28 results about "Classification procedure" patented technology

A feature extraction unit (510) outputs first and second feature vectors of an input image. An averaged first / second feature calculation unit (520a, 520b) calculates an averaged first / second feature vector by averaging first / second feature vectors of a given class and obtains an averaged first / second feature matrix by aggregating averaged first / second feature vectors of all classes. A first / second feature similarity calculation unit (532a, 532b) calculates a first / second similarity from the first / second feature vector of the input image and a first / second weight matrix. The averaged first / second feature calculation unit (520a, 520b) replaces the first / second weight matrix of the first / second feature similarity calculation unit (532a, 532b) with the averaged first / second feature matrix.

To provide a data classification device capable of preventing deterioration of classification accuracy of an object.SOLUTION: An object classification device includes a data acquisition unit (101) that acquires sensor data from a sensor (20), a data processing unit (104) that generates classification data from the sensor data acquired by the data acquisition unit (101) using representative shape data that is sensor data indicating a feature of a representative shape of an object belonging to a class to be classified, and a classification unit (105) that classifies the object on the basis of the classification data generated by the data processing unit (104).SELECTED DRAWING: Figure 3

The present application provides a method for road classification, a device, an apparatus, a system, a medium, and a product thereof, all of which can be used in the field of vehicle control. The method captures a set of chassis signals from a vehicle and then inputs them into a road detection model for classification to determine a road type. A corresponding driving parameter of the vehicle is then determined, according to which the vehicle operates. The present embodiment effectively improves the adaptability of the road type to the degree of influence of the road on the vehicle by performing the classification using the chassis signals related to vehicle operation and the road detection model resulting from a subsequent reclassification of the road.

Improve the accuracy of subject classification by using information highly relevant to the subject in the image. [Solution] The associative label extraction unit 10 extracts multiple associative labels associated with the true label of the main subject of the image from the associative knowledge graph. The embedding representation vector calculation unit 20 calculates the embedding representation vectors of the multiple associative labels and calculates the embedding representation vectors of multiple surrounding labels obtained from the image for the true label of the main subject and averages them. The similarity calculation unit 30 calculates the cosine similarity between the embedding representation vector of each associative label and the averaged embedding representation vector of the surrounding labels. The associative label selection unit 40 selects the embedding representation vector of the associative label that has a high similarity to the averaged embedding representation vector of the surrounding labels. The surrounding label expansion unit 50 uses the selected associative labels as surrounding labels to generate a cell graph with the main subject as the center node and the surrounding labels as side nodes.

Fault classification device (1) for detecting a location that is the cause of a fault in a mechanism unit of a machine with multiple components, comprising: a fault unit detection unit (12) that captures a data set in which a fault unit of a machine is correlated with one or more components, wherein the fault unit is a group of one or more components whose causal fault locations cannot be separated from each other when designing the machine; a fault history recording unit (11) that records a fault history containing an event of faults that have occurred in the past and countermeasures for eliminating the faults; and a correlation unit (13) which stores information about one or more events in correlation with the fault unit in a fault history database (21) by comparing the component with the countermeasure component; a recovery receiving unit (15) that receives a recovery request containing a search keyword; and a recovery execution unit (16) that recovers information about the event based on the search keyword and outputs the fault unit correlated with the information about the event in order to specify the location in the mechanism unit of the machine that caused the fault.

Systems, computer program products, and methods are described herein for process optimization using advanced computational models for data analysis and automated processing. The present disclosure is configured to collect metadata from a node associated with data, wherein the node is configured to process the data, and wherein the metadata comprises real-time parameters of the data; train an instantaneous process identifier (IPI) using the metadata, wherein the IPI comprises a deep learning neural network; analyze the metadata using a classification procedure, wherein the classification procedure determines resources needed to process the data; determine a processing node to process the data, wherein determining the processing node is based on the processing node's availability and the processing node's processing capabilities; and allocate resources to process the data, wherein allocating the resources comprises an instance-based allocation determined by the IPI.

Methods and devices for a condition classification of a power network asset of a power network asset are disclosed. The methods and devices may combine an automatic classification procedure with a missing data replacement procedure.

The invention relates to a network flow control method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: generating a classification program; the classification program is an eBPF program running in a kernel mode; controlling the network flow through the classification program; wherein the classification program is configured to classify the network traffic, and if the network traffic belongs to a first category, the network traffic is sent to an auxiliary container; and if the network traffic belongs to the second category, sending the network traffic to a service container. By adopting the method, the communication efficiency can be improved.

