Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
7133results about How to "Improve generalization ability" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Voice print system and method
InactiveUS20030009333A1Increase in sizeImprove generalization abilitySpeech recognitionSpeech segmentationSpeaker verification
The voice print system of the present invention is a subword-based, text-dependent automatic speaker verification system that embodies the capability of user-selectable passwords with no constraints on the choice of vocabulary words or the language. Automatic blind speech segmentation allows speech to be segmented into subword units without any linguistic knowledge of the password. Subword modeling is performed using a multiple classifiers. The system also takes advantage of such concepts as multiple classifier fusion and data resampling to successfully boost the performance. Key word / key phrase spotting is used to optimally locate the password phrase. Numerous adaptation techniques increase the flexibility of the base system, and include: channel adaptation, fusion adaptation, model adaptation and threshold adaptation.
Owner:SPEECHWORKS INT
Multi-task learning convolutional neural network-based face attribute analysis method
ActiveCN106529402AImprove generalization abilityCalculation speedCharacter and pattern recognitionTask networkMulti attribute analysis
The present invention discloses a multi-task learning convolutional neural network (CNN)-based face attribute analysis method. According to the method, based on a convolutional neural network, a multi-task learning method is adopted to carry out age estimation, gender identification and race classification on a face image simultaneously. In a traditional processing method, when face multi-attribute analysis is carried out, a plurality of times of calculation are required, and as a result, time can be wasted, and the generalization ability of a model is decreased. According to the method of the invention, three single-task networks are trained separately; the weight of a network with the lowest convergence speed is adopted to initialize the shared part of a multi-task network, and the independent parts of the multi-task network are initialized randomly; and the multi-task network is trained, so that a multi-task convolutional neural network (CNN) model can be obtained; and the trained multi-task convolutional neural network (CNN) model is adopted to carry out age, gender and race analysis on an inputted face image simultaneously, and therefore, time can be saved, and accuracy is high.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
Unmanned aerial vehicle patrol detection image power small component identification method and system based on Faster R-CNN
ActiveCN106504233ABreak through the time bottleneckImprove recognition rateImage enhancementImage analysisFeature extractionUncrewed vehicle
Owner:STATE GRID INTELLIGENCE TECH CO LTD
An image semantic segmentation method based on deep learning
ActiveCN109711413ARich coordinate featuresImprove generalization abilityInternal combustion piston enginesCharacter and pattern recognitionData setRgb image
The invention discloses an image semantic segmentation method based on deep learning. The method comprises four parts of data set processing, deep semantic segmentation network construction, deep semantic segmentation network training and parameter learning, and semantic segmentation on a test image. The RGB image and the gray level image of the input image are used as the input of the network model, the edge information of the gray level image is fully utilized, and the richness degree of input characteristics is effectively increased; a convolutional neural network and a bidirectional threshold recursion unit are combined, and more context dependency relationships and global feature information are captured on the basis of learning image local features; coordinate information is added tothe feature map through the first coordinate channel module and the second coordinate channel module, the coordinate features of the model are enriched, the generalization ability of the model is improved, and a semantic segmentation result with high resolution and accurate boundary is generated.
Owner:SHAANXI NORMAL UNIV
Image quality testing method based on deep convolutional neural network
InactiveCN103544705ARefine image featuresImprove accuracyImage analysisImaging qualityConvolutional neural network
The invention discloses an image quality testing method based on a deep convolutional neural network. The image quality testing method based on the deep convolutional neural network comprises the following steps of firstly, establishing a sample set, establishing a deep convolutional neural network model, training the deep convolutional neural network model under different initial conditions, connecting the optimal deep convolutional neural network models obtained through the multiple times of training in parallel to obtain an image quality testing system, and using the obtained image quality testing system for testing images to be tested. According to the image quality testing method based on the deep convolutional neural network, characteristic learning is carried out by simulating the learning process of human brains, the problem that characteristics are difficult to select in an existing image quality testing method is solved, the contingency of predicting results is reduced, integration is high, generalization ability is strong, and testing effects are good.
