536 results about "Sample number" patented technology

Methods and apparatus are provided for analysis and correlation of phenotypic and genotypic information for a high throughput sample on a cell by cell basis. Cells are isolated and sequentially analyzed for phenotypic information and genotypic information which is then correlated. Methods for correlating the phenotype-genotype information of a sample population can be performed on a continuous flow sample within a microfluidic channel network or alternatively on a sample preloaded into a nano-well array chip. The methods for performing the phenotype-genotype analysis and correlation are scalable for samples numbering in the hundreds of cells to thousands of cells up to the tens and hundreds of thousand cells.

Systems and methods for using sample numbers to pair timed metadata samples with media or hint samples is provided. A timed metadata sample can be paired with media or hint samples since a sample number contained in the time media sample is provided relative to the appropriate media or hint track. Additionally, an offset of sample numbers, applicable to scenarios where a plurality of timed metadata samples exist, may be added to the provided sample number to obtain the actual sample number within the media or hint track.

The invention discloses a target space knowledge and two-stage prediction learning-based target detection method. By utilizing various data conversion methods, a sample number is increased and samplediversity is improved; two deep neural networks including SSD and newly designed RefineNet are trained; for a prediction target with a relatively high probability in a primary prediction result of theSSD, the accuracy of judgment is further improved through the RefineNet; and by establishing peculiar spatial structure constraint rules of the target, the wrong prediction is reduced, thereby obtaining a final detection result. Compared with a few existing methods, the method provided by the invention has the advantages that visual and spatial characteristics of a remote sensing target are considered at the same time; and end-to-end target candidate selection, feature extraction and classified locating are realized by utilizing the deep networks with excellent feature extraction capabilities, so that the detection rate of the remote sensing target is remarkably increased and the false alarm rate is reduced.

The invention belongs to the technical field of image processing, discloses a method for identifying and positioning power transmission line insulators in unmanned aerial vehicle aerial images, and solves the problems in the prior art that the detection precision of an identification algorithm of the insulators is not high, the robustness is low, and the identification algorithm is easy to be affected by sample number. A group of Gabor wavelet basis with different sizes and different directions and training sample images are taken as convolutions so as to form a group of characteristic vectors which accurately describe sample image texture characteristics. A random forest machine learning algorithm with a semi-supervised learning mode is used to train sample data sets of the known category and the unknown category so as to obtain an insulator identification model. Through the mode from left to right and from top to bottom, a detection window with the same size as the training sample traverses the input images with different sizes. The detection window combining the identification model detects and positions the positions of the insulators in the input images with different sizes. And finally the accurate positions of the insulators in the input image with the original size are determined by using a non-maximum inhibition method.

An OFDM receiver recovers an rf signal as in-phase (I) and quadrature phase (Q) components of a baseband signal sampled in an A / D converter. The I and Q components of a received symbol are correlated at all sampling points. Correlation values are averaged over the latest L frames and saved in an L-deep FIFO. Symbol amplitude and phase are computed and passed to an offset estimator and an OFDM frame synchronization estimator. A phase-locked loop oscillator provides a sample number identifying the OFDM frame boundary to the offset estimator. An estimated offset value is selected as the negative of the phase angle of the auto correlation.

The invention relates to the field of big data machine learning, and discloses a method and a system for training a distributed CTR (Click To Rate) prediction model. Logistic regression calculation can be carried out under a distributed parallel framework, parameters and data can be distributed to multiple primary clusters for being processed; the primary clusters can be further subdivided into multiple secondary clusters, a MapReduce framework is adopted by the secondary clusters in each primary cluster, the gradient can be calculated by Map, and the iteration direction and the step length can be calculated by Reduce; and a task backup mechanism can be adopted in the secondary clusters. According to the method and the system, disclosed by the invention, the sample number and the characteristic parameters, which can be processed, in the logistic regression calculation can be increased, the integrated running efficiency of the system can be simultaneously increased, the calculating and processing time can be shortened, and the phenomenon that the whole calculation is interrupted as a node is in massive failure can be prevented from happening.

