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30results about How to "Improve classification recognition ability" patented technology
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Underwater acoustic signal target classification and recognition method based on deep learning
ActiveCN109800700AEffective Voiceprint FeaturesImprove robustnessBiological neural network modelsCharacter and pattern recognitionFeature extractionDecomposition
The invention belongs to the technical field of underwater acoustic signal processing, and particularly relates to an underwater acoustic signal target classification and recognition method based on deep learning. The method comprises the following steps: (1) carrying out feature extraction on an original underwater acoustic signal through a Gammatone filtering cepstrum coefficient (GFCC) algorithm; (2) extracting instantaneous energy and instantaneous frequency by utilizing an improved empirical mode decomposition (MEMD) algorithm, fusing the instantaneous energy and the instantaneous frequency with characteristic values extracted by a GFCC algorithm, and constructing a characteristic matrix; (3) establishing a Gaussian mixture model GMM, and keeping the individual characteristics of theunderwater acoustic signal target; And (4) finishing underwater target classification and recognition by using a deep neural network (DNN). According to the underwater acoustic signal target classification and recognition method, the problems that a traditional underwater acoustic signal target classification and recognition method is single in feature extraction and poor in noise resistance can be solved, the underwater acoustic signal target classification and recognition accuracy can be effectively improved, and certain adaptability is still achieved under the conditions of weak target acoustic signals, long distance and the like.
Owner:HARBIN ENG UNIV
Network encryption traffic classification method and system based on multi-feature learning
ActiveCN113037730AImprove classification accuracyImprove classification recognition abilityNeural architecturesData switching networksData packData set
The invention belongs to the technical field of network security, and particularly relates to a network encryption traffic classification method and system based on multi-feature learning, and the method comprises the steps: carrying out the preprocessing of an original traffic data set, and obtaining a traffic data package vector used for the input of a deep learning model; respectively inputting the traffic data packet vectors into a trained multi-channel CNN model and a trained LSTM model for parallel learning, extracting the data packet space features through the multi-channel CNN model, and extracting traffic time sequence features through the LSTM model; carrying out vector splicing on the data packet space feature and the traffic time sequence feature to obtain an omnibearing traffic feature vector; and inputting the omni-directional traffic feature vector into a neural network full-connection layer, and obtaining an encrypted traffic classification type through a traffic type probability. According to the method, the traffic features can be comprehensively and automatically extracted and utilized from the angles of the spatial features and the time features, the classification capability of the encrypted traffic is improved, and the method has good application value.
Owner:PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU +1
Neural network image classification and recognition method based on optimized KPCA algorithm
ActiveCN112488205AEasy extractionImprove classification recognition abilityInternal combustion piston enginesCharacter and pattern recognitionAlgorithmOriginal data
The invention discloses a neural network image classification and recognition method based on an optimized KPCA algorithm, and the method comprises the steps of calculating the cosine similarity of different vectors in a high-dimensional space, carrying out the dimension reduction of an original matrix of the KPCA algorithm through matrix rank minimization, reserving the effective information of original data to the maximum degree, and extracting better feature vectors to serve as weight values of convolutional layers, therefore, the problems that when an original KPCA algorithm is used for convolutional neural network image classification prediction, convolution kernel initialization calculation is complex, dimensionality disasters are likely to be caused, reliable features cannot be extracted, a whole network is difficult to train, and a network architecture is sensitive to image noise are solved. Therefore, the robustness and prediction performance of the whole network model are improved, and the effect of image classification and recognition is finally improved.
