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45results about How to "Feature extraction is simple" patented technology
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Vehicle attribute identification method based on multi-task convolutional neural network
ActiveCN106599869AFeature extraction is simpleStrong feature expression abilityRoad vehicles traffic controlCharacter and pattern recognitionNetwork modelComputer vision
The invention provides a vehicle attribute identification method based on a multi-task convolutional neural network. The method comprises a training process and an identification process. Particularly the method comprises the steps of acquiring a picture of a to-be-identified vehicle, designing a multi-task convolutional neural network structure and training a network model vehicle attribute identification, identifying a vehicle model and returning vehicle window position coordinate of the vehicle, designing a vehicle image mask and generating a new vehicle image, extracting a multi-task convolutional neural network characteristic of the new vehicle image, training an SVM classification model, and identifying vehicle color. The vehicle attribute identification method is advantageous in that manual characteristic definition and re-classification by a user are not required; the multi-task convolutional neural network structure can simultaneously receive and process a plurality of tasks; and furthermore based on the multi-task convolutional neural network, structure information of the vehicle in the vehicle image is acquired for realizing an effective vehicle color identification method and improving identification accuracy, thereby supplying accurate basis for intelligent traffic.
Owner:合肥市正茂科技有限公司
Classification and identification method for foggy water surface image and clear water surface image
ActiveCN104217215AImprove intelligenceEasy to identifyCharacter and pattern recognitionFeature extractionVisual perception
The invention belongs to the field of image identification, and particularly relates to a classification and identification method for a foggy water surface image and a clear water surface image. The method comprises the steps of acquiring water surface images to be identified; establishing a water surface image database; extracting features of the water surface images; training and learning classification and identification features of the fog of the water surface images; and identifying the foggy water surface image to be identified and the clear water surface image to be identified. The method can greatly improve the intelligence of a visual system of a water surface aircraft. The features can be extracted simply, few features for identification are available, and the identification rate is high; and being used for earlier stage processing of the visual system of water surface ship or an unmanned surface vehicle, the method can adaptively judge water surface weather environment, and can effectively improve the performances of post-defogging, and target detection, tracking and identification.
Owner:HARBIN ENG UNIV
Method for automatic identifying steel slab coding and steel slab tracking system
InactiveCN102663360AIncrease the level of automationIncrease productivityOptical transmission adaptationsCharacter and pattern recognitionImage pre processingTracking system
Owner:CENT SOUTH UNIV
Traffic light positioning method
ActiveCN104851288ASimple adaptationThe implementation process is simpleDetection of traffic movementCharacter and pattern recognitionComputation complexitySignal light
The invention discloses a traffic light positioning method. The method comprises: expanding an object region where traffic lights are in in advance, obtaining a constraint region, and performing binaryzation on a grayscale image of the constraint region, extracting proportion of bright regions, the number of small area bright regions, the number of standard light area bright regions, the number of large area bright regions, and the average gray value of dark regions from binarization images, according to the features, determining day or night of an intersection image where the object region is in, if the image is a night image, detecting signal lights whose states are lightened through the binarization image, and according to the positions of the signal lights, adjusting positions of the traffic light; if the image is a day image, detecting a black frame in a traffic light structure through the binaryzation image, and according to the position of the black frame, adjusting the positions of the traffic light. The method is advantaged by low calculation complexity, fast operation speed, good environment adaptability, and simple realization.
Owner:宁波中国科学院信息技术应用研究院 +1
Method for estimating abundance of hyperspectral image end member
InactiveCN103258330AImprove decomposition efficiencyShorten the timeImage analysisAlgorithmRectangular coordinates
The invention discloses a method for estimating the abundance of a hyperspectral image end member. The method for estimating the abundance of the hyperspectral image end member comprises a first step of extracting the image end from an image and selecting mixed pixel points, conducting linear decomposition and obtaining a corresponding abundance value, a second step of evaluating a normalized spectral characteristic value which corresponds to the end member, a third step of tracing points under a rectangular coordinate system, a fourth step of conducting curve fitting and obtaining a quadratic curve expression, a fifth step of obtaining the abundance of the rest points by means of mapping the spectral characteristic value, and a sixth step of evaluating the root mean square error RMSE between an estimated value and an actual value and judging whether the RMSE meets the evaluated precision. According to the method for estimating the abundance of the hyperspectral image end member, an abundance value is rapidly predicted through establishment of certain relationship between the spectral feature value and the abundance of the end member, the defect that the corresponding abundance can be obtained when the liner decomposition is conducted on all mixed pixel points is overcome. In actual application process, due to the fact that the linear decomposition is conducted on only a small amount of pixel points which are evenly distributed, the abundance value which corresponds to end members of all pixel points can be obtained, and the time of decomposition of the pixel points can be effectively shortened.
