48 results about "Limited sampling" patented technology

An analyte test sensor for use in measuring the concentration of a particular analyte in a test sample includes a non-conductive substrate, a reference electrode deposited on the substrate, a working electrode deposited on the substrate and a compensation electrode deposited on the substrate. The compensation electrode is provided with a resistive ladder and is designed to correct for test result inaccuracies which are the result of variances in the manufacturing of the test sensor. Specifically, in one embodiment, the compensation electrode corrects for test result inaccuracies in an analog manner by shunting a portion of the working current away from working electrode. In another embodiment, the compensation electrode corrects for test result inaccuracies in a digital manner by providing a calibration code which is proportional its resistance value. A batch of analyte test sensors are preferably manufactured in the following manner. An initial batch of the test sensors is constructed. Then, a limited sampling of the sensors is tested for accuracy using a control sample. Based on the test results, the resistance value of the compensation electrode for each remaining sensor in the batch is adjusted accordingly.

A moored buoy floating at the ocean surface and anchored to the seafloor precisely measures acceleration, pitch, roll, and Earth's magnetic flux field of the buoy over a limited sampling period. The system includes: 1) A buoy, 2) A mooring system, 3) An electronic data logger controlling communications between the system and the on-board remote telecommunications system, 4) an embedded computer for data input/output, temporary or permanent data storage, and algorithms to convert the measured time series data into surface ocean wave spectra and quality assurance statistics and encode the results for transfer to the data logger, 5) Sensors include one or three acceleration sensors, three orthogonal angular rate sensors, and three orthogonal magnetometers to measure the Earth's magnetic flux field, 6) A telecommunications system that links the buoy data logger and a shoreside processing system, and 7) A shoreside processing system that decodes the transmitted data, performs, quality control, and computes derived wave parameters.

The invention relates to a vehicle active safety control method, and particularly relates to a vehicle lateral stability nonlinear integration control method. Firstly, a simplified vehicle dynamics model is built; then, design of a nonlinear model predictive controller is carried out, expected yaw angle velocity information is inputted to the nonlinear controller module, according to the value of the expected yaw angle velocity and sideslip angle velocities of a front wheel and a rear wheel of the vehicle and the yaw angle velocity fed back in real time, a model predicative control method is used for predicting future dynamic performance of the system, optimization is carried out at the same time, an additional yaw moment and the optimized steering wheel angle information are decided and outputted to an execution mechanism corresponding to the vehicle, and the vehicle is kept in a yaw stability state. The method of the invention is successfully realized by the controller through FPGA full hardware, the FPGA uses a parallel hardware calculation method for acquiring the optimized control sequence in a limited sampling time domain, requirements of high real-time performance and miniaturization on the vehicle-mounted nonlinear model predictive controller can be met, and the calculation performance of the control system is improved.

The invention relates to an arrival direction self-correction method for a sensor array. According to the method, a noise subspace estimated value is determined; an array phase error of the sensor array is initialized, and an initial value of the signal arrival direction is estimated through utilizing an MUSIC algorithm; based on the calculation value of the signal arrival direction, a Hermitian positive definite matrix is calculated, and the Hermitian positive definite matrix satisfies a first equation; an array element phase error is made to satisfy the first limit condition, and the array phase error is converted to satisfy a second equation; the first equation and the second equation are solved through utilizing a Lagrangian multiplier method to acquire a vector estimated value of the array phase error; a correction value of the signal arrival direction is acquired through utilizing the MUSIC algorithm or a high resolution subspace algorithm; and iteration of the previous steps is repeatedly carried out till iteration stop conditions are satisfied. The method is advantaged in that dependence on special structure characteristics of an array output covariance matrix is avoided, excellent performance is realized under the condition of limited sampling data, and influence of the sensor array error on the arrival direction can be effectively reduced.

The invention discloses a CT sparse projection image reconstruction method and a CT sparse projection image reconstruction device at a limited sampling angle. The method comprises the steps of obtaining a pseudo-inverse matrix of a projection equation according to projection data, generating a random solution set according to the pseudo-inverse matrix, reserving or replacing each solution in the current random solution set correspondingly, selecting an optimal solution from the current random solution set when the number of iterations reaches a preset maximum value, and adopting the selected optimal solution as a to-be-obtained reconstruction result. The device comprises a memory for storing at least one program and a processor for loading at least one program to execute the method of theinvention. According to the method, the pseudo-inverse of a discretized projection reconstruction equation is used as an initial solution of the algorithm, so that the quality of the initial solutionis guaranteed. A set of solutions are generated through random walk, and then the iterative optimization is carried out respectively. The diversity of optimization paths is guaranteed. Troubles causedby the defects of an initial solution and an iteration path of a traditional reconstruction method can be overcome. The method and the device are applied to the technical field of image processing.

