210 results about "Self estimation" patented technology

Techniques for performing accurate and automatic head pose estimation are disclosed. According to one aspect of the techniques, head pose estimation is integrated with a scale-invariant head tracking method along with facial features detected from a located head in images. Thus the head pose estimation works efficiently even when there are large translational movements resulting from the head motion. Various computation techniques are used to optimize the process of estimation so that the head pose estimation can be applied to control one or more objects in a virtual environment and virtual character gaze control.

Aspects of the present disclosure relate to using an object detected at long range to increase the accuracy of a location and heading estimate based on near range information. For example, an autonomous vehicle may use data points collected from a sensor such as a laser to generate an environmental map of environmental features. The environmental map is then compared to pre-stored map data to determine the vehicle's geographic location and heading. A second sensor, such as a laser or camera, having a longer range than the first sensor may detect an object outside of the range and field of view of the first sensor. For example, the object may have retroreflective properties which make it identifiable in a camera image or from laser data points. The location of the object is then compared to the pre-stored map data and used to refine the vehicle's estimated location and heading.

The invention belongs to unmanned aerial vehicle autonomous carrier landing field, and in particular relates to a pose estimation system and method for visual carrier landing navigation on mobile platform. The system and method can resolve the pose information of a moving ship relative to a UAV on a real-time basis through treatment of a collaborative landmark, have the advantages of small amount of calculation in the algorithm, good real-time property, and simple implementation, and provides a premise for safe landing of the UAV. The method uses a camera as a main sensor to reduce development costs and shorten the development cycle.

In the proposed low complexity technique a hierarchical approach is created. An initial FFT based detection and range estimation gives a coarse range estimate of a group of objects within the Rayleigh limit or with varying sizes resulting from widely varying reflection strengths. For each group of detected peaks, demodulate the input to near DC, filter out other peaks (or other object groups) and decimate the signal to reduce the data size. Then perform super-resolution methods on this limited data size. The resulting distance estimations provide distance relative to the coarse estimation from the FFT processing.

The invention discloses a prediction method for the q-precentile life of an intelligent meter and relates to the technical field of reliability estimation of accelerated degradation data. The method comprises the following steps: performing degradation path modeling according to test data, and obtaining the pseudo life estimation of the intelligent meter under all stress levels according to the failure threshold value of given parameters; building a Weibull distribution-based accelerated degradation model of three parameters, such as temperature, humidity and electrical stress according to the pseudo life estimation results of the intelligent meter under all stress levels of an accelerated degradation test, so as to obtain the point estimation and covariance estimation of the acceleration model; and obtaining the lower estimation of parameter service life of the intelligent meter under a given reliability degree and a given confidence degree in normal use conditions according to the point and covariance matrix of the parameters of the accelerated degradation model. Due to the adoption of the method, the test time is effectively shortened, the test expense is reduced, and the quick and accurate estimation of a stable period of an accelerometer is realized.

The invention relates to a method for real-time estimation of the queuing length of a signalized intersection based on a vehicle track. The method comprises the following steps: (1) judging a vehicle traffic state according to real-time vehicle traffic information; (2) estimating a vehicle queue-joining key point and a vehicle queue-leaving key point according to the real-time vehicle track information, vehicle traffic state and kinematical equation; (3) identifying an upstream reached fleet according to the estimated vehicle queue-joining key point; (4) performing queuing wave and evanescent wave estimation according to the estimated vehicle queue-joining key point, the queue-leaving key point and the identified fleet; and (5) estimating the intersection signal parameter and the queuing length according to the estimated queuing wave and evanescent wave. Compared with the prior art, the method is adaptive to a data environment with low sampling frequency and low sampling rate, and has the advantages of strong robustness, high real-time performance, good accuracy and the like.

The invention provides a two-stage linear weighted least-square power system state estimation method which is characterized by comprising the following steps of: forming a network model, and calculating a node admittance matrix and a branch-node relationship matrix; transforming a measurement vector and a state vector; forming an accurate linear measurement equation; performing the first stage of linear weighted least-square estimation to obtain the estimated value of the transformed state vector; performing inverse transformation, and performing the second stage of linear weighted least-square estimation to obtain the estimated values of the voltage amplitudes and phase angles of all nodes; and identifying bad data. According to the two-stage linear weighted least-square power system state estimation method provided by the invention, more scientific state estimation result can be obtained, the calculation efficiency is higher, and the engineering application prospect is good.

