614 results about "Labelling" patented technology

This invention is for a reader/writer device (terminal) for accessing item-related data in rewritable tags of the "RF-ID" type wherein each tag includes non-volatile memory. In a farm animal application each animal is implanted with an identified tag. From time to time, the terminal can be given a selected security level by reading password-like and individual identity data from a special tag held by an individual. As a result the individual can gain "read" and/or "write" to selected fields in the data record held within the tag. Further information may be added from time to time by empowered individuals. Schemes are enabled for the enhanced management of animals having such tags include accurate descriptors, proving ownership, showing compliance with disease control programmes and withholding periods after possible treatments, traceability of meat products from the shop to the farm, breeding schemes and performance recording. The use of security levels and the traceability of entries adds to the reliability of the stored information.

A label comprises a first area having a first barcode printed thereon and a second area having a second barcode printed thereon. The first barcode is of a first format, the second barcode is of a second format and each barcode encodes at least a subset of identical information. The subset of identical information encoded in the first barcode and the second barcode may include information identifying the patient, the type of sample, or date or time of sampling, or any combination thereof.

The invention discloses a method and a device for data identification based on a multitask deep neural network. The method comprises the following steps of: step 1, establishing the multitask deep neural network; step 2, regarding the two adjacent layers of the multitask deep neural network as a limited Boltzman machine, and pre-training the multitask deep neural network by a method of training layer by layer without supervision from bottom to top, so as to obtain an initial connection weight between the adjacent layers; step 3, minimizing a target function about the network weight with supervision by virtue of a back propagation algorithm, so as to obtain an optimized network weight; and step 4, inputting to-be-identified data in the multitask deep neural network with the optimized network weight, so as to obtain an output layer node value, thus obtaining the type of the to-be-identified data according to the output layer node value. In the method, relevance among different labels is excavated by virtue of the neural network, so that a high image labelling accuracy can still be ensured in large-scale image labelling with a high label quantity.

A labelling device includes a linearly displaceable piston operably connectable to a moving mechanism for moving the piston from a resting position to a labelling position. A label carrying mechanism is arranged at the distal end of the piston for carrying an adhesive label and affixing the adhesive label to an object via physical contact at the labelling position of the piston. A force-switching-state system is provided to maintain the piston in relation to the moving mechanism in an attracting force state and the piston moves with the moving mechanism. The physical contact creates an opposite repelling force from the object onto the label carrying mechanism causing separation of the displaceable piston from the moving mechanism. This separation causes a change of the force state of the displaceable piston from an attracting to a repelling force state, causing the linearly displaceable piston to move back to the resting position.

The invention discloses a multiple features fused bidirectional recurrent neural network fine granularity opinion mining method. The method comprises the following steps of: capturing comment data of a specific website through internet and carrying out labelling and preprocessing on the comment data to obtain a training sample set; carrying out training by using a Word2Vec or Glove model algorithm to obtain word vectors of the comment data; carrying out vectorization after carrying out part of speech labeling, dependence relationship labeling and the like; and inputting the vectors into a bidirectional concurrent neural network to construct a bidirectional recurrent neural network fine granularity opinion mining model. According to the method, attribute words in fine granularity opinion mining is extracted and emotional polarity judgement is carried out through the training of a model, so that plenty of model training time is further saved and the training efficiency is improved; no professionals are required to carry out manual extraction on the attribute words, so that a lot of manpower cost is saved; and moreover, the model can be trained by using a plurality of data sources, so that cross-field fine granularity opinion analysis can be completed, thereby solving the problem of long-distance emotional element dependency.

The invention discloses an abnormality detecting method and device and relates to the technical field of abnormality detection. The method comprises acquiring target log data to be detected; acquiring a first probability that the target log data belong to the category of abnormality through preset categorization models which are acquired by training a plurality of access sample data with categories determined; determining whether the first probability is higher than a preset threshold value; if so, determining abnormality of the target log data. Therefore, the abnormality detecting method solves the technical problem such as large data labelling volume, high labelling costs, sensitive parameters and linear inseparability in existing abnormality detecting methods and has the advantages of reducing data labelling volume and labelling costs and improving detecting performance.

The invention relates to a public culture knowledge graph platform-based comprehensive recommendation system. The system comprises a labelling unit, a label library, a label mining unit, a recommendation unit and a visualization unit, wherein the labelling unit is used for distributing resource labels for extracted resources and distributing user interest labels for extracted user data; the label library is used for storing the resource labels and the user interest labels; the label mining unit is used for carrying out mining analysis on the labels in the label library; the recommendation unit is used for carrying out comprehensive public culture information recommendation according to a mining analysis result of the label mining unit; and the visualization unit is used for carrying out visualized display on a comprehensive recommendation result of the recommendation unit. According to the system provided by the invention, the cross-field, cross-category, cross-data structure and cross-data source public culture knowledge recommendation is realized.