A differential protection system and a differential protection method for a flexible DC distribution network line are provided. An FPGA-based protection device sends a sample pulse to a data collector. To achieve synchronized sampling and data window synchronization, line dual-end protection devices are arranged as a reference end and a following end respectively. The following end firstly sends a synchronization request command including a number of a sample and a corresponding time of the sample. The reference end returns information including a sample number and a corresponding sample time of the reference end after receiving the command sent by the following end. After receiving a corresponding data message of the reference end, the following end calculates a communication transmission time delay and a deviation between the sample times of the two sides. Through compensation of the communication transmission time delay and a secondary interpolation sampling synchronization method, synchronized sampling of the following end and the reference end is achieved. Through calculating the differential current of two ends of a direct current line, the method determines whether differential current protection criteria are satisfied and achieves differential protection of the direct current line. The system and the method can ensure correct actions on internal faults and correct non-actions on external faults.

The invention discloses a current transformer saturation detecting method and aims at solving the technical problem of improving the saturation detection accuracy of a current transformer. The current transformer saturation detecting method comprises the following steps: calculating the amplitude values of Im, I2m, I3m, I5m, In, I2n, I3n and I5n by a relay protection device; calculating according to the following equations: Id = Im + In, I2d = I2m + I2n, I3d = I3m + I3n, and I5d = I5m + I5n by the relay protection device; calculating according to the following equations: id = imp + inp, ir = imp - inp, and dir = | (imp - im ( p - N ) ) | + | ( inp - in ( p - N ) ) | by the relay protection device; meeting the following conditions: 1) dir > 0.8in, 2) the sample number meeting ir > 0.8in is smaller than N, 3) the sample number meeting id > 0.8in is smaller than 3, and the difference between the sample number meeting the condition 2) and the sample number meeting the condition 3) is bigger than 3; failing to meet the condition: I2d + I3d + I5d < 0.15Id; and judging that the current transformer is saturated. Compared with the prior art, the current transformer saturation detecting method has the advantages that the method not only ensures that false differential protection operation is not carried out during external major faults with CT (Current Transformer) saturation, but also can improve differential protection sensitivity during internal faults with CT saturation through the judgment of time difference between differential current and brake current and the harmonic properties of the differential current in combination with harmonic properties during the CT saturation.

A method for audio / video synchronization and an apparatus thereof are provided. At least one dynamic synchronization checkpoint is disposed on the playback sample number axis of a video. Whether or not the audio and the video are synchronized is checked when the playback sample number of the video is set the dynamic synchronization checkpoint. The video is paused playing if the playback speed of the video is too fast, and those delayed playback sample number of the video having not been played are skipped if the playback speed of the video is too slow. Furthermore, the location of the dynamic synchronization checkpoint and the frequency of performing synchronization detection are adjusted according to the playback time jitter between the audio and the video.

The invention provides a method for estimating a traffic state parameter (speed) based on confidence optimization by analyzing data characteristic of the float car, introducing a confidence factor and mainly considering float car data sample number, the speed and time continuity of a traffic state. The average speed of the float car is estimated by confiding most of vehicles and high-speed vehicles and combining speed data of a previous time interval and historical speed data. The influence of abnormal data on a calculation result is effectively reduced, so that the calculation result is closer to an actual road situation; meanwhile, the problem of traffic state parameter estimation under the condition of sample size deficiency is solved, so that the accuracy and the stability of the traffic state parameter estimation are greatly improved.

The exemplary embodiments of this invention provide in one aspect thereof an ability to signal multiple decoding times for each sample in a file format level in order to allow, for example, different decoding times for each sample (or sample subset) between decoding an entire stream and decoding a subset of the stream. An alternate decoding time box is specified to allow for the signaling of multiple decoding times for each sample. Such a box can contain a compact version of a table that allows indexing from an alternate decoding time to a sample number, where an alternate decoding time is a decoding time to be used with a sample when only a subset of an elementary stream stored in a track is to be decoded. Furthermore, each entry in the table provides the number of consecutive samples with the same time delta, and the delta between those consecutive samples. By adding the deltas a complete time-to-sample map can be constructed.

A fraud score calculating program primarily for use in determining the possibility of credit card fraud can calculate a score reflecting the reliability of a model created based on Bayesian theory. A model which is stored in a fraud detection model database 120 obtains new authorization data and continues learning as the number of data samples increases. Calculation of the score is performed by a calculation logic provided in a scoring subsystem 110. The sample number data for a case corresponding to the authorization data are obtained from the model, and the probability of the occurrence of fraudulent use is calculated. The reliability of the model is also calculated on the basis of, for example, the number of the registered samples, and a fraud score is calculated using both the calculated probability of the occurrence of fraud and the calculated reliability of the model.

