1328 results about "Data dimension" patented technology

The present invention relates generally to methods, software and systems for measuring and valuing the quality of information and data, where such measurements and values are made and processed by implementing objectively defined, measurable, comparable and repeatable dimensions using software and complex computers. The embodiments include processes, systems and method for identifying optimal scores of the data dimension. The invention further includes processes, systems and method for data filtering to improve the overall data quality of a data source. Finally, the invention further includes processes, systems and method for data quality assurance of groups of rows of a database.

Configuring a set of guided documents for operation of a business activity based on a workflow for the activity, industry expertise, and a plurality of models of source data that is relevant to the business activity, wherein points of entry to access the guided documents are organized around one or more industry-specific data dimensions.

The invention provides a robot adaptive grabbing method based on deep reinforcement learning. The method comprises the following steps: when distanced a certain distance away from an object to be grabbed, a robot obtaining a picture of a target through a pick-up head in the front, then according to the picture, calculating position information of the target by use of a binocular distance measurement method, and applying the calculated position information to robot navigation; when the target goes into the grabbing scope of a manipulator, taking a picture of the target through the pick-up head in the front again, and by use of a DDPG-based deep reinforcement learning network trained in advance, performing data dimension reduction feature extraction on the picture; and according to a feature extraction result, obtaining a control strategy of the robot, and the robot controlling a movement path and the posture of the manipulator by use of a control strategy so as to realize adaptive grabbing of the target. The grabbing method can realize adaptive grabbing of objects which are in different sizes and shapes and are not fixedly positioned and has quite good market application prospect.

Systems and methods in accordance with various embodiments of the present invention provide for representing a plurality of source data values as graphical elements in a default treemap visualization, where each data value is associated with a plurality of data dimensions. A first data dimension is selected to be mapped to an area cell characteristic based on the first data dimension having a quality of numeric and a quality of non-negative. A second data dimension is selected to be mapped to a color cell characteristic based on the second data dimension having a quality of numeric and a quality of previously unmapped. The default treemap visualization is generated based on the selected first data dimension and the selected second data dimension.

A computer-implemented data processing method comprises receiving an index specification, storing data in a data repository, and indexing the data to create an index of the date stored in the data repository. The index specification comprises a user-specific index parameter. The data is indexed along a dimension of the data specified by the user-specified index parameter. The is received from data source computers and may be indexed as the data is received from the source computers.

The invention discloses a city-level intelligent traffic simulation system. With multi-source heterogeneous data as input, the vehicle queue length of each lane at an intersection can be predicted, the evolution of the vehicle queue length is further dynamically simulated, and the simulation system comprises a data collection module, a multidimensional database, an algorithm module, a traffic state prediction module, a traffic simulation derivation module and an interaction visualization module. In the data collection module, the collected data comprise traffic dynamic data and static data. The multidimensional database is used for receiving various traffic dynamic information in real time and storing road infrastructure configuration information. In the algorithm module, a generated confrontation network is used for data generation and state prediction on the vehicle queue length, and the processing process comprises data feature extraction, data dimension processing, confrontation network generation, and hyperparameter optimization. The vehicle queue length of each lane at the intersection can be predicted, and the evolution of the vehicle queue length is further dynamically simulated.

A system and method for managing the development and manufacturing process of a pharmaceutical is disclosed. The method comprises capturing and recording the development and manufacturing history of the pharmaceutical drug in order to generate a product history. The product history is stored on a computer and is searchable in multiple data dimensions in order to easily retrieve information. The system automatically provides compliance management procedures in order to comply with regulatory standards for the pharmaceutical industry.