Owner:SOUTH CHINA UNIV OF TECH
Method for detecting X-ray mammary gland lesion image based on feature pyramid network under transfer learning
ActiveCN110674866AImprove robustnessImprove the extraction effectImage enhancementImage analysisData setImage detection
The invention provides a method for detecting an X-ray mammary gland lesion image based on feature pyramid network under transfer learning. The method comprises the steps: 1, establishing a source domain data set and a target domain data set; 2, establishing a deformable convolution residual network layer by a deformable convolution and extended residual network module; 3, establishing a multi-scale feature extraction sub-network based on a feature pyramid structure through a feature map up-sampling and feature fusion method in combination with a deformable convolution residual network layer;4, establishing a deformable pooling sub-network sensitive to the focus position; 5, establishing a post-processing network layer to optimize a prediction result and a loss function; and 6, migratingthe training model to a small sample molybdenum target X-ray mammary gland focus detection task so as to improve the detection precision of the network model on the focus in the small sample image. According to the method, a transfer learning strategy is combined to realize focus image processing in a small sample medical image.
Owner:LANZHOU UNIVERSITY OF TECHNOLOGY
Zero sample image classification method based on combination of variational autocoder and adversarial network
ActiveCN108875818AImplement classificationMake up for the problem of missing training samples of unknown categoriesCharacter and pattern recognitionPhysical realisationClassification methodsSample image
The invention discloses a zero sample image classification method based on combination of a variational autocoder and an adversarial network. Samples of a known category are input during model training; category mapping of samples of a training set serves as a condition for guidance; the network is subjected to back propagation of optimization parameters through five loss functions of reconstruction loss, generation loss, discrimination loss, divergence loss and classification loss; pseudo-samples of a corresponding unknown category are generated through guidance of category mapping of the unknown category; and a pseudo-sample training classifier is used for testing on the samples of the unknown category. The high-quality samples beneficial to image classification are generated through theguidance of the category mapping, so that the problem of lack of the training samples of the unknown category in a zero sample scene is solved; and zero sample learning is converted into supervised learning in traditional machine learning, so that the classification accuracy of traditional zero sample learning is improved, the classification accuracy is obviously improved in generalized zero sample learning, and an idea for efficiently generating the samples to improve the classification accuracy is provided for the zero sample learning.
Owner:XI AN JIAOTONG UNIV
Named-entity recognition model training method and named-entity recognition method and device
InactiveCN104615589AImprove recognition accuracyImprove generalization abilitySpecial data processing applicationsNamed-entity recognitionLabeled data
An embodiment of the invention provides a named-entity recognition model training method and a named-entity recognition method and device. The method used for training a recurrent neutral network (RNN) named-entity recognition model includes: acquiring multiple labeled sample data, wherein each sample datum includes a text string and multiple term segment labeled data thereof, and each term segment labeled datum includes segmented terms separated from the text string and a named-entity attribute tag in the text string; mapping the segmented terms in the labeled sample data to be term vectors, taming the sample data as training samples, training the RNN named-entity recognition model, and learning parameters of the RNN named-entity recognition model. By the named-entity recognition model training method and the name-entity recognition method and device, the trained model has better generalization ability, the named entity in the natural language tests can be recognized rapidly, and recognition accuracy of the named entity is improved.
Owner:BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
Urban rail transit panoramic monitoring video fault detection method based on depth learning
InactiveCN106709511AImprove robustnessImprove generalization abilityCharacter and pattern recognitionNeural architecturesData setModel testing
The invention provides an urban rail transit panoramic monitoring video fault detection method based on depth learning. The method comprises a data set construction process, a model training generation process and an image classification recognition process. The data set construction process processes a definition abnormity video, a colour cast abnormity video and a normal video in an urban rail transit panoramic monitoring video. A training set and a test set are classified. The model training generation process comprises model training and model test. The model training is to train a fault video image recognition model based on a convolution neural network. The convolutional neural network comprises a plurality of convolution layers and a plurality of full connection layers. The model test is to calculate the test accuracy. If expectation is not fulfilled, the fault video image recognition model is optimized. The image classification recognition process comprises the steps that a single-frame image to be recognized is input into the model, and the fault video image recognition model outputs an image classification result to complete the fault image detection of the urban rail transit panoramic monitoring video.
