166 results about "Real time classification" patented technology

A method of inspecting Web elements for real-time classification and detection of Web elements spoofing attempts, according to which trustworthy Web locations are identified for generating a database of safe zones. For each inspected element, it is checked whether or not its top frame URL is included in the database, and if it is included, the element is classified as suspected in Web elements location spoofing attempt.

A new method, system and apparatus is provided that enables muscle signals that correspond to muscle contractions to be mapped to one or more functions of an electronic device such as a prosthetic device or gaming apparatus. Muscle signals are classified in real-time from self-selected intentional movements. A self-training protocol allows users to select and label their own muscle contractions, and is operable to automatically determine the discernible and repeatable muscle signals generated by the user. A visual display means is used to provide visual feedback to users illustrating the responsiveness of the system to muscle signals generated by the user.

A classification system has a classification server that receives data for an email and determines if the email message is suspicious, legitimate but failing authentication, forwarded or fully authenticated and legitimate when the domains are owned, or not owned, by the domain owner, Email messages are categorized and presented in a report that enables the email sender to identify and fix a network, malicious traffic, and legitimate messages that have failed authentication beyond control. It also highlights where everything is going well.

A new method, system and apparatus is provided that enables muscle signals that correspond to muscle contractions to be mapped to one or more functions of an electronic device such as a prosthetic device or gaming apparatus. Muscle signals are classified in real-time from self-selected intentional movements. A self-training protocol allows users to select and label their own muscle contractions, and is operable to automatically determine the discernible and repeatable muscle signals generated by the user. A visual display means is used to provide visual feedback to users illustrating the responsiveness of the system to muscle signals generated by the user.

A method for the multiplexed diagnostic and genetic analysis of enzymes, DNA fragments, antibodies, and other biomolecules comprises the steps of constructing an appropriately labeled beadset, exposing the beadset to a clinical sample, and analyzing the combined sample / beadset by flow cytometry. Flow cytometric measurements are used to classify, in real-time, beads within an exposed beadset and textual explanations, based on the accumulated data obtained during real-time analysis, are generated for the user. The inventive technology enables the simultaneous, and automated, detection and interpretation of multiple biomolecules or DNA sequences in real-time while also reducing the cost of performing diagnostic and genetic assays.

A new method, system and apparatus is provided that enables muscle signals that correspond to muscle contractions to be mapped to one or more functions of an electronic device such as a prosthetic device or gaming apparatus. Muscle signals are classified in real-time from self-selected intentional movements. A self-training protocol allows users to select and label their own muscle contractions, and is operable to automatically determine the discernible and repeatable muscle signals generated by the user. A visual display means is used to provide visual feedback to users illustrating the responsiveness of the system to muscle signals generated by the user.

Method and system using a designated known secure computer for real time classification of change events in a computer integrity system are disclosed. In the embodiment of the invention, the known secure computer is dedicated for providing permissible change events, which are compared with change events generated on client operational computers. An alert is raised when the change event at the client operational computer and the respective permissible change event provided by the known secure computer differ.

The present invention discloses a sudden event emergency information mining method based on social media. The method comprises the steps of (S1) using an open platform API or a web crawler to acquire social media data, the social media data being a document set; (S2) using a MongoDB cluster to store the document set; (S3) preprocessing the document set; (S4) using an LDA to mark the preprocessed document set, and obtaining a known sample; (S5) forming a word characteristic set by all words in each document of the known sample, word frequency of each word characteristic in the document being the weight of the word characteristic in the document; (S6) constructing a short text real-time classification model; (S7) classifying real-time sudden events by using the short text classification model, and predicting themes of the sudden events; and (S8) performing information mining according to the social media data of the classified sudden events. Classification of social media short texts can be automatically and rapidly achieved, and therefore sudden event emergency information is mined.

A method is provided to classify network traffic flows in real-time using spectral analysis techniques to extract regularities inside the network traffic flows. In one embodiment of the invention, subspace decomposition on power spectral density feature vectors and minimum coding length criterion are utilized for training traffic flows of different classifications. Experimental results are shown to demonstrate the effectiveness and robustness of the invention.

Methods and computer program products are disclosed for the real-time classification of text from a region of interest within an image sample. A feature extractor extracts feature data associated with a plurality of region features from the region of interest. The plurality of region features are selected as to minimize the time necessary for feature extraction. A neural network preclassifier selects one of a plurality of associated source classes for the region of interest according to the extracted feature data. A plurality of classification systems are each associated with one of the plurality of source classes. Each of the plurality of classification systems are operative to classify individual characters within the region of interest when the associated source class of the classification system is selected.

