similar probability density distribution, reasonable extrapolation of the actually measured rain flow matrix is realized, and an expected effect is achieved.

The invention discloses a method for detecting remote sensing image change based on non-parametric density estimation, which mainly solves the problem that the estimation to the statistic items which relevant to a change type and a non-change type in a differential chart in the prior art has error. The realizing process of the method is that inputting two remote sensing images with different time-phase, removing noise of each channel of each image, obtaining noise-removing images of the two time-phase, and constructing difference images through adopting the change time-vector method, gathering the difference images into change type and a non-change type through applying K-means clustering algorism, obtaining the initial sorting results, and estimating the statistic items relevant to the change type and the non-change type in differential images through adopting non-parameter density estimation, carrying out the self-adapting space restriction combining the variable weight markov random field model, and obtaining the final change detecting results. The experimentation shows that the invention can effectively keeps the structure information of the images, removes insulation noise, improves the change detection processing efficiency, and can be used for the fields of disaster surveillance, land utilization and agriculture investigation.

The invention discloses a WSN (wireless sensor network) intra-network data fusion method based on kernel density estimation and non-parameter belief propagation, which comprises data acquisition and data fusion. The data acquisition is that monitoring unions which are respectively composed of no less than three sensor nodes for gathering the monitoring data are constructed in a monitoring region, each monitoring union is corresponding provided with a union header node for collecting the monitoring data, the sensor nodes in each monitoring union are respectively used for gathering the monitoring data of an object entering the monitoring region; and the data fusion is that the gathered monitoring data are subjected to KDE (kool desktop environment) processing by the sensor nodes in the monitoring unions respectively, the processed data are transmitted and collected to the union header nodes through NBP (name bind protocol) processing, the collected data are subjected to gauss mixing by the union header nodes, the data after gauss mixing are subjected to Gibbs sampling fusion, and the fused result is acted as a characteristic of the monitoring data. The accuracy of the monitoring data can be improved under a noisy or an uncertain environment, and the accurate fusion characterization of the monitoring data of the multi-node unions can be realized.

The invention relates to a crowd density estimation and people flow statistics method, a multi-scale fusion crowd density estimation model is used, the model is composed of a deep layer network and ashallow layer network, and the deep layer network is designed based on VGG-16. The shallow network is mainly used for learning the characteristics of a target with small pixel occupation on a picture,and the multi-scale fused crowd density estimation model extracts the characteristics of different convolutional layers of the deep network, and performs crowd density estimation by fusing the characteristics with the outputs of the deep network and the shallow network. Meanwhile, the output of the crowd density estimation model is used as the input of the people flow statistical model, so that the two models are fused together, the training speed of the neural network can be greatly increased, and the application in actual life is wider. According to the method, the accuracy of the crowd density task and the visitor flow rate same task is improved, and meanwhile, the crowd density estimation task and the visitor flow rate statistical task are completed in one model.

The invention discloses a steering engine reliability simulation sampling method based on Markova chain Monte Carlo, which comprises four stages: 1, Markova process simulation, namely selecting the initial state of a Markova chain, determining a random transition sampling probability density function, determining the next state of the Markova chain and constantly repeating to generate random sample points, of which the limit distribution is asymptotically optimal, of an importance sampling density function; 2, kernel density estimation, namely selecting a kernel density function, determining a window width parameter and a local bandwidth factor and generating a mixed importance sampling probability density function by using a self-adaptive width and kernel density estimation method according to Markova state points; 3, importance sampling, namely performing importance sampling according to the mixed importance sampling probability density function generated in the second stage; and 4,statistical calculation, performing failure probability estimation according to the important sample points generated in the third stage and calculating the failure probability of the system. The method effectively solves the problems of low simulation efficiency, low precision and mixed system.

A system estimates the spectral noise power density of an audio signal includes a spectral noise power density estimation unit, a correction term processor, and a combination processor. The spectral noise power density estimation unit may provide a first estimate of the spectral noise power density of the audio signal. The correction term processor may provide a time dependent correction term based, at least in part, on a spectral noise power density estimation error of the actual spectral noise power density. The correction term may be determined so that the spectral noise power density estimation error is reduced. The combination processor may combine the first estimate with the correction term to obtain a second estimate of the spectral noise power density that may be used for subsequent signal processing to enhance a desired signal component of the audio signal.

In a method for determining speech quality using an objective measure, in order to enhance prediction reliability of the evaluated quality parameters, distortions of the mean spectral envelope are extensively corrected with a weighting function WT(f) before comparing spectral properties. Additionally, the fixed band limits for integration of spectral power density are suppressed and other band limits are searched for instead in a predetermined optimization area in which the resulting spectral intensity representations of the voice signal to be evaluated and the reference voice signal have maximum similarity. The solutions described can supplement known methods and can be incorporated into their structures.