Owner:DALIAN MARITIME UNIVERSITY
Signal identification method based on extraction of signal power spectrum fitting characteristic
ActiveCN108566253ARealize unified identificationFeature extraction is simpleTransmission monitoringFeature vectorRound complexity
The invention discloses a signal identification method based on extraction of signal power spectrum fitting characteristic, and belongs to the field of the wireless communication. The signal identification method comprises the following steps: firstly dividing power spectrum data into a training set and a testing set, intercepting data sample fragments with equal length from the power spectrums corresponding to service class signals of different types in the training set; performing polynomial fitting on one data sample fragment A by using the least square linear regression algorithm to construct a cost function J, and minimizing the cost function J to acquire a parameter of the fitting polynomial; respectively selecting different polynomial orders, repeating w times of polynomial fittingand extracting the highest order item parameter, and acquiring all elements in a characteristic vector of the data sample fragment A; repeating the above steps to obtain a characteristic vector set Fof the service class signal, thereby constructing a training set matrix; and finally constructing a multi-layer neural network classifier model, searching an optimal solution by adopting a self-adaptive moment estimation algorithm, and identifying and classifying power spectrum signals in the testing set. Through the signal identification method disclosed by the invention, the characteristic extraction is simple and efficient, the signal identification rate is high, and the computing complexity is reduced.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Graph-structured-data generation method based on deep convolutional generative adversarial networks (DCGAN)
ActiveCN108053454AHave attributesOutstanding featuresImage codingGraph structured dataGenerative adversarial network
Owner:HUAZHONG UNIV OF SCI & TECH
A method and device for detecting head key points in human body posture recognition
ActiveCN109685013AImprove recognition accuracyFeature extraction is simpleCharacter and pattern recognitionHuman bodyCrucial point
The invention provides a method and a device for detecting head key points in human body posture recognition. The method comprises the following steps: providing an image to be processed; Performing target detection processing on the to-be-processed image to obtain one or more first head detection frames; Judging the head posture of each human body in the to-be-processed image; When the head posture of the human body is the front face or the back face, the midpoint of each edge of the first head detection frame serves as four first head key points; When the head posture of the human body is aleft side surface, taking the middle point of the right longitudinal edge, the left lower vertex and the middle point of the upper transverse edge of the first head detection frame as three first headkey points; And when the head posture of the human body is the right side surface, taking the middle point of the left longitudinal edge, the right lower vertex and the middle point of the upper transverse edge of the first head detection frame as three first head key points. According to the invention, the accuracy of head key point detection in human body posture recognition can be improved.
Owner:SHANGHAI XIAOI ROBOT TECH CO LTD
A pavement type estimation method based on depth convolution neural network without loss function
ActiveCN109460738AFeature extraction is simpleReduce difficultyCharacter and pattern recognitionNeural architecturesHistogramSupport vector regression machine
The invention discloses a method for estimating pavement type based on depth convolution neural network without loss function, which comprises the following steps: step 1, collecting pavement workingcondition image, calibrating pavement type and establishing pavement working condition database; The depth convolutional neural network based on loss-free function is trained to obtain image features,and then binary hash coding and histogram processing are performed to obtain image feature output vectors. According to the feature output vector of the image and the corresponding road type, the support vector machine is trained and the parameters are selected, and the road type discrimination function is determined. 2, collect that working condition image of the pavement to be tested, obtainingthe characteristic output vector of the pavement to be tested according to the step 1, and determine the type of the pavement to be tested by using the trained support vector machine. It simplifies the feature extraction of convolution neural network depth learning model, and uses support vector machine to classify images, which greatly reduces the difficulty of convolution training and improvesthe efficiency of classification.