The invention discloses an interference subspace tracking-based high-efficiency self-adaptive monopulse angle measurement method. According to the invention, for the massive computation and the independent and identically distributed training sample number for covariance matrix estimation of the self-adaptive monopulse technology in the high-dimensional system, the MPASTd method is applied to the self-adaptive monopulse to carry out recursive estimation of the interference subspace; a new self-adaptive sum and difference beam weight is calculated; and a signal source space angle is obtained by using an angle estimation formula. With the method, the computation load is obviously reduced and the convergence speed is accelerated; and the good angle measurement precision is realized under the circumstances of small samples.

The invention discloses a method and a system for rapidly capturing a relay satellite measurement and control system signal. The method comprises the following steps: S1, analog-to-digital conversion: performing band-pass sampling on an analog intermediate-frequency signal through a high-speed analog-to-digital converter by comprehensive digital baseband equipment to obtain a digital intermediate-frequency signal; S2, down-sampling processing; S3, implementation of a capturing time shorter than a data sampling time: controlling two memories to implement seamless caching of data streams, wherein a caching depth is a sampling number of coherent integration, and a switching interval between the two memories is a sampling time of captured data; and S4, implementation of a search of a signal dynamic range: converging a Doppler frequency range by adopting a two-round frequency search according to a speed of outputting a frequency search unit result in a data sampling period, and obtaining a code phase offset of cached data in a third-round phase search. The system is used for implementing the method. The method and the system have the advantages of easiness in implementation, high accuracy, capability of satisfying a current engineering condition, and the like.

The invention discloses a human body tumble detection method and a human body tumble detection system based on big data. The human body tumble detection method is characterized in that characteristic vectors can be established according to information acquired by cell phone built-in sensors; a machine learning model can be used to identify whether the tumble occurs; when the user is in the tumbling state, the acquired information data can be transmitted to a big data platform in a real-time manner, and can be stored according to individual similarities; the platform is used to analyze the uploaded data by adopting the similarity metric algorithm, and is used to determine whether the updated data exists; when the updated data exists, the new data sample can be generated by the platform, and at the same time, a new machine learning classification model can be generated according to the data sample. When the tumbling state of the human body is determined by the system, the cell phone is used to trigger an alarming system automatically, and therefore the tumbling user can be rescued timely, and at the same time, the accuracy of the system can be increased continuously with the increasing sample number. The human body tumble detection method and the human body tumble detection system can be used for monitoring the activity safety of the children, the elderly, and the patients.

The present invention discloses a classification model training method and a device, belonging to the technical field of data processing. The method comprise a step of carrying out model training according to a current round of positive example samples and a current round of negative example samples and obtaining a current round of classification model, a step of using the current round of classification model to classify all samples if the current round of classification model does not satisfies a specified condition and selecting a specified sample in all samples according to a classification result, a step of taking the current round of positive example samples and the specified samples as a next round of positive example samples and determining a next round of negative example samples according to the next round of positive example samples, and a step of continuously executing the above model training and sample processing process according to the next round of positive example samples and the next round of negative example samples until the classification model which satisfies the specified condition is obtained. With the increase of the positive example sample number, the potential positive example samples in the negative example samples decrease, the purity of the negative example samples can be effectively improved, the stability of model obtained through training according to superimposed number of positive example samples and negative example samples, and the classification accuracy is high.

A method for measuring particle diameter distribution includes placing particle samples in glass sample tank set on back light plate uniformly with particle sample number of 50 - 500, placing size calibration object in the tank, using digital camera to shoot image of said particle sample, guiding image to computer for processing the image and obtaining roundness and diameter of each particle, outputting particle sample distribution results of number, volume and weight according to division of particle diameter.

The invention provides an OPTICS clustering algorithm based on a Spark big data platform, and relates to a computer information obtaining and processing technology. Parallel data is structurally partitioned, the optimal data set partitioning is obtained, a corresponding RDD is generated, neighbor sample numbers and core distances are calculated in parallel, partitions are subjected to parallel execution of the OPTICS algorithm to obtain a cluster sequence of the partitions, and the cluster sequence is obtained persistently; clusters are given to the partitions according to the cluster sequence, and samples can obtain global cluster numbers by combining the partitions. By means of the Spark distributed parallel technology, the optimal partitioning structure is found, and the cluster sequence of the partitions is obtained through parallel calculation. According to the OPTICS cluster sequence, a user can observe the inherent clustering structure of a data set from different levels of structures, the method can process a large data set which cannot be processed by a serial algorithm, and the time for obtaining the clustering result is greatly shortened.