Owner:GUILIN UNIV OF ELECTRONIC TECH
Grass and original ecology data monitoring and intelligent decision-making integrated solution system
ActiveCN112990262AImprove collection efficiencyHigh data accuracyCharacter and pattern recognitionNeural architecturesEcological environmentResearch data
The invention discloses a grassland ecological data monitoring and intelligent decision-making integrated solution system, which comprises an ecological environment data acquisition module, a target area video data acquisition module, a sensor module, a data processing module and a cloud service platform module, the system can establish a cloud ecological database for a large-scale grassland ecological environment in a short time, and can perform a series of scientific analysis and decision-making on the ecological state of a target area from multiple dimensions in combination with big data and artificial intelligence application, including plant variety identification, ecological disaster early warning and plant disease and insect pest degree analysis. A user can take reasonable improvement measures while fully knowing the ecological state of the target area, valuable scientific research data is provided for scientific research personnel, and powerful technical support is formed for restoration and protection of the grassland ecological environment.
Owner:INNER MONGOLIA UNIVERSITY
Audio scene recognition method combining deep neural network and topic model and system thereof
InactiveCN109448703AImprove classification recognition abilityBuild accuratelyCharacter and pattern recognitionSpeech recognitionMatrix decompositionTraining phase
The invention discloses an audio scene recognition method combining deep neural network and a theme model as well as a system thereof. The method trains the audio event classification DNN neural network, a PLSA theme model, and an audio scene recognition DNN neural network at a training phase. In a testing phase, a test audio file passes through the audio event classification DNN neural network frame by frame; then an audio file-audio event co-occurrence matrix is constructed by the output of the neural network, the co-occurrence matrix is matrix-decomposed by the PLSA theme model, and decomposition is carried out to obtain the theme distribution of the test audio file on the potential theme; finally, the audio file-theme distribution is used as the input of the audio scene recognition DNNneural network, and the recognition result is obtained. The method innovatively combines the deep neural network with the theme model, and the introduction of the theme model is beneficial to providemore useful information for the deep neural network, and the method improves the classification and recognition capability of the network.
Owner:SHANDONG NORMAL UNIV
Rapid ship target detection method, storage medium and computing equipment
PendingCN111914924AGood precisionEfficient removalCharacter and pattern recognitionNeural architecturesData setComputer science
The invention discloses a rapid ship target detection method, a storage medium and computing equipment, and the method comprises the steps: establishing a feature pyramid full-convolution network anda double-branch module which sequentially comprise an input layer, a feature extraction layer, a feature fusion layer and an output layer, determining a ship data set, inputting a generated training set into the built feature pyramid full-convolution network, and generating a target detection result. According to the method, the ship in the image can be rapidly and accurately detected; and the detection result is accurate and rapid, the requirement on embedded equipment is low, and the method has very high practical application value in various aspects such as military affairs, civil use and the like.
Owner:XIDIAN UNIV
Underwater acoustic communication signal modulation mode identification method based on improved gating network and residual network
ActiveCN113269077AImprove recognition generalization abilityImprove discrimination abilityCharacter and pattern recognitionNeural architecturesFeature extractionResidual neural network
The invention discloses an underwater acoustic communication signal modulation mode identification method based on an improved gating network and a residual network. The identification method comprises the following steps: receiving an underwater acoustic signal; improving the gated recurrent neural network to extract time features of the underwater acoustic signals; improving the residual neural network, and introducing a self-attention mechanism to carry out weighted reprocessing to obtain underwater acoustic signal spatial features; based on the correlation between the time features and the spatial features of the underwater acoustic signals, using an adaptive fusion strategy to fuse the obtained time features and the obtained spatial features of the underwater acoustic signals; sending the fused features to a full-connection neural network for network training; and after feature extraction and fusion are carried out on a to-be-recognized underwater acoustic signal, inputting the to-be-recognized underwater acoustic signal to the trained full-connection neural network for recognition, and finally outputting a recognition result. The technical scheme finally realizes underwater acoustic communication modulation mode intelligent identification with strong anti-interference and high accuracy.
Owner:QINGDAO UNIV OF SCI & TECH
Electronic nose signal feature fusion method based on separability degree and dissimilarity degree
ActiveCN106127259AImprove classification recognition abilitySolve redundancyCharacter and pattern recognitionInformation processingDimensionality reduction
The invention provides an electronic nose signal feature fusion method based on the separability degree and the dissimilarity degree and belongs to the technical field of electronic noise signal and information processing. The method comprises a step one of subjecting an electronic nose signal to feature extraction, a step two of performing feature selection and a step three of performing feature weighted fusion. The invention reserves classification information to the greatest degree while reducing dimensions and eliminating redundancy, and thus the classification identification rate is greatly improved, and the classification identification performance of an electronic nose is improved further.