The invention discloses a rail vertical irregularity estimation method and system based on extended Kalman filtering. The system is characterized in that vibration acceleration sensors and gyroscopes are installed on a train body and a frame, the output ends of the vibration acceleration sensors, the gyroscopes and train speed sensors are connected to a central processing unit, and spatial sampling of train body vertical vibration, train body nodding angular speed, frame vertical vibration, frame nodding angular speed and the like is realized through train speed signals. A vehicle rail coupling system state equation is established according to a kinetic equation for a vehicle rail coupling model; a sensor observation equation is established according to signals acquired by all sensors; a filtering iterative equation is configured to obtain time updating equations and measurement updating equations of state estimation and estimation error covariance; an optimal state at each moment is obtained according to extended Kalman filtering, and rail irregularity estimation is finally obtained after inversion by using the time updating equation of the state estimation. The rail vertical irregularity estimation method and system based on extended Kalman filtering have the advantages of low cost, good engineering feasibility, simplicity in operation and online real-time detection.

The invention relates to a correlation filtering tracking algorithm based on significance detection and robustness scale estimation. The method comprises steps that filter training, and a cycle matrix training filter is constructed through utilizing the target prior information; significance detection, l2 regularized constraints of an original regularized ridge regression problem are converted into l2 and 1 constraints; secondly, a significance area is extracted from a candidate area through utilizing boolean map saliency (BMS); target position prediction, relevant filtering is carried out for a sample containing the significance information, the maximum value of a filter response map corresponds to the position of a target, and parameters of the filter are updated; robustness scale estimation, two-dimensional scale change of the target in length and width can be acquired through a two-dimensional scale filter. The method is advantaged in that significance detection is introduced under the relevant filter framework, scale change of the target can be acquired through employing the two-dimensional scale filter, and tracking accuracy and the success rate are improved.

The present invention is directed to a context-based image retrieval (CBIR) system for disease estimation based on the multi-atlas framework, in which the demographic and diagnostic information of multiple atlases are weighted and fused to generate an estimated diagnosis, on a structure-by-structure basis. The present invention demonstrates high accuracy in age estimation, as well as diagnostic estimation in Alzheimer's disease. The system and the pathology-based multi atlases can be used to estimate various types of disease and pathology with the choice of patient attributes. The present invention is also directed to a method of context-based image retrieval.

The invention provides a road surface attachment coefficient and road surface gradient synchronous real-time estimation system and method for a hub-motor-driven vehicle. The road surface attachment coefficient and road surface gradient synchronous real-time estimation system comprises a wheel trackslip rate calculating module, a wheel side-slip angle calculating module, a wheel vertical force calculating module, a Dugoff tire model module for deformation treatment, an air resistance calculating module, a fading memory UKF parameter estimation algorithm module, a wheel longitudinal lateral force calculating module, a wheel rotating dynamics module and a longitudinal force feedback and correction module. According to the road surface attachment coefficient and road surface gradient synchronous real-time estimation method, through fading memory weighting processing on a traditional UKF algorithm, obsolete measured data of the algorithm are rejected in time, the weight of newly measured data is increased, and accordingly the estimation precision of parameters is improved; and the advantage that the torque of all wheels of the hub-motor-driven vehicle can be measured precisely is fullycombined, and accurate wheel longitudinal force information obtained by the wheel rotating dynamics module is utilized to correct wheel longitudinal force information obtained by a Dugoff tire model,so that the accuracy of longitudinal normalized force is ensured, and accordingly the estimation precision of the road surface attachment coefficient is improved indirectly.

The invention provides an accurate linearization method of a measurement equation for electric power system state estimation. The accurate linearization method is characterized by including steps of establishing a network model and calculating a nodal admittance matrix; selecting state vectors and measurement vectors; and forming a jacobian matrix so as to get the accurately linearized measurement equation. The accurately linearized measurement equation is obtained by selecting the state vectors and the measurement vectors, and the state vectors can be solved by means of any types of existing state estimation according to the accurately linearized measurement equation, and estimation values such as all branch power and node injection power in the network can be acquired, so that complexity of the state estimation model is reduced greatly and the equation is easy to solve.

