161results about How to "Improve the correct recognition rate" patented technology

The invention discloses a method for identifying a modulation mode of a communication signal. The method comprises the following steps that: a network side receives a communication signal, constructs and stores characteristic parameters of the communication signal according to the characteristics of the received communication signal; the network side processes the constructed characteristics parameters of the communication in a GDA mode, and stores the processed characteristic parameters; and the network side judges the processed characteristic parameters in a mode of a decision tree judgmentclassifier of an AdaBoost, and identifies the modulation mode of the received communication signal. Due to the adoption of the method, the problem that the conventional identification method has low identification accuracy in a low signal-to-noise ratio is solved; and a higher correct identification rate is achieved.

The invention discloses an enterprise association recognition method and system. The method includes the steps that an enterprise relevant database of a uniform data format is established; enterprise relevant data of a set area are collected through the network technology; relevant data fields of all enterprises in the enterprise relevant database are called, and the data fields are preprocessed; enterprise shareholder data fields are extracted, and whether the data fields belong to persons or the enterprises are judged; data docking is performed on the same enterprise; data association between different enterprises is established; information query of the associated enterprises is performed. The method and system have the advantages that the data fields are preprocessed, enterprise shareholder data fields and the like are intelligently recognized, whether the data fields belong to persons or the enterprises are judged, the correct recognition rate of the system for persons and the enterprises can be increased to the maximum extent, the system accuracy is improved, and enterprise association accuracy is improved.

The present invention discloses a logistics industry-oriented system and method for cascade protection of user privacy information. The method comprises: in a collection stage, using an electronic express form instead of a conventional express form, thereby ensuring that user information is transparent to a courier; in an express delivery transfer stage, applying an idea of secret division, so that each station can acquire only part of station information; in an express delivery notification stage, using a server proxy notification method, so that a courier can issue a notification without knowing contact information of a recipient; in a signing-for-accepting-the-express-delivery stage, verifying the identity of the recipient by using a verification code that is generated by the server in the previous stage; and at the same time, encrypting a content in network communication by combining symmetric encryption and asymmetric encryption.

The invention discloses a vibration event mode identification method. The method includes acquiring and obtaining the original vibration signals, including the vibration signal and the non-vibration signal, via a vibration sensor; separating the vibration signal in the original vibration signals; denoising the vibration signal; performing feature extraction on the denoised vibration signal and obtaining the characteristic vector including three characteristics of: A performing wavelet packet decomposition on the time-frequency domain and obtaining the energy characteristic vector, B performing the cepstrum analysis and extracting the cepstrum parameter characteristics, and C extracting the signal characteristic on the time domain; and establishing an identification model composed of two grades of classifiers, wherein the first grade of classifier is a classifier based on SVM and divides the vibration events into non-intrusive events and intrusion events by taking the characteristic vector extracted from the vibration signal as the input, and the second grade of classifier performs the identification based on the artificial nerve network for the intrusion events to obtain the classification result.

The invention discloses a method for test recognition based on verification and a device thereof, the steps of the method comprise receiving a verification request, splicing a generated identifying code segment and a to-be-recognized image segment comprising test characteristics into an identifying code image to transmit to a terminal, receiving user recognition information which is transmitted by the terminal and is input by users after the users recognize the identifying code image, and extracting text characteristics included by the image segment in the user recognition information to be the recognition result of the image segment. The scheme of the invention increases the accurate recognition rate of the recognition result, and resolves the limitation of the computer recognition.

The invention discloses a method to identify flaw on surface of woven. Input the image signal which gained from image acquisition system to computer and proceed filtering and enhancement. Extract energy, entropy, contrast, deficit moment and correlation from vertical and horizon high frequency detail of image signal on woven flaw as five texture distinction parameter of woven. Use optimal edge detection algorithm on image signal of woven flow. Abstract apsidal ratio from image signals as a form characteristic parameter of woven. Input these six parameters to neural network system to proceed identifying and assorting on flows. The invention put texture and morphological character as evidence of differentiating flows of woven. Using neural network system to proceed identifying and assorting, the accuracy of flows can be increased obviously. Because of simple operation, fast detection and high efficient, the method has good prospect for applying.

