53 results about "Granular computing" patented technology

The application borrows terminology from data mining, association rule learning and topology. A geometric structure represents a collection of concepts in a document set. The geometric structure has a high-frequency keyword set that co-occurs closely which represents a concept in a document set. Document analysis seeks to automate the understanding of knowledge representing the author's idea. Granular computing theory deals with rough sets and fuzzy sets. One of the key insights of rough set research is that selection of different sets of features or variables will yield different concept granulations. Here, as in elementary rough set theory, by “concept” we mean a set of entities that are indistinguishable or indiscernible to the observer (i.e., a simple concept), or a set of entities that is composed from such simple concepts (i.e., a complex concept).

The invention discloses a monitoring video multi-granularity marking method based on generalized multi-marking learning. The monitoring video multi-granularity marking method of the invention takes the backdrop of public security video monitoring content analysis and carries out a research according to video characteristic multi-layer acquisition and multi-granularity representation theory and method. The monitoring video multi-granularity marking method comprises steps of analyzing and extracting characteristics of different layers of an object in a video on the basis of a multi-marking learning theory and a deep learning theory , constructing a generalized multi-mark classification algorithm on the basis of a multi-mark learning theory and a deep learning theory, and characterizing a multi-granularity representation model of video information on the basis of a granular computing theory and a nature language understanding technology. The monitoring video multi-granularity marking method, targeting the monitoring video content field, carries out a research going deep into the system, constructs a multi-mark learning algorithm through the deep learning theory and can provide an effective theory and method to multi-layer video information. Through simulating the way that human recognize and describe the image, the monitoring video multi-granularity marking method establishes the multi-granularity video representation theory and method, provides a new idea to the video content analysis, and lays theory and application foundations for pushing development of future video monitoring intelligentalization.

The invention discloses a parallel analysis of digital terrain oriented data splitting and distributing method, and belongs to the crossing technical field of digital terrain analysis and parallel computing. The method comprises the following steps: (1) reading DEM (digital elevation model) data, and establishing a data granularity model; (2) calculating the granularity of the minimum data based on a memory paging schedule strategy; (3) calculating the granularity of composite data based on a quadtree storage strategy; (4) calculating a calculation method and a cutting mode of the numbers of retardant rows and lines of node data granularity; (5) calculating the distribution number of node data based on the composite data granularity; and (6) distributing node data by a main node accordingto the distribution number of nodes. The method provided by the invention is independent of the number of idle nodes, the composite data granularity is used as a basic unit of node data distribution,so that the communication amount of data is reduced; and load balance is guaranteed among calculated nodes with the same performance.

In order to overcome the defects of an existing gingival margin curve extraction mode, the present invention discloses a gingival margin curve design method for a personalized implant tooth. The gingival margin curve design method for the personalized implant tooth comprises the following steps of: a step 1 of respectively reconstructing gingival margin profiles of a residual tooth of a patient suffering from tooth missing and a three-dimensional dental model of a sample, extracting feature regions of gingival margin curves according to an obtained maximum principal curvature value, extracting gingival margin feature lines of the residual tooth of the patient suffering from tooth missing and the sample by utilizing a granular computing and cellular automaton combining method, then respectively fitting the gingival margin feature curves of the residual tooth of the patient suffering from tooth missing and the dental model of the sample, and finally, constructing a single gingival margin curve of the residual tooth of the patient suffering from tooth missing and the sample; a step 2 of constructing a gingival margin biological multivariate statistical analysis model of the sample; and a step 3 of designing a personalized gingival margin curve of a missed tooth of the patient. The method disclosed by the present invention aims to improve accuracy and efficiency of designing the implant tooth gingival margin curve, improve the repaired gingival margin profile form of the implant tooth, and improve a repair success rate of the personalized implant tooth.

Provided is a rough set and granular computing merged method for mining online data of distributed heterogeneous mass urban safety data flows. A rough set theory and a data mining technology are introduced to achieve analysis and mining of the urban safety data flows, a distributed heterogeneous mass data flow concept formalized description model is firstly established, coupling analysis is performed on the concept model, node association is mined and found by adopting a rough set mass data partitioning method based on attribute reduction according to concept lattice based node pair association rules, finally key event information influencing urban safety is obtained through flexible granular computing, and urban digital management is achieved. The rough set and granular computing merged method for mining the online data of the distributed heterogeneous mass urban safety data flows is high in mining accuracy, good in timeliness and good in data validity.

The present invention relates to the field of traffic information release and traffic management and control and discloses a road traffic flow parameter prediction method based on granular computing. The method comprises the steps of (1) replacing a data point by information particulate to be the basic unit of data mining analysis, (2) with granular computing ideology throughout the whole prediction framework, taking granular processing as a data processing method with a unified structure, allowing a policy maker to clearly understand the positions of various forms of systems in mutual interaction, grasping the communication mode of the systems, and establishing an enhanced harmonious environment among different ways, (3) with a fuzzy time series and the Gath-Geva cluster theory as the basis, by focusing on the commonalities of existing formal methods, recognizing the orthogonality of an existing good frame ( such as the probability theory and the probability density functions of various variables), with variable granularity concept as a basis, establishing the interval length analysis model of a granularity range according to a numerical entity, and thus realizing pattern recognition and speculation on the above basis.

