712 results about "Initial sample" patented technology

The instant invention provides an economical flow-through method for determining amount of target proteins in a sample. An antibody preparation (whether polyclonal or monoclonal, or any equivalent specific binding agent) is used to capture and thus enrich a specific monitor peptide (a specific peptide fragment of a protein to be quantitated in a proteolytic digest of a complex protein sample) and an internal standard peptide (the same chemical structure but including stable isotope labels). Upon elution into a suitable mass spectrometer, the natural (sample derived) and internal standard (isotope labeled) peptides are quantitated, and their measured abundance ratio used to calculate the abundance of the monitor peptide, and its parent protein, in the initial sample

The invention relates to the technical field of image information processing, in particular to a multiclass image classification method based on active learning and semi-supervised learning. The method comprises five steps: initial sample selection and classifier model training, BvSB active learning sample selection, CST semi-supervised learning, training sample set and classifier model updating and assorting process iteration. Through the operations of BvSB active learning sample selection, CST semi-supervised learning and SVM classification, the invention has efficient image classification effect under the condition of less manual tagging, does not increase overmuch computation burden, can quickly provide classification effects and also can take consideration of the demand of a classification system on the computation complexity.

A system for audio processing comprising an initial background statistical model system configured to generate an initial background statistical model using a predetermined sample size of audio data. A parameter computation system configured to generate parametric data for the audio data including cepstral and energy parameters. A background statistics computation system configured to generate preliminary background statistics for determining whether speech has been detected. A first speech detection system configured to determine whether speech was present in the initial sample of audio data. An adaptive background statistical model system configured to provide an adaptive background statistical model for use in continuous processing of audio data for speech detection. A parameter computation system configured to calculate cepstral parameters, energy parameters and other suitable parameters for speech detection. A speech / non-speech classification system configured to classify individual frames as speech frames or non-speech frames, based on the computed parameters and the adaptive background statistical model data. A background statistics update system configured to update the background statistical model based on detected speech and non-speech frames. A second speech detection system configured to perform speech detection processing and to generate a suitable indicator for use in processing audio data that is determined to include speech signals.

The method according to the invention allows the formation of oil and the retention phenomenon in the mother rock to be modelled. Organic matter characterization experiments are used to establish the molecular model (MM) of the initial sample (E). The thermal cracking reaction of this molecular model is reproduced by dynamic molecular simulation computations with a reactive force field (RMD) and validated by comparison with experimental data. The reaction mechanism obtained (SR) allows to carry out a kinetic study (C) by variation of the temperature parameter. The phase equilibria (PES) of the reaction medium are determined at any time from dynamic simulation. The successive phase equilibrium assessments at various progress stages of the cracking reaction allow following the physicochemical evolution (PC) of the thermal maturation of the organic sample studied. The free hydrocarbons (liquid and gaseous) that are not retained in the solid residue can be quantified throughout numerical modelling of the sample maturation; representing, in the sedimentary basins, the hydrocarbons that are not retained in the organic matrix of the mother rock (Q). This quantity can be used as an indicator or an input value for the retention threshold in basin models.

A solution for reducing interference in noninvasive spectroscopic measurements of tissue and blood analytes is provided. By applying a basis set representing various tissue components to a collected sample measurement, measurement interferences resulting from the heterogeneity of tissue, sampling site differences, patient-to-patient variation, physiological variation, and instrumental differences are reduced. Consequently, the transformed sample measurements are more suitable for developing calibrations that are robust with respect to sample-to-sample variation, variation through time, and instrument related differences. In the calibration phase, data associated with a particular tissue sample site is corrected using a selected subset of data within the same data set. This method reduces the complexity of the data and reduces the intra-subject, inter-subject, and inter-instrument variations by removing interference specific to the respective data subset. In the measurement phase, the basis set correction is applied using a minimal number of initial samples collected from the sample site(s) where future samples will be collected.