The invention discloses a human body action recognition method and a mobile intelligent terminal. The human body action recognition method comprises the steps that human body action data are acquired for training so that feature extraction parameters and template data sequences are obtained, and the data requiring performance of human body action recognition are acquired in one time of human body action recognition so that original data sequences are obtained; feature extraction is performed on the original data sequences by utilizing the feature extraction parameters, and the data dimension of the original data sequences is reduced so that test data sequences after dimension reduction are obtained; and the test data sequences and the template data sequences are matched, and generation of human body actions corresponding to the template data sequences to which the test data sequences are correlated is confirmed when the successfully matched test data sequences exist. Dimension reduction is performed on the test data sequences so that the requirements for the human body action attitudes are reduced, and noise is removed. Then the data after dimension reduction are matched with the templates so that calculation complexity is reduced, accurate human body action recognition is realized and user experience is enhanced.

Systems and methods in accordance with various embodiments of the present invention provide for representing data values of a data set as a plurality of graphical elements in a data visualization, where each data value is associated with a plurality of data dimensions. A first data visualization may be generated based on a first configuration of the datatset. The first data visualization and a first user interface are displayed on a data visualization display page. The first user interface includes the plurality of graphical elements in the first data visualization. A selection of a element of the plurality of graphical elements is received through the first user interface. Moreover, the data values based on the received selection are filtered. A second data visualization representing the data set excluding the filtered data values is displayed.

Data visualization systems and methods for generating 3D visualizations of a multidimensional data space are described. In one embodiment a 3D data visualization application directs a processing system to: load a set of multidimensional data points into a visualization table; create representations of a set of 3D objects corresponding to the set of data points; receive mappings of data dimensions to visualization attributes; determine the visualization attributes of the set of 3D objects based upon the selected mappings of data dimensions to 3D object attributes; update a visibility dimension in the visualization table for each of the plurality of 3D object to reflect the visibility of each 3D object based upon the selected mappings of data dimensions to visualization attributes; and interactively render 3D data visualizations of the 3D objects within the virtual space from viewpoints determined based upon received user input.

The invention discloses a method of deep neural network based on a discriminable region for dish image classification. The method relates to the field of image processing, integrates a significant spectrum pooling operation, and fuses low-level features and high-level features in a network. The method adopts a convolution kernel filling operation, effectively preserves important information on characteristic spectra, and is matched with data dimensions of a full connection layer, so that the full connection layer can utilize a VGG-16 pre-training model at a training state, thereby improving the training efficiency and network convergence speed. Each image to be classified is subjected to normalization processing based on the model which is learned in a constructed database, the image is tested by using a trained convolutional neural network, the classification precision is measured by using Softmax loss, a classification result of the image is obtained, real categories and predicted categories of targets in all test images are compared, and a classification accuracy rate is obtained through calculation. The method is used for testing on a self-established data set CFOOD90, and theeffectiveness and the real-time performance of the method are verified.

The invention belongs to the technical field of automatic radar HRRP (high resolution range profile) object identification and particularly relates to compressive sensing based range profile object identification. Range profile object identification includes the steps: constructing a matching dictionary according to a radar echo model, selecting an appropriate test matrix for compressive sensing of a training sample range profile and to-be-identified test sample range profile which are known in type information so as to achieve data dimension reduction; then, subjecting data subjected to compressive sensing to sparse reconstruction so as to obtain sparse coefficients of the training sample range profile and the test sample range profile under the matching dictionary; utilizing the sparse coefficient of the training sample range profile as a pattern vector, and identifying the test sample range profile according to a nearest neighbor method. By the aid of compressive sensing based range profile object identification, since sparse coefficient characteristics of objects under the dictionary are extracted, redundancy is avoided, calculating amount is decreased and unnecessary noise is avoided.

