105results about How to "Guaranteed recognition rate" patented technology

The invention discloses a method for sorting postal letters according to addresses driven by an address information base, wherein each delivery address in the address information base at least comprises a character expression containing address information for realizing transit sorting, local port sorting and road sections; the address character expression obtained by image acquisition and character identification is in transversal matching with the delivery addresses in the address information base; and sorting information of the postal letters can be obtained according to the matching degree, so that the transit sorting, the local port sorting and the road section delivery after the local port sorting can be realized. The invention is actually applied to an identification module for an information sorter; and under the conditions that the address base is relatively accurate and complete and the identification rate is basically ensured, the method can be effectively used for analyzing and correcting a letter address identification result to obtain accurate letter sorting information, i.e. the method can be used for sorting the letters in complete accordance with the letter address identification result.

The invention discloses a method for identifying synonyms. The method is executed in a computing device, multiple query records of multiple users are stored in the computing device, and each query record comprises user identification, query time and a query phrase. The method comprises the steps that multiple conversations are determined according to multiple stored query records within preset time, each conversation comprises one or more query phrases of the same user identification; for each conversation, each query phase in the conversation is divided into one or more words; candidate synonym pairs are extracted from each conversation according to preset rules; for each candidate synonym pair, word vectors corresponding to two words in the candidate synonym pair are determined according to a word vector model, the similarity of the two word vectors is calculated, and if the similarity is larger than a first threshold value, the two words in the candidate synonym pair serve as the synonyms. The invention further discloses a device capable of implementing the method and a computing device comprising the device.

The invention discloses an identification method for truck brake shoe breakdown. The identification method for the truck brake shoe breakdown comprises extracting segmentation characteristics of three angles from a current image; conforming a characteristic region of a truck brake shoe according to the segmentation characteristics of the three angles; extracting truck brake shoe characteristics from the characteristic region of the truck brake shoe in the current image; utilizing the identification method of a support vector machine (SVM) for the truck brake shoe breakdown and calculating characteristics values of the truck brake shoe; judging whether the truck brake shoe failures or not according to the characteristic values and the presetting fault indentification values. The invention discloses an identification device for the truck brake shoe breakdown. Manual-acting faults can be avoided by utilizing the identification device for the truck brake shoe breakdown. The identification rate of the breakdown can be guaranteed. The accidents can be prevented timely. Therefore, operation safety can be guaranteed.

A voice processing method and a terminal. The method comprises: retrieving voice information, of which the usage frequency reaches a designated value in a server end, and locally saving the voice information (S11); after input information is received, recognizing voice feature information from the information and converting same into a voice instruction (S12); and according to the voice instruction, performing matching in the locally saved voice information, and if no corresponding voice information is matched, sending the voice instruction for matching to the server end (S13). The method and terminal can increase voice recognition flexibility, controllability, expansibility and resource reusability on the premise of guaranteeing the recognition rate.

The invention relates to a facial expression recognition algorithm based on discriminative component analysis, and particularly relates to a facial expression recognizing method based on an improved DCA (discriminative component analysis). The method comprises the steps of firstly obtaining a sample subset in accordance with principles of a maximum type inner distance and a minimum type inter-distance; secondly calculating a projection matrix of the sample subset; and finally multiplying samples with the projection matrix, and judging the types of the samples through a nearest neighbor method so as to accomplish a facial expression recognition task. With the adoption of the method, not only is the DCA algorithm migrated to the field of facial expression recognition, but also an improved algorithm based on the DCA is provided according to practical conditions; the effectiveness of the algorithm is proved through the experiment result; and 95.71% of average recognition rate is ultimately achieved in a JAFFE (Japan female facial expression) image library.

The invention discloses a system and method for transmitting data using barcode. The system comprises a first terminal and a second terminal; each of the first terminal and the second terminal is provided with a HD digital interface, and the first terminal and the second terminal are connected through a HD video wire, wherein the first terminal further comprises a barcode conversion module for acquiring to-be-transmitted data from the first terminal, converting the data into the barcode capable of bearing data and data identification, and transmitting the barcode to the second terminal; the second terminal further comprises a video acquiring card and a barcode analysis module, the video acquiring card is used for acquiring the barcode and sending the barcode to the barcode analysis module; the barcode analysis module is used for analyzing the barcode, and transferring the analyzed barcode to the second terminal. Through the adoption of the system disclosed by the invention, the data exchange can be performed between the computers in network isolation.

The invention provides a weight loss feather extraction method based on an acceleration transducer, comprising the following steps: (1) receiving the measuring data of an acceleration transducer; (2) according to the measuring data, vertically correcting accelerated speed to obtain net accelerated speed after removing gravity in the vertical direction; and (3) according to the net accelerated speed obtained in step (2), detecting the state of weight loss, and extracting weight loss feather data.