Apparatus and methods for real-time traffic classification based on off-line determined traffic classification rules are provided. Traces of real traffic are obtained and subjected to statistical analysis. The statistical analysis identifies the multidimensional domain space of characteristic traffic parameters. Classification rules associated with the identified domains are derived and provided to traffic classification points for real-time traffic classification. Traffic classification points, typically edge network nodes, sample packets in aggregate streams with a predetermined probability. Statistical information regarding the sampled flows is tracked in a table, the number of time a flow was sampled providing a probabilistic measure of the flow's duration before the flow terminates. The table entries, which predominantly track high bandwidth flows, are subjected to the classification rules for real-time classification of the sampled flows. Optionally, rules include an action to be taken in respect of flows having characteristics matching thereof. Advantages are derived from low overhead on-line real-time classification of high-bandwidth flows at low overheads before flow termination.

The invention discloses a convolutional neural network method based on a parallel multi-scale time convolution kernel, and mainly solves the problems of low detection accuracy and difficulty in effectively detecting imaginary movement of a user in the prior art. According to the implementation scheme, the method comprises the following steps: collecting imaginary motion electroencephalogram data,preprocessing the imaginary motion electroencephalogram data, and making a data set by using the preprocessed electroencephalogram data; constructing a convolutional neural network, training the convolutional neural network by using the training set and the verification set, testing the convolutional neural network by using the test set, and performing fine tuning on the tested convolutional neural network by using the electroencephalogram data of the testee to obtain a final convolutional neural network suitable for the testee to perform an online experiment; and obtaining an online imagination motion electroencephalogram signal of the testee in real time, and sending the online imagination motion electroencephalogram signal to the final convolutional neural network to obtain a real-timeclassification result. The method can effectively detect the imaginary movement of the user, improves the classification accuracy of the imaginary movement electroencephalogram signals, can be used for medical service, and serves as an auxiliary tool to participate in rehabilitation treatment of stroke patients.

Process for real time sorting of signals from several semi-conductor detection elements in which:a) during a calibration phase:amplitude data and signal rise time data are established for each event signal,a biparametric detection spectrum with amplitude and rise time data is acquired,a biparametric acceptance window corresponding respectively to an amplitude-rise time correlation is established, andb) during an examination phase:an amplitude, rise time data pair is established in real time for each event signal detected,the events are sorted in real time, selecting the signals according to whether their amplitude and rise time data are or are not within the window.

The invention discloses a water chilling unit fault feature learning and classification method based on 1DCNN and GRU fusion, and solves the problems that an existing method consumes time and is difficult to process mass data. The method comprises the following steps: firstly, preprocessing collected continuous-time fault sequence information under different working conditions to construct a sample set; secondly, carrying out preliminary feature extraction on the sample set by adopting a dimension reduction algorithm; then, creating a 1DCNN-GRU neural network model; during training, enabling fault features of a training sample at the same moment to act as input of a time step of the network model, adopting a cross entropy loss function, model parameters are adjusted in combination with a back propagation BP algorithm, enabling Softmax regression to act as a classification algorithm, conducting iterative updating on the network model parameters, and finishing model training. The methodnot only can extract the dynamic information between the local features of the sequences and the sequences, but also can realize real-time classification diagnosis of faults.

The invention provides a method for classifying the rail failures of a high-speed rail. The main idea is that the method comprises the steps of extracting local features of a time domain and a frequency domain of damaged signals by using a wavelet analysis method; building a three-dimensional tensor signal for a same measuring point by combining different compartments; expanding data to a multi-dimensional space to obtain a non-negative tensor; taking an alternate least squares algorithm as an iteration criterion of the non-negative tensor decomposition; introducing SVD (Singular Value Decomposition) to improve the initialization of the non-negative tensor; extracting hidden features by an improved non-negative tensor decomposing method; and finally, introducing an extreme learning machine algorithm to realize real-time classification on the rail failures. According to the method for classifying the rail failures of the high-speed rail provided by the invention, the signals of rail defects and failures can be classified accurately, the classifying speed and accuracy of the for classifying the rail failures can be improved, and the robustness can be realized; furthermore, the classifying method based on the g the rail failures is prior to an existing method, the better recognition effect can be obtained, and the method can be extensively applied to the field of classifying the for classifying the rail failures.

The invention provides a human body posture identification method and system, and belongs to the technical field of security engineering. The method comprises the following steps of (1) at least collecting six parameters, including linear acceleration and angular acceleration, of a human body in X, Y and Z directions of space; (2) analyzing data features presented by the parameters, and selectingt data statistics features for each type of data as condition attributes for judging human body postures, wherein t is an integer greater than 1; (3) extracting the data statistics features of a dataset under various human body postures to form a sample set, and dividing the sample set into training samples and test samples; (4) according to the training samples and the test samples, building a decision tree classification model; and (5) based on the decision tree classification model, performing real-time classification on collected data to identify the human body postures. The human body posture identification method and system is high in identification accuracy, high in identification speed, high in adaptability to multiple input samples, and strong in robustness; and output of a classification result has high error tolerance.