The invention provides a hexapod-robot real-time gait planning method based on deep reinforcement learning. The method comprises the following steps of using a hexapod robot to acquire environment road condition information and making an integral movement track; through a camera, acquiring an environment photograph, according to the photograph, using a binocular range finding method to calculate road condition information of a target track, and using the calculated road condition information of the track in robot center of mass movement track navigation; in a foot end swinging space range of robot legs, taking photographs of a road condition environment, and through a trained deep reinforcement learning network based on a depth determinacy policy gradient (DDPG), carrying out data dimension reduction and feature extraction on the photograph; and according to a feature extraction result, acquiring a control policy of a hexapod robot, wherein the hexapod robot controls foot laying of a robot according to the control policy so that real-time walking of the hexapod robot is realized. By using the gait planning method, a complex and non-structural environment of a road condition can be planned in real time. The method has an important meaning for increasing an environmental adaptive capacity of the hexapod robot.

A method for presenting seismic data in a multidimensional visualization. Specifically, in the visualization technique of the current invention, seismic data is displayed in a multidimensional plan view utilizing at least four dimensions associated with the seismic data, such as for example, x, y, time / depth and offset. In the method of the invention, a plurality of time or depth windows are defined along a reflector or any other time or depth surface of interest on the prestack data as presented in standard CMP displays. In one embodiment of the invention, for each CMP gather, a window is defined around the data representing the reflector of interest. Passing through each window are individual seismic traces. The window, being defined on the seismic display, is associated with a finite time / depth segment and will contain several offsets. In addition, since each CMP gather has a constant x and y coordinate, the window is associated with specific spatial coordinates. These spatial coordinates are used to plot the window on an x-y plan view. Each window represents a segment of the seismic data associated with a reflector or other time / depth window. The data within each window can be analyzed to determine such things as, for example, the accuracy of the particular velocity model selected for data processing methods, such a migration. Furthermore, as multiple windows are plotted on the plan view, trends in the data become more prevalent to an observer. The resulting multidimensional plan view thereby permits presentation of the data utilizing at least four dimensions of the data. In another embodiment, additional information can be extracted from the multidimensional plan view by overlaying this plan view on additional representations of the data, such as for example, the underlying seismic structure. In addition, the visualization techniques could be used on poststack data to visualize several stacked traces around a point of interest.

The present invention relates generally to methods, software and systems for measuring and valuing the quality of information and data, where such measurements and values are made and processed by implementing objectively defined, measurable, comparable and repeatable dimensions using software and complex computers. The embodiments include processes, systems and method for identifying optimal scores of the data dimension. The invention further includes processes, systems and method for data filtering to improve the overall data quality of a data source. Finally, the invention further includes processes, systems and method for data quality assurance of groups of rows of a database.

The present invention provides an advertising intelligent recommendation method. The method comprises the steps of: collecting historical behavior data of all the users, classifying the historical behavior data of all the users, generating a user portrait data dimension table in different dimensions, and making corresponding feature tags for the users according to a corresponding relation of different historical behavior data and the user portrait data dimension table in different dimensions; employing a preset screening rule to perform screening of an advertising to be put according to the user feature tags and an advertising preset condition, and obtaining a candidate advertising set; and finally, performing statistics of the real-time behavior data of all the users, employing a preset scoring formula to perform scoring of the candidate advertising in the candidate advertising set, performing descending sort, and giving priority to recommend the advertisings ranking higher to the users. The advertising intelligent recommendation method can recommend advertisings for the users so as to improve the advertising input accuracy. The present invention further provides an advertising intelligent recommendation device and a storage medium.

The embodiment of the invention relates to an automatic recognition and classification method of electrocardiogram heartbeat based on artificial intelligence. The method includes the following steps that received original electrocardiogram digital signals are processed, and heartbeat time sequence data and lead heartbeat data are obtained; according to the heartbeat time sequence data , the lead heartbeat data is cut to generate lead heartbeat analysis data; the lead heartbeat analysis data is subjected to data combination, and a one-dimensional heartbeat analysis array is obtained; accordingto the one-dimensional heartbeat analysis array, data dimension amplification and conversion are conducted, and four-dimensional tensor data is obtained; the four-dimensional tensor data is input to aLepuEcgCatNet heartbeat classification model obtained through training, and heartbeat classification information is obtained. The method overcomes the defect that a traditional method only depends onsingle lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

