897results about How to "Effective classification" patented technology

An improved method for efficiently retrieving data associated with linked network locations utilizing a computer network is disclosed. First, data is retrieved from a network location which has a plurality of links to related network locations. Next, in response to a single user input, the plurality of links and their network addresses are automatically classified into a folder having common subject matter. A user can later efficiently retrieve data associated with the classified network locations utilizing the common category of network addresses.

Scanning of computer files for malware uses a classifying technique to classify an input file as a clean file or a dirty file. The parameters of the classifying technique are derived to train the classification on a corpus of reference files including clean files known to be free of malware and dirty files known to contain malware. The classification is performed using a representation of the files in a feature space defined by a set of predetermined features for respective file formats, the features being a predetermined value or range of values for one or more data fields of given meanings. The representation of a file is derived by determining the file format, parsing the file on the basis of the structure of data fields in the determined file format to identify the data fields and their meaning, and determining, on the basis of the identified data fields, which of the set of predetermined features are present.

A method for classifying electronically posted documents includes receiving two posted documents and generating corresponding metadata summaries for each, wherein each of the metadata summaries includes at least one sub-tree structure. The structures of the two summary sub-trees within the respective metadata summaries are subsequently compared. If the two summary sub-trees are different, the two documents are deemed distinct. If the two summary sub-trees are the same, attribute values and text content of the metadata summaries are compared over a portion of the metadata summaries. If the compared attribute values and text content are determined to be the same, the documents are deemed duplicative.

The invention belongs to the field of data processing technology and discloses a text sentiment classification method facing Chinese Web comments. The text sentiment classification method includes a training process and a classification process. The training process includes the steps of carrying out training text preprocessing, carrying out feature selecting, carrying out vectorization representation of a text and obtaining a training classifier. The classification process includes the steps of carrying out test text preprocessing, carrying out feature selecting, utilizing the classifier to classify and outputting a classification result. On the basis of an original document classification method, document frequency (DF) and information gain (IG) are used and a sentiment dictionary of negative words, degree adverbs and dynamic sentiment words are built to distinguish sentiment tendency of Chinese feature words, select feature words, calculate a feature weight value and build a feature vector. Moreover, a NaiveBayes classification algorithm is used for training to obtain the classifier, carrying out sentiment classification on the text, providing effective data mining for users and then carrying out analysis processing.

A system and method of providing an autocomplete recommended word, which classify a recommended word list according to indexes of various languages, store the recommended word list for each index, extract a corresponding autocomplete recommended word according to a user query and a setting mode which is received from a user's web browser, provide the user with the corresponding autocomplete recommended word, and thereby may propose a suitable recommended word according to the user query.

The invention enables the digital management of large scale image databases, to efficiently classify and index image data independent of language. Complex processing requirements are required only on reduced and operably small subsets of the entire collection, thereby effectively scaling large document collections. Embodiments of the present invention provide image-based classification and retrieval of documents based on image recognition, e.g., signatures, logos, stamps, or word-spotting; in documents within real time for large datasets such as in the millions of documents.

A system and method provide for efficient classification of long strings of data, such as network messages. The system, which may be a classification engine for use in a network device, is configured to include one or more stages having one or more banks of ternary content addressable memories (TCAMs). The TCAM banks of each stage are organized into one or more groups and each group processes the network messages for a different set of the network device's ports. The system further includes at least one memory resource that is shared by the TCAM banks of all groups. That is, the parallel banks of TCAMs operate in a distributed fashion while the shared memory resource operates in a centralized fashion. Accordingly, the system can process network messages at high speed while minimizing the number of required components.

The invention discloses a fabric defect detection method based on a depth neural network. The method comprises the following steps: (1), an image acquisition system is built to acquire an image; (2), the image is segmented into experimental samples, fabric sample image data are enhanced at the same time, and a fabric image after enhancement serves as a training sample; (3), a depth neural network is designed; (4), parameters are set, the depth neural network is initialized, the training sample is fed to the depth neural network for training, and after network training is completed, the network model is saved; and (5), an inputted new fabric sample is fed to the network model for detection. According to the fabric defect detection method based on the depth neural network provided by the invention, with a convolutional neural network as a core, feature extraction is performed by a convolutional layer, a pooling layer retains effective features and reduces the amount of calculation, and a full connection layer is used for classification. A mini-batch gradient descent method is used for optimization, the generalization ability is enhanced through L2 regularization, defect recognition is carried out through determining the corresponding position of the maximum component outputted by a classifier, effects are shown in Figure 4, Actual presents the actual category of the sample, and Pred presents the predicted category of the sample.

