Patents
Literature

911results about How to "Improve recognition accuracy" patented technology

Rotating shaft geometric error identification method commonly used for five-axis numerical control machine tool

The invention discloses a rotating shaft geometric error identification method commonly used for a five-axis numerical control machine tool. The method comprises the steps that according to the type of the five-axis numerical control machine tool, a ball rod instrument measuring mode is determined; according to a numerical control machine tool geometric error model, a ball rod instrument sensitive vector is used for obtaining a ball rod instrument reading model; according to the ball rod instrument reading model, the ball rod instrument measuring mode, the property of a geometric error item and a horizontal moving shaft geometric error item, a machine tool rotating shaft geometric error item expression is obtained; proper mounting parameters are selected, the machine tool is operated, and corresponding ball rod instrument reading is obtained; and three horizontal shaft geometric error items of the five-axis numerical control machine tool are input, and according to the rotating shaft geometric error item expression and the ball rod instrument reading, 16 items of geometric errors of two rotating shafts of the five-axis numerical control machine tool are obtained. The method is suitable for different types of five-axis numerical control machine tools, all the 16 items of geometric errors of the two rotating shafts of the machine tool can be obtained, meanwhile, mounting is easy, measuring time is short, the identifying results accord with the property of the geometric error items, and measuring accuracy is high.
Owner:ZHEJIANG UNIV

Detecting system and detecting method for fatigue driving of driver

The invention discloses a detecting system and a detecting method for fatigue driving of a driver, and belongs to the technical fields of image processing and pattern recognition. The system comprises a human face localization module, a human eye state judging module, a mouth state judging module and a fatigue determining module, wherein the human face localization module is connected with the human eye state judging module and the mouth state judging module for communication; the determining result is sent to the fatigue determining module. The detecting method comprises the following steps: 1, localizing and detecting the human face; 2, judging the human eye state; 3, judging the mouth state of the driver; 4, judging the mental state of the driver. The detecting system and the detecting method combine two obvious facial characteristics of the driver, namely the human eyes and the human mouth, for complex judging; compared with recognition detection using a single method, the method provided by the invention has a higher recognition accuracy; through determining the mental state of the driver, the driver under fatigue state is reminded to stop the vehicle for rest, so that traffic accidents are effectively reduced, and the safety of life and property is strongly guaranteed.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA

Semi-supervised-transfer-learning character recognition method and system based on convolutional neural network

The invention provides a semi-supervised-transfer-learning character recognition method based on a convolutional neural network. The method includes: using batch character image samples, which are ina target domain and have no class label, as a test sample set to input the same to the convolutional neural network after semi-supervised transfer learning, and identifying character images of the test sample set, wherein the convolutional neural network after semi-supervised transfer learning is obtained by joint training of batch character image samples which are in a source domain and have class labels, batch character image samples which are in the target domain and have class labels, and batch character image samples which are in the target domain and have no class label, and recognitionprecision is improved. According to the semi-supervised-transfer-learning character recognition method based on the convolutional neural network provided and a semi-supervised-transfer-learning character recognition system based on the convolutional neural network provided by the invention, the large number of source-domain samples with the class labels, the small number of target-domain sampleswith the class labels and the relatively easily obtained target-domain samples without a class label can be utilized for semi-supervised transfer learning, and adaptation ability of the convolutionalneural network on the target-domain samples is improved.
Owner:TSINGHUA UNIV

Feature image extraction method based on deformable convolutional layer and feature image extraction device thereof

The invention discloses a feature image extraction method based on a deformable convolutional layer. The method comprises the steps that a target image is acquired; and pixel values are extracted from the target image through the sampling points of the convolutional kernel in the convolutional layer so as to obtain a feature image, wherein the actual coordinate values of the sampling points are the actual coordinate values calculated according to the preset initial coordinate values and the deviation variable trained in advance. According to the method, one deviation variable can be additionally arranged for each sampling point in the convolutional kernel so that a convolutional neural network is enabled to realize the capacity of learning image space geometric deformation, the convolutional kernel is enabled to randomly sample near the present position, the adaptability of the convolutional layer for the deformed image in extracting the feature image can be increased and the identification accuracy for the deformed object can be increased. The invention also discloses a feature image extraction device based on a deformable convolutional neural network. The feature image extraction device also has the beneficial effects.
Owner:GUANGDONG UNIV OF TECH

Non-intrusive resident load identification method based on S_Kohonen

The present invention discloses a non-intrusive resident load identification method based on S_Kohonen. The method comprises the steps: the step 1: determining a switching event according to the changing of an active power at a home electric power inlet, and when the switching event happens, collecting current samples of electric appliances having generation of the switching events at the home electric power inlet; the step 2: performing frequency-domain analysis of the collected electric appliance current samples, extracting the frequency-domain harmonic amplitudes of the collected electric appliance current samples as load features of each electric appliance, and forming a load feature base; the step 3: designing an S_Kohonen neural network suitable for resident load identification, and determining the number of nerve cells of the input layer and the output layer of the S_Kohonen neural network and the scale of the competition layer to determine a selection mode for obtaining the nerve cells and a learning algorithm of weight regulation; the step 4: performing parameter initialization; the step 5: performing training of the S_Kohonen network through a training set and performing test of the training set; and the step 6: regulating the network parameters to realize optimal network performances.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)