The invention belongs to the field of signal processing, and particularly relates to a robust direction of arrival (DOA) estimation method based on sparse and low-rank recovery. According to the technical scheme, firstly, based on a low-rank matrix decomposition method, a received signal covariance matrix is modeled as the sum of a low-rank noise-free covariance matrix and a sparse noise covariance matrix; then the convex optimization problem about a signal and noise covariance matrix is constructed based on a low-rank recovery theory; then a convex model about the sampling covariance matrix estimation error is constructed, and a convex set explicitly includes the convex optimization problem; and finally, based on the obtained covariance matrixes, DOA estimation is achieved through a MVDRmethod. In addition, based on the statistical characteristic that the sampling covariance matrix estimation error submits to progressive normal distribution, an error parameter factor selection criterion is derived to reconstruct the covariance matrixes. Numerical simulation shows that under the limited sampling conditions, compared with traditional CBF and MVDR algorithms, a proposed algorithm ishigh in DOA estimation accuracy and robust in performance.

The invention provides a modified continuous-variable quantum key distribution method for resisting an actual attack. The method comprises the following steps of step A carrying out continuous-variable quantum information transmission and carrying out real-time monitoring on local oscillator light; and step B carrying out a data post-processing step including a preprocessing operation, wherein the step B includes the steps that Bob obtains an original key according to peak-to-valley value lookup and Gaussian post-selection, obtains accurate parameter estimation through combination with local oscillator light intensity evaluation monitored by real-time AD oversampling and finally obtains a final key through data negotiation and privacy amplification. According to the method in the invention, the limited sampling bandwidth effect can be effectively eliminated, and meanwhile, all existing actual attacks for Gaussian-modulation CVQKD are defended, the method only changes the steps of a local oscillator light intensity monitoring method and a data post-processing software algorithm, the actual cost for defending the actual attack is simplified and the system complexity is reduced. The method can eliminate the limited sampling bandwidth effect, so that the key output rate of a Gaussian-modulation CVQKD system is improved.

The invention provides a freezing sampler for sediments in different depth of rivers or lakes. The upper end of a conical head is fixedly connected with the lower end of a sampling tube. A liquid nitrogen inducing tube is arranged in the sampling tube. The liquid nitrogen inducing tube and a venting joint are respectively and fixedly connected with an end cover 5. The lower end of the venting joint is communicated with the sampling tube. The upper end of the sampling tube is in threaded connection with the end cover. A sealing gasket is arranged between the sampling tube and the end cover. Cable holes are arranged on the end cover. The freezing sampler has a maximum sampling depth of 1.5 m in relative to the limited sampling depth of a common gravity-type columnar sampler, and the depth range of sediment sampling is enlarged. The freezing sampler, in relative to a freezing sampler which uses dry ice as a freeze agent, has the sampling tube with an appropriate thickness of tube diameter, is suitable for both loose and hard sediments, and the scope of application is expanded, and the freezing sampler has the advantages of low cost and fast and convenient sampling. Through use of the freezing sampler, disturbance on the sediments is smaller, undisturbedness of the sediments in different depth can be greatly maintained, and the accuracy of the sample analysis of the sediments can be improved.

The invention provides a vector array steady focusing processing method with a combined constraint optimization form. The method comprises the following steps of: (a) generating a vector array data matrix Sv(r, theta, phi); (b) obtaining a sampled data covariance matrix R<(v)> having a symmetrical structure, and performing Cholesky decomposition on the R<(v>) to obtain a Cholesky decomposition factor U<(v)>; (c) generating vector array focusing-oriented vectors of different scanning positions; (d) applying a steady combined constraint optimization condition to the vector array focusing-oriented vectors and the decomposition factor U<(v)>, and summarizing to obtain a vector array steady focusing processor form which can be used for performing combined constraint on vector array focusing-oriented vector mismatch and a limited sampling effect simultaneously; (e) obtaining an optimal weight vector w; (f) obtaining optimized array output power; and (g) setting an appropriate step length, and repeating the step (c) to the step (f), searching for a complete plane, comparing, outputting a power spectrum, and determining the position of a noise source at a spectral peak position. The vector array steady focusing processing method is suitable for positioning an underwater noise source under a vector array testing condition.