Owner:HUAZHONG NORMAL UNIV +1
Method and system for enhancing features by aid of bidirectional long-term and short-term memory recurrent neural networks
InactiveCN104952448AEnhancement effect is goodImprove performanceSpeech recognitionShort-term memoryData mining
The invention discloses a method and a system for enhancing features by the aid of bidirectional long-term and short-term memory recurrent neural networks. The method includes steps of training inputted features with noise and corresponding features without noise by the aid of bidirectional long-term and short-term memory recurrent neural network models; enhancing the features by the aid of the bidirectional long-term and short-term memory recurrent neural network models. The method and the system have the advantages that the long-term and short-term memory recurrent neural network models can be built in two directions by the aid of the inputted features, and accordingly models of contexts of current frames can be effectively built; long-term and short-term memory cells and certain control variables are introduced into the neural networks, and accordingly the models can be built in the dependence on the long contexts; excellent enhancement effects can be realized by the models owing to long-term dependence when the features are enhanced by the aid of the models, and accordingly the performance of voice recognition systems and audio event classification systems can be improved.
Owner:å¼ çˆ±è‹± +1
Near infrared spectrum nondestructive testing method and device for material component content
InactiveCN101915744AReduce complexityImprove modeling efficiencyColor/spectral properties measurementsInfraredNonlinear calibration
The invention discloses a near infrared spectrum nondestructive testing method and a near infrared spectrum nondestructive testing device for material component content. The method comprises the following steps of: collecting calibration set samples by using a spectrometer, pretreating a spectrum, selecting an optimal calibration sample set through sample optimization and establishing a nonlinear calibration model by using the optimal calibration sample set, collecting spectrum samples with unknown component content by using the spectrometer, pretreating the spectrum in a mode which is the same as that of pretreating the calibration set samples, and detecting the component content of the unknown sample through the established nonlinear calibration model. The method and the device can effectively solve the problems of complex calibration model, slow training speed and difficult hardware implementation in the conventional near infrared nondestructive testing method for the material component content and obviously improve the accuracy and stability of a nondestructive testing result of the material component content.
Owner:BEIHANG UNIV
Automotive exhaust emission data fusion system
ActiveCN106650825ADo not interfere with drivingAvoid harmCharacter and pattern recognitionNeural architecturesDiagnostic systemAir pollutant concentrations
The invention discloses an automotive exhaust emission data fusion system. The automotive exhaust emission data fusion system comprises a roadside air pollutant concentration estimation module, a roadside air pollutant concentration prediction module, a city global atmospheric environment prediction module, an automotive exhaust emission factor estimation module and an automotive exhaust emission feather analysis module, wherein the five modules are used for respectively realizing different data analysis functions, and the different functions can be realized by virtue of the different modules; the modules can be independently used, or two or more modules can be combined for use, so as to realize the storage, analysis and fusion of automotive exhaust telemetering data, automotive attributes, driving working stations, detection time and meteorological condition data; and by combining with a vehicle-mounted diagnosis system database, a portable emission test system database, a vehicle inspection station offline database, a traffic information database and a geographic information database, automotive exhaust telemetering data is analyzed, and the highest discriminatory key indexes and statistical data are acquired, so that effective supports are provided for the formulation of relevant decisions of government departments.
Owner:UNIV OF SCI & TECH OF CHINA
Deep learning-based image high-density population counting method
PendingCN107301387AStrong generalization abilityGood robustnessImage enhancementImage analysisPopulationNetwork model
The invention discloses a deep learning-based image high-density population counting method. The method comprises the following steps of S1, establishing a depth complementation convolutional neural network by utilizing a deep learning framework caffe; S2, performing image data enhancement on an image according to operations of angle rotation, image multi-scale zooming, image mirroring and image pyramid zooming; S3, performing Gaussian kernel fussy normalization processing on the enhanced image data to obtain a real crowd density graph, outputting an estimated density graph and the real density graph by the network, and performing continuous iterative training optimization on the whole network structure according to a loss function; and S4, inputting a crowd picture and a tag picture to the network for training, and performing continuous iterative optimization to obtain a trained network model finally. According to the method, the end-to-end convolutional neural network is designed; a picture is given and input, and the estimated density graph corresponding to the picture is output, so that an estimated crowd number is obtained; and by outputting the density graph, more useful information is reserved.