Identification method for handwritten character strings in air

The invention discloses an identification system and method for handwritten character strings in air. The system comprises a gesture identification module, a finger track extraction module, a pretreatment module and a character string identification module, wherein the gesture identification module is connected with the finger track extraction module and used for recognizing and identifying gestures for starting and ending writing; the finger track extraction module is connected with the pretreatment module and used for performing finger track extraction and record on input handwriting actions; the pretreatment module is connected with the in-air handwritten character string identification module and used for pre-treating finger track data; and the character string identification module is connected with the pretreatment module and used for performing character string identification on the finger track data. The invention further discloses the identification method for the handwritten character strings in air. According to the identification system and method, the supported writing mode is novel and convenient, the identification is accurate and fast, a more humanized and intelligent handwriting input mode other than the traditional handwritten mode is provided for people, and the system and the method can be widely applied to human-computer interaction systems such as game operation, television control, a teaching system and the like.
Owner:北京中科阅深科技有限公司

Hardware trojan identification method based on single classification supporting vector machine

A hardware trojan identification method based on a single classification supporting vector machine belongs to the detection and identification field of hardware trojan chips. The invention solves the problems of huge error and low efficiency in the prior art because the technology of utilizing chip side channel information to identify the chip hardware trojan requires to observe an image manually. The method provided by the invention comprises the following steps: I, preprocessing, and acquiring a side channel information matrix; II, selecting a backward analysis chip to perform backward analysis, and determining whether a hardware trojan is contained; III, dividing the backward analysis chip not containing the hardware trojan into a train sample and a train optimizing sample; IV, utilizing the side channel information matrix of the train sample to establish the characteristic space of the chip; V, acquiring the side channel characteristic data matrix of a chip to be detected; VI, performing normalization processing; VII, picking up the normalized data of the train sample and the train optimizing sample; VIII, training the single classification supporting vector machine to constitute a minimum hypersphere; and IX, if beyond the minimum hypersphere, then determining that the chip to be detected is a hardware trojan chip.
Owner:HARBIN INST OF TECH AT WEIHAI

Method and device for acquiring number of paper currency or financial bill, and method and device for identifying paper currency and financial bill

The invention discloses a method for acquiring a number of a paper currency or a financial bill. The preset position on a conveying passage is provided with a number acquisition device according to the layout position of the number. The method comprises the steps of: acquiring a regional image where the number may appear when the paper currency and the financial bill to be detected reaches the preset position; detecting the regional image, and judging whether the regional image contains the number of the paper currency; and extracting the number from the image. The invention also provides a device for acquiring the number of the paper currency or the financial bill, which comprises an image acquisition unit, a detection unit and a number extract ion unit. The invention also provides a corresponding method and a corresponding device for identifying the paper currency or the financial bill. According to the invention, only a region which may contain the number needs to be extracted, which can ensure that the processed redundant information amount is minimum, the resolution of the region to be identified is highest, the useful information amount is increased, and the identification accuracy is improved, so that the number of the paper currency or the financial bill can be quickly and accurately acquired and can be identified and managed.
Owner:BEIJING NUFRONT SOFTWARE SCI TECH

Intelligent voice service development cloud platform and method

The invention discloses an intelligent voice service development cloud platform and method. The intelligent voice service development cloud platform comprises an application optimization database, a content service optimization module, a voice recognition module and a semantic comprehension module, wherein the content service optimization module is used for receiving an input sentence pattern of a user customized according to a built application and output data corresponding to the input sentence pattern and storing the input sentence pattern and the output data into the application optimization database; the voice recognition module is used for obtaining the input sentence pattern from the application optimization database and updating a voice recognition model according to the obtained input sentence pattern; the semantic comprehension module is used for obtaining the input sentence pattern and the output data from the application optimization database and updating a semantic comprehension model according to the obtained input sentence pattern and the output data. Thus, optimization and customization for applications are realized, all optimization operations are done by users on line without participation of background technical support personnel of the platform, the development period for optimization and customization is shortened, a developer can rapidly implement optimization conveniently, and a special intelligent voice interaction application is customized.
Owner:BEIJING UNISOUND INFORMATION TECH +1

Video frequency objects recognition method and system based on supporting vectors machine

The invention discloses a video object identification method and a relevant system based on support vector machines; with structure training samples and according to resolution that is selected from the training samples, utilize a method combined with wavelet outline description symbol, shape factor and invariant torque, to describe outline characteristics of the training samples; gain a support vector machine model according to the outline characteristic training, and meanwhile, determine decision-making function parameters at the optimum category aspects in the support vector machine model; then, extract outline characteristics from the video object to be identified; the support vector machine model after training can follow the video object outline characteristics that is input, in order to perform category for the acquired video objects with a decision-making function operation for the optimum category face. The invention has advantages of high calculation speed, high identification accuracy, reliable arithmetic performance and multi-category identification; and moreover, with increase of objects to be identified, the identification performance can still be kept stable, and the identification speed can still meet real-time monitoring demands.
Owner:HUAWEI TECH CO LTD

Packet sampling and application signature based internet application flux identifying method

The invention relates to an internet application flow rate identification method based on message sampling and application signing, comprising the following steps: firstly, message sampling capture: in accordance with sampling strategy and sampling rate the message is captured and decoded; secondly, decoding: the flow information and application data of the message is analyzed by decoding the message; thirdly, flow classification: according to the flow information of the message, a flow state table is found and maintained; fourthly, flow state distinguishing: the signature is matched if the application type of the flow state found through the flow classification is unknown; finally, signature matching: according to the application signature bank, the application data of the message is matched, if matched successfully, the application type of the flow state is updated, and the flow information and application type of that data stream is output. The method is of high accuracy in identification, high efficiency in processing, good expandability, high possibility in realization, and is applicable not only for message processing, but also for flow data analysis. The invention can be achieved in not only the network equipment, but also the network analysis system.
Owner:BEIJING JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products