The invention discloses an enterprise industry classification method. According to the method, main business keywords of enterprises are effectively extracted by utilizing semi-supervised learning-based image split clustering algorithm, the extracted keywords are used as features on the basis of a gradient enhancement decision-making tree, and a training cascade classifier is used for classifyingthe enterprises according to industries, so that the problem that artificial classification is tedious is solved. The method specifically comprises the following steps of: 1) extracting main businesskeywords of enterprises by utilizing a word vector and a semi-supervised image split clustering algorithm, getting rid of junk words and constructing a keyword library; and 2) inputting the extractedkeywords which are taken as features into a training cascade classifier, the enterprises are classified by each level of classifier, and the unclassified enterprises are classified according to the next level of classifier. According to the method, keywords can be automatically constructed, updated and classified, the problem of classifying millions and millions of enterprise industries is solved,and the problem of artificial labelling is effectively solved.

Embodiments described herein may provide a method for generating a three-dimensional vector model of the interior of a structure. Methods may include: receiving sensor data indicative of a trajectory; receiving sensor data defining structural surfaces within a structure; generating a three-dimensional point cloud from the sensor data defining structural surfaces within the structure; segmenting the three-dimensional point cloud into two or more segments based, at least in part, on the sensor data indicative of trajectory; generating a three-dimensional surface model of an interior of the structure based on the segmented three-dimensional point cloud with semantic recognition and labelling; and providing the three-dimensional surface model of an interior of the structure to an advanced driver assistance system to facilitate autonomous vehicle parking.

The invention discloses a method for real-time detection of a road vehicle based on a deep learning SSD frame and mainly solves a problem of low detection accuracy of the existing technology under the conditions of traffic congestion and complicated weather. An implementation scheme of the method comprises the following steps that 1.shooting videos of a plurality of running vehicles at a vital communication line and grabbing window information of the vehicle in each frame of image in the video in a manual labelling way; 2.taking VGG-16 in a classification network as a basic network, constructing an SSD300*300 detection frame, extracting features of some feature extraction layers to carry out connection as recognized feature vectors and inputting the feature vectors into a loss function; 3.training through a training sample and taking a trained detection model into the detection frame; and 4.setting a correlation threshold and detecting the vehicles in the test videos by the trained detection model. According to the method, the detection accuracy is greatly improved and an effect of real-time detection is realized, and the method can be used for vehicle detection under the complex scene.

The invention discloses a three-dimensional face modeling method and a three-dimensional face modeling printing device based on video streaming and face multi-attribute matching. The method comprises the following steps that: establishing a universal three-dimensional face model library; through a pre-trained multi-task learning deep neural network, carrying out face detection and face key point information extraction; and utilizing the pre-trained multi-task learning deep neural network to carry out face attribute analysis prediction, and combining face key point data with face attribute information to carry out coarse registration with the universal three-dimensional face model library to obtain a universal face model which is most similar to real-time acquisition. The printing device comprises an input unit, a feature point labelling unit, a universal three-dimensional face model library unit, a face multi-attribute classification unit, a face key point optimization unit, a texture refining unit and a printing and outputting unit. The method and the printing device have the advantages of high accuracy, simple implementation way, good user friendliness, high automation degree and the like.

A computer-implemented system and method for text classification is provided that applies a hybrid approach for text classification. The system and method includes a text pre-processor which prepares unclassified articles in a format which can be read by a two-stage classifier. The classifier employs a hybrid approach. A keyword-based model achieves machine-labelling of the articles. The machine-labelled articles are used to train a machine learning model. New articles can be applied against the trained model, and classified.

The invention discloses a multi-dimension labelling and model optimization method for audio and video. The method specifically comprises the following steps: first, carrying out sample management andsorting, carrying out de-duplication aiming at sample data of an input system, carrying out numbering, and establishing a sample labelling task library; at the preprocessing stage of audio data, carrying out audio extraction on video data of the task library, and completing the preprocessing operation for the audio data; at the audio content analysis and feature extraction stage, after the audio preprocessing is completed, carrying out deep analysis according to a labelling standardized system configured at the background, and outputting label data; S304, at the video content analysis and feature extraction stage, carrying out image analysis on the video content, and carrying out deep analysis according to the labelling standardized system configured at the background, and outputting the label data; S305, carrying out feature fusion and label generation, namely, fusing the recognition features and label information, and outputting a label result of the sample; carrying out manual rechecking and model optimization, wherein the label result data generated by the system can be subjected to artificial re-check conformation.

The invention discloses a CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews. The method comprises the steps as follows: a, a CRFs model is adopted, review language characteristics are taken as sequences, then position labelling is performed on review languages according to the sequences, corresponding rules are adopted to perform stratified filtering on error labels, and extraction for sentiment subjects and sentiment words is finished; and b, an SVM model is adopted to perform sentiment orientation analysis on word pairs according to the extracted sentiment subjects and sentiment words as well as introduced sentence structure features. According to the invention, the sentiment subjects and the sentiment words in review sentences are extracted together, further, sentiment classification accuracy in the sentiment orientation analysis is improved, so that the sentiment element extraction and sentiment judgment are improved, and F value is up to 76.3%; due to introduction of word meaning codes, the generalization ability and the robustness of a system are improved by virtue of the word meaning codes, and the accurate rate and recall rate of review result analysis are greatly improved.