Owner:JILIN UNIV
Image forensics method for natural image and compressed and tampered image based on DWT
InactiveCN103903271AGet rid of the gameFeature extraction is simpleImage analysisSupport vector machineHough transform
The invention provides an image forensics method for a natural image and a compressed and tampered image based on DWT. According to the method, the natural image and the compressed image based on the DWT can be effectively distinguished, meanwhile, good distinguishability is achieved on certain specific image tampering carrying out compression trace elimination on the compressed image, the joint probability histogram of a wavelet transform coefficient of the natural image and the tampered image is calculated through the method, the histogram is normalized, then Hough transform is carried out, the mean value, variance value, skewness value and kurtosis value of a Hough transform coefficient matrix are extracted as characteristic values of a support vector machine, and a training set is formed by the characteristic values. A classification model is generated by the support vector machine through the training set in a training mode, unknown characteristic value samples are classified through the model, and whether compression or anti-compression forensics processing is carried out on an image or not is judged. The method is stable in performance, easy and convenient to implement, efficient, high in accuracy and suitable for forensics detection of the natural image and the tampered image in other aspects.
Owner:BEIHANG UNIV
Pedestrian reidentification method and system based on color texture distribution characteristic
ActiveCN108345900AAvoid interferenceFeature extraction is simpleCharacter and pattern recognitionTraining periodFeature extraction
The invention belongs to the image processing technology field and relates to a pedestrian reidentification method based on a color texture distribution characteristic. The method comprises the following steps of inputting N image pairs to be matched including training data and test data and a corresponding label ln, wherein the n=1,..., N; extracting the color texture space distribution characteristic expression of input image data; acquiring the consistent characteristic expression of the color texture space distribution characteristic expression through multi-scale characteristic matching;and constructing two classifiers for the acquired consistent characteristic expression and outputting a probability expression describing a same target. The method has advantages that characteristic extraction is simple and a speed is fast; a training period is short; robustness is high; and precision is high.
Owner:ZHEJIANG UNIV
JPEG image double-compression automatic detection method
InactiveCN106960435AAccurate detectionPromote resultsImage enhancementImage analysisSupport vector machineFeature vector
The invention discloses a JPEG image double-compression automatic detection method, and the method comprises the steps: carrying out the preprocessing of a JPEG image; extracting a first effective number feature of a DCT coefficient and adjacent coefficient difference features of the JPEG image based on the Markov model, carrying out the fusion of the features, and carrying out the feature dimension reduction; and training and recognizing a support vector machine. The fusion features can be effectively used for various compression conditions (besides the common compression conditions, also comprising a condition that two compression qualities are equal and the second compression quality is 95). Moreover, after the extraction of the fusion features, the dimension reduction of a feature matrix is carried out, and the matrix after dimension reduction is taken as a feature vector for subsequent operation. The method reduces the calculation complexity, improves the algorithm efficiency, and is easy to use in reality.
Owner:HUAZHONG NORMAL UNIV
Machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data
PendingCN111782624AFeature extraction is simpleEasy to install and deployDigital data information retrievalMeasurement/indication equipmentsFeature (machine learning)Engineering
The invention discloses a machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data. According to the method, on the premise of big data, artificial intelligence methods such as machine learning and deep learning are combined to perform feature extraction, feature transformation and feature learning on vibration signals generated by key devices soas to monitor and analyze the key devices. The beneficial effects of the invention are that the vibration analysis is simple in feature extraction, is convenient to install and deploy, and does not damage the structure of a measured object body.