Owner:SOUTHWEST UNIVERSITY
Method and device for identifying safe dressing of electric power worker
PendingCN113536842ASmall amount of calculationImprove the performance of safe clothing classification and recognitionData processing applicationsCharacter and pattern recognitionMachine learningPersonnel safety
The embodiment of the invention provides a method and a device for identifying safe dressing of an electric power worker. The method comprises the following steps: acquiring a dressing image of the electric power worker; and inputting the dressing image of the electric power worker into a preset dressing identification model to perform detection of the head and the body of the personnel, safety helmet classification identification and tool classification identification, and obtaining a safety helmet identification result and a tool identification result. According to the method and the device for identifying the safe dressing of the electric power worker provided by the embodiment of the invention, a lightweight network MobileNet is adopted as a basic feature extraction network, an optimized SSD target detection algorithm is combined, the head part and the body part of the personnel are preliminarily judged, and then different hierarchical features and different scale splicing features of the network are applied to different classification identification tasks. And the calculated amount is reduced, and meanwhile, the safety dressing classification and identification performance is improved.
Owner:POTEVIO INFORMATION TECH
Unbalanced big data set-oriented unsupervised text topic related gene extraction method
PendingCN111460161AAvoid predefinedReduce distractionsCharacter and pattern recognitionSpecial data processing applicationsData setSmall sample
The invention discloses an unbalanced big data set-oriented unsupervised text topic related gene extraction method, which comprises the following steps of: obtaining a clustering cluster of a high-dimensional sample set by adopting factor analysis and a density peak algorithm, and labeling an unlabeled sample; improving a CHI statistical matrix-based feature selection method by utilizing the average local density and the information entropy, and forceening the feature expression degree of low-density and small sample clusters; a fast fixed point algorithm based on negentropy is adopted, high-order statistical correlation between multi-dimensional data is analyzed, independent implicit topic feature genes are extracted, and removal of high-order redundancy between components is completed. Large-scale labeled samples do not need to be adopted for training, so that the pre-definition of a sample category relationship and a feature structure can be effectively avoided; and the influence ofan over-sampling or under-sampling method on the category distribution of the original unbalanced data set is overcome. The performance of the CHI statistical selection method is improved by correcting the feature category structure; effective feature dimension reduction under the condition of keeping the sample set recognition capability is also realized.
Owner:XIAN UNIV OF POSTS & TELECOMM
Remote sensing image target detection model building method based on context enhancement and application
PendingCN112200045AImprove classification recognition abilityEasy to detectScene recognitionNeural architecturesImaging processingComputer science
The invention discloses a remote sensing image target detection model establishment method based on context enhancement and an application, and belongs to the technical field of image processing, andthe method comprises the steps: building a to-be-trained target detection model based on a neural network, and carrying out the target detection and training on a remote sensing image, obtaining a remote sensing image target detection model based on context enhancement; in the target detection model, using each module for extracting a multi-scale feature map F of the remote sensing image; extracting global context information of the F to obtain M; respectively enhancing boundary information and category information in the F to obtain M<E> and M<E><cl> and respectively capturing informationassociation between channels in M<E> and M<E><cl> to obtain channel weights W<d> and W<c>; and fusing the M and M<E> according to the W<d> to obtain a boundary information enhanced feature map F<E>, fusing the M and M<E><cl> according to the W<c> to obtain a category information enhanced feature map F<E><cl>, and fusingF, F<E>, and F<E><cl> to obtain a feature map F<E><ct>, and carrying outtarget detection on the feature map F<E><ct>. The method can improve the target detection precision of a remote sensing image.