The invention belongs to the radar signal sorting technology field and particularly relates to a radar signal sorting method based on probability data association. The method comprises steps that sampling data of one sampling period under the clutter environment is utilized to determine an initial radar parameter database through a logic method; combined probability data association is carried out for the sampling data, a confirmation matrix is split into interconnection matrixes, and combined event probability is calculated, including calculating an innovation covariance, a target gain matrix and a state estimation covariance; the radar parameter database is updated; steps are repeated till association of the sampling data is accomplished. The method is advantaged in that the stable radar signal parameters can be accurately and effectively acquired under conditions of relatively low signal to noise ratio and limited sampling data accuracy, probability interconnection of data of each sampling point at each moment in the sampling period and the radar database data is carried out, the radar database parameters are further predicted and updated, and thereby a purpose of sorting accurate radar signals under clutter and signal coexistence conditions can be realized.

The invention discloses a remote sensing image-based inland water body blue-green algae classification and recognition method, which comprises the following steps of 1) obtaining an optical remote sensing data source image, and processing the obtained data source image to obtain a pre-processed optical image capable of being recognized; 2) performing radiation calibration and atmospheric correction on the identifiable pre-processed optical image obtained in the step 1); and 3) establishing a deep neural network for extracting blue-green algae information of the water body by using the image obtained in the step 2) after over-radiation calibration and atmospheric correction, and identifying the blue-green algae information through the deep neural network. According to the method, the remotesensing image is introduced into monitoring of the blue-green algae, so that the problem of high cost, long time consumption and the like due to limited sampling frequency in a traditional method formanually collecting a water sample on site for water quality analysis are solved. The remote sensing image has the characteristics of high spatial coverage and high time resolution, and can well detect the lake blue-green algae bloom.

The invention discloses an on-line diagnosis method for a turn-to-turn short circuit fault of a non-electromagnetic coupling low-thermal coupling double redundancy permanent magnet synchronous motor coil among each winding based on an instantaneous power principle. When a coil turn-to-turn short circuit of a certain set of three-phase winding occurs, corresponding equivalent resistance, equivalentinductance and equivalent induction electromotive force of the phase where the fault coil is located change, fault winding parameters are differed from normal three-phase winding parameters when 3/2static coordinate system switching is conducted, currents of the two three-phase winding are almost the same, and therefore the situation where instantaneous active power or instantaneous reactive power is consumed by the fault three-phase winding is differed from the normal three-phase winding. According to the method, the motor is in a braking state or an electric state, the absolute value and the average value of the difference of the instantaneous active power or the difference of the instantaneous reactive power of the two windings, whether the absolute value or the average value is positive or negative and the forward or reverse rotation state of a motor within the latest limited sampling control periods are calculated in real time to judge coil turn-to-turn short circuit fault occurs on which three-phase winding on line.

A bandwidth limited sampling circuit of high linearity may be implemented by using a first circuit portion to limit the bandwidth of the input signals, and using a second circuit portion to sample the bandwidth limited input signal. The first circuit portion and the second circuit portion may be implemented using separate components. In an alternative embodiment, bandwidth limiting is implemented by taking a difference of a sampled input signal from a sampled high frequency components of the input signal.

The invention opens an elastic image registration method based on limited sampling and global optimization. The steps of the registration are: First read the deformation image; compute the maximum frequency of defined target function, perform the average sampling calculation on the deformation coefficients with two times of the maximum frequency of target function; get the sequence of deformation images; then read into the reference images; deform the deformation images to finish the image registration with the deformation function that corresponds to minimum target function value. Furthermore, start from the coefficient value of deformation function in the rough registration; search the global minimum point in target function with the gradient descent algorithm.