The invention discloses a method for recognizing a cloud based on a mie-scattering laser radar with equalized value distribution. The method comprises the steps that a mie-scattering laser radar signal is input; semi-discretisation data processing based on noise estimation is carried out on the mie-scattering laser radar signal; data distribution equalization is carried out on the mie-scattering laser radar signal; the background base line of the mie-scattering laser radar signal is generated; the mie-scattering laser radar signal is layered and recognized based on the background base line. Through the method, the importance of a smoothness problem in the cloud recognition process can be weakened, meanwhile, the background base line adapting to signal changes by itself is found out, and therefore the problem that a current algorithm overly depends on smoothness or missing detection happens to weak cloud echo signals is solved, the recognition rate of cloud signals in the observation process is guaranteed, and the data quality of cloud observation is improved, and more accurate observation data are provided for weather and climate study and forecasting.

The invention discloses a KAZE feature-based hand back vein recognition method, which comprises the following steps of: 1, preprocessing an acquired hand back vein image, and carrying out feature point extraction on the preprocessed image based on a KAZE algorithm; step 2, performing coarse matching on the feature points extracted from the two images by using a nearest neighbor ratio method to obtain coarse matching feature points; and step 3, performing fine matching on the coarse matching feature points again by using an RANSAC algorithm to obtain matching points. The invention provides a KAZE feature-based hand back vein recognition method, which can significantly reduce the false recognition rate and the recognition time, and has good recognition accuracy and real-time performance.

The invention discloses an action living body recognition method based on attitude estimation and action detection, and relates to the technical field of biological recognition. The method comprises the steps: recognizing a real person and an attack by enabling a user to cooperate to do an action, wherein an action instruction set is random, so whether the user is a living body or not can be judged more accurately; before a living body is made, in order to improve the one-time passing rate of a real person, carrying out the light recognition, face recognition in a circle, face alignment screenrecognition and face occlusion recognition on a user; estimating a depression angle, a deflection angle and a rotation angle of the user by adopting a posture estimation PSECNN model, and performinghead shaking, nodding and head raising recognition on the user, so as to accurately recognize the head action of the user; when blinking and mouth opening actions are identified, adopting an MCNN model and an ECNN model to replace mainstream detection based on feature points, so the blinking and mouth opening identification rate is ensured in precision; in vivo detection, action instructions are random, actions and action sequences need to be accurate, anti-copying recognition is carried out, and safety is higher.

The invention discloses a method of recognizing ear speech in a normal speech flow under the condition of a small database, comprising the following steps: building a speech recognition and classification system which comprises a digital speech input module, a feature extraction module, and a deep neural network composed of a deep belief network and a Softmax output layer, wherein the deep belief network is composed of restricted Boltzmann machines stacked from bottom to top, spectrum feature is the input feature of the deep belief network, and the Softmax output layer correspondingly outputs a target value of the deep neural network; acquiring training data set samples and processing the samples in at least one of the following ways: (1) artificially extending the data set 8 to 24 times by repetition; and (2) scrambling the data set, and training the speech recognition and classification system; and using the trained classification system to recognize a to-be-recognized speech flow. The recognition performance of the system can be improved in a small database, and the recognition rate of normal speech is guaranteed while ear speech recognition is realized.

The invention belongs to the technical field of invoice information extraction, and particularly relates to an invoice information extraction method combining two-dimensional code recognition, which comprises the following steps: S1, carrying out bill text recognition; s2, carrying out bill two-dimensional code identification; and S3, selecting an identification result: after output information ofbill text identification and bill two-dimensional code identification is obtained, analyzing and accepting or rejecting the two groups of information, deriving an approximate position of a two-dimensional code according to a text identification result, and performing positioning identification on the two-dimensional code by using a deep learning technology. The two-dimensional code identificationrate is far higher than that of a traditional method. According to the method, text recognition and two-dimensional code recognition results are accepted or rejected according to the confidence coefficient, the recognition accuracy is further guaranteed, meanwhile, the text and the two-dimensional code are recognized, and when information of one party is defective, the result of the other party can supplement the final result advantageously. The method can adapt to more complex and changeable scenes, and has higher robustness.

The invention discloses a Spark big data platform-based neighborhood density imbalance data mixed sampling method, and relates to a computer information acquisition and processing technology. According to the invention, the data is stored in the RDD through the Spark; carrying out normalization processing; The method comprises the following steps: dividing RDD into a positive domain space, a negative domain space and a boundary domain space according to neighborhood density in combination with a three-branch decision theory, sampling data of a boundary domain by adopting an SMOTE algorithm, sampling data of a negative domain by adopting a mixed sampling algorithm, and finally merging the data in the three domains to obtain a final data set. By dividing each piece of data into different domains and processing the data according to the characteristics of the different domains, a small number of types of data can be properly added, and meanwhile, most types of data are properly reduced. And finally, calling an MLLib algorithm library, and evaluating the effect by using a machine learning classifier. According to the method, the problem of inter-class proportion imbalance of unbalanceddata can be effectively alleviated, and the precision of the algorithm is improved.