The invention discloses a freight wagon blocking key missing fault identification method based on a support vector machine, and belongs to the field of freight wagon running fault testing. The freight wagon blocking key missing fault identification method includes a training process of the support vector machine and a process of online blocking key missing identification of the support vector machine. With the freight wagon blocking key missing fault identification method based on the support vector machine, when a train passes through, images of a running freight wagon body, freight wagon type and freight wagon information can be acquired; whether the images include the blocking key component or not can be confirmed through image naming; coarse positioning of a blocking key can be completed through positioning shaft ends so as to further complete fin positioning of the blocking key;HOG (Histogram of Oriented Gradients) features of blocking key images are extracted, and blocking key identification classifiers are inserted to complete blocking key missing fault identification. With the freight wagon blocking key missing fault identification method, training samples can be conveniently added, and corresponding classifiers can be trained based on different detection stations. The freight wagon blocking key missing fault identification method can be run automatically without mannual interference, running parameter can be controlled through configuration files; identification speed is fast, and automatically identified results can be stored in a friendly mode so as to facilitate verification and review of the identified results by train examination staff.

In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

A method for identifying iris of human eye includes detecting four boundary points of pupil and accurately positioning pupil centre, detecting four outer boundary point of its and confirming iris centre, expanding eye image in 360 degree around iris center to be rectangle for searching pupil boundary, carrying out topological conversion to realize iris image noralization.

The invention discloses a method and a device for avoiding a misoperation by fingerprint confirmation, and a mobile terminal. The method comprises the following steps: obtaining operation instruction information input by a user; judging whether the operation instruction information has a mapping relationship or not in a mapping relationship table, wherein the mapping relationship table comprises the mapping relationship between multiple pieces of operation instruction information and application program operation; if the operation instruction information has the mapping relationship in the mapping relationship table, obtaining a fingerprint pattern input by a user through a fingerprint sensor; verifying whether the obtained fingerprint pattern is matched with at least one preset standard fingerprint pattern or not; and if the obtained fingerprint pattern is matched with at least one preset standard fingerprint pattern, executing the application program operation corresponding to the operation instruction information. The method can effectively reduce the misoperation caused by that the touch screen of the mobile terminal is touched or operated under an unconsciousness situation to cause the touch screen to respond so as to enter an operable state according to the definition and the detection judgment of fingerprint confirmation information, user demands can be more accurately met, a correct recognition rate of a black screen gesture is improved, the operation experience degree of the user is obviously improved, and meanwhile, the cruising ability of a mobile phone is improved.

The invention provides high-dimensional and imbalance data classification-oriented integration. The integration is characterized in that preprocessing policies are reduced to two types by adopting a sequence of dimension reduction and sampling; based on a reproducibility principle of an experiment conclusion, a few standard data sets of data mining and machine learning are selected as experiment data; in selection of a preprocessing method, a wrapper feature selection method and an oversampling method are added; the influence of the preprocessing method on the imbalance data classification performance is researched in two aspects of an attribute number and an imbalance degree, a completer preprocessing experiment policy is adopted, and different conclusions are obtained: before high-dimensional imbalance data classification, features are reduced firstly and then data is balanced, so that the average AUC performance is better and the automation level is high; and different preprocessing combination policies are adopted for relieving the influence of high dimension and imbalance on classification.

The invention relates to a shape and color integration insulator identification and defect detection method. According to the method, a potential insulator region on an unmanned plane inspection image is identified according to insulator sheet arrangement characteristics, an error identification region is further detected according to insulator color significant characteristics, an incomplete-identification insulator region can be compensated according to main color components of an insulator prior knowledge model, and lastly, an insulator chip shell defect is diagnosed according to an insulator sheet position relationship. Through integrating shape and color characteristics of an insulator, the insulator region can be accurately and completely identified from a complex background, the insulator chip shell defect is detected through analyzing the insulator sheet position relationship, and the method can be effectively applied to unmanned plane high voltage transmission line inspection business.