Owner:HUAZHONG UNIV OF SCI & TECH
Target application program updating method and device based on feedback information
PendingCN112748941AEasy to identifyImprove recognition accuracyCharacter and pattern recognitionNeural architecturesApplication procedureNetwork model
The invention discloses a target application program updating method and device based on feedback information. The method comprises the steps that actual scene information input in a target application program and result feedback information corresponding to the actual scene information are obtained; according to the actual scene information and the result feedback information, a target neural network model in the target application program is updated, the target neural network model is a neural network model obtained by conducting model training on a pre-trained neural network model through a training sample set, the training sample set comprises at least two sets of sample scene information, the different groups of sample scene information correspond to different actual sample recognition results of the target object, the loss function used during model training corresponds to different weights in the different groups of sample scene information, and the weights corresponding to the different groups of sample scene information are in negative correlation with the number of the sample scene information in the different groups of sample scene information.
Owner:TENCENT TECH (SHENZHEN) CO LTD
Face recognition method and system
ActiveCN112257600AIncrease the number ofImprove classification recognition abilityCharacter and pattern recognitionFeature vectorInformation transmission
The invention discloses a face recognition method and system. The method comprises the steps of: extracting feature vectors of face images with labels and face images without labels to obtain a firstfeature vector set and a second feature vector set; calculating soft label information of the label-free face image; calculating inter-class divergence and intra-class divergence of the face image training set and executing a linear discriminant analysis algorithm to obtain a discriminant projection matrix; performing dimension reduction on the feature vector of the to-be-detected face image, thefirst feature vector set and the second feature vector set by utilizing the identification projection matrix to obtain respective low-dimensional feature vectors, and inputting the low-dimensional feature vectors into a collaborative representation classifier to obtain respective collaborative representation codes, and calculating a reconstruction residual error by utilizing the collaborative representation code corresponding to each type, wherein the type of label with the minimum reconstruction residual error is a to-be-detected sample label. According to the method, the label information with the label data is transmitted to the data without the label, the number of the training samples with the label is increased, all the samples are identified and analyzed, and the CRC precision and discriminability are improved.
Owner:武汉新可信息技术有限公司
Bone marrow cell classification and identification method and system based on multiple features and multiple classifiers
ActiveCN113177927AEnsure consistencyEnsure clarityImage enhancementImage analysisData setMultiple classifier
The invention discloses a bone marrow cell classification and identification method and system based on multiple features and multiple classifiers. The method comprises the following steps: training and testing bone marrow cells based on three transfer learning classifiers; respectively extracting a texture feature LBP, a shape feature HOG and a color feature HSV of the bone marrow cell image data set, performing fusion to obtain a feature fusion image, and performing fusion on the three migration classifiers by using a Keras model fusion algorithm to obtain a fusion classifier; training the fusion classifier by using the feature fusion image with the best test accuracy in the single classifier to obtain a multi-feature multi-classifier fusion model with the best test accuracy; positioning and segmenting bone marrow cells in the bone marrow cell image to obtain a model application data set; and testing the data set by using the multi-feature multi-classifier fusion model with the best test effect to obtain a final model application effect. The advantages of different features and different classifiers are combined, and the test classification accuracy of the bone marrow cells is improved.
Owner:XI AN JIAOTONG UNIV
Zero-sample image classification method and system based on unknown similar category set
ActiveCN112257765AImprove accuracyIncrease the degree of relationship weightCharacter and pattern recognitionComplex mathematical operationsSample imageClass prediction
Owner:UNIV OF JINAN
Litchi disease and insect pest identification method based on deep learning
PendingCN114463741AReduce storageReduce computational overheadCharacter and pattern recognitionNeural architecturesBiotechnologyDisease
Aiming at the limitation of the prior art, the invention provides a litchi disease and insect pest identification method based on deep learning. According to the method, a deep learning training result is applied, various litchi diseases and insect pests can be automatically identified, and the problems of low efficiency, poor identification effect and the like of a traditional artificial disease and insect pest identification method are solved; according to a litchi disease and insect pest recognition model used in the scheme, an attention mechanism SimAM is introduced on the basis of a lightweight convolutional neural network ShuffleNetV2, an activation function Hardswitch is used, and a Dropout regularization method is adopted in a full connection layer; the litchi disease and insect pest recognition model can effectively extract important features, suppress interference of non-important features and improve network classification recognition performance, the number of network model parameters is not additionally increased, and storage and calculation expenses of the model are reduced.