A fusion estimator is determined as the location of the most significant mode of a density function, which takes into account the uncertainty of the estimates to be fused. A mode detection method relies on mode tracking across scales. The fusion estimator is consistent and conservative, while handling naturally outliers in the data and multiple source models. The new estimator is applied for multiple motion estimation. Numerous experiments validate the theory and provide very competitive results. Other applications include generic distributed fusion, robot localization, motion tracking, registration of medical data, fusion for automotive tasks.

The invention relates to a detection method for a construction change of a remote-sensing image based on a DSM and a kernel density estimation. The detection method comprises the following steps that a first image and a second image of the panchromatic remote-sensing image are preprocessed; angular points are extracted respectively; the center point of a candidate construction is searched, and the kernel density estimation is conducted by using a symmetrical Gaussian probability-density function; results of the kernel density estimation are overlapped, and two time phase kernel density estimation images are obtained; a difference algorithm is carried out on the kernel density estimation images, and a difference image Pdif is obtained; the difference image Pdif is labeled, and a change region CH is obtained; purification is carried out on the change region CH. The problems that a false detection rate and a missing detection rate of the construction change of the high spatial resolution remote-sensing image are high, the algorithm is complex are solved, and the method can be used in update of an urban geography database and fast recognition of illegal constructions.

The invention provides a high-speed service area crowd density estimation system based on Wi-Fi data, and the system comprises: a data collection device which is configured to collect the geographic position related information of each service area and the Wi-Fi data; and a data processer which is used for estimating the number of people in the service area by using a regression model, wherein theindependent variable of the regression model is the number of people connected with Wi-Fi in each hour interval of each service area, the dependent variable is the number of people in the corresponding service area, the slope of the independent variable is the reciprocal of the connection willingness, and the connection willingness is formed by multiplying the environment characteristics, the function positioning characteristics, the day characteristics and the hour characteristics of each service area by corresponding learning parameters, the environment features and the function positioningfeatures are extracted from Wi-Fi data and geographic position related information of the corresponding service areas respectively, the day features and the hour features are preset piecewise functions; behavior preferences of the users are predicted according to the estimated number of people in the service area and the sequence of the connection APs of the users entering the service area, further the number of people in each functional area in the service area is estimated, and finally the crowd density of the service area is obtained.

The invention discloses a multichannel electroencephalogram data fusion and dimension descending method. The multichannel electroencephalogram data fusion and dimension descending method comprises the following steps of (1) reading in multichannel electroencephalogram data; (2) performing kernel density estimation on the electroencephalogram data by using a Parzen window to obtain an estimation value of the electroencephalogram data; (3) performing kernel transformation on the electroencephalogram data by using a polynomial kernel function, mapping the electroencephalogram data to corresponding kernel space to form kernel matrixes and fusing all the kernel matrixes corresponding to electroencephalogram of all channels into a synthetic kernel matrix by using different weight numbers; (4) calculating an eigenvalue and an eigenvector of the synthetic kernel matrix; and (5) performing entropy component analysis on the eigenvalue of the synthetic kernel matrix G and the eigenvector of the synthetic kernel matrix G by using a map of kernel entropy principal component analysis (KECA) to obtain low-dimension eigenvalue and eigenvector data and implement fusion and dimension descending of the multichannel electroencephalogram data. By the multichannel electroencephalogram data fusion and dimensional descending method, the electroencephalogram data of each channel are subjected to kernel function mapping, and effective fusion and dimension descending of the multichannel electroencephalogram data can be implemented through multi-kernel entropy component analysis.

A video camera may overlook a monitored area from any feasible position. An object flow estimation module monitor the moving direction of the objects in the monitored area. It may separate the consistently moving objects from the other objects. A object count estimation module may compute the object density (e.g. crowd). A object density classification module may classify the density into customizable categories.

The invention relates to an interpolation method applied to pseudo measurement generating and belongs to the fields of electricity system dispatching automation technologies and power grid simulation technologies. The method comprises the steps that firstly, load data collected by an electric quantity metering system are used as load measurement; an unknown predicted value is obtained by utilizing a certain algorithm; afterwards, the predicted value is utilized, historical data serve as a basis, and then an efficient equidistant-node interpolation method is combined; accordingly, the defects of a distribution network measurement device are made up for. According to the pseudo measurement generating method, the load data in the distribution network metering system are fully utilized, and the algorithm of the method is simple, and convergence performance is guaranteed; required accuracy can be always obtained based on an equidistant-node method as long as the distances between nodes are small enough; calculation is fast, the accuracy of a pseudo measurement load of a non-measurement point can reach or approach an actual measured value, the smoothness of the load data is maintained, and then the state estimation accuracy of a distribution network is improved.