The invention discloses a hyper-spectral remote sensing image classifying method based on AdaBoost. A traditional mode identification method cannot meet the requirements of carrying out high-efficiency and high-precision classification on hyper-spectral data with high data dimensions and great data quantity; and although a neural network and a support vector machine can effectively classify remote sensing data, an ideal selection method of parameters does not exist. The hyper-spectral remote sensing image classifying method based on the AdaBoost comprises the following steps of: pre-processing the hyper-spectral data to remove abnormal wave bands influenced by factors including atmosphere absorption and the like; then utilizing MNF (Minimum Noise Fraction) conversion to carry out wave band preferential selection to achieve the aims of optimizing data, removing noises and reducing dimensions of the data; then, dividing a training sample and a test sample; selecting a decision stump as a weak classifier and utilizing an AdaBoost algorithm to train the weak classifier to obtain a strong classifier; selecting suitable iterations; and finally, utilizing a one-to-one method to establish a plurality of the classifiers. According to the hyper-spectral remote sensing image classifying method based on the AdaBoost, the convergence rate is enhanced and the classification performance of a hyper-spectral image is improved.

The invention relates to a frequency domain full-waveform inversion seismic velocity modeling method. The method comprises the following steps of: 1) acquiring an original seismic shot gather record, focus wavelet information and an initial model used by inversion; 2) analyzing information acquired in the step 1), and determining basic inversion parameters and a full-waveform inversion frame from low frequency to high frequency based on a forward modeling algorithm and an optimization algorithm; 3) calculating to acquire the most appropriate forward and inversion model network for different frequencies; 4) compressing data dimensions which participate in inversion by a principal component analysis method during low-frequency inversion; 5) judging whether projection matrix dimensions corresponding to different frequencies meet the threshold value conversion standard, if the conversion standard is met, performing a next step, and if the conversion standard is not met, returning to the step 4); 6) introducing a focus encoding method, and pressing crosstalk noise by a random phase encoding method; 7) judging whether an iteration stopping condition is met, if the iteration stopping condition is met, performing a next step, and if the iteration stopping condition is not met, returning to the step 6); and 8) if the inversion of all the frequencies is not finished, returning to the step 3) until the inversion of all the frequencies is finished, acquiring the final velocity model, and outputting the velocity model.

The invention relates to an improved parallel channel convolutional neural network training method. The improved parallel channel convolutional neural network training method comprises steps that characteristic extraction of data of the convolutional neural network is carried out through utilizing direct connection and convolutional channels to acquire characteristic matrixes; the characteristic matrixes are merged, and data dimension reduction is further carried out; the convolutional neural network is trained, and a loss value of network training at present is calculated; error items and a weight gradient of each layer are calculated; whether the network is in a convergence state is determined according to the loss value, if not, an initialization parameter of the convolutional neural network is adjusted according to the weight gradient, and re-training is further carried out; if yes, the network training result is outputted. The method is advantaged in that data circulation in the network can be guaranteed through introducing the direct connection channel, a problem of gradient instability during deep convolutional neural network training is solved, and deeper networks can be trained; through maximum pooling and mean value pooling, characteristic matrix dimensions of two times of characteristic extraction can be made to be consistent, and advantages of two pooling methods are integrated.

The invention discloses an on-line analytical processing (OLAP) massive multidimensional data dimension storage method. Firstly, OLAP multidimensional data are divided according to dimensions, dimension hierarchical encoding is built, a high definition (HD) File dimension storage file structure is designed, only relevant dimension corresponding data needs to be accessed for aggregation calculation, and therefore retrieval of unrelated data is avoided; secondly, a B+ tree index based on the dimension hierarchical encoding is built for rapid positioning of the dimension storage data, and therefore input (I) / output (O) overhead is saved; and at last, a high-efficiency parallel query algorithm is designed, and OLAP query efficiency is further improved. Therefore, the OLAP massive multidimensional data dimension storage method which is high in efficiency, easy to use and scalable is provided for massive data analysis application for scientific experimental statistics, environmental meteorology, bioinformatics computing and the like.