Owner:SOUTH CHINA AGRI UNIV
Multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching
ActiveCN112860183AFix performance degradationOvercoming the Negative Transfer ProblemInput/output to record carriersManufacturing computing systemsData setDomain testing
The invention discloses a multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching, and the method comprises the steps: building a multi-source data set through the operation data collected from a plurality of mechanical devices, carrying out the preprocessing, and dividing the multi-source data set into a source domain data set, a target domain training data set, and a target domain test data set; constructing a multi-source distillation-transfer learning network model based on high-order moment matching, and performing high-order moment matching, maximum classifier difference and multi-source distillation training by using the source domain data set and the target domain training data set; and taking the target domain test data set as test input, and synthesizing outputs of the plurality of classifiers by using an adaptive weighting strategy to complete cross-domain fault diagnosis. According to the method, features of a source domain and a target domain are aligned at domain and category levels by utilizing multi-source data, the classification capability of the model on target samples is improved through multi-source distillation, and adaptive weighting is provided to integrate diagnosis results, so that the problem that the performance of a traditional method is reduced in cross-domain diagnosis is solved, and the performance of a deep model is greatly improved.
Owner:XI AN JIAOTONG UNIV
Reservoir computing hardware implementation method and device based on coupled MEMS resonator
PendingCN112163358AImprove dynamic mapping capabilitiesImprove classification recognition abilityDesign optimisation/simulationCAD numerical modellingComputer hardwareTime delays
The invention discloses a reservoir computing hardware implementation method and device based on a coupled MEMS resonator, and the method comprises the steps: carrying out the preprocessing of a to-be-detected time sequence signal, so as to enable the to-be-detected time sequence signal to correspond to a virtual node of the coupled MEMS resonator; designing a nonlinear vibration equation of the coupled MEMS resonator, and regulating and controlling the coupled MEMS resonator to a preset nonlinear working point according to the equation; respectively detecting two signal test ends of the MEMScoupled resonator to obtain a first output signal and a second output signal corresponding to the to-be-tested signal corresponding to each moment, and feeding back the output signal corresponding tothe to-be-tested signal at the current moment to the virtual node corresponding to the next moment through a bidirectional time delay feedback loop; and performing regression training on the preset target value and the first output signal and the second output signal corresponding to the to-be-measured signal corresponding to each moment to obtain a weight coefficient required by calculation of the storage pool. According to the method, the data mapping dimension and the memory performance are enhanced, and richer nonlinear characteristics are provided for reserve pool calculation.