The invention provides a system and method for determining a state of a user at least through utilization of self-stated data and biological measurement data. The method comprises the steps of receiving first biological measurement data of the user; receiving first self-stated data from the user, wherein the self-stated data comprises the state of the user; and storing the data to a memory, thereby enabling the first biological measurement data to be linked with the first self-stated data on relationship and time. The data can be linked on the relationship through utilization of a single fileand / or at least one public ID number. The data can be linked on the time through utilization of at least a time stamp, offset, a sample rate and / or the sample number. The method also comprises the steps of receiving second biological measurement data, determining the state of the user through utilization of the second biological measurement data and the data stored in the memory device, and performing at least one action in response to the state.

The invention discloses a method for forecasting a Mooney viscosity in a rubber mixing process, which comprises the following steps of: establishing an initial off-line model by using a process parameter and a Mooney viscosity accumulated in the process production as a data sample, then applying the off-line model to a practical process line, and acquiring new sample data; if the forecasting error of the model on the new sample exceeds a forecasting error threshold value, judging the sample as a valid sample, and adding the valid sample into the model; performing on-line dynamic update on model parameters; and when the number of the samples in the model exceeds the preset maximum sample number, removing the redundant samples in the model so that the number of the samples of the model is controlled within the maximum sample number. The method continually improves the precision of the model based on the cutting technology, and can effectively control the complexity of the model at the same time; the model has good convergence effect; and the method reduces the calculation quantity of the conventional model, and can provide accurate on-line real-time forecast for the Mooney viscosity in the rubber mixing process.

The invention discloses a supervised snore source identifying method. The method includes data pretreatment, training and identification; the main steps include, firstly, performing Mel frequency conversion on actual measured snore data, and acquiring a data sample; secondly, arranging a structure of a convolution neutral network, quantity of output feature drawings of a convolution layer and convolution core size, pooling size, weight vector updating studying rate, batch training sample number, and training iterations; thirdly, using a snore time frequency spectrogram of a training set as the convolution neutral network to input; performing network initialization on the arranged network structure; completing the training process through forward process, direction bias transmission, weight value updating and offset until the appointed iterations; at last, delivering the testing set to the trained network model and acquiring the identifying result. The supervised snore source identifying method can effectively identify the snore source, and is exact in identifying result and good in performance.

The invention discloses a tire rubber carbon black dispersity evaluation method and a system based on neural network image processing. The method comprises the following steps: S1, rubber material images in a sample set are acquired, the rubber material images are pre-processed, and feature data are extracted; S2, feature data of a part of rubber material images in the sample set are acquired as training data, the training data are trained according to a BP network model and an RBF network model respectively, the optimal mixing coefficient is obtained through an adaptive genetic algorithm, the two network models are parallelly connected according to the optimal mixing coefficient, and a BP-RBF mixed neural network evaluation model is obtained; and S3, feature data of the other part of rubber material images in the sample set are acquired as evaluation data, the data are put in the BP-RBF mixed neural network evaluation model obtained through training, and the carbon black dispersity grade in the rubber material is acquired through output. The accuracy is high; parameters can be adjusted and optimized along with increasing of the sample number in use, and the robustness is strong.

A device for high accurate measurement of time intervals is based on the conversion of the time interval to a sequence of samples of the response of a surface acoustic wave filter excited at the beginning and at the end of the measured interval. In one of its configurations, the time interval measurement device includes the input of the pulse signal, the filter exciter, the surface acoustic wave filter, the amplifier, the sampler, the analog-to-digital converter, the sample registers, the register of sample numbers, the voltage comparator, the control circuits, the sample counter, the computer, the output of the reference clock signal source, and the output of the measured time intervals.

The invention provides a pipeline leakage weighted positioning method and device based on pressure waves and sound waves. The method comprises the following steps: obtaining a signal transmission time difference obtained through multiple instruments and multiple methods; and carrying out exponential weighting processing on the signal transmission signal difference by adopting an iterative method to obtain positioning results. The device comprises an obtaining module and a calculating module. According to the pipeline leakage weighted positioning method and device, comprehensive positioning precision for multiple signal sources can be remarkably improved by an exponential weighting algorithm established based on physical characteristics of a signal, and the pipeline leakage weighted positioning method is superior to a weighted positioning method based on probability statistics in requirements on sample number, positioning inaccuracy, calculated amount and the like.