Techniques for performing channel estimation in a multi-carrier system are described. A frequency response estimate is initially obtained for a wireless channel based on a narrowband pilot sent on different sets of subbands in different symbol periods or a wideband pilot sent on all or most subbands in the system. Spectral estimation is performed on the frequency response estimate to determine at least one frequency component of the frequency channel estimate, with each frequency component being indicative of a delay for a channel tap in an impulse response estimate for the wireless channel. A channel estimate for the wireless channel is then obtained based on the frequency component(s) determined by the spectral estimation. This channel estimate may be a channel profile, the impulse response estimate, an improved frequency response estimate, a signal arrival time, or some other pertinent information regarding the wireless channel.

The invention discloses a thunder and lightning transient signal identification method and system based on spectral density attenuation. In an existing thunder and lightning transient signal identification scheme, a traveling wave head is difficult to identify, a sampling rate requirement is high, and setting lacks a theoretical basis and so on. The method of the invention is characterized by extracting the transient signal of a direct current power transmission system and carrying out wavelet transform, calculating a wavelet singular entropy, determining the ratio of a spectrum density, and identifying a fault signal, a lightning shielding failure interference signal, and a counter interference signal; and determining according to a recorded data energy distribution coefficient, and identifying a common short circuit fault signal and a lightning fault signal. In the invention, a thunder and lightning transient signal can be well identified, and the method and the system are less affected by factors such as a fault distance, a transition resistance and the like.

The invention provides an anomaly detection method and device based on kernel density estimation. The method comprises the following steps: acquiring at least three feature vectors subjected to data processing in advance; determining a density estimate corresponding to each of the feature vectors; determining a probability density function of the at least three eigenvectors based on each of the density estimates; obtaining a probability of occurrence of each of the feature vectors according to the probability density function; determining an offset corresponding to each of the probabilities; normalizing each of the offset quantities to obtain a corresponding standard value; determining whether each of the feature vectors is abnormal according to each of the standard values and a preset threshold value. The scheme has wide adaptability.

The invention discloses a method for estimating lightning density distribution and annual average ground flash density (NG) values by kernel density estimation. The method comprises the following steps of: 1, constructing and maintaining a thunder and lightning space database and establishing a thunder and lightning real-time positioning monitor system by combining the geographic information system (GIS) technology to acquire the source of information on the occurrence of thunder and lightning, and displaying the positioning distribution of the lightning in real time in a space element mode; 2, acquiring the distribution of regional lightning density by a kernel density estimation process, and cutting graph regions which are selected by users and are used as condition layers to form a regional density distribution map and eliminate an edge effect; and 3, calculating a total regional area (kilometer) and annual total lightning time of the kernel density estimation to solve the NG values of the corresponding regions and form a weather product which combines the lightning density map and the numerical values, and releasing and sharing a thunder and lightning data analysis product by a sharing network finally. The fundamental aim of thunder and lightning service is fulfilled by the lightning density statistical method.

The invention discloses a cancer cell detection method based on Faster R-CNN and density estimation. The cancer cell detection method comprises the following steps: step 1, making a data set; step 2,optimizing a network structure: step 2.1, generating a density map by using a regression-based density estimation method; step 2.2, measuring a regression-based density estimation loss function by using the Euclidean distance between the density map obtained by network prediction and a real value; and step 3, obtaining a detection classification result. The cancer cell detection method based on Faster R-CNN and density estimation provided by the invention effectively improves the detection accuracy under the conditions of shielding and high density.

The invention relates to a phase related-sub-pixel matching method based on maximum-kernel-density estimation. The method is specifically as follows: 1. calculating an inter-image cross-power spectrum Q(u,v) is calculated; 2. separating a real part and an imaginary part of the Q(u.v) and applying phase edge filtering denoising to solve a phase angle matrix Psi(u,v); 3. unwrapping the phase angle matrix so as to obtain a corresponding plane matrix Psi unwrap(u,v); 4. using an MKDE method to perform fitting on the plane matrix so as to obtain plane equation parameters x0 and y0 and according to a proportion relation of the phase angle and an airspace pixel, obtaining a sub-pixel matching result. Compared with the prior art, the matching result is excellent in stability so that effects of gross errors on the plane fitting of the angle phase in the phase angle matrix can be prevented effectively so that the matching precision is improved and at the same time a pixel locking effect of the sub-pixel matching result is reduced significantly.