Owner:SOUTH CHINA UNIV OF TECH
Subword-based speaker verification with multiple-classifier score fusion weight and threshold adaptation
InactiveUS6539352B1Increase in sizeImprove generalization abilitySpeech recognitionSpeech segmentationSpeaker verification
Owner:BANK ONE COLORADO NA AS AGENT +1
Image classification method based on confrontation network generated through feature recalibration
ActiveCN108805188AImprove generalization abilityClassification task performance improvementCharacter and pattern recognitionNeural architecturesGenerative adversarial networkClassification methods
The invention discloses an image classification method based on a confrontation network generated through feature recalibration. The image classification method based on the confrontation network generated through feature recalibration is suitable for the field of machine learning and comprises the steps that to-be-classified image data are input into a confrontation network model for network training; a generator and a discriminator which are constituted by a convolutional network are constructed; random noise is initialized and input into the generator; the random noise is subjected to multilevel deconvolution operation in the generator through the convolutional network, and finally, generated samples are obtained; the generated samples and authentic samples are input into the discriminator; and the input samples are subjected to convolution and pooling operation in the discriminator through the convolutional network, thus a feature graph is obtained, a compressed and activated SENetmodule is imported into an intermediate layer of the convolutional network to calibrate the feature graph, thus the calibrated feature graph is obtained, global average pooling is used, and finally,image data classification is output. The SENet module is imported into the intermediate layer of the discriminator, the importance degree of each feature channel is automatically learned, useful features relevant to a task are extracted, features irrelevant to the task are restrained, and thus semi-supervised learning performance is improved.
Owner:JIANGSU YUNYI ELECTRIC
An aluminum material surface defect detection algorithm based on deep learning
ActiveCN109711474AImprove generalization abilityEasy to detectImage analysisCharacter and pattern recognitionData setTest set
The invention relates to an aluminum material surface defect detection algorithm based on deep learning, and the algorithm comprises the steps: (1) employing a camera to shoot the surface of an aluminum material, obtaining a related data set, employing a labelImg tool to label an image, and obtaining label information; (2) dividing the image into a training set and a test set, and performing dataenhancement on the training set; (3) inputting a defective image, a non-defective image and label information of the defective image into the network at the same time every time to carry out model training; and (4) inputting the test image into the trained aluminum material surface defect detection model, and obtaining the position and the corresponding category of the defect. According to the method, a defective image and a non-defective image can be effectively utilized; the generalization ability and the detection precision of the model are improved, the detection performance is further improved by fully utilizing context information around the candidate region, the detection performance of dense small defects can be improved by utilizing a soft non-maximum suppression algorithm, and the method is an efficient aluminum material surface defect detection algorithm.
Owner:SUN YAT SEN UNIV
Face recognition method and device
ActiveCN107423690AImprove accuracyCalculation speedCharacter and pattern recognitionFeature vectorFeature extraction
The embodiment of the invention discloses a face recognition method and a device. The method comprises the steps of extracting the Haar feature of a current to-be-recognized face image, and detecting the human face area of the to-be-recognized face image by adopting an ADaBoost classifier so as to obtain a face region image; performing the multi-scale feature extraction on the face region image by utilizing a convolution neural network model, and obtaining a feature vector of the face region image; inputting the feature vector, a pre-built legal face database and a preset user similarity threshold value into a multi-task learning model pre-constructed according to a Softmax loss function and a Triplet loss function, and judging whether the to-be-recognized face image is a legal user or not according to the output value of the multi-task learning model. The extracted feature is good in robustness and good in generalization ability. Therefore, not only the face recognition rate improved, but also the accuracy of face recognition is improved. The safety of identity authentication is improved.
Owner:GUANGDONG UNIV OF TECH
Artificial intelligent training platform for intelligent networking vehicle plan decision-making module
InactiveCN107506830AImprove generalization abilitySafe and "Real Training Scenarios"Mathematical modelsMachine learningFrame basedReinforcement learning algorithm
The invention, which relates to the technical field of an intelligent vehicle automatic driving and traffic simulation, relates to an artificial intelligent training platform for an intelligent networking vehicle plan decision-making module and aims at improving the intelligent level of the intelligent vehicle plan decision-making module based on enriched and vivid traffic scenes. The artificial intelligent training platform comprises a simulation environment layer, a data transmission layer, and a plan decision-making layer. The simulation environment layer is used for generating a true traffic scene based on a traffic simulation module and simulating sensing and reaction situations to the environment by an intelligent vehicle, thereby realizing multi-scene loading. The plan decision-making layer outputs a decision-making behavior of the intelligent vehicle by using environment sensing information as an input based on a deep reinforcement learning algorithm, thereby realizing training optimization of network parameters. And the data transmission layer connects the traffic environment module with a deep reinforcement learning frame based on a TCP / IP protocol to realize transmission of sensing information and vehicle control information between the simulated environment layer and the plan decision-making layer.