Owner:GTCOM TECH QINGDAO CO LTD
Classroom student behavior detection method
InactiveCN111353468ADetection area reducedImprove manual inspection efficiencyData processing applicationsBiometric pattern recognitionEngineeringDeep learning
The invention discloses a classroom student behavior detection method, relates to image processing and recognition, and mainly solves the technical problems of low accuracy, low speed and poor robustness of a current full-image detection method. The classroom student behavior detection method comprises the following steps: converting an original image of a classroom student into a grey-scale map;inputting the grey-scale map into a deep learning model to carry out head and shoulder frame detection and prone table identification, and obtaining a head and shoulder frame; determining a hand raising detection area according to the head and shoulder frame, and performing hand raising detection in the hand raising detection area. The hand raising detection area is determined according to the head and shoulder frame, and hand raising detection is performed in the hand raising detection area so that the detection input image can be greatly reduced, the detection area can be greatly reduced, the hand detection efficiency can be effectively enhanced, the hand detection accuracy can also be greatly enhanced and the robustness is high.
Owner:YULIN NORMAL UNIVERSITY
Method for identifying mutual information of cotton foreign fiber
InactiveCN101598675AIncrease contrastImprove accuracyCharacter and pattern recognitionOptically investigating flaws/contaminationPlastic filmMutual information
The invention relates to a method for identifying mutual information of cotton foreign fiber, comprising: performing classified statistics on the read cotton foreign fiber image in aspects of area and category to obtain foreign fiber fixed scanning background; performing pretreatment on the image to improve contrast ratio of the cotton foreign fiber image; performing precise identification on hairs, colored polypropylene fiber silk and plastic sheets which are easy to identify; performing intact piece information extraction on foreign fiber which is not identified; identifying the extracted intact piece foreign fiber; and performing and calculating mutual information treatment on the identified intact piece cotton foreign fiber image. The identification method can improve preciseness, periodicity and timeliness of foreign fiber detection, improve detection rate and effectively reduce harm of foreign fiber.
Owner:SHANDONG AGRICULTURAL UNIVERSITY
Aircraft voiceprint recognition method and system, electronic equipment and storage medium
ActiveCN114743562AFeature extraction is simpleQuick identificationSpeech analysisCharacter and pattern recognitionFast Fourier transformTime domain
The invention provides an aircraft voiceprint recognition method and system, electronic equipment and a storage medium, and belongs to the technical field of aircraft voiceprint data processing. The method comprises the following steps: acquiring sound signals in an environment in real time; sampling and quantizing the collected sound signals to obtain time domain features of the sound signals; converting the time domain feature into a frequency domain feature by using FFT (Fast Fourier Transform); and carrying out three-layer identification on the frequency domain characteristics to obtain an aircraft sound signal, and sending an indication signal. Based on the scheme, the problem that in the prior art, airplane signals are difficult to identify quickly in real time in an outdoor unmanned environment is solved.
Owner:CHENGDU KAITIAN ELECTRONICS
Spatio-temporal interest point feature encoding method in human motion recognition
ActiveCN105046211AHigh precisionImprove the ability to distinguishCharacter and pattern recognitionVideo retrievalFeature extraction
The invention discloses a spatio-temporal interest point feature encoding method in human motion recognition. Human motion recognition in a video has broad application prospects in aspects of intelligent monitoring, video retrieval and the like. A human motion recognition method based on spatio-temporal interest point features has the advantages of simple extraction, strong anti-interference ability, good robustness and the like, and thus wide attention is got. However, during a process of encoding the spatio-temporal interest point features through vector quantization so as to acquire video representation vectors, problems of large representation errors, weak encoding discrimination ability and lost spatial position information exist. In order to solve the above problems, the invention discloses spatial regulation local constraint encoding algorithm. During a feature encoding process, local constraints are introduced for reducing representation errors, spatial regulation is introduced for enhancing discrimination of an encoding result and using the spatio-temporal interest point features for the spatial position information, and finally, the precision of human motion recognition is improved.
Owner:NAT UNIV OF DEFENSE TECH
Lower limb action recognition method based on pressure and acceleration sensor
InactiveCN107092861BFeature validEffective Feature ExtractionCharacter and pattern recognitionDiagnostic recording/measuringFeature extractionMedicine
The invention discloses a lower limb movement recognition method based on a pressure and acceleration sensor. The specific implementation steps of the method are as follows: firstly, the pressure sensor signal of the lower limb movement of the human body is collected in real time, and after preprocessing the pressure sensor signal, according to the pressure sensor data rising The edge and falling edge mark the start and end of the lower limb movement. When the rising edge of the pressure is detected, the three-axis acceleration signal of the acceleration sensor will be collected and stored. When the falling edge of the pressure is detected, the three-axis acceleration signal of the acceleration sensor will be collected. The three-axis signal of the acceleration sensor collected between the edge and the falling edge is called the acceleration signal segment. Then the frequency domain features and statistical features are extracted from the acceleration signal segment extracted in the previous step. After the features are extracted, the data dimensionality reduction is performed on the extracted features. Finally, the trained classifier is used to classify the feature data after dimension reduction, and the classification result of the action pattern is obtained.