The invention discloses a method for identifying a disturbance event in a distributed type optical fiber pipeline security early-warning system. When the disturbance event exists, wavelet de-noising is conducted on two routes of sampling signals. The characteristic values of one sampling signal where wavelet de-noising is conducted are extracted, wherein the characteristic values include the vibration fragment length, the time domain energy, the k-order original point distance, the k-order center distance, the skewness, the kurtosis and low frequency wavelet coefficient energy Ej, obtained through wavelet decomposition, of all layers, and j ranges from 1 to 7. The thirteen extracted characteristic values are sent to a decision tree classification device, and the type of the disturbance event is obtained through the decision tree classification device. Man-machine interaction incremental learning is achieved by changing the type of the disturbance event stored in a database under the condition that a new type of the disturbance event appears or the type, obtained through the decision tree classification device, of the disturbance event is wrong, and online training is conducted on the decision tree classification device according to the modified type of the disturbance event. By means of the method, the type of the disturbance event can be accurately obtained.

Disclosed are a mobile terminal and a method for controlling a 3D multi-angle view thereof. Multi-angle views of a 3D image captured by a plurality of cameras are displayed, switched (converted) and selected according to a touch input. And, a selected angle view of the 3D image undergoes a capturing or a recording. While appreciating different-angle views of a 3D image (broadcast image) captured by a plurality of cameras, a desired angle view of the 3D image may be played, recorded and stored with using a user interface. This may enhance a user's convenience.

The invention relates to a radar-simulation-image-based human body motion classification method of a convolution neural network. The method comprises: step one, establishing a time-frequency image data set containing a variety of human motions; step two, enhancing radar time-frequency image data; step three, establishing a convolution neural network model; to be specific, using a handwriting recognition network LeNet as a basis, introducing a modified linear unit ReLU to replace an original Sigmoid activation function as an activation function of the convolution network based on three convolution layers, two pool layers and two fully connection layers, adding a pool layer and reducing a fully connection layer to form a convolution neural network structure including three convolution layers, three pool layers and one fully connection layers, and adjusting an interlamination structure and an in-layer structure of the network and training parameters to realize a good classification effect; and training the convolution neural network model.

The present invention relates to an online advertisement classified pushing method and system based on a consumer behavior data analysis and classification technology. Compared with the prior art, the online advertisement classified pushing method and system overcome the defect that potential customers cannot be mined to carry out network online advertisement pushing. The online advertisement classified pushing method comprises the following steps of: carrying out data collection and preprocessing, i.e. collecting behavior data of consumers from online mobile terminals, establishing a data pool, carrying out preprocessing operation on the data in the data pool and providing data support for subsequent data analysis and modeling; aiming at the behavior data of the consumers, carrying out modeling, i.e. establishing a topic model facing the behavior data of the consumers so as to mine relations between the consumers and online advertisement categories as well as a purchase time period; and aiming at the consumers, carrying out effective classification and aiming at different consumer categories, pushing the corresponding types of advertisements online. According to the present invention, by collecting the behavior data of the consumers on various mobile terminals, carrying out analysis and modeling on behaviors of the consumers and mining consumption habits of different consumers, effective classification of the consumers is implemented.

The present invention relates to a process for classifying the panel MURA in the module inspection of a liquid crystal display panel.

The invention includes a MURA area logical operation process (S5) of photographing the liquid crystal display panel from different angles of visibility, performing an image processing for detecting a MURA area for a group of images taken and then performing an image logical operation process, an upper-level classification process (S6) of classifying the shape of MURA, and a lower-level classification process of classifying the panel MURA by combining an upper-level classification with other parameters. With this invention, the panel MURA is correctly detected without setting up the complicate parameters. This invention contributes to labor saving at the final inspection step for manufacturing and leads to quality assurance and higher reliability of the liquid crystal display panel in the liquid crystal display panel manufacturing field.

A defect is efficiently and effectively classified by accurately determining the state of overlap between a design layout pattern and the defect. This leads to simple identification of a systematic defect. A defective image obtained through defect inspection or review of a semiconductor device is automatically pattern-matched with design layout data. A defect is superimposed on a design layout pattern for at least one layer of a target layer, a layer immediately above the target layer, and a layer immediately below the target layer. The state of overlap of the defect is determined as within the pattern, over the pattern, or outside the pattern, and the defect is automatically classified.