The instant invention provides an economical flow-through method for determining amount of target proteins in a sample. An antibody preparation (whether polyclonal or monoclonal, or any equivalent specific binding agent) is used to capture and thus enrich a specific monitor peptide (a specific peptide fragment of a protein to be quantitated in a proteolytic digest of a complex protein sample) and an internal standard peptide (the same chemical structure but including stable isotope labels). Upon elution into a suitable mass spectrometer, the natural (sample derived) and internal standard (isotope labeled) peptides are quantitated, and their measured abundance ratio used to calculate the abundance of the monitor peptide, and its parent protein, in the initial sample.

The present invention puts forward a front vehicle information structured output method base on concatenated convolutional neural networks, for mainly solving the problem that a traditional method cannot quickly detect and identify a vehicle body, a license plate and a vehicle logo in one time in a complex environment. The realization process of the front vehicle information structured output method comprises the steps of: 1, acquiring a sample set and selecting a vehicle body initial sample set from the sample set; 2, training the vehicle body initial sample set through a BING (Binarized Normed Gradients) method; 3, respectively training convolutional neural networks of vehicle body, license plate and vehicle logo; 4, judging the area and color of the vehicle body according to the well trained convolutional neural network of vehicle body; 5, judging the area of the license plate and identifying a license plate number according to the well trained convolutional neural network of license plate; 6, judging the area and class of the vehicle logo according to the well trained convolutional neural network of vehicle logo; and 7, outputting the all obtained information to a frame image. The front vehicle information structured output method of the present invention can accurately detect and identify front vehicle information in a complex environment, and can be used for intelligent monitoring, intelligent traffic, driver auxiliary and traffic information detection.

The invention relates to an interactive method of image semi-automatic annotation, comprising S1 dividing an initial sample into three different types of annotation samples according to different category attributes; labeling the three types of labeling samples manually to get different kinds of labeling results, and then using three models of Mask-RCNN, Fast-RCNN and FCN to train separately; S2 processing the data set of the picture to be annotated in an offline manner, wherein the annotating process is that the data set of the picture to be annotated passes through the three depth learning models in turn to output the json format files of all types and coordinate points of the data samples; S3 calling the relevant attribute tag value and coordinate point value of the json format file according to the name of the annotated image; S4 displaying the corresponding automatic marking result in the marking software, and judging whether the category and area marking of the target object arestandardized and reasonable by manpower; S5 carrying out data augmentation on the correctly labeled labeling samples and feeding back the augmented data to the model for retraining.

The invention relates to a nonlinear fault detection method based on semi-supervised manifold learning, which belongs to the field of electromechanical equipment fault diagnosis. The method comprises the following steps that (1) vibration signal data acquisition and preprocessing are performed on monitored electromechanical equipment, and hybrid-domain feature extraction is performed to obtain an initial sample set which represents an operating state of the equipment; (2) a semi-supervised Laplacian Eigenmap algorithm is adopted to perform manifold feature extraction on an equipment sample, so as to obtain essential manifold features sensitive to faults; and (3) an intelligent diagnosis model based on an LS-SVM (Least Squares-Support Vector Machine) is established in low-dimensional manifold feature space, so as to realize mode recognition and diagnosis decision to the operating state of the equipment faults. By using a semi-supervised manifold learning algorithm adopted by the invention, nonlinear geometric manifold features of a vibration signal sample can be effectively extracted, the fault category of the equipment operating state is judged, and the fault detection pertinence and accuracy are improved. The nonlinear fault detection method can be widely used for fault detection and diagnostic analysis of all kinds of mechanical equipment.

The invention relates to the correlation technical field of order picking operation, in particular to a method and device for generating order picking collection lists and a method for optimizing an order picking route. The generation method comprises the steps: obtaining position information of N order forms, using the order forms as samples, and using the position information of the order forms as sample two-dimensional coordinates of the corresponding samples, wherein N is greater than one; computing the number of clusters included by the order picking collection lists; selecting c samples from the N samples as initial samples; computing the distances between the sample two-dimensional coordinates of (N-c) samples except for the initial samples and c clustering centers, regarding the cluster where the clustering center with the shortest distance is located as a cluster to be classified, and returning the samples to the cluster to be classified; generating the corresponding order picking collection lists by the clusters if the iteration end condition is met, and continuing to perform iteration if the iteration end condition is not met. According to the order picking route generated by the optimized order picking collection lists, the order picking efficiency is improved greatly compared with the order picking efficiency in the prior art.