Owner:SOUTH CHINA UNIV OF TECH +1
Training method and detection method of flow detection model of asymmetric convolutional network
ActiveCN111092873AFeature extraction is simpleImprove detection accuracyError preventionCharacter and pattern recognitionSymmetric convolutionFeature extraction
The invention discloses a training method and a detection method of a flow detection model of an asymmetric convolutional network. The flow detection model of the asymmetric convolutional network comprises an asymmetric convolutional self-encoding network and a classification network. The training method comprises the steps: constructing the symmetric convolutional self-encoding network, wherein the symmetric convolutional self-encoding network comprises an encoding network and a decoding network; training the symmetric convolutional self-encoding network by using a training sample; removing adecoding network in the trained symmetric convolutional self-encoding network to obtain an asymmetric convolutional self-encoding network; and extracting abstract features of a training sample by using the asymmetric convolutional self-encoding network, and training a classification network by using the abstract features so as to complete training of a flow detection model of the asymmetric convolutional network. Compared with the existing detection model, the method has higher detection accuracy and lower false alarm rate, and the detection model only reserves the coding network, so that themodel is lighter and easier for feature extraction, and the overhead is saved.
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
Triangular linear frequency modulation continuous signal parameter estimation method based on differential envelope detection
InactiveCN109683142AFeature extraction is simpleReduce computational complexityWave based measurement systemsSample sequenceTime complexity
The invention belongs to the technical field of signal detection and estimation, and discloses a triangular linear frequency modulation continuous signal parameter estimation method based on differential envelope detection. A receiver samples an observed triangular linear frequency modulation continuous wave signal from the radar to obtain a sampling sequence; the receiver carries out differentialoperation, Hilbert transform and low-pass filtering on the sampling sequence to obtain a denoising envelope sequence; the receiver calculates according to the denoising envelope sequence to obtain signal characteristic parameters including a positive modulation frequency, a negative modulation frequency, a frequency modulation signal period, a minimum frequency in a sweep frequency interval and amaximum frequency in the sweep frequency interval. The triangular linear frequency modulation continuous signal parameter estimation method based on differential envelope detection has the advantagesof characteristic extraction and calculation in the time domain and lower time complexity, and can be used for solving the parameter estimation problem of the triangular linear frequency modulation continuous signal; the characteristic parameters of the triangular linear frequency modulation continuous signal can be obtained by utilizing the sequence difference and the envelope detection, and a parameter estimation value is calculated according to the envelope slope and the change time of the envelope slope.
Owner:ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
Two-dimensional face fraud detection classifier training and face fraud detection method
ActiveCN107103266BFully extractedFeature extraction is simpleSpoof detectionSupport vector machineFeature vector
The present invention provides a method for generating a two-dimensional human face fraud detection model. The method includes: first, preprocessing all human face pictures in the training set to obtain normalized human face images; Extract LBP eigenvectors, Gabor wavelet eigenvectors and one-dimensional pixel eigenvectors from the integrated face image; thirdly, splice these three eigenvectors to form the final eigenvector; fourthly, use support vector machine to The final feature vector is trained to obtain a two-dimensional face fraud detection classifier; this method extracts the feature information of the difference between the face and the photo; the feature extraction is simple and efficient, does not require the user's deliberate cooperation, and can be used in low-resolution situations Get good results. In addition, based on the two-dimensional face fraud detection classifier obtained by the above method, the present invention also proposes a face fraud detection method, which has the advantage of high detection accuracy and can effectively prevent face fraud.