The invention relates to a test platform for an absolute grating ruler. The test platform for the absolute grating ruler comprises a complementary metal oxide semiconductor (COMS) sensor, an optical focusing mirror, a COMS eye lens, a collimated light source, a grating ruler supporter, a double-grating-strip grating ruler, a grating ruler supporter, a mobile platform, a linear slide rail, a linear propulsion system, a stepping motor and a base, wherein a linear movement system is composed of the stepping motor, the linear propulsion system and the linear slide rail, and a sampling system of image processing is composed of the COMS sensor, the optical focusing mirror, the COMS eye lens, the collimated light source and the double-grating-strip grating ruler. According to the test platform and the test method for the absolute grating ruler, accuracy and reliability of encoding measurement are improved, so that accurate positioning can be achieved. The test platform for the absolute grating ruler has the advantages of high accuracy, high controllability and high reliability. The invention aims at providing a method for the absolute grating ruler of double encoding strips with optical amplification. In addition, through reasonable and reliable image analysis and closed-loop control, mobility, reliability and accuracy of the grating ruler are enabled to be greatly improved.

The invention discloses a noise suppression method and device and a mobile device. The method comprises the following steps: determining real-time position of the mobile device in a working environment; obtaining a fixed position of a noise source in the working environment; determining a relative position of the noise source with respect to the mobile device according to the real-time position and the fixed position; and controlling the mobile device to carry out suppression on a noise signal of the noise source according to the relative position. Therefore, the mobile device can eliminate the noise signal of the fixed noise source very well.

The invention discloses a convolutional neural network model training method and device, an electronic device and a computer readable storage medium. The method comprises the following steps of dividing the convolutional neural network into a plurality of convolutional stages, wherein the convolution stage is composed of at least one convolution layer; determining the parameters of the convolutional neural network; inputting a positive training sample set into the convolutional neural network for training to obtain the feature images of a plurality of convolutional stages corresponding to each sample image; for each sample image, fusing the feature images of a plurality of corresponding convolution stages; obtaining a positive sample convolutional neural network model according to the fused feature image of each sample image, wherein the positive sample convolutional neural network model is used for identifying a target area. According to the embodiment of the invention, the feature images of the plurality of convolution stages of the convolutional neural network are fused during the training process of the positive sample convolutional neural network model, so that the correct recognition rate of the positive sample convolutional neural network model to the target area can be improved.

The invention discloses a voice-control-based vehicle driving automation system and method. The system includes a voice input button which is used for triggering voice control, a voice input module which is used for collecting voice signals, a semantic recognition module which is used for identifying control command items expected by the voice signals, an identity recognition module which is usedfor identifying whether the voice signals come from a driver, a control module which determines whether to execute the control command items based on a semantic recognition result, an identity recognition result, the state of the voice input button, and the state of a driving automation unit to be controlled and then issues control commands and feedback information, a voice output module which isused for voice broadcast of the feedback information, a display module which is used for displaying the feedback information, and the driving automation unit which executes control commands issued bythe control module. The voice-control-based vehicle driving automation system and method can quickly and conveniently finish setting through voice control, driving burden is low, and driving safety and comfort are improved.

The invention discloses a double-layer license plate character segmentation method based on projection and recognition. The method comprises the following steps: S1, projection in a horizontal direction is carried out on the double-layer license plate, and the double-layer license plate is segmented into an upper part and a lower part; S2, a vertical projection method is used for carrying out character segmentation on the license plate of the lower layer; S3, after segmentation of characters of the lower layer is carried out, if the number of the characters is 5, a step 4 is directly carried out, if the number of the characters is larger than 5, a recognition module is used for acquiring reliability of character recognition, and through the highest reliability, the position information and the length and width information of the best five characters on the license plate of the lower layer are acquired; S4, according to the character segmentation information for the license plate of the lower layer, the width of the best character is obtained; and S5, according to the width of the best character, initial positions of the first character and the second character on the license plate of the upper layer are calculated, and segmentation is carried out.

The invention discloses a lung texture recognition method based on deep neural network extraction appearance and geometric features, which belongs to the field of medical image processing and computervision. 217 lung 3D computed tomography (CT) images are used as source data, a plurality of groups of data sets are obtained through preprocessing, and each group of data comprises a CT image small block, a corresponding geometric information image small block and a category label. A dual-channel residual network framework is built, the CT image small block and the corresponding geometric information small block are used as input of each channel respectively, the appearance information and the geometric information of the lung textures are learnt respectively through the dual-channel residualnetwork, the information is effectively fused, and the recognition rate is thus high. Besides, the provided network has a distinct structure, and building and realization are easy.