Owner:AEROSPACE INFORMATION RES INST CAS
Mobile application traffic identification method and system based on machine learning
PendingCN114500387AIncrease richnessImprove training effectArtificial lifeMachine learningTraffic balancingEngineering
The invention relates to a mobile application traffic identification method and system based on machine learning, and belongs to the field of traffic identification. The method comprises a flow acquisition stage, a flow processing stage, a feature extraction stage, a flow marking stage, a flow balancing stage and a model training stage. The system comprises a flow monitoring module, a flow processing module, a flow display module, a feature extraction module, a feature display module, an application identification module and a result display module. According to the method, a multi-feature fusion feature extraction scheme is provided, the information richness is improved, the model training effect is optimized, and the classification accuracy is improved; a model training mode combining an SMOTE + ENN sample balance algorithm and a random forest algorithm is designed, so that the misclassification rate of minority class samples is reduced, and the classification and recognition capability of a classifier is improved.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Audio scene recognition method and system combining deep neural network and topic model
InactiveCN109448703BImprove classification recognition abilityBuild accuratelyCharacter and pattern recognitionSpeech recognitionTraining phaseDocumentation
Owner:SHANDONG NORMAL UNIV
Feature selection method and system for iterative integration of multi-class imbalanced genomics data
InactiveCN105389480BImprove classification recognition abilityImprove classification accuracyBiostatisticsProteomicsGenomicsSystem integration
Owner:SHENZHEN UNIV
Waybill number identification method and device based on bill-of-lading scanning copy big data
PendingCN112200177AImprove performanceEasy to call directlyCharacter and pattern recognitionNeural architecturesAlgorithmEngineering
The invention discloses a waybill number identification method and device based on bill-of-lading scanning copy big data, belongs to the technical field of big data processing, wherein the device comprises a plurality of convolutional neural network (CNN) models, a voting mechanism and an image segmentation technology. The plurality of convolutional neural network (CNN) models establish a classification and identification model based on the bill-of-lading scanning copy big data, and the voting mechanism performs bill-of-lading scanning copy classification and identification. According to the invention, the plurality of models are adopted to carry out classification and identification on ship companies to which the bill-of-ladings canning copies belong, the classification result is obtainedaccording to majority voting rules, the classification and identification effect can be fully improved, an iterative updating mechanism of the identification model can be established for newly addedbill-of-ladings canning copies, and the performance improvement of the models is effectively maintained; therefore, the bill-of-lading scanned copies are accurately recognized, the bill-of-lading scanned copies are automatically renamed according to the recognition result, other services can be directly called conveniently, and the bill-of-lading scanned copies can be directly output as a file tobe archived.
Owner:山东文多网络科技有限公司
Distance-adaptive thermal infrared face recognition method
ActiveCN114821703AQuick checkAvoid slow recognitionInternal combustion piston enginesCharacter and pattern recognitionComputer scienceThermal infrared
The invention relates to the technical field of image recognition, in particular to a distance-adaptive thermal infrared face recognition method, which comprises the following steps: constructing and training an improved thermal infrared image super-resolution enhancement network Retinex-CNN, and processing a thermal infrared image by using the trained improved thermal infrared image super-resolution enhancement network Retinex-CNN; on the basis of prior information obtained by near-infrared, different feature algorithms are utilized, local feature extraction is carried out on near-infrared and processed thermal infrared images, full feature fusion is carried out on the extracted different features, dimension reduction processing is carried out on the fused features, and then classification and recognition are carried out on the features after dimension reduction. Through the recognition method, the problem that face recognition is difficult due to the fact that thermal infrared imaging is greatly influenced by the distance when the distance changes can be effectively solved.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
A classification and recognition method of underwater acoustic signal targets based on deep learning
ActiveCN109800700BImprove robustnessThe voiceprint feature is validBiological neural network modelsCharacter and pattern recognitionAlgorithmGammatone filter
Owner:HARBIN ENG UNIV
Speaker identification method and system
PendingCN113077796AImprove recognition accuracyImprove classification recognition abilitySpeech analysisHuman brainSpeech sound
The invention provides a speaker identification method and system, and belongs to the technical field of speaker identification. The method comprises the steps of analyzing voice signals by using a confirmation network, extracting voiceprint features of the voice signals, and confirming whether the voice signals are from the same speaker or not; and analyzing the voiceprint features of the voice signals by using an identification network, and identifying the identity of a speaker of the voice signals. According to the method, specific voiceprint features of the speaker are extracted, channel noise interference is reduced, and the identification accuracy of the speaker is improved; the speaker confirmation network and the speaker identification network can assist each other by adopting a multi-task learning form, so that the confirmation effect and the identification effect are improved; a human brain thinking mode is simulated by combining a speaker confirmation technology, and identification is carried out by combining the confirmation technology under the condition that the number of speakers is large and the identification difficulty is high; and through the simulation application of the human brain thinking model, the intelligence of the algorithm is improved, and the identification ability of the model under the condition of high difficulty is enhanced.