A spur detection and spur cancellation apparatus in a multiple sub-carrier digital communication receiver includes a spur detection block that estimates, using one or more Fourier transforms, a frequency location of a narrowband interference spur in a received digital signal that includes a plurality of sub-carriers, and a spur cancellation block that attenuates the estimated narrowband interference spur. The spur detection block may use a fast Fourier transform (FFT) and / or a discrete Fourier transform (DFT) to locate a frequency and to measure a discrete power spectra of the narrowband interference spur. A channel state information block in the receiver may adjust a channel state information metric based on the located frequency and / or the measured discrete power spectra of the narrowband interference spur.

Provided is seismic imaging, particularly, a time-domain reverse-time migration technique for generating a real subsurface image from modeling parameters calculated through waveform inversion, etc. A reverse-time migration apparatus according to an example includes a source estimator configured to estimate sources by obtaining transmission waveforms from data measured by a plurality of receivers, through waveform inversion, and a migration unit configured to receive information about the estimated sources, and to perform reverse-time migration in the time domain. The source estimator estimates sources, by solving first-order matrix equation including a Toeplitz matrix composed of autocorrelation values of the Green's function, and a cross-correlation matrix of measured data and the Green's function, through Levinson Recursion. In more detail, the migration unit includes a back-propagation unit configured to back-propagate the measured data; a virtual source estimator configured to estimate virtual sources from the sources estimated by the source estimator; and a convolution unit that configured to convolve the back-propagated data with the virtual sources and output the results of the convolution.

The invention discloses a hybrid measurement based power distribution network dynamic state estimation method, which comprises the steps of reading current network parameters and a network topology structure of a power distribution network, and forming a node admittance matrix and a branch-node association matrix; reading SCADA data and PMU data of each node of the power distribution network, configuring a mapping relation between the two kinds of data, and processing differences between the two kinds of data; building a state transition equation and a measurement equation which are adapt to PMU / SCADA hybrid measurement; adding a fault-tolerant mechanism to a traditional extended Kalman filter method, and performing state estimation on the power distribution network by adopting the improved extended Kalman filter method. Dynamic state estimation is applied to hybrid measurement, and the accuracy of state estimation is improved. Meanwhile, an adaptive data detection mechanism is introduced, and the accuracy of dynamic state estimation is improved.

The invention discloses a position and speed combined estimation method for satellite navigation, which solves the problem of real-time estimation of the position and speed of a receiver when an accurate carrier phase measured value cannot be acquired by modeling a recurrent Doppler measured value of the receiver and performing coupled solution with a pseudo-range measurement model based on the correlation between the Doppler measured value and the position of the receiver. The method comprises the following steps of: performing combined estimation on positioning and speed measurement at each new epoch moment according to the current pseudo-range measured value and Doppler measured value; and weighting least square based on a nonlinear equation system to solve an estimated position in an iterative process to replace a pseudo-range positioning result to serve as a reference position for the speed estimation of the receiver, so that the speed estimated value is gradually converged along with the position estimation. Meanwhile, estimation error comprises speed error to ensure that the algorithm convergence judgment is stricter, and the search step of an algorithm around the actual position is reduced so as to acquire higher estimation accuracy. By the method, real-time positioning and speed measurement can be reliably realized, and the method is suitable for various satellite navigation systems such as a global positioning system (GPS), a global orbit navigation satellite system (GLONASS) and the like.

The invention belongs to the technical field of signal processing, and relates to a speech enhancement method based on speech presence probability and phase estimation. The method comprises the following steps: (1) estimating the speech presence probability; (2) estimating the pure voiced phase; (3) estimating the pure speech magnitude spectrum; and (4) estimating pure speech signals. The Q value is estimated through a multivariable linear regression technology, and the accuracy of speech presence probability estimation is improved. The pure voiced phase is estimated between adjacent bands using a harmonic model of short-time Fourier transform domain. The pure speech magnitude spectrum is estimated based on the phase difference, and the compensation effect of phase on the pure speech magnitude spectrum is fully utilized. The speech enhancement method of the method is a single-channel speech enhancement method. Speech signals with noise are collected using one microphone. The method is easy to implement. The problem that estimation of speech presence probability is inaccurate can be well solved. The compensation effect of the voiced phase and the phase difference on the pure speech magnitude spectrum is fully utilized. Thus, the performance of the speech enhancement method is improved.