A method for classification of samples comprising providing a trained statistical model based upon a set of initial samples. Receiving a set of first samples and training a first statistical model base upon the first set of samples, where the first statistical model is of the same class as the trained statistical model. Receiving a set of second samples and training a second statistical model base upon the second set of samples, where the second statistical model is of the same class as the trained statistical model. The trained statistical model, the first statistical model, and the second statistical model, being independent of each other and collectively used to classify another sample.

Adaptive sampling of pixels is disclosed. In some embodiments, an initial rendering of a scene that determines texture at each portion of the scene is generated, and a ray traced rendering of the scene is generated by tracing an initial sample of rays. The following steps are iterated until all portions of the ray traced rendering satisfy a noise threshold: subtract the initial rendering of the scene from the ray traced rendering of the scene to determine a measure of noise at each portion of the ray traced rendering and trace another sample of rays in the ray traced rendering for each portion of the ray traced rendering that does not satisfy the noise threshold. The completed ray traced rendering is outputted. Different portions of the completed ray traced rendering are sampled with different numbers of samples of rays.

The invention discloses a traffic sign recognition method based on YOLO v4tiny. The method comprises the steps: collecting a traffic sign data set, carrying out the data enhancement of an initial sample traffic sign data set, and dividing the initial sample traffic sign data set into a training set, a verification set and a test set; for a real target frame in the training set, clustering six priori frame sizes with different sizes by taking an intersection-parallel ratio as an index, and embedding a channel attention mechanism and a space attention mechanism into a YOLO v4tiny framework to obtain a YOLO v4tinyCBAM network model; and training the network model through the training set, performing verification through the verification set, and finally testing the performance of the networkmodel through the test set. According to the method, a channel attention and spatial attention mechanism is introduced into the YOLO v4tiny lightweight network, so that the generalization ability is stronger, and the recognition precision is higher.

The invention discloses a remote sensing image classification method based on image block active learning and belongs to the technical field of image information processing. The method comprises the following steps of remote sensing image blocking, initial sample selection, classifier model training, active learning sample selection, training sample set and classifier model updating, classification process iteration; image block classification prediction, and conversion of a block classification result into a pixel classification result. The remote sensing image classification method serves an image block as a research object, compared with a traditional remote sensing image classification method based on active learning of pixel points, under the same experiment condition, the classification result of the image is more accurate, a block sample screened out by the active learning can more rapidly and accurately conduct manual annotating, constitutive properties of the classified image are stronger, spots directly brought by classification of the pixel points are greatly reduced, and the better visual effect is brought to people.

The invention provides a preparation method and failure analysis method for a transmission electron microscope (TEM) sample. The method comprises the following steps: performing rough positioning on a failure area on a substrate, cutting an initial sample comprising the failure area, accurately positioning a specific failure point in the initial sample by using the TEM, and thinning the initial sample to form a final sample, namely the TEM sample for TEM electronic penetration can be provided. Therefore, the technical problems that a focused ion beam (FIB) is insufficient in resolution ratio, a failure point on a specific layer with small enough thickness inside a semiconductor cannot be accurately positioned and a TEM sample cannot be manufactured in the traditional technology are solved.

This invention describes a nucleic acid testing procedure in a form of portable device or a test kit for the purposes of clinical genetic testing, infectious disease diagnostics, biodefense, forensic analysis, paternity testing, pet and cattle breeding, food testing, etc. This testing does not include toxic chemicals and is simple enough to be used by an average individual without any special laboratory training. The procedure includes collecting the sample, potential isothermal amplification of the whole genomic DNA or a fragment of genomic DNA, denaturing double-stranded DNA into single-stranded form, hybridizing the denatured sample DNA to single-stranded allele-specific tester oligonucleotides complementary to the analyzed DNA sequence of interest, selective removal of single-stranded DNA from DNA hybrids, and finally detecting the label in double-stranded hybrids to determine the presence or absence of a particular sequence in the initial sample.