The invention provides a reliability analyzing method of an initiating explosive device testing system, and relates to the field of quality detection of initiating explosive devices in aerospace systems. The reliability analyzing method comprises the following step ofproviding a reliability evaluation method based on a dynamic bayes network for the reliability of a flexible testing system of an initiating explosive device in an aerospace system. The aerospace-stage initiating explosive device has extreme requirements forthe reliability, and the reliability has to reach 100%. The quality of theinitiating explosive device is determined by adopting an ignition test, the initiating explosive device belongs to a disposable product, only a limited sampling ignition test is performed on each batch of initiating explosive devices to evaluate the overall quality characteristics of the initiating explosive device, and a testing system is an important guarantee to accurately evaluate the qualityreliability of the initiating explosive device. Since the varieties of theaerospace initiating explosive devices are large, and initiating explosive devices of different varieties are equipped with different testing instruments and different testing channels and have different parameter configurations. The reliability of the testing system is related to time and product task batches, a dynamic bayes network structure chart of the testing system is established by using the dynamic bayes network evaluation method, the timing sequence causality relationship between different time slices is combined with the causality relationship in time slices, and dynamic analysis and prediction are performed through quantitative reasoning.

The invention provides a method and a device for monitoring a water environment in a sunk ponding region. The method for monitoring the water environment in the sunk ponding region, provided by the invention, comprises the following steps: determining a current range of the sunk ponding region according to current satellite image data; acquiring current satellite spectrum data corresponding to the sunk ponding region according to the current range of the sunk ponding region; determining a current reflectance value of the sunk ponding region according to the corresponding current satellite spectrum data; and determining a current water environment parameter of the sunk ponding region according to the current reflectance value and a model of the reflectance and the water environment parameter of the sunk ponding region. According to the method for monitoring the water environment in the sunk ponding region, provided by the invention, the problems of limitation by manpower, material resources, weather and hydrologic conditions when the water environment is monitored in a water region in a fixed point and fixed profile manner, limited sampling quantity and scale, and high cost of long-term and large-scale tracking and monitoring in the prior art are solved.

The invention provides a radar wave flow velocity meter and a flow velocity measurement method applied to the radar wave flow velocity meter. A corrected electromagnetic wavelength is obtained throughcarrying out calculation according to a height, a temperature and air pressure of a position where the radar wave flow velocity meter is located, and the corrected electromagnetic wavelength is utilized to carry out flow velocity calculation, so that the flow velocity calculation correctness is improved. Furthermore, vibration interference signals in electromagnetic wave reception signals are filtered, so that the Doppler frequency value calculation correctness is improved and then the flow velocity calculation correctness is improved. By utilizing a related Fourier transform algorithm, high-precision Doppler frequency values can be calculated under limited sampling point data, so that the flow velocity calculation result can be accurate to 0.1 mm/s.

The embodiment of the invention provides a method and controller for traffic engineering (TE) in a communication network. The communication network comprises a controller and a plurality of nodes, wherein the plurality of nodes include flexible nodes and non-flexible nodes, the flexible nodes are directly connected with the controller, and the non-flexible nodes are indirectly connected with the controller. The method comprises the following steps: the controller determines at least one sampling node from the flexible nodes and a time frequency of traffic sampling, notifies the at least one sampling node to perform traffic sampling at the time frequency, and receives the traffic sampling data acquired by all sampling nodes during each sampling; the controller determines the parameter information required for the TE according to the traffic sampling data acquired during each sampling and the total traffic information of links of which the link utilization ratio is greater than a first preset value or smaller than a second preset value in the communication network; and the controller controls the TE according to the parameter information. According to the method and controller provided by the embodiment of the invention, the TE control under limited sampling can be realized.

The invention relates to a complex electrical equipment optimizing method based on a space division technology, and belongs to the technical field of engineering optimization. The method aims at the difficulty that a large and complex engineering electromagnetic field is long in problem and performance analysis consumed time, and optimizing design is hard to achieve, an overall design space is subdivided into a plurality of sub design spaces according to the change range of each design variable, and a globally optimal solution is found through combination of a particle swarm optimization algorithm and the Kriging modeling technology based on self-adaption sampling. Due to the fact that the sub design spaces are introduced, Kriging response model building and parallel computing of the optimizing process are achieved, time is saved, and high efficiency is achieved. Meanwhile, for the complex problem of multiple design variables, the number of samples is reduced, under limited sampling information, the high precision of the Kriging model is guaranteed, and it can be guaranteed that the globally optimal solution is obtained to the maximum degree.