A device and method are provided for real-time classification of covert speech. The device comprises a plurality of sensors for capturing real-time bio-signal data for brain monitoring in response to mental tasks delivered to a user, and a brain computer interface with memory storing instructions to configure a processor to perform a method of real-time classification of covert speech. The method comprises capturing real-time bio-signal data for brain monitoring in response to mental tasks delivered to a user, pre-processing the raw bio-signal data, extracting a vector of features from the raw bio-signal data, selecting features from the vector of features, building classification model to generate classified covert speech data using the selected features, and controlling a display device with visual elements based on the classified covert speech data.

Method and system using a designated known secure computer for real time classification of change events in a computer integrity system are disclosed. In the embodiment of the invention, the known secure computer is dedicated for providing permissible change events, which are compared with change events generated on client operational computers. An alert is raised when the change event at the client operational computer and the respective permissible change event provided by the known secure computer differ.

The invention discloses an information entropy-based self-adaptive integrated classification method of data streams. Concept drift can be detected, and duplicate concepts can also be identified. In asystem, a new classifier is reconstructed and put into a classifier pool only when existence of a new concept is detected, the problem of duplicate training caused by duplicate concept appearance is prevented, model updating frequency is reduced, and real-time classification ability and classification effect of a model are improved. Through carrying out performance analysis comparison with classical data stream algorithms on a synthetic dataset and a real dataset, experiments show that the method of the invention can cope with multiple types of concept drift, improves anti-noise ability of theclassification model, and also has lower time cost consumption on the premise of ensuring higher classification accuracy. The method of the invention can be applied to many practical problems of sensor network anomaly detection, credit-card fraud behavior detection, weather forecasting, electricity price prediction and the like.

A method, system and computer readable media for real-time chromakey matting using image statistics. To identify the chroma key spectrum, the system / method executes in three stages. In an off-line training stage, the system performs semi-automatic calibration of the chroma key parameterization. In the real-time classification stage, the system estimates the alpha matte on a GPU. Finally, an optional error minimization stage improves the estimated matte, accounting for misclassifications and signal noise. Given the resulting matte, standard alpha blending composites the virtual scene with the video feed to create the illusion that both worlds coexist.

A method of performing a real-time classification of a data stream exchanged between a client and a server over a telecommunications network, comprises: receiving a data segment of the data stream indicating a data offset; updating a bitmap table with the received data segment, wherein the bitmap table comprises a number of blocks of data, the data blocks being ordered by block sequence numbers in the bitmap table; after updating the bitmap table, performing measurements on a complete data block to obtain at least one measurement result without waiting for all the data blocks having block sequence numbers that are lower than the first block sequence number to be completed; performing a statistical analysis based on measurement results obtained for several complete blocks; classifying the data stream into one group among a plurality of groups, based on the performed statistical analysis at least.

The invention discloses a digital drilling real-time classification method for a surrounding rock of underground engineering. The method comprises the following steps: making a field drilling test onan underground engineering field to obtain a drilling parameter, obtaining uniaxial compressive strength of a rock body through a model of relationships between drilling parameters and uniaxial compressive strength of rock bodies, obtaining a rock body breaking coefficient on the basis of a model of relationships between drilling parameters and rock body breaking coefficients, obtaining equivalentuniaxial compressive strength from the uniaxial compressive strength of the rock body and the rock body breaking coefficient by use of a uniaxial compressive strength correction formula, obtaining displacement of the surrounding rock by use of a model of relationships between equivalent uniaxial compressive strength and displacement of surrounding rocks, and further classifying the surrounding rock in real time according to the displacement of the surrounding rock. The method has the characteristic of real-time accurate prediction; the displacement of the surrounding rock is reversely deducedaccording to the drilling parameter obtained in field, thereby accurately classifying the surrounding rock in real time by use of the stability of the surrounding rock and providing a basis for design and construction of underground engineering; disturbance in a process of delivering a rock sample to a laboratory is avoided, meanwhile, the test time is shortened to a great extent and manpower andmaterial resources are saved.

The invention relates to an RNNs-based method for automatic safety checking of a short message. The method specifically comprises the steps that (1) historical data is pre-processed, wherein preprocessing comprises noise removal and Chinese word segmentation; (2) characteristics of the historical data which is already pre-processed at the step (1) are extracted, so word vectors can be generated; and (3) a classification model which integrates RNNs and Naive Bayes is used to conduct real-time classification of short message texts. According to the invention, workloads of police officers in manual checking are reduced.