The invention provides a data parallel processing method. The data parallel processing method comprises the following steps that 1, a main management node receives data and acquires the incidence relation of the data; 2, the main management node calculates allocatable GPUs and GPU work loads of work computing nodes; 3, the main management node partitions the data and distributes the partitioned data to all the work computing nodes; 4, the work computing nodes perform parallel processing on the received data and transmit processing results back to the main management node; 5, the main management node merges the results and then outputs the results. The data parallel processing method has the following advantages that a master-slave architectural pattern is adopted to be used for high-performance large-scale data parallel processing, operation stage partition is performed on specific operations converted by application programs according to DNA feature modeling, node granularity grade operation deployment is performed according to a partition result, and the execution efficiency of a parallel task of data flow in a single node is improved by adopting a thread parallel optimization mechanism and fully utilizing multiple computing kernels.

The classification system of a network device includes a cache in which a mapping between predefined characteristics of TCP/IP packets and associated actions are stored in response to the first “Frequent Flyer” packet in of a session. Selected characteristics from subsequent received packets of that session are correlated with the predefined characteristics and the stored actions are applied to the received packets if the selected characteristics and the predefined characteristics match, thus reducing the processing required for subsequent packets. The packets selected for caching may be data packets. For mismatched characteristics, the full packet search of the classification system is used to determine the action to apply to the received packet.

A statistical system and method for generating patterns and performing online handwriting recognition based on those patterns. A plurality of predetermined patterns may be generated by performing feature extraction operations on one or more character samples utilizing a Gabor filter. An online handwritten character may be acquired. The online handwritten character may be pre-processed. One or more feature extraction operations, utilizing a Gabor filter, may be performed on the online handwritten character to produce a feature vector. One or more patterns may be generated, using a statistical algorithm, for the online handwritten character, based on the feature vector. The online handwritten character may be statistically classified based on a comparison between the one or more patterns generated for the online handwritten character and the plurality of predetermined patterns.

The scheme of the invention discloses a vehicle assistance driving system based on the infrared thermal imaging and millimeter wave radar technology. The main functions include pedestrian and vehicledetection, image and sound fusion and collision early warning in driving of the vehicle. The early warning system uses the following technical characteristics: the infrared camera and the millimeter wave radar specialized for vehicle-mounted assistance driving, the data acquired by the radar, such as the distance, the direction and the speed and the images acquired by the infrared camera are fusedfor screening and determining the pedestrians or the vehicles, the pedestrian or vehicle characteristics are extracted through the algorithm, finally the result is displayed on the visual interface and multidimensional early warning information including images, numbers and sound is provided.

The invention discloses a method for selecting and rapidly comparing robust features of iris images. The method comprises: S1, adopting regional multipole sequencing measurement features to express iris texture; S2, selecting and fusing iris features by taking similarity as guidance; and S3, rapidly comparing iris characteristic templates on the basis of a hierarchical classifier. The method uses a feature-selecting method taking similarity as the guidance to select regional measurement features and construct a combined classifier, and adopts a hierarchical classifier structure to optimize and combine the obtained combined classifier again so as to rapidly compare the iris characteristic templates. The method automatically learns the obtained iris regional sequencing measurement features, and has the advantages of greatly raising the speed of comparing the iris characteristic templates and effectively solving the problem of applying iris recognition to large-scale databases, along with good robustness and high recognition precision. The method can be widely used in a large number of application systems using iris for identity recognition and security protection.

The invention provides a handwritten Chinese character layout evaluation method. The method comprises the following steps: (1), pretreatment; (2), performing evaluation to the position layout of each stroke in a handwritten Chinese character; (3), performing layout evaluation to all the parts in the handwritten Chinese character by part layout evaluation; (4), performing evaluation to the integral form of the handwritten Chinese character by integral font evaluation; and (5), combining the results of evaluation in the steps (2), (3) and (4) to obtain the evaluation conclusion. The invention adopts the five steps of pretreatment, stroke layout evaluation, part layout evaluation, integral form evaluation and overall evaluation, and obtains the evaluation conclusion by measuring nine kinds of indexes, the conclusion takes the character reasoning as a primary way and the grading as an auxiliary way, and the writing quality of the users is effectively classified by grade according to the index value.