The invention discloses a deep learning-based crack identification method. According to the method, correct identification of crack positions and crack attributes is realized at the same time by utilizing a deep convolutional neural network which comprises branches; on the trunk of the network, convolutional layers and de-convolutional layers are combined to realize end-to-end crack position prediction; and on the branches, attribute identification is realized. In order to overcome the difficulty that the labelling of crack samples wastes time and labor, a simulation crack drawing algorithm is designed to realize the automatic drawing and labeling of cracks, so that the workload of manual labeling is greatly lightened, training samples in large data volume are provided for deep learning, overfitting of deep network models is avoided, and the convergence and convergence efficiency during real crack training are improved; and according to the method, the identification correctness is greatly enhanced, the universality is stronger, the reliability is higher and demands of industrial detection can be satisfied.

An image processing system and method having a statistical appearance model for interpreting a digital image. The appearance model has at least one model parameter. The system and method comprises a two dimensional first model object including an associated first statistical relationship, the first model object configured for deforming to approximate a shape and texture of a two dimensional first target object in the digital image. Also included is a search module for selecting and applying the first model object to the image for generating a two dimensional first output object approximating the shape and texture of the first target object, the search module calculating a first error between the first output object and the first target object. Also included is an output module for providing data representing the first output object to an output. The processing system uses interpolation for improving image segmentation, as well as multiple models optimised for various target object configurations. Also included is a model labelling that is associated with model parameters, such that the labelling is attributed to solution images to aid in patient diagnosis.

The present invention relates to a method and apparatus for monitoring on a micro-array a PCR amplification of a nucleotide molecule being present in a solution. The method includes the steps of: providing a support having fixed upon its surface a microarray having at least a capture molecule being immobilized in specifically localized areas of the support and a reaction chamber; introducing a solution containing the nucleotide molecule into the reaction chamber and reagents for nucleotide molecule amplification and labelling; submitting the solution to at least 2 thermal cycles having at least 2 and preferably 3 different temperature steps in order to obtain labelled target nucleotide molecule by PCR amplification; performing at least a measurement of the labelled target nucleotide molecule in at least one thermal cycle by incubating the labelled target nucleotide molecule under conditions allowing a specific binding between the target nucleotide molecule and its corresponding capture molecule and measuring the light emission from the bound labelled target nucleotide molecule in response to excitation light with the solution being present in the chamber and containing the labelled target nucleotide molecule. The surface of emission for a localized area is between about 0.1 μm² and about 75 mm². The method further includes processing the data obtained in at least one thermal cycle in order to detect and/or quantify the amount of nucleotide molecule present in the solution before the amplification.

Embodiments of the invention provide a method and device for multilingual hybrid model establishment and data acquisition, and electronic equipment. The method includes determining a modeling unit ofan acoustic model according to a speech unit contained by multilanguage, and establishing the acoustic model based on a deep neural network, wherein the modeling unit is the context free speech unit;obtaining multilingual hybrid speech training data, converting a hybrid speech signal in the multilingual hybrid speech training data into an eigenvector sequence, and converting a hybrid labelling text corresponding to the hybrid speech signal into a hybrid label sequence of the modeling unit based on the acoustic model; training the acoustic model by using the eigenvector sequence and the hybridlabel sequence; obtaining multilingual hybrid corpus data to train a language model; and establishing a multilingual hybrid speech recognition system according to the acoustic model and the languagemodel. According to the embodiments, recognition accuracy of speech data mixing multiple languages can be enhanced.

This is provided a method of labelling a message or group of messages. An input is received (208) at a neural network (300, 302) including at least one recurrent layer, which may comprises LSTM memory blocks (300). The input comprising at least one word vector (x_t), which represents at least one word in a message, and the at least one word vector defines a meaningful position in a word vector space. Typically the input is a sequence of word vectors corresponding to a sequence of words. The input is then processed to generate a plurality of network outputs. Each network output corresponds to a respective one of a plurality of labels. Based on the network outputs, a probability score for each of the labels is then generated (210). If it is determined (212) that at least one of the probability scores meets at least one criterion, the at least one label corresponding to the at least one probability score for which the at least one criterion is met is assigned (214) to the message.

The present invention relates to novel fluorinated 3,6-diaminoxanthene compounds derived from the basic structural formula (I) and to their uses as photostable fluorescent dyes, e.g. for immunostainings and spectroscopic and microscopic applications, in particular in conventional microscopy, stimulated emission depletion (STED) reversible saturable optically linear fluorescent transitions (RESOLFT) microscopy, and fluorescence correlation spectroscopy. The claimed compounds are also useful as molecular probes in various spectroscopic applications.