Sets of vectors are generated based on respective output signals from at least M+N detectors detecting different respective output modes emitted from one or more linear-optical interferometers (LOIs). A first set of vectors corresponds to an M-mode quantum state emitted from at least one of the LOIs. A second set of vectors corresponds to an N-mode quantum state emitted from at least one of the LOIs. A classifier is trained to classify each of a plurality of pairs of X-dimensional input vectors as a member of one of two or more different classes based on an X-1 dimensional hyperplane determined at least in part by the training, which comprises: receiving training data comprising at least one vector from the first set and at least one vector from the second set, and updating the X-1 dimensional hyperplane based at least in part on the training data (X ≥ M; X ≥N).

An X-ray fitting analysis method and an X-ray fitting analysis system based on a multi-physics variable model. The X-ray fitting analysis method includes: obtaining multiple X-ray measurement signals of an inspection target by an X-ray measurement apparatus; establishing multiple fitting models according to a target architecture; performing an initial fitting procedure to generate multiple initial fitting results and multiple initial parameter ranges; performing a first co-fitting procedure to obtain multiple first fitting results that satisfy a first fitting condition; performing, according to a data type of the first fitting results, a classification procedure to generate multiple classification fitting results; counting to obtain multiple classification parameter ranges; and performing a second co-fitting procedure to obtain multiple second fitting results that satisfy a second fitting condition and respectively correspond to multiple structural parameters.

A classification device includes processing circuitry configured to acquire an operation log related to operation information and identify each operation performed by a user using the operation log, create a vector of each operation based on a co-occurrence relationship between operations identified, calculate a similarity between a predetermined number of operations adjacent to each other in chronological order by using the vector of each operation created, determine a division point of operations by using the similarity calculated and divide a time-series operation into operation sets based on the division point, and classify the operation sets divided into classes based on a number of types of operations common to the operation sets.

To automatically generate a label corresponding to each cluster obtained by clustering each sentence of a model document, and to automatically create sentence classification information which is teacher data.SOLUTION: The cluster configuration unit 130 clusters a plurality of sentence units 511 obtained as a result of decomposing the model document 52 into a plurality of clusters. The label setting unit 140 acquires a label setting result in which a label is set to each cluster based on the clustering result. The model learning unit 150 acquires the text classification information 53 in which the text units included in each of the plurality of clusters are associated with the labels based on the label setting result. Here, the text classification information 53 is training data used for learning of the classification model 60 that classifies text units included in the classification target document.SELECTED DRAWING: Figure 1

Provided is a test case classification apparatus including a memory and a processor. The processor includes a test case classification section that performs classification of one test case that is included in test cases describing test contents for system software, by comparing a manual testing execution time representing an amount of time required to manually execute a test based on the one test case and an automated testing execution time representing an amount of time required to automatically execute the test and setting a mode of running the one test case, and a result output section that outputs a result of the classification performed by the test case classification section.

A multi-label fluid segmentation and classification system according to an embodiment of the present invention comprises: a collection unit for collecting optical coherence tomography (OCT) images; a memory that stores a multi-label fluid segmentation and classification program for segmenting and classifying multi-label fluids representing body fluids accumulated in a plurality of spaces between a plurality of retinal layers included in the OCT image; and a processor for executing the program so as to classify and segment the multi-label fluids in the OCT image, wherein the processor includes a training unit for training, with the multi-label fluid classification and segmentation method, a deep learning model, which extracts features from the OCT image and simultaneously performs, by using the features, classification prediction for the multi-label fluids and segmentation for each of the multi-label fluids.

Object classification procedures, including: the receipt of sensor data (402) associated with an object observed by a sensor system (28) of an autonomous vehicle (10); determining a limiting curve (810, 820, 901, 902) associated with the sensor data (402) using a processor; determining a variety of boundary curve features based on a set of convexities (811, 813, 815, 817, 821, 823, 825, 827, 829, 911, 913, 921, 923) and concavities (812, 814, 816, 818, 822, 824, 826, 828, 912, 922) that are assigned to the boundary curve (810, 820, 901, 902); and classifying the object by applying the multitude of boundary curve features to a machine learning model and obtaining a classification output that classifies the object to assist in controlling the autonomous vehicle (10); where the boundary curve features include slopes (971, 972, 973, 974) between adjacent convexities (811, 813, 815, 817, 821, 823, 825, 827, 829, 911, 913, 921, 923) and concavities (812, 814, 816, 818, 822, 824, 826, 828, 912, 922).

This class classification device: acquires feature vectors extracted from input data; generates, by using a trained generator, weight vectors that continuously change according to the values of the feature vectors, for a plurality of classes to be classified; calculates, by using a trained calculator, scores for the plurality of classes on the basis of the feature vectors and a plurality of the weight vectors generated for the plurality of classes; classifies a classification target of the input data into any of the plurality of classes on the basis of a plurality of the scores calculated for the plurality of classes; and outputs the classification result.