The invention belongs to the abnormal monitoring field of cloud monitoring, in particular to log monitoring, and discloses a log file processing method and device, a computer device and a storage medium. The method comprises the following steps of obtaining log files generated by an application program and identifying a file format corresponding to the log files; determining a file parsing rule corresponding to the recognized file format; parsing the log file according to the determined file parsing rules to obtain a unified coding specification file, and analyzing the unified coding specification file through preset text parsing rules to obtain log fields; generating a data dimension statistical table according to the log field, and comparing the data dimension statistical table with a historical data dimension statistical table to obtain difference information; generating alarm information according to the difference information, and outputting the alarm information. The method improves the processing speed of log files in different formats and reduces the workload of developers.

A system (10) for optimization using multi-dimensional data includes a server (12) that uses a multi-dimensional data model to organize data stored at one or more data storage locations (14). The multi-dimensional data model includes a number of data dimensions (50, 70) that each include a hierarchy of members (54, 74). The server (12) receives input from a user specifying a problem instance to be solved using an optimization engine (20). The problem instance is specified by the user in a multi-dimensional format and the optimization engine (20) is unable to solve the problem instance in the multi-dimensional format. The system (10) also includes a transformation module (22) that receives the problem instance in the multi-dimensional format, transforms the problem instance into a format appropriate for the optimization engine (20), and communicates the transformed problem instance to the optimization engine (20) to be solved.

The invention discloses an effective micro-expression automatic identification method which comprises the steps of micro-expression frame sequence preprocessing, micro-expression information data study and micro-expression identification. The method for micro-expression frame sequence preprocessing comprises the steps that frames of obtained micro-expression sequences are detected, data of an image of each frame are extracted so that graying processing can be conducted on the data, and all the micro-expression sequences are interpolated into the frame of the unified number through the linear interpolation method. The method for micro-expression information data study comprises the steps that the micro-expression sequences obtained in the preprocessing stage are written in a tensor mode, then, the intra-class distance of the same class of micro-expressions is minimized in a tensor space through the discriminating analysis method of tensor expression and the between-class distance of different classes of micro-expressions is maximized, so that data dimension reduction is achieved, and characteristic data are ranked in a vectorized mode according to a class discriminating capacity descending order. A nearest neighbor classifier is used for micro-expression identification. Compared with the methods of MPCA, GTDA, DTSA and the like, the effective micro-expression automatic identification method has the advantages of being high in rate of identification, low in computer performance requirement and easy to achieve.

The invention discloses a longitudinal federated learning system optimization method, apparatus and device and a readable storage medium. The longitudinal federated learning system optimization methodcomprises the steps: obtaining a sample alignment result obtained by conducting sample alignment on local training sample sets of all participation devices, wherein data characteristics of samples owned by all the participation devices are not completely the same; according to the sample alignment result, cooperating with each participation device to obtain multiple groups of input data with different data dimensions; and training a preset to-be-trained machine learning model with variable input data feature dimensions according to the multiple groups of input data to obtain a target machinelearning model. According to the longitudinal federated learning system optimization method, when a participant of longitudinal federated learning uses the model trained through longitudinal federatedlearning, the participant can use the model independently without cooperation of other participants, so that the application range of longitudinal federated learning is expanded.

A system and method for managing the development and manufacturing process of a pharmaceutical is disclosed. The method comprises capturing and recording the development and manufacturing history of the pharmaceutical drug in order to generate a product history. The product history is stored on a computer and is searchable in multiple data dimensions in order to easily retrieve information. The system automatically provides compliance management procedures in order to comply with regulatory standards for the pharmaceutical industry.