The invention discloses a thermal process soft sensor modeling method based on least squares and support vector machine ensemble, and belongs to the technical fields of thermal process and artificial intelligence intersection. The method includes selecting auxiliary variables as an input of a model and key variables to be predicted as an output of the model, selecting running data as an initial training sample, utilizing the soft fuzzy c-means clustering (SFCM) method to divide the initial sample into sub-datasets which are overlapped and which are provided with differences, establishing individual models on each sub-dataset, and synthesizing predicted outputs of the individual models to obtain estimation of the key variable; aiming to optional new acquired sample xk, obtaining a corresponding predicted value. According to the thermal process soft sensor modeling method, the soft fuzzy C-means clustering method is adopted, predicting accuracy is improved by means of establishing integrated models, calculating of the models is easier, and calculating efficiency is improved; boundary samples are processed effectively, the process is convenient to implement, the key variable can be predicted accurately, and important significance is provided to optimized operation of the thermal process system.

The invention relates to a mass online historical data-based power system transient stability evaluation method. The method comprises the steps of selecting mass online historical data to generate emulated data; based on the emulated data, extracting statistic features and generating an initial sample set; extending an instable sample and compressing a stable sample in the extended-boundary manner so as to form a calculation sample; based on the calculation sample, conducting the classification model training and the parameter optimization based on the SVM algorithm to finally form a classification model, and evaluating the transient stability of a power system. According to the technical scheme of the invention, a reusable theoretical solution is established from an original online calculation result to a main problem involved in forming an actual available SVM model. The above technical scheme reflects some characteristics of the mass data processing process and provides a novel method for the mass data technical packet of the power system.

The present invention describes a method including: determining field-clustering scheme; selecting initial sample plan; establishing initial model of overlay, the initial model of overlay comprising components; and establishing efficient model of overlay from the initial model of overlay including: constructing matrices; identifying redundant components and eliminating the redundant components; and identifying highly-correlated components and determining whether to eliminate the highly-correlated components.

The invention relates to an assessment method for transient voltage stabilization of a load area of an electrical power system and belongs to the electrical power system stability analyzing and assessment field. The assessment method for the transient voltage stabilization of the load area of the electrical power system includes that using data measured by a synchronous phasor measurement unit as basis, and building an initial sample database for data mining through a lot of simulation samples; extracting characteristics for reflecting the stabilization degree of each node through the quantitative evaluation for each node in the area; identifying through a multiple linear regression method to obtain sensitivity coefficients for reflecting the mutual influence relations between the nodes in a local area network; using a semi-supervised clustering method to demarcate all the samples; using a decision tree algorithm to perform classified learning to obtain a decision tree model, and using the decision tree model for online monitoring to assess the global transient voltage stabilization state of the load area of the electrical power system.

An apparatus for sharing social media content includes a content management unit for, when sharable content is input by a user, extracting profile information about the sharable content by analyzing the sharable content, and generating social media content by associating the extracted profile information about the sharable content with profile information about a user to store the generated social media content in a database. Further, the apparatus for sharing the social media content includes a content searching unit for extracting an initial sample by searching the database based on keywords requested to be searched for in response to a search request of a user, and searching for the sample by comparing profile information about each piece of content included in the initial sample with one of the keywords.

The invention provides more rapid and cost-effective methods of deglycosylating target glycoproteins. In methods of the invention, the target glycoprotein is isolated from initial samples, which may contain multiple other glycoproteins, by subjecting the initial sample to a solid phase containing an affinity ligand, such as a deglycosylated antibody, that interacts specifically with the target glycoprotein. Once separated from the sample, the target glycoprotein can be deglycosylated in situ, or eluted from the solid phase, quantitated, and then deglycosylated.

The invention discloses a method for preparing simulative lunar soil. The method comprises the following steps of: selecting and collecting source rock samples; pulverizing the collected source rock samples and selecting the source rock samples with a certain precision from the pulverized source rock samples; performing whole rock analysis on the selected source rock samples; preparing initial samples of the simulative lunar soil according to the whole rock analysis result; sintering the prepared initial samples of the simulative lunar soil into glass; and pulverizing the sintered glass and then mixing the pulverized glass and the prepared initial samples of the simulative lunar soil in a certain ratio to obtain the simulative lunar soil. By the method, the simulative lunar soil is prepared from the earth source rock samples; and the simulative lunar soil samples with the properties similar to those of authentic lunar samples can be prepared for research in the aspects of lunar science, engineering and the like.