Embodiments of the invention disclose a two-dimensional code generation method and device. The two-dimensional code generation method comprises the following steps of: obtaining an original two-dimensional code and a plurality of self-defined two-dimensional code elements, wherein the original two-dimensional code comprises a plurality of two-dimensional code elements, and the self-defined two-dimensional code elements have first shape features; recognizing a two-dimensional code element combination in the original two-dimensional code, wherein the two-dimensional code element combination comprises at least two two-dimensional code elements which have the adjacency relation and has a second shape feature; matching the first shape features of the plurality of self-defined two-dimensional code elements with the second shape feature of the two-dimensional code element combination; and if a target self-defined two-dimensional code element with the matched shape feature, replacing the two-dimensional code element combination in the original two-dimensional code by the target self-defined two-dimensional code element so as to obtain a target two-dimensional code. According to the methodand device, data safety of two-dimensional codes is enhanced.

The invention belongs to the field of wireless sensor networks, and relates to a node optimal deployment method suitable for a wireless positionable sensor network. The method aims at processing optimal deployment of wireless positionable sensor network nodes, and comprises the following steps: acquiring a sensor node antenna gain estimation model in a discrete state; constructing a double-dipoleantenna radiation gain model and a field intensity estimation model in a simultaneous state; obtaining an optimization objective function through communication among a mobile charger, a sensing node and a common node; and optimizing a target function by adopting a multitask evolutionary algorithm based on an information positive migration mechanism to obtain an optimal deployment mode of the sensing nodes. The method has the characteristics that the energy utilization rate of the charger is effectively improved, the charging inactivation time is shortened, and meanwhile, the requirements of high-precision positioning and large-range coverage of the whole system are met.

The embodiment of the invention discloses a weight precision configuration method and device, equipment and a storage medium. The method comprises the following steps: determining a current recognition rate threshold value from at least two candidate recognition rate threshold values smaller than the target recognition rate threshold value, reducing and adjusting the weight precision correspondingto each layer in the neural network based on the current recognition rate threshold; and training the reduced and adjusted neural network to adjust the weight parameter value of each layer, the training target being to improve the recognition rate of the reduced and adjusted neural network, and determining the final configuration result of the weight precision of each layer according to the relationship between the current recognition rate and the target recognition rate threshold. By adopting the technical scheme, the resource utilization rate in the artificial intelligence chip bearing theneural network can be improved, the chip performance can be improved and the chip power consumption can be reduced under the condition of ensuring the identification rate of the neural network.

The invention provides an advertisement image detection method and device. The advertisement image detection method comprises the steps of extracting images at reference positions in advertisement video stream and at least one image before and after the reference positions; taking pixels with high identification rates in the selected images as identification points for matching images; segmenting the extracted images in a mode of dividing the images at the reference positions and the at least one image before and after the reference positions of the extracted images into segments, thereby forming image segments; dividing the image segments into multiple parts, thereby forming image groups; carrying out parallel similarity matching on the identification points of the reference position images in each image segment of the image groups and the identification points of the at least one image before and after the reference positions, thereby obtaining an advertisement segmentation time vector of each image group; and obtaining start time and end time of each piece of advertisement in the advertisement video stream according to the advertisement segmentation time vector of each image group.

The embodiment of the invention provides a pavement repair information detection method, device and equipment and a storage medium. The method comprises the steps of acquiring multiple frames of pavement sample images; training a plurality of convolutional neural network models according to the plurality of road surface sample images to obtain a plurality of trained convolutional neural network models; determining a target convolutional neural network model in the plurality of trained convolutional neural network models; and inputting a to-be-detected pavement image into the target convolutional neural network model to obtain pavement repair information in the to-be-detected pavement image. The method provided by the embodiment of the invention can solve the problems that the identification rate of repair information cannot be ensured and time and resources are wasted in the prior art.

The invention relates to the field of workpiece identification, in particular to a shielded workpiece identification method based on template matching. The method comprises: preprocessing a color image; then manufacturing a plurality of standard templates; and finally, judging whether a workpiece exists or not through standard template matching. The system comprises an image alignment module, a preprocessing module, a standard template module and a template matching module. According to the recognition method, the workpiece in the non-shielding state and the workpiece in the shielding state can be effectively recognized, the recognition rate can reach 94.4%, and the workpiece in the non-shielding state and the workpiece in the shielding state can be effectively recognized.