The invention discloses a hyperspectral remote sensing classification method based on support vector machine under particle optimization. High-efficiency high-accuracy classification of hyperspectral data which are high in data dimension and large in data volume can not be met by existing methods, and no ideal selection method is provided for parameters of a support vector machine method. According to the method, hyperspectral data is preprocessed, abnormal wave bands are removed under the influence of factors such as atmospheric absorption, then a certain proportion of data of various types are selected at random to serve as training data, a Gauss radical basis function is selected to serve as a kernel function mode, a classifier based on the support vector machine is trained, a speed updating formula of changing weight is designed, a certain proportion of particle mutation is guaranteed, an optimal classifier parameter is selected and obtained according to a particle swarm optimization algorithm, a plurality of second classifiers are trained, and a type which wins most votes is selected to be a final predicted type of data points according to a voting mode. According to the method, parameter optimization convergence ability of the classifier is strengthened, and the classification performance of hyperspectral remote sensing images is improved.

The invention discloses a semi-supervised dimension reduction-based hyper-spectral image classification method for mainly solving the problems of high calculation quantity caused by over high hyper-spectral image data dimensions and low classification accuracy of the conventional method. The method comprises the following steps of: expressing each pixel point of a hyper-spectral image by using a feature vector, and selecting a marked training set, a test set and a total training set; constructing local inter-class and local intra-class dissimilarity matrixes of the marked training set respectively to obtain a total local dissimilarity matrix; constructing and solving a feature value equation to obtain a projection matrix; projecting the marked training set and the test set to a low-dimensional space respectively to obtain a new marked training set and a new test set; and inputting the new marked training set and the new test set to a support vector machine, and performing classification to obtain class information of the test set. By adopting the thought of semi-supervision, higher classification accuracy can be acquired; and the method can be applied to the fields of map design, vegetation survey and military intelligence acquisition.

The invention discloses a fault diagnosis method and a fault diagnosis device of a power transformer. The method comprises the following steps: establishing a state characteristic data table based on an in-oil dissolved gas sample with a definite fault type; carrying out normalized treatment on the state characteristic data table and establishing a normalized fault table; calculating based on the normalized fault table to obtain various fault type clustering centers; based on the clustering centers, establishing a state standard spectrum matrix; calculating through an improved main component analysis method to obtain a characteristic value, a characteristic vector and a main component contribution rate; setting a threshold value and correspondingly selecting a main component; and calculating an Euler distance between a sample to be detected and the main component of a state characteristic sample main component and taking a state characteristic sample corresponding to a minimum distance value as a diagnosis result. The fault diagnosis method and device of the power transformer have the following advantages that a state standard spectrum is calculated by utilizing fuzzy clustering, and subject data removal and sample quantity restriction are avoided; meanwhile, the dimension of the data can be reduced and main characteristics for representing fault types are refined; and the accuracy of latent fault diagnosis in the power transformer is effectively improved.

The invention discloses a power distribution network visual platform construction method based on an SG-CIM. The method includes the steps that the SG-CIM is adopted as an information model for information interaction of a power distribution network, and an interaction standard is determined; then a visual platform provided with a three-layer structure including a data source layer, a platform supporting layer and an application development layer is built. The data source layer provides original data of the power distribution network, data objects of a system are classified according to master data dimensions, the data are packaged according to the uniform data model interface standard of the SG-CIM, and the data are pushed to the platform supporting layer. The platform supporting layer comprises a sharing fusion platform, a mobile operating platform and a GIS platform, the sharing fusion platform provides data aggregates, analyzes and extracts aggregate objects and provides corresponding sharing service, the GIS platform carries out data construction and provides external service, and the mobile operating platform provides mobile operating service. The application development layer is used for developing high-grade applications. According to the method, by extending the SG-CIM and determining the uniform interaction standard based on IEC61970 / 968, visual management on the power distribution network is achieved, and the management capacity of the power distribution network is achieved.