A method including identifying an emotion identifier from a message using an emotion identifier table is provided. The method includes separating emotion identifier from the message, splitting a portion into multiple tokens delimited by a space or a punctuation mark, and determining a score for the message based on an emotion score in the emotion identifier table, and on at least one of the multiple tokens. The method includes adding a token from the multiple tokens to the emotion identifier table based on a contribution from the token to the message score, associating a sentiment score for the token in the emotion identifier table based on the contribution of the token to the message score, and modifying an emotion score of the token when the token is already included in the emotion identifier table based on the contribution of the token to the message sentiment score.

The invention discloses an on-site tunnel real-time surrounding rock classification system and a method. The system includes a bearing platform, the bearing platform bears a measuring ruler, a point load tester for conducting point load test on an on-site surrounding rock sample, and a geological compass for determining weak structural plane occurrence, the measuring ruler, the point load tester and the geological compass are detachably disposed on the bearing platform, and the lower part of the bearing platform is provided with moving components for driving the bearing platform to move. The setting of the whole system is convenient for moving a measurement component to the site for real-time measurement, realizes real-time classification work of tunnel surrounding rock, effectively improves the efficiency of tunnel surrounding rock classification work, improves the flexibility of classification work, and can acquire real-time feedback of rock mass classification near a tunnel face, thus being convenient for directly carrying out follow-up work.

The invention relates to a network traffic classification method based on semi-supervised learning. The method comprises the steps of obtaining network traffic of which type is marked and not marked and extracting traffic feature in each piece of network traffic to obtain network traffic feature vectors; calculating information gain of each traffic feature through utilization of marked data and carrying out feature weighting; mixing and clustering the network traffic of which type is marked and not marked; obtaining the number of the marked network traffic in each cluster and determining a proportion of each type in each cluster; when the sum of the total number of the marked network traffic in clusters is smaller than a preset network traffic threshold value, judging that the clusters areunknown protocol clusters, otherwise, judging that the clusters as the types with the maximum proportions in the marked network traffic; repeating the steps until the traffic types of the traffic clusters are judged, and training an online real-time classifier through utilization of the traffic clusters. The invention relates to a computer device. The device comprises a processor, a memory and acomputer program which is stored on the memory and can be operated on the processor.

The invention discloses a word distribution and document feature based automatic classification method for spam comments. The method comprises: firstly, collecting network comments and performing word segmentation on the comments to obtain a keyword set; secondly, establishing a word distribution matrix, training a language model, and calculating a classification probability of unlabeled network comments belonging to normal comments or the spam comments; thirdly, extracting document features of the network comments, and calculating the classification probability of the unlabeled network comments; and finally, calculating a weighted average of the classification probabilities, and repeating the steps until the classification probabilities calculated for successive two times are same or a given number of iterations is reached. The method comprehensively considers word distribution features and the document features in the network comments, automatically finishes network comment classification through a self-learning policy, and assists in identification of the spam comments in the network comments. The method is simple in calculation and high in universality and expansibility, can carry out real-time classification on a great amount of comments by only a small amount of network comments with labels, and meets an application demand of quickly identifying the spam comments in the instantly updated network comments.

In flow cytometry, particles (2) can be distinguished between populations (8) by combining n-dimensional parameter data, which may be derived from signal data from a particle, to mathematically achieve numerical results representative of an alteration (48). An alteration may include a rotational alteration, a scaled alteration, or perhaps even a translational alteration. Alterations may enhance separation of data points which may provide real-time classification (49) of signal data corresponding to individual particles into one of at least two populations.

A method and system has an architecture that employs a unique hybrid approach for data mining that integrates advanced three-dimensional computer visualization and inference-based data generalization techniques. The present method and system is geared towards the interactive acquisition and display of visual knowledge representations. Knowledge representations, called knowledge landscapes, are employed for robust real-time classification of incoming data as well as for forecasting new unexpected trends. Knowledge landscape visualization techniques contribute to better human decision-making insights through facilitation of spatial operations such as navigation and zoom operations. A graphically appealing human computer interface and capability to visualize large and complex knowledge bases through spatial and graphical depictions of knowledge components adds to advantages and widespread applicability.

The invention discloses garbage classification equipment and a garbage classification method based on chemical element properties as well as application. The garbage classification method comprises the following steps: carrying out pre-treatment including bag breaking, crushing, sieving and the like on garbage; putting a garbage mixture which is pre-treated on a garbage conveying device; carrying out detection analysis on garbage by using equipment for detecting the garbage on line and in real time based on the chemical element properties; receiving a signal detected by the detection equipment by a computer control system, and sending an instruction to a device for classifying the garbage in real time; and classifying the garbage. The garbage classification method is applied to garbage recycling and preparation of raw materials with the stable chemical element properties; the garbage can be recycled and resources are saved; the garbage also can be prepared into the raw materials with the stable chemical element properties; and when the raw materials are used for processes including pyrolysis, gasification, combustion and the like, and good conditions are created for stable and effective operation of the processes, so that the reduction of the garbage is realized, and the aims of recycling and energy generation are realized.