In a system and method of estimating and classifying a barcode using heuristic and statistical measures, a classification determination is generated for each of a plurality of estimated barcode digits that correspond to a decoded barcode estimation. The classification determination is selected from one of a first classification determination and a second classification determination. The first classification determinations among a plurality of estimation determinations are aggregated. The aggregated first classification determinations are compared to a first predetermined threshold to determine a validity of the decoded barcode estimation.

The method includes measuring an operating parameter comprising at least one of a phasor current and phasor voltage of an electrical machine. The method further includes determining a diagnostic parameter indicative of a fault of the electrical machine based on the measured operating parameter. The method also includes estimating end of life of the electrical machine based on the diagnostic parameter using an error minimization method.

The invention provides an output feedback control method of a motor position servo system based on state estimation. The method comprises the steps that firstly, a mathematical model of the motor position servo system is established; secondly, a motor output feedback controller based on state observation is arranged; thirdly, parameters in a robustness controller based on finite time interference estimation are adjusted to enable the system to meet the control performance index. According to the output feedback control method, the motor position servo system model is established according to the characteristics of the motor position servo system. The motor output feedback controller based on state observation is designed, estimates the system state and is used for designing controllers, the effect on the controllers of measurement noise is avoided, the problem of uncertain nonlinearity of the motor servo system can be effectively solved, the system control precision can meet the performance index under the interference conditions, and the design of the controllers can be simplified by means of the output feedback control method.

The present invention discloses a parameter identification method for a dynamic oscillation signal model. When a model parameter is identified, actual constraints on the parameter is considered. The method comprises the steps of: firstly, giving a state estimation value and an initial value of a state estimation error covariance; secondly, within a maximum iteration moment range, obtaining a state predictive value and a predictive error covariance at a next moment by using a prediction step of extended Kalman filter; thirdly, updating the state predictive value and the predictive error covariance at the moment by using a filtration step of the extended Kalman filter, and obtaining the state estimation value and the estimation error covariance at the moment; fourthly, judging whether the state estimation value at the moment meets a corresponding actual constraint condition, and if so, going to the state estimation at the next moment; and if not, performing optimization on the state estimation value at the moment by using an improved particle swarm algorithm, and obtaining an optimal state estimation value that meets the constraint condition at the moment, and on the basis, performing estimation on the state at the next moment.

Autonomous remaining useful life estimation equipment interacts with the operator through natural speech, voice and sound and provides active failure prevention through automatic and / or continuous remaining useful life estimation of a material under evaluation. The equipment comprises at least one computer and a material features acquisition system operable to detect a plurality of material features. The features are then evaluated according to rules that capture the multidiscipline knowledge of experts and are already inputted into the computer. The computer iterations are processed until an acceptable conclusion is made regarding the condition of the material under evaluation, thus alleviating the need for multidiscipline experts to examine and analyze all the material data manually, a very slow and expensive process. Further, the natural speech and voice interaction preferably allows the operator to operate the equipment while wearing gloves or with dirty hands as he / she will not need to constantly physically manipulate the equipment. The remaining useful life estimation capability may also be retrofitted into conventional inspection systems by extracting pertinent features through spectral frequency analysis and sensor normalization and utilizing those features in the autonomous remaining useful life estimation system.

A pulse can be stably estimated based on an image data suitable for extracting a pulse signal. A pulse estimation device includes image input portion (21) to which time-sequential captured images including at least a portion of subject H as an object are input from camera (2); region extractor (22) that extracts a skin-color region from each of the captured images; zoom commander (28) that transmits a zoom command to camera (2) that has imaged the captured images or a user of camera (2) in order to adjust a size of the skin-color region; and pulse estimator (23) that estimates a pulse of subject H based on the skin-color region of the captured images obtained by imaging with camera (2), after the zoom command is transmitted.

In a system and method for noise estimation with music detection described herein provides for generating a music classification for music content in an audio signal. The music detector may classify the audio signal as music or non-music. The non-music signal may be considered to be signal and noise. An adaption rate may be adjusted responsive to the generated music classification. A noise estimate is calculated applying the adjusted adaption rate. The system and method may mitigate the noise modeling algorithms being misled by the music components.