Owner:SHANDONG NORMAL UNIV
Face recognition method and system based on certificate photos and on-site photos
ActiveCN108875559BQuick comparison workImprove classification recognition abilityCharacter and pattern recognitionComputer graphics (images)Facial recognition system
The invention relates to the technical field of biological feature recognition, specifically provides a face recognition method and system based on ID photos and on-site photos, and aims to solve the technical problem of how to improve the accuracy of face recognition based on ID photos and on-site photos. For this purpose, the face recognition method in the present invention can calculate the similarity of the two according to the image features of the ID photo image and the on-site photo image, and then judge whether the ID photo image and the on-site photo image are similar according to the similarity value. Based on this, when comparing large-scale ID photos with on-site photos, based on the similarity judgment, the image comparison can be quickly completed. Meanwhile, the face recognition system in the present invention can execute and realize the above face recognition method.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
A face recognition method and system
ActiveCN112257600BIncrease the number ofImprove classification recognition abilityCharacter and pattern recognitionFeature vectorInformation transmission
Owner:武汉新可信息技术有限公司
Method for evaluating pore structure of reservoir
InactiveCN106950606ASolve the problem of continuous characterization of pore structure changesImprove the ability to explain and evaluate in detailElectric/magnetic detection for well-loggingDetection using electron/nuclear magnetic resonanceSpectral curveNMR - Nuclear magnetic resonance
The invention relates to a method for evaluating the pore structure of a reservoir. The method comprises steps of: 1) extracting a pore constituent component curve from a nuclear magnetic resonance T2 spectral curve; 2) determining a pore structure type based on mercury intrusion data of a typical rock core; 3) extracting a pore size component and corresponding pore size ratio data on the pore constituent component curve after typical rock core depth returns and determining the pore size limits of different pore structure types; 4) scoring depth points one by one according to the depth within a nuclear magnetic logging measurement depth range, specifically, assigning values to respective pore size components at a certain depth point according to the pore sizes reflected by the respective pore size components, and comprehensively scoring the depth point in combination with the pore size ratios corresponding to respective pore size components; and drawing a pore structure classification indication curve according to a scoring result. The method can reflect the continuous change of the pore structure goodness in the longitudinal depth, so as to further identify the goodness and badness of the same-class pore structure, and improve pore structure evaluation accuracy.
Owner:中石化石油工程技术服务有限公司 +1
Method and device for adjusting equivalent Q value of silicon microresonator RC (Resistance-Capacitance) system by mechanical pumping
PendingCN114417529AImprove dynamic mapping capabilitiesImprove classification recognition abilityGeometric CADDesign optimisation/simulationCapacitanceData set
The invention relates to a method and a device for adjusting an equivalent Q value of a silicon microresonator RC (Resistance-Capacitance) system by utilizing mechanical pumping. The method comprises the following steps of: determining a resonator modal frequency and a mechanical pumping signal; the equivalent Q value is dynamically adjusted through mechanical pumping to verify the experiment; further performing complex nonlinear response on the pre-processed to-be-tested signal by using a coupling MEMS resonator RC system under specified conditions according to a verification experiment; filtering and collecting a response signal of the resonator; accurate control, data detection, downsampling, logical operation and the like of a digital delay feedback control program; and training and testing the test data set. According to the method, energy exchange between different modes of the resonator is achieved through mechanical pumping, and dynamic adjustment of the equivalent Q value of the used modes is achieved under the condition that it is guaranteed that the system works under the strong non-linear rich condition (the high vacuum environment). According to the method, the nonlinear mapping performance of reservoir calculation is ensured, and the calculation speed is increased.
Owner:AEROSPACE INFORMATION RES INST CAS
Human behavior micro-Doppler classification and recognition method based on migration deep neural network
InactiveCN110275147BLower requirementReduce training dataset requirementsWave based measurement systemsNeural learning methodsHuman behaviorData set
Owner:NAT UNIV OF DEFENSE TECH
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