Owner:TONGJI UNIV
Depth convolution wavelet neural network expression identification method based on auxiliary task
ActiveCN107292256AImprove generalization abilityEfficient complete feature transferNeural architecturesAcquiring/recognising facial featuresFeature selectionExpression Feature
The invention discloses a depth convolution wavelet neural network expression identification method based on auxiliary tasks, and solves problems that an existing feature selection operator cannot efficiently learn expression features and cannot extract more image expression information classification features. The method comprises: establishing a depth convolution wavelet neural network; establishing a face expression set and a corresponding expression sensitive area image set; inputting a face expression image to the network; training the depth convolution wavelet neural network; propagating network errors in a back direction; updating each convolution kernel and bias vector of the network; inputting an expression sensitive area image to the trained network; learning weighting proportion of an auxiliary task; obtaining network global classification labels; and according to the global labels, counting identification accuracy rate. The method gives both considerations on abstractness and detail information of expression images, enhances influence of the expression sensitive area in expression feature learning, obviously improves accuracy rate of expression identification, and can be applied in expression identification of face expression images.
Owner:XIDIAN UNIV
Special audio event layered and generalized identification method based on SVM (Support Vector Machine) and GMM (Gaussian Mixture Model)
InactiveCN102799899AReduce distractionsEasy extractionCharacter and pattern recognitionSvm classifierData mining
The invention relates to a special audio event layered and generalized identification method based on a combination of an SVM (Support Vector Machine) and a GMM (Gaussian Mixture Model), and belongs to the technical field of a computer and audio event identification. The special audio event layered and generalized identification method comprises the following steps of: firstly, obtaining an audio characteristic vector file of a training sample; secondly, respectively carrying out model training on a great quantity of audio characteristic vector files (of the training samples) with various types by using a GMM method and an SVM method, so as to obtain the GMM model with generalization capability and an SVM classifier, and complete offline training; and finally, carrying out layered identification on the audio characteristic vector files to be identified by using the GMM model and the SVM classifier. With the adoption of the method provided by the invention, the problems that the conventional special audio event identification is low in identification efficiency on a continuous audio stream, very short in continuing time, high in audio event false dismissal probability can be solved. The method can be applied to searching a special audio and monitoring a network audio based on contents.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
LSTM-RNN model-based air pollutant concentration forecast method
ActiveCN106599520AImprove perceptionTake advantage ofForecastingDesign optimisation/simulationTest sampleModel parameters
The invention relates to an LSTM (Long Short-Term Memory)-RNN model-based air pollutant concentration forecast method. The method comprises the steps of monitoring and collecting air pollutant concentration data in a relatively long time; preprocessing historical data to construct training, verification and test sample data of a to-be-trained LSTM-RNN model; obtaining a pre-trained LSTM-RNN model through the training sample data, then performing fine adjustment and training through the constructed verification and test sample data to obtain LSTM-RNN model parameters, improving model precision by further correcting model parameters, and taking a corrected LSTM-RNN model as an air pollutant concentration forecast model; and finally taking the preprocessed air pollutant concentration data of a target city in the relatively long time as input data of the LSTM-RNN model, and performing model output to obtain a forecast result of air pollutant concentration at a current moment or at a moment in the future.
Owner:UNIV OF SCI & TECH OF CHINA
Convolutional neural network based crowd density distribution estimation method
ActiveCN106326937AImprove robustnessImprove generalization abilityCharacter and pattern recognitionNeural learning methodsDensity distributionImage segmentation
The invention relates to a convolutional neural network based crowd density distribution heat map generation method, which comprises the steps of dividing a crowd picture set into a training sample set and a test sample set, performing crowd label image segmentation by using convolutional neural network architecture, and carrying out number regression by using a convolutional neural network; correcting a density map through a multi-scale template operation, generating a crowd density distribution heat map according to the corrected density map and the regression number, and completing crowd density distribution estimation. According to the invention, deep characteristics of an image are extracted by using a powerful learning ability of the full convolutional neural network so as to perform accurate crowd segmentation, and low efficiency and blindness of density calculation of a traditional method for full image characteristics are overcome; a crowd near-far perspective effect is overcome to a certain extent through multi-scale template correction; and mapping is performed in allusion to the estimated number of people, lateral comparison can be performed on heat maps of different cameras, the method is applicable to various crowd scenes, and the crowd density distribution heat map can be acquired in real time.