The invention relates to a method for on-line estimation of internal resistance of a power lithium battery. The method for on-line estimation of the internal resistance of the power lithium battery comprises the steps that an automobile is energized to travel, and a battery management system obtains the working condition of the automobile in real time; a current changing curve graph with the working condition same as or close to the real-time traveling working condition of the automobile is selected according to the real-time traveling working condition of the automobile; the maximum current value I1 and the minimum current value I2 in the current changing curve graph are selected; in the process of traveling discharging process of the automobile, when the current value at present is equal to the maximum current value I1, the battery voltage value at the moment is recorded as the minimum voltage value U2; when the current value at present is equal to the minimum current value I2, the battery voltage value at the moment is recorded as the maximum voltage value U1; the formula R=(U1-U2) / (I1-I2) is used for calculating the battery direct-current resistance R. Compared with the prior art, an auxiliary device or other externally-connected measuring devices are not needed, internal resistance value of the power battery can be estimated in an on-line mode in the traveling process of the automobile, the method is used only according to real-time battery voltage data and real-time battery current data in the traveling process of the automobile, and the method is simple, easy to implement, practical, and easy to popularize and use.

In a method and magnetic resonance (MR) apparatus for MR image reconstruction, an image reconstruction algorithm is used that operates on one calibration matrix that is formed by reorganizing a number of individual, undersampled k-space data sets respectively acquired by multiple reception coils in a parallel MR data acquisition from a subject exhibiting motion. The motion causes the k-space data sets to exhibit errors. In order to use the calibration matrix in the reconstruction algorithm, it is subjected to an iterative rank reduction procedure in which, in each iteration, a residual is calculated for each data point that represents how poorly, due to motion-induced corruptions, that data point satisfies the low rank constraint, and non-satisfying data points are removed from the data point for the next iteration. The resulting low rank matrix at the end of the iterations is then used to produce images with fewer motion-induced errors.

A method for state of health estimation and misalignment correction in a vehicle lane management system. Two lane sensing systems onboard a vehicle provide lane information to a lane management system, where one of the lane sensing systems may be a dedicated forward-viewing lane sensing system, and the other may use images from a surround-view camera system. The lane information is stored in a fixed-length, moving-window circular data buffer. A correlation coefficient is recursively computed from the lane information from the two lane sensing systems and used to calculate a state of health of the lane management system. A linear regression relationship is also computed between the data from the two lane sensing systems, and the scale factor and offset value are applied to the lane information from the second lane sensing system before a fusion calculation is performed on the lane information from the two lane sensing systems.

Systems and methods are disclosed for generating cost estimates. In one implementation, a cost estimation system includes cost object metadata describing a mapping between functions at a cost estimation system and corresponding functions at a cost evaluation system. Based on the metadata, the cost estimation system determines whether a cost object requires evaluation in the cost evaluation system and, based on that determination, provides an evaluation request for the cost object to the cost evaluation system. In response to the evaluation request, the cost estimation system receives cost data determined by the cost evaluation system and calculates the cost estimate.

A method for a receiver to estimate phase of a carrier wave, including receiving a carrier wave carrying pilot symbols and data symbols extending between the pilot symbols, determining phase of the carrier wave at received pilot symbols, and interpolating the phase of the carrier wave at points between pilot symbols based, at least in part, on an estimated phase of demodulated data symbols. Apparatus for estimate phase of a carrier wave, including a unit for determining phase of the carrier wave at received pilot symbols, and a unit for interpolating the phase of the carrier wave at points between pilot symbols based, at least in part, on an estimated phase of demodulated data symbols. Related apparatus and methods are also described.

The invention discloses a scale estimation-based human face detection method. According to the method disclosed in the invention, a size of a human face on an image is estimated via human face scale estimation, the image is zoomed based on a human face scale, a suggestion frame extraction is performed rapidly via a full convolution network, and a human face detection result can be obtained via twotimes of cascaded classification and regression. According to the method, human face detection is performed based on combination of human face scale estimation and a cascaded convolution neural network, an overall calculation amount of human face detection can be reduced, overall time consumption for human face detection can be reduced, and human face detection effects can be ensured.