Owner:INST OF ACOUSTICS CHINESE ACAD OF SCI +1
A graph-structured data generation method based on deep convolutional generative adversarial networks
ActiveCN108053454BHave attributesOutstanding featuresImage codingGraph structured dataData ingestion
Owner:HUAZHONG UNIV OF SCI & TECH
Noise frequency modulation signal identification method
ActiveCN112543159AAccurate identificationSolve the indistinguishable digital signalModulation type identificationPhase-modulated carrier systemsTime domainTelecommunications
The invention provides a noise frequency modulation signal identification method, and belongs to the technical field of communication. According to the method, the intrinsic characteristics of noise frequency modulation are extracted and judged by utilizing the characteristics of the noise frequency modulation in frequency spectrum and energy distribution, so that the purpose of accurately identifying the noise frequency modulation signal is achieved. Specifically, digital MPSK modulation signal judgment, digital MASK modulation signal judgment, digital MFSK modulation signal judgment and analog modulation judgment are sequentially carried out, and the residual frequency hopping signals are recursively sorted by utilizing the exclusiveness of two frequency hopping signals in the same period. Features are extracted through a signal time domain and a signal frequency domain, parameter estimation is carried out by means of spectral lines and frequency spectrums, noise frequency modulationsignal recognition is carried out according to a priority step-by-step confirmation method, and the method has the advantages of being simple in algorithm, small in calculated amount, easy in featureextraction and low in engineering implementation difficulty.
Owner:NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
A Signal Recognition Method Based on Feature Extraction of Signal Power Spectrum Fitting
ActiveCN108566253BRealize unified identificationFeature extraction is simpleTransmission monitoringFeature vectorFeature extraction
The invention discloses a signal identification method based on extraction of signal power spectrum fitting characteristic, and belongs to the field of the wireless communication. The signal identification method comprises the following steps: firstly dividing power spectrum data into a training set and a testing set, intercepting data sample fragments with equal length from the power spectrums corresponding to service class signals of different types in the training set; performing polynomial fitting on one data sample fragment A by using the least square linear regression algorithm to construct a cost function J, and minimizing the cost function J to acquire a parameter of the fitting polynomial; respectively selecting different polynomial orders, repeating w times of polynomial fittingand extracting the highest order item parameter, and acquiring all elements in a characteristic vector of the data sample fragment A; repeating the above steps to obtain a characteristic vector set Fof the service class signal, thereby constructing a training set matrix; and finally constructing a multi-layer neural network classifier model, searching an optimal solution by adopting a self-adaptive moment estimation algorithm, and identifying and classifying power spectrum signals in the testing set. Through the signal identification method disclosed by the invention, the characteristic extraction is simple and efficient, the signal identification rate is high, and the computing complexity is reduced.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Pedestrian re-identification method and system based on color texture distribution feature
ActiveCN108345900BAvoid interferenceFeature extraction is simpleCharacter and pattern recognitionImaging processingColor texture
The invention belongs to the technical field of image processing, and relates to a pedestrian re-identification method based on color texture distribution features, comprising the following steps: inputting N image pairs to be matched including training data and test data and their corresponding labels 1 n , where n=1,...,N; extract the color texture spatial distribution feature representation of the input image data; obtain the consistent feature representation of the color texture spatial distribution feature representation through multi-scale feature matching; Feature representation builds a binary classifier that outputs a probabilistic representation describing the same object. The invention has the advantages of simple and fast feature extraction, short training period, high robustness and high precision.
Owner:ZHEJIANG UNIV
Method for automatic identifying steel slab coding and steel slab tracking system
InactiveCN102663360BIncrease the level of automationIncrease productivityOptical transmission adaptationsCharacter and pattern recognitionImage pre processingTracking system
The invention discloses a method for automatic identifying steel slab coding and a steel slab tracking system. The method is characterized in that: from the starting of a first frame original image, automatic identification of slab coding is successively carried out on each frame of original image by the following steps: step 1, carrying out image preprocessing on the original image; step 2, carrying out binarization processing on the image after the preprocessing and carrying out slab coding detection and coding position positioning on an obtained binary image, wherein the slab coding detection and coding position positioning employ a projection processing method; and step 3, splitting the original image according to a boundary coordinate that is obtained by the coding position positioning to obtain a plurality of single character images and carrying out character identification on the each single character image, thereby completing slab coding identification on the original image of the current frame; and then returning to the step 1. According to the invention, the method for automatic identifying steel slab coding and the steel slab tracking system have characteristics of high automation degree and high coding identification efficiency.