Owner:ZHENGZHOU JINHUI COMP SYST ENG
An orchard pedestrian detection method based on a YOLOv3 algorithm
PendingCN109934121AImprove robustness and generalizationReduce hardware requirementsCharacter and pattern recognitionNeural architecturesMachine learningOrchard
The invention discloses an orchard pedestrian detection method based on a YOLOv3 algorithm. The method specifically comprises the steps of collecting pedestrian images in an orchard; Preprocessing thecollected images, and constructing a standard pedestrian detection data set; Placing a training set into the modified Darknet-53 network structure to extract pedestrian features; Generating a predicted pedestrian boundary frame by using a K-means clustering method, performing category prediction by using a binary cross entropy loss function, and performing multi-scale fusion prediction by using asimilar FPN network; Finally, removing redundant prediction boundary frames through the Soft-NMS, and outputting final prediction boundaries and categories. The pedestrian detection accuracy is high,the real-time performance is good, the robustness of a training model to a complex background is enhanced by aiming at data augmentation methods such as Random Eraging proposed in an orchard environment, and through the adopted Soft-NMS algorithm, the recall rate of detection can be increased, and the introduced group normalization Group Normalizations can reduce the requirements of a trained model on hardware.
Owner:JIANGSU UNIV
Network constructing method for human face identification, identification method and system
ActiveCN105095833AImprove learning abilityImprove generalization abilityBiological neural network modelsCharacter and pattern recognitionAge estimationGroup of pictures
The invention discloses a deeper layer network constructing method used for gender identification or age estimation based on human face. The method includes a step (101) dividing all training pictures into a plurality of groups; (102) extracting high layer features of a group of pictures based on a convolution neural network and thereby obtaining a first matrix composed of the high layer feature vectors, and extracting low layer and global features of the same group of the training images based on an artificial neural network and thereby obtaining a second matrix composed of the low layer feature vectors, obtaining a group of gender identification or age estimation results based on the extract first matrix, the second matrix and the defined judgment formula, wherein the values of a first weight matrix W1, a second weight matrix w2, an offset matrix b and an adjusting weight beta in the defined judgment formula are updated by utilizing an error back propagation algorithm and the final values of the parameters are obtained and the network construction is completed. Judgment of age and gender of a human face is performed based on the judgment formula determined according to the values of the parameters when the network construction is completed.
Owner:HENGFENG INFORMATION TECH CO LTD
Method for remain useful life prognostic of lithium ion battery with model active updating strategy
InactiveCN103778280AEasy Adaptive AcquisitionFlexible inferenceElectrical testingSpecial data processing applicationsHealth indexEngineering
The invention relates to a method for remain useful life prognostic of a lithium ion battery with a model active updating strategy. According to a time series obtained through a voltage range of a discharge curve, conversion is conducted so that an equivalent discharge difference series obtained by discharge circulation at each time can be obtained, and therefore a health index time series of the ion battery is obtained; according to correspondence of a discharge voltage series and a time series, prognostic is conducted on the health index series to determine the remain useful life of the battery. Sampling entropy characteristic extraction and modeling are conducted on a charge voltage curve so that a relationship between a complete and accurate charge / discharge process and a battery performance index can be provided. On the basis of a performance index model, a short-term time series prognostic result is continuously updated to a known performance index data series and correlation analysis is conducted. According to the difference of the correlation degrees, retraining is conducted in the mode of training set expansion. The method is different from an existing iteration updating draining method, the prognostic model is updated dynamically, and therefore the prognostic precision is improved.