The invention provides a hyper-spectral image classification method based on a Gaussian process classifier collaborative training algorithm-. The method belongs to the technical field of image processing, and mainly solves the problem of low classification precision when the number of marked samples of hyper-spectral data is small in the prior art. The method comprises the following implementation processes of: firstly, randomly dividing feature vectors of samples into two sub feature vectors which are used as two visual angles of the samples; performing Gaussian process classifier collaborative training by using the two visual angles of partial non-marked samples and existing marked samples to obtain two final Gaussian process classifiers; and marking the two visual angles of all the non-marked samples by using the two final Gaussian process classifiers respectively, wherein the class mark with higher probability is used as the classification result of the non-marked samples. The method applied to hyper-spectral image classification can be used for remarkably improving the classification accuracy under the condition of small marked sample number.

In an imaging device according to one aspect of the invention, an amplitude demand Ac is specified according to a wobbling amplitude Ai required for detection of a hill-climbing direction based on an F value and a wobbling amplitude Ag required for detection of the hill-climbing direction based on an AGC gain. An allowable amplitude Am set for preventing a potential image shake caused by a magnification change with a position shift of a compensator lens in wobbling is specified corresponding to a zoom position and an object distance. When Ac>Am, the control procedure restricts the wobbling amplitude to Am and increases a sampling number of AF evaluation values to be detected at respective positions in wobbling, in order to ensure the sufficient detection performance of the hill-climbing direction. When Ac□Am on the other hand, the control procedure sets Ac to the wobbling amplitude. This arrangement of the imaging device efficiently ensures the sufficient detection performance of the hill-climbing direction for autofocusing control, while effectively preventing deterioration of the quality of a captured image due to the magnification change in the wobbling motion.

The invention provides a method and a device for separating a single karyocyte, in particular a method and a device for separating single karyocyte of cord blood, marrow and peripheral blood. The single karyocyte can be separated without a hundred grades purification environment, and the method and the device are not limited by sample numbers and volumes, and has high efficiency, simple structure and no pollution, thereby being applied to laboratory research and human body treatment, and being easy to produce in batch.

The invention provides a CNN and transfer learning-based disease intelligent identification method and system, which can reduce interference of a picture background, can achieve high recognition accuracy under the condition of a limited sample number, and supports higher training sample multi-classification operation efficiency. The disease image identification method comprises the following steps: image preprocessing: normalizing the size of the image, quickly positioning a disease region by using Fast-RCNN, and eliminating background interference; image feature extraction: using a triplet similarity measurement model to extract image features, and then using SIFT features as compensation features to perform weighted fusion; and disease classification and recognition: learning a first image feature of a normal plant image by adopting a deep convolutional neural network, then learning a second image feature of the disease plant image by using transfer learning, and finally performing classification and recognition by combining the first image feature and the second image feature.

The invention discloses a sand-dust type aerosol inversion method based on support vector machine identification. The method comprises the steps of selecting a sand-dust aerosol layer with obvious characteristics, a thick cloud layer with obvious characteristics and a thin cloud layer with obvious characteristics as classification samples, training classifiers through various sample numbers and characteristic vectors, then confirming an optimal classifier, classifying data of a satellite borne laser radar sand-dust source region, obtaining a sand-dust aerosol identification result with high accuracy, and inverting level height and optical thickness of an aerosol. According to the sand-dust type aerosol inversion method based on the support vector machine identification, a support vector machine is used for confirming characteristics of a hyper-plane through support vectors, the requirements for the number of the samples can be reduced, interference on classification accuracy due to sample uncertainty is reduced, and 532nm polarization detection data and level height information of a satellite borne laser radar are effectively used for distinguishing non-spherical ice crystal cloud and sand-dust type aerosol particles. The sand-dust type aerosol inversion method based on the support vector machine identification is particularly suitable for processing satellite borne laser radar detection data of a sand-dust season in northwest region of our country.

The invention discloses a ground feature classifying method based on simplified polarization synthetic aperture radar data, which relates to the synthetic aperture radar remote sensing information processing technology. The ground feature classifying method includes steps: a) obtaining Stokes parameters after filtering; b) extracting characteristic parameters including polarizability m, relative phase difference delta among orthogonal channels and total polarization power span for showing scattering mechanisms; c) calculating power ingredients corresponding to the basic scattering mechanisms, and determining main scattering mechanisms of pixels and dividing data into three types of pixels with identical scattering characteristics; d) further dividing each type of pixels with identical scattering characteristics into a plurality of types of pixels with equal sample numbers by the aid of the span; and e) realizing iterative Wishart classification. The ground feature classifying method is simple in principle and convenient in calculation, and can be used for analyzing and classifying ground feature scattering characteristics.