Owner:CENT SOUTH UNIV
Parameter Estimation Method for Triangular LFM Continuous Signal Based on Differential Envelope Detection
InactiveCN109683142BFeature extraction is simpleReduce computational complexityWave based measurement systemsTime domainEnvelope (radar)
The invention belongs to the technical field of signal detection and estimation, and discloses a triangular linear frequency modulation continuous signal parameter estimation method based on differential envelope detection. A receiver samples an observed triangular linear frequency modulation continuous wave signal from the radar to obtain a sampling sequence; the receiver carries out differentialoperation, Hilbert transform and low-pass filtering on the sampling sequence to obtain a denoising envelope sequence; the receiver calculates according to the denoising envelope sequence to obtain signal characteristic parameters including a positive modulation frequency, a negative modulation frequency, a frequency modulation signal period, a minimum frequency in a sweep frequency interval and amaximum frequency in the sweep frequency interval. The triangular linear frequency modulation continuous signal parameter estimation method based on differential envelope detection has the advantagesof characteristic extraction and calculation in the time domain and lower time complexity, and can be used for solving the parameter estimation problem of the triangular linear frequency modulation continuous signal; the characteristic parameters of the triangular linear frequency modulation continuous signal can be obtained by utilizing the sequence difference and the envelope detection, and a parameter estimation value is calculated according to the envelope slope and the change time of the envelope slope.
Owner:ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
A malicious domain name processing method, apparatus, device and machine-readable storage medium
ActiveCN113328994BReduce false alarm rateImprove accuracyPlatform integrity maintainanceTransmissionDomain nameWeb site
The present disclosure provides a malicious domain name processing method, device, device and machine-readable storage medium. The method includes: acquiring training samples that meet preset requirements, extracting sample features from the training samples according to preset dimensions; and according to the extracted sample features , perform sample clustering on the training samples, and use BiLstm to train and generate a classifier; use the classifier to process the features extracted according to the domain name to be determined, and determine whether the domain name to be determined belongs to the DGA domain name; the preset dimensions include : Domain name length, initial proportion, numerical proportion, domain name entropy. Through the technical solution of the present disclosure, the initials of pinyin are used as one of the dimensions of the training model, and the algorithm model obtained can effectively reduce the false alarm rate of domestic websites. At the same time, the training method provided by the present disclosure has simple and efficient feature extraction, and can determine the DGA domain name. High accuracy and low resource consumption.
Owner:NEW H3C SECURITY TECH CO LTD
A grnn-based identification method
ActiveCN109165556BIncrease training speedImprove training accuracyCharacter and pattern recognitionDigital data authenticationEcg signalData set
The technical solution of the present invention includes a GRNN-based identity recognition method, which is used to realize: obtain an ECG signal sample data set including multiple cycles of heartbeat data of multiple users, and use wavelet transform to remove the ECG signal noise; the denoised signal is segmented to construct the morphological features of the ECG signal, and the heartbeat feature database of the training set and the heartbeat feature database of the test set are respectively constructed; the singular value decomposition method is used to remove the redundancy in the ECG signal. Additional features; using linear discriminant analysis to reduce the dimensionality of the heart beat feature data set; training a generalized regression neural network classifier, and outputting the identity information of the individual according to the principle of multi-heart beat voting. The beneficial effects of the present invention are: the redundant information in the electrocardiogram signal is effectively removed, and the accuracy rate of subsequent identification is improved, while the reduction of the feature dimension of the heartbeat by LDA and the use of the GRNN neural network as a classifier greatly improve the identification speed.
Owner:JILIN UNIV +1
A Method for Estimating Endmember Abundance in Hyperspectral Images
InactiveCN103258330BImprove decomposition efficiencyShorten the timeImage analysisRectangular coordinatesAlgorithm
Owner:DALIAN MARITIME UNIVERSITY
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