In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

The invention discloses an efficient recognition method for the flight control signal of a low-altitude, slow-speed and small unmanned aerial vehicle. The method comprises the steps of step 1, carrying out primary detection on a flight control link signal; step 2, extracting key features, and constructing a target feature database; step 3, carrying out feature fusion and optimization; step 4, analyzing the signal according to the fused optimized features so as to obtain the type, the frame format and the keyword segment of the signal, and storing the obtained analysis result into the target feature database; step 5, according to a learning algorithm based on a neural network, realizing the accurate classification and recognition of the target, and obtaining a correct recognition result. According to the method, the recognition problem for the flight control signals of low-altitude, slow-speed and small unmanned aerial vehicles can be solved. Therefore, the recognition, management and control for a specific airspace can be realized. Meanwhile, the high-efficiency early warning and defense effect for various low-altitude, slow-speed and small unmanned aerial vehicles which have illegally intruded into secret-related and sensitive airspaces can be realized. The method is higher in correct recognition rate, lower in calculated amount, higher in processing efficiency and strong in applicability.

The invention discloses a method for identifying a behavior based on feature enhancement and decision fusion. The method comprises the following steps: pre-treating data of an accelerometer; calculating multi-dimensional feature vectors of the pre-treated data; using the forward sequence selection algorithm to select the optimal feature vector among the multi-dimensional feature vectors; using the Relief-F algorithm to select a proper feature value for feature enhancement; training one basic classifier and three weak classifier; taking the identification results from the basic classifier and the three weak classifiers as 4 parameters, meanwhile taking the identification result of the last action as another parameter, determining the weights of the 5 parameters through training; identifying human body behaviors by using secondary classification, a first classification using the basic classifier for identification, a second classification using weighted voting decision fusion to obtain a final identification result. According to the invention, the method can effectively differentiate similar actions that are difficult to differentiate, such as walking, going upstairs and going downstairs, and increase identification of human body behaviors.

The invention discloses a quick near-infrared brain-computer interface method. The method is mainly characterized in that a multi-channel near-infrared optical brain signal collecting system is used for obtaining multi-channel near-infrared optical brain signals generated by a user in the brain-computer interface task executing process in real time, after the signals are pre-processed, feature space transformation is carried out to extract feature vectors, and mode recognition is carried out to judge the type of a brain-computer interface task executed by the user. The method has the advantages that the recognition rate and recognition speed of brain-computer interface tasks are obviously improved compared with a traditional near-infrared brain-computer interface method which carries out recognition with amplitude as feature vectors.

The invention discloses a particle filter-based improved RAIM (Receiver Autonomous Integrity Monitoring) anti-deception jamming method. The invention relates to an improved RAIM anti-deception jamming method. For solving the problems of measurement failure of a single satellite, positioning misleading of a receiver by a control resolving flow, and neglecting of dependency and similarity among remaining vectors by RAIM, the invention provides a particle filter-based improved RAIM anti-deception jamming method. The particle filter-based improved RAIM anti-deception jamming method comprises the following steps: 1, forming a formula described in the specification; 2, obtaining a Rho n and three-dimensional coordinates of the satellite; 3, calculating a parameter described in the specification; 4, calculating y (m); 5, calculating w (m); 6, selecting wmax; 7, calculating a parameter described in the specification; 8, calculating a maximal visible satellite SLmax; 9, calculating a judgment threshold gamma; 10, judging the existence of a deception satellite; 11, estimating a sequence number of the deception satellite; and 12, if the mark F of the deception satellite is equal to 1, removing jamming, carrying out positioning resolving, and if F is not equal to 1, carrying out positioning resolving. The particle filter-based improved RAIM anti-deception jamming method is applied to the field of improved RAIM anti-deception jamming.

The invention discloses a vehicle license plate recognition method and system. The vehicle license plate recognition method comprises the steps that A, when the situation that a vehicle passes on the site is detected, a video image of a license plate of the vehicle is acquired; B, the acquired video image is processed, whether a license plate number can be automatically recognized through the processed video image or not is judged, if not, the step C is executed, and if yes, the step E is executed; C, the acquired video image is sent to an operating terminal; D, the received video image is displayed by the operating terminal, and a license plate number input by an operator according to the displayed video image is received; E, the license plate number is recorded and stored. The invention further provides the vehicle license plate recognition system. By the implementation of the technical scheme, the vehicle license plate recognition method and system increase the correct recognition rate of the license plate numbers and are more reasonable in practical application.