The invention provides a method for detecting the change of urban surface features in remote sensing image based on Siamese convolution network, the Siamese convolution network is a twin convolution neural network SCNN, Based on the data enhancement technique, the initial sample set is selected from the registered two-phase city images, the twin convolution neural network (SCNN) is set up, the twin convolution neural network (SCNN) is trained based on the initial sample set, and the initial sample set is expanded by the data enhancement technique. The twin convolution neural network (SCNN) istrained based on the expanded sample set, and the trained SCNN model is obtained to detect the change of urban terrain. The invention realizes the expansion of the sample through the data enhancementtechnology, and designs a Siamese convolution neural network, avoids the tedious steps of artificially designed features in the traditional change detection method, and realizes the end-to-end operation; Fully considering the spatial attributes of the image, the accuracy and reliability of the change detection are improved.

A method is provided in one example embodiment and includes creating an initial sample set comprising a plurality of notification messages, where each of the notification messages is associated with one of a plurality of bearers each of which has a first parameter associated therewith. The method further comprises prioritizing the notification messages of the initial sample set according to a value of the first parameter of the associated bearer to create a prioritized sample set and optimizing the prioritized sample set to create an optimized sample set. The method further comprises applying a throttle factor to the optimized sample to remove a number of low priority notification messages from the prioritized sample set to create a final set of notification messages to be transmitted to a network element.

The invention discloses a real-time video based vehicle detecting and tracking method. The method includes S101, acquiring an initial sample set; S102, training an initial classifier; S103, iterating a training classifier; S104, generating a vehicle candidate area; S105, confirming and tracking vehicles; S106, counting traffic flowrate; S107, performing algorithm interaction. The real-time video based vehicle detecting and tracking method has the advantages that the vehicles appearing in a real-time video can be detected and tracked stably, the traffic flowrate can be counted, high robustness, adaptability to noise, illumination and weather changes and a certain adaptability to shielding are achieved, and the method is high in processing speed and accuracy and capable of meeting operation requirements of a real-time system.

The invention provides a power system dynamic security assessment comprehensive model and a spatiotemporal visualization method. The method comprises the following steps: constructing a dynamic security index and establish an initial sample set for dynamic security assessment based on the historical operation data of a power system and a predicted accident set; 2) constructing a feature selectionframework, performing feature selection on the initial sample set and forming a processed high-efficiency sample set; 3) constructing an online dynamic security assessment comprehensive model based ona random bit forest and performing offline training and updating on the model by using the high-efficiency sample set; and 4) using the continuously updating dynamic security assessment model to complete the online assessment of the dynamic security state and using the spatiotemporal visualization method to realize the visual presentation of the dynamic security information. The purpose of the present invention is to provide the online dynamic security assessment comprehensive model and the spatiotemporal visualization method which are beneficial to the system operators to take timely preventive and control measures, avoid large power outage caused by accidents and improve the secure operation level of the power grid.

The present invention describes a method including: determining field-clustering scheme; selecting initial sample plan; establishing initial model of overlay, the initial model of overlay comprising components; and establishing efficient model of overlay from the initial model of overlay including: constructing matrices; identifying redundant components and eliminating the redundant components; and identifying highly-correlated components and determining whether to eliminate the highly-correlated components.

The invention discloses a global sensitivity analysis method for complex information system performance, which includes: experimental design of initial sample generation; construction of performance evaluation model; generation of proxy model; experimental design of sensitivity analysis data generation; Sensitivity analysis: By adjusting the value of sensitive indicators, the system performance can be maximized, and then the system components associated with them can be found by sensitive indicators, and improved according to the values. The advantages of the present invention are: for complex information system efficiency evaluation model with many parameters, high dimensions, wide range of values, and complex evaluation process, the agent model based on extreme learning machine is introduced to replace the complex efficiency evaluation model, and the global model based on variance is used. Sensitivity analysis method, improving the performance of complex information systems within a limited budget and time, invented a sensitivity analysis method to quickly improve complex information systems.