Owner:SHANGHAI JIAO TONG UNIV
Workflow scheduling method based on depth enhancement learning
InactiveCN106228314AGuaranteed time and efficiencyImprove generalization abilityResourcesNeural learning methodsDeep neural networksSample pool
The present invention discloses a workflow scheduling method based on depth enhancement learning. The method comprises the following steps of A) collecting M task execution workflow directed acyclic graphs (DAG) in an actual execution environment as a sample pool; B) carrying out the Markov decision process (MDP) modeling on each workflow DAG to generate a task state set S; C) according to a training method DQN of a neural network, using the task state sets S generated by the M workflow DAG and the corresponding known action sets A as the input to substitute into a deep neural network formula, and obtaining the value of a neural network parameter matrix. According to the present invention, and by the above method, the defects that the execution time of the workflow scheduling method is long and the generalization performance is poor under a current distributed environment, are solved, the time efficiency of an algorithm is guaranteed acceleratedly, at the same time, the generalization performance of the algorithm itself is increased, and a scheduling machine can learn a scheduling strategy autonomously according to the actual scene characteristics.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA
Business risk assessment method and device, and risk control system
InactiveCN107067157AImprove generalization abilityImprove processing efficiencyResourcesAssessment methodsRisk behavior
The invention provides a business risk assessment method and device, and a risk control system. The method comprises steps that to-be-assessed business data is received, and a risk behavior rule set matched with the business data is identified according to a preset risk rule engine; for multiple rules of the rule set, a risk value of each corresponding rule is assessed according to a pre-established risk assessment model; the risk values of the corresponding rules are accumulated, the accumulated risk value sum is compared with a total risk score threshold of each preset risk grade, and a risk grade of the business data is determined. The method is advantaged in that generalization capability and processing efficiency of a risk system can be improved.
Owner:BEIJING QIYI CENTURY SCI & TECH CO LTD
Vehicle detection method based on improved YOLOv3
ActiveCN110796168AEasy extractionEasy to detectInternal combustion piston enginesCharacter and pattern recognitionData setEngineering
The invention belongs to the field of deep learning and intelligent vehicle road detection, and specifically relates to a vehicle detection method based on improved YOLOv3. The method comprises the following steps: redesigning a convolutional neural network structure between a Darknet layer and three yolo layers, and designing a YOLO-TN network by referring to an idea of TridentNet weight sharing;carrying out model pruning on the YOLO-TN convolutional neural network; constructing a vehicle detection data set, and labeling vehicle position information in the data set; respectively training vehicle detection models based on the YOLO-TN and the YOLOv3, completing a vehicle detection task, and comparing detection results of the YOLO-TN and the YOLOv3. The method has high average precision while guaranteeing real-time detection, and is lower in omission ratio and more accurate in positioning when used for detecting distant vehicles and small-scale targets.
Owner:JIANGSU UNIV
Analogue circuit fault diagnosis neural network method based on particle swarm algorithm
InactiveCN101221213AImplement extractionEliminate the effects ofElectronic circuit testingPhysical realisationNetwork ConvergencePrincipal component analysis
The invention discloses a neural network method for diagnosing analog circuit failures which is based on a particle swarm algorithm, and comprises the following steps: imposing an actuating signal to an analog circuit to be tested, measuring an actuating response signal in the testing nodes of the circuit, extracting the candidate signal of failure characteristics by implementing noise elimination and then wavelet packet transformation on the measured actuating response signal, extracting the failure characteristics information by further implementing orthogonal principal component analysis and normalization processing on the candidate signal of failure characteristics, and sending the failure characteristics information as samples to the neural network for implementing classification. The method adopts the particle swarm algorithm instead of a gradient descent method in traditional BP algorithms, thus leading the improved algorithm to be characterized in that the algorithm avoids the local minimum problem and has better generalization performance. The BP neural network method for diagnosing the analog circuit failures which is optimized on the basis of particle swarm can obviously reduce iteration times in the algorithm, improve the precision of network convergence, and improve diagnosis speed and precision.
Owner:HUNAN UNIV
Improved CNN-based facial expression recognition method
InactiveCN108108677AReduce training parametersSmall amount of calculationImage enhancementImage analysisFace detectionSvm classifier
The invention provides an improved CNN-based facial expression recognition method, and relates to the field of image classification and identification. The improved CNN-based facial expression recognition method comprises the following steps: s1, acquiring a facial expression image from a video stream by using a face detection alignment algorithm JDA algorithm integrating the face detection and alignment functions; s2, correcting the human face posture in a real environment by using the face according to the facial expression image obtained in the step s1, removing the background information irrelevant to the expression information and adopting the scale normalization; s3, training the convolutional neural network model to obtain and store an optimal network parameter before extracting feature of the normalized facial expression image obtained in the step s2; s4 loading a CNN model and the optimal network parameters obtained by s3 for the optimal network parameters obtained in the steps3, and performing feature extraction on the normalized facial expression images obtained in the step s2; s5, classifying and recognizing the facial expression features obtained in the step s4 by using an SVM classifier. The method has high robustness and good generalization performance.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com