The invention relates to a kernel density estimation-based license plate character segmentation method. At present, the conventional methods have the defect that binaryzation cannot be performed well under the extreme condition. The method comprises the following steps of: performing preprocessing of normalization, edge sharpening and noise removal on a license plate image; determining a character area of a license plate; finding a pixel value of which the probability of occurrence is maximum in the current kernel probability density curve and the kernel density half width of the point, and performing image binaryzation by utilizing the two parameters; and extracting a segmentation result of which the score is maximum within the width range to be used as a final character segmentation result. By the method, the distribution of character pixels can be determined accurately; and compared with the common iteration binaryzation method, the method has the advantage that the ambient interference resistant capacity is enhanced greatly.

The invention provides an unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation. In the method, a Gaussian mixing probability hypothesis filter is used to estimate a multi-target number and states. The method is characterized by firstly, introducing a clutter space sparsity estimator based on linear measurement into a passive cooperation positioning system under non-linear measurement; and secondly, during a clutter density estimation process, introducing multi-frame measurement, and using feedback of a threshold technology and posterior strength Gaussian mixture to reject potential target measurement in measurement data so as to reduce an influence of real target measurement on clutter density estimation. In the invention, multi-target tracking performance of an unknown clutter passive cooperation positioning system can be effectively increased and a multi-target tracking problem under an unknown clutter is solved.

The invention relates to a kernel density estimation-based non-invasive power load identification method. The method comprises the following steps of: selecting a common household power load as a research object, acquiring power consumption data of the research object, carrying out sub-state division and extraction power distribution; generating a household working state set according to the power distribution, and calculating simulation power consumption data under each state; carrying out kernel density estimation to obtain probability distribution reference model of each state simulation data; identifying household working state transition points in the reference models, and dividing each household working state data segments; and for each data segment, searching a household working state which is closest to the probability distribution of the data segment, and comparing the household working state with the probability distribution so as to complete an identification task. According to the method, the main data features of power load power consumption can be effectively extracted, the main data distribution features are highlighted, and the influences of random power consumption data and abnormal fluctuation are weakened, so that effect can be well decomposed in the aspect of non-invasive identification, and the method is suitable for the changing and complicated working environment of the current household power grid.

The invention discloses a vibration response characteristic and wave trough control-based vibration input spectrum parameter determination method. The method comprises the following steps: 1) determining the key position, to be concerned during the vibration, of a product according to the characteristics of the product; 2) measuring to obtain all the characteristic frequency ranges of a vibration response curve of the key position; 3) determining the power spectral density of each characteristic frequency range in a used input spectrum, and calculating to obtain the power spectral density amplification factor of each characteristic frequency range; 4) obtaining the spectral density of each characteristic frequency range in a target response line, and calculating to obtain the spectral density of each characteristic frequency range in a designed input spectrum; and 5) ranking the obtained characteristic frequency ranges from small to large, and calculating to obtain the spectral density gain or attenuation slopes of adjacent frequency ranges, wherein the spectral density gain or attenuation slopes are input spectrum parameters. According to the method disclosed in the invention, the unexpected screening results of the product due to product superiority frequency response can be effectively avoided, and the hidden reliability danger caused by excessive screening experiments can be avoided.

The invention relates to a converter fault diagnosis method based on kernel density estimation. The method comprises the steps of: performing pre-processing of collected data through cubic B-spline wavelet analysis based on a mallat algorithm to obtain samples with fault features; employing a KDE fault classifier to perform offline training to select better parameters of the fault classifier and accurately dividing the normal conditions and each type of fault condition included in the training samples, and using the better parameters into a classifier network to obtain the optimal parameters;implanting the classifier network with the optimal parameters into online simulation to perform real-time online monitoring fault diagnosis of an actual circuit; and allowing the classifier network with completion of optimal parameters to distinguish known fault type samples and normal samples, complete the location of the known fault types of faults and identify the unknown faults for achievementof circuit protection in a condition of generation of unknown types of faults. The converter fault diagnosis method based on kernel density estimation can determine the health condition of the converter more accurately and more reliably, and also can improve the efficiency of the fault diagnosis of the converter.

A method for testing a signal comprises obtaining a signal, determining whether the signal has at least one period, measuring that period and providing the measurement as output. A power spectral density estimation can be used for signals having a single period, and an autocorrelation function with slicing can be used in an iterative procedure for finding multiple periods within signals.