A data classification device includes: a data acquisition unit configured to acquire data to be classified; a classification unit configured to classify the data into one of a plurality of classes by using a learned model learned using a neural network; a similarity calculation unit that calculates the similarity between the data and learned data used to generate the learned model; and a determination unit configured to determine whether the data belongs to the classes on the basis of the similarity, and, when the data does not belong to any of the classes, determine that the data belongs to an unknown class.

This classification device executes: a calculation process for calculating a first training result evaluation value indicating the extent to which data being classified, to which no correct-answer label is attached, contributes to supplemental training of a prediction model that is capable of accessing a training dataset in which correct-answer labels are attached and that is trained using the training dataset, the calculation being carried out on the basis of a first degree of uncertainty indicating the level of ambiguity in a first prediction result outputted as a result of having inputted the data being classified to the prediction model; a classification process for classifying the data being classified as either one of supplemental training data or non-supplemental training data for the prediction model, the classification being carried out on the basis of the first training result evaluation value calculated through the calculation process; a setting process for configuring a setting so that a correct-answer label can be attached to the supplemental training data classified through the classification process; and a supplementation process for supplementing the training dataset with the supplemental training data to which the correct-answer label was attached in the setting process.

To provide a classification method, a classification device and a classification program for detecting communications related to malware files from large-scale traffic data. A classification method for classifying content in network communications. The classification method includes receiving traffic data on communications, extracting an attached file from the traffic data, analyzing a type of the attached file, and analyzing whether or not the attached file contains malware by using a machine learning model suitable for the analyzed type of the attached file.

The category classification device acquires a feature quantity vector extracted from input data, generates a weight vector that continuously changes in accordance with the value of the feature quantity vector for each of a plurality of classified categories using a learned generator, and generates a weight vector that continuously changes in accordance with the value of the feature quantity vector for each of the plurality of classified categories using a learned calculator. A score for each of the plurality of categories is calculated on the basis of the feature quantity vector and a plurality of weight vectors generated for each of the plurality of categories, a classification object of the input data is classified into any one of the plurality of categories on the basis of the plurality of scores calculated for each of the plurality of categories, and a classification result is output.

Methods of evaluating animal activity are disclosed including examples running artificial intelligence classification routines. Predictive atmospheric and celestial data may be used together with predictive artificial intelligence data analysis to obtain probabilities of certain animals being present at certain times. The evaluations may occur in conjunction with the operation of a feeder such that the method includes both feeding a selected species and predicting the activities of that species.

To provide a time series classification device, a method, and a program for performing highly-accurate classification in a low-cost calculation process with a small amount of calculation.SOLUTION: A method includes: compressing actual training time series data into representative training time series data for each class that includes, for each event, a minimum value and a maximum value of a plurality of time-series event values, a minimum value time series and a maximum value time series that include representative values between the minimum value and the maximum value, and a representative value time series; calculating, for each class, a plurality of FMS values for event values of a plurality of time stamps in a test time series to be classified, based on the representative training time series data for each class; and classifying a class in which an average value for the FMS values of the plurality of time stamps is minimum into the class in the test time series. If an event value in the test time series is equal to or less than an event value in the minimum value time series or equal to or greater than an event value in the maximum value time series, the FMS value is calculated as 1.0, if the event value in the test time series is between the minimum value and the event value in the representative value time series, the FMS value is calculated as α×A, and if the event value in the test time series is between the representative value and the event value in the maximum value time series, the FMS value is calculated as Δ×B.SELECTED DRAWING: Figure 11

In a method for detecting discomfort in an infant (102) in a motor vehicle (104), at least the following steps are performed: A detection unit (106) detects a verbal utterance from the infant (102) in an interior space (108) of the motor vehicle (104) and generates corresponding audio data. A processing unit (116) classifies the verbal utterance based on the audio data and using a classification procedure to determine a need of the infant (102) and generates corresponding classification data. A control unit (118) controls at least one functional unit of the motor vehicle (104) based on the classification data to satisfy the identified need of the infant (102).

An object of the present disclosure is to provide a defect classification device capable of easily grasping an appropriate recipe update timing of an imaging device when classification accuracy for classifying defects existing on a semiconductor wafer is decreased. The defect classification device according to the present disclosure calculates classification accuracy by further acquiring a result of a manual classification for defects spanning a plurality of classification spaces as a result of an automatic classification, and comparing the result of the automatic classification with the result of the manual classification (see FIG. 5).

To classify a plurality of differences between text files into a plurality of patterns even when classification patterns are not determined.SOLUTION: A classification system has a processor and a memory, and comprises: a difference detection part which detects a difference between first text data and second text data updated from the first text data; a classification pattern generating part which generates a plurality of classification patterns for classifying differences on the basis of the plurality of detected differences; a difference classification part which classifies the plurality of detected differences on the basis of the generated classification patterns; and a result output part which outputs a classification result classifying the differences.SELECTED DRAWING: Figure 1