The invention provides an underwater acoustic target recognition method based on a weighted support vector machine. The method comprises the steps of performing framing preprocessing, performing feature extraction, building a weighted support vector machine recognition model, searching an optimal kernel function parameter and a penalty factor by utilizing a grid search method, training the weighted support vector machine recognition model, reflecting a recognition result of a classifier on an underwater acoustic target through a confusion matrix, and counting the recognition accuracy of the classifier. According to the method, an appropriate feature extraction method is selected for the characteristics of the underwater acoustic target, the method has the capability of autonomously selecting model parameters, the correct recognition rate of the underwater acoustic target is 80% or above, and the stability of a classifier is higher than that of an existing classification method.

The invention discloses a track correlation method by taking topological sequences as characteristics, which comprises steps of constructing target topological sequences of a single sensor and matching multi-sensor topological sequences and belongs to the field of track correlation of multi-sensor networking. The method comprises the following steps of: forming the topological sequences of each target on the basis of a target position data set of the single sensor; filtering the topological sequences of the each sensor according to the similarity of a spatial location to construct F statistic which is submitted to chi square distribution; and judging that the topological sequences which meet statistical tests are from the same target by angle fine turning. In the track correlation method by taking the topological sequences as the characteristics, the topological sequences are taken as a state variable for identifying the target and the method has high accuracy, higher robustness, and high correlation performance.

The invention discloses a DSSS (direct sequence spread spectrum) frequency domain interference detection method, relating to an algorithm of interference detection identification in the DSSS communication field. In the method, an FFE (fast Fourier transform) frequency domain transform is carried out on a received signal in the DSSS communication system so as to obtain the frequency domain information of the DSSS communication system; and under the condition that no interference prior knowledge is required, all kinds of interferences happening in the communication can be analyzed, detected and identified with different methods according to different types, so that two communication parties can select correct anti-interference measures according to different interferences. The method provided by the invention has the characteristics of strong robustness in collecting the characteristic parameters of the interferences and high correct recognition rate of the interferences and the like, does not need interference prior knowledge, and is especially for the DSSS system with interferences at different signal to interference ratios and signal to noise ratios.

The invention discloses a DSmT (Dezert-Smarandache Theory)-based image target multi-characteristic fusion recognition method. The method comprises the steps: firstly, multiple characteristic quantities, such as moment, outline and the like of an image target, are extracted to serve as a data source to obtain enough useful complementary information; then, a target recognition rate matrix is constructed by a PNN (Probability Neural Network) widely used in classification; and then, a value is assigned to the basic reliability by the target recognition rate matrix according to an initial recognition result of the PNN and a rule similar to the maximum likelihood thought in the statistics; and finally, decision-level data infusion is carried out by using a DSmT combination rule to complete the recognition of the three-dimensional target. With the DSmt-based image target multiple features fusion method, the problem that the target recognition rate is not high, resulting from inaccuracy, indeterminacy and incompleteness of the obtained information and the high-degree conflict and the like is solved.

The invention discloses a method for identifying the subcarrier modulation constellation diagram of wireless optical communication under atmospheric turbulence. The method comprises the following steps that: an atmospheric turbulence channel mathematical model is established; the constellation diagram of subcarrier modulation signals which have passes through an atmospheric turbulence channel is obtained according to the atmospheric turbulence channel mathematical model, fuzzy C-means clustering is performed, and the hard trend mean feature parameter of cluster centers is extracted; and an improved three-layer BP neural network classifier is designed, the feature parameter is adopted to train the improved BP network, and the trained network is adopted to identify the constellation diagram.The method for identifying the subcarrier modulation constellation diagram of the wireless optical communication under the atmospheric turbulence of the present invention has the advantages of simpletechnical routes, economical performance, high practicability, high feasibility and easiness in implementation. With the method adopted, the correct recognition rate of subcarrier high-order modulation under severe light intensity fluctuation caused by atmospheric turbulence can be improved, and the influence of the atmospheric turbulence on the detection of the constellation recognition can be well suppressed.