The invention discloses a granular computing method for aiming at a large-scale economical scheduling problem of a power grid. The method comprises the steps of 1, establishing an economical scheduling model which comprises an objective function and a restricting condition of the model; 2, performing layered granulating on the power grid; 3, performing parameter equalization; 4, performing granularity dividing; 5, processing a restraining condition; and 6, optimizing set output by means of the granular computing method. The granular computing method has beneficial effects of realizing comprehensive factor consideration, improving computing precision, presetting layered granulating and applying an analytic hierarchy process for solving the problem, greatly reducing computing time and improving computing efficiency. For a large-scale power grid, if an appropriate set dividing method is utilized and particle parameter equalization is performed, a problem of difficult convergence can be settled and furthermore computing speed can be improved.

The embodiment of the invention provides an intelligent control device for a micro intelligent vehicle. The intelligent control device comprises a camera (10), a signal detection module (20), a control rule module (31), a control parameter calculation module (32), an Arduino control panel (33), a motor driver (40) and a steering engine driver (50). The invention also provides an intelligent control method for the micro intelligent vehicle. The control rule is acquired by employing the granular computing theory, the micro intelligent vehicle is intelligently controlled, and a control method for traditionally controlling a precise mathematical model required to be established, controlling in real time by establishing hierarchical control rules and gradually fining is avoided. The intelligent control device has the advantages of high control precision, high real-time property and high interference resistance.

Embodiments of the present invention provide systems and methods for providing security in a computing environment. These systems and methods can be applied to cloud computing environments. Interfaces allow a user to request and gain user access to applications (and their equivalents) even if the applications prior to implementing the present invention do not allow the user to request or gain user access to the applications. The embodiments of this invention can operate at the granular computing level.

The invention discloses a full-coverage granular computing based K-medoids text clustering method. The method comprises the steps of 1) preprocessing texts, including, Chinese word segmentation and stop word removal; 2) performing characteristic extraction on the texts, setting a high frequency word threshold and a low frequency word threshold, filtering away high-frequency words with insufficientdiscrimination degrees and low-frequency words with weak representativeness, and then building a word vector spatial model by utilizing a TF-IDF algorithm; and 3) clustering the texts, firstly performing coarse clustering on the texts by utilizing single-pass and calculating an initial clustering center candidate set by utilizing a concept of granularity importance of a full-coverage granular computing theory, and then calculating an initial clustering center based on the density and a maximum-minimum distance algorithm, and finally performing text clustering by utilizing a k-medoids algorithm. The full-coverage granular computing based K-medoids text clustering method solves the problems of iteration times increase and relatively big fluctuation of clustering results of the traditional K-medoids clustering algorithm in which the initial clustering center is selected randomly, and also solves the problem that the initial clustering center is located at the same type of the cluster inthe currently improved K-medoids clustering algorithm.

The invention relates to a multi-granularity-based cloth image retrieval method. According to the method, the granular computing theoretical research is adopted, and characteristic granularities of cloth images can be adjusted flexibly; by means of the method, multi-granularity retrieval can be conducted on the cloth images. Color characteristics and texture characteristics are used for describing the cloth images, meanwhile, three different color combination granularities (the single-main-color color combination granularity, the double-main-color color combination granularity and the three-main-color color combination granularity) are established for the color characteristics, the knowledge granularities in the field of the cloth images are used in cooperation, and therefore the cloth image retrieval method more conforming to human visual perception is realized.

The invention discloses a matrix dynamic attribute reduction method in which objects and attributes are simultaneously added, which relates to the technical field of rough set and granular computing theory in data mining. Firstly, the minimum attribute reduction REDU of a decision table before change is calculated. When objects and attributes are added to the decision table at the same time, whether the relative knowledge granularity of the minimal attribute reduction of the decision table is equal to the relative knowledge granularity of the changed decision table is calculated, If so, REDU is the minimum attribute reduction after the change, otherwise, the matrix method and incremental mechanism are used to calculate the external importance of all attributes of the changed decision tableexcept REDU, Select the largest attribute to add to the minimum attribute reduction, calculate the relative knowledge granularity until it is equal to the relative knowledge granularity of the changed decision table, delete the redundant attributes, and obtain the minimum attribute reduction of the changed decision table. The invention effectively solves the problem that the minimum attribute reduction can be quickly calculated when the objects and the attributes in the decision table are simultaneously increased, and contributes to improving the efficiency of the dynamic data knowledge mining.

The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.

The invention discloses a big data method based on granularity calculation in a cloud environment. The method comprises the following steps: (1) establishing a variable-precision fuzzy rough set modeloriented to hybrid data analysis; an extended ziarko variable-precision rough set thought is combined with a fuzzy rough set theory; wherein the innovation point of the variable-precision fuzzy roughset model is a determination rule of upper and lower approximate sets, information table elements are considered in the approximation of the upper and lower sets to evaluate the inclusion degree of the decision approximate set, and the elements are included in the approximate set with high enough inclusion degree; (2) a data roughness measurement method based on random entropy is provided, and aneffective roughness measurement technology is convenient to research; and (3) designing a mass data parallel attribute reduction acceleration algorithm based on granular calculation, fully combiningbig data analysis and processing with a cloud calculation platform, and adopting a model-data parallel research method to solve the problem of mass data and high-dimensional complex data attribute reduction.

The invention relates to a multi-application fine-grained unloading method and system architecture for a cloud-side collaborative network, and the method for realizing multi-application fine-grained unloading comprises the following steps: uploading application data to be unloaded to a decision controller by a mobile device; storing the uploaded application data into an application data pool; calculating a probability B-Level for each task in the application data; taking out the ready tasks from the application data pool, and arranging the ready tasks in a queue according to a set sorting level; taking out the ready tasks from the sorting queue in sequence and performing scheduling planning on the ready tasks; and distributing the task to the target computing service equipment according to the scheduling plan. According to the multi-application fine-grained unloading method, an enforceable scheme in the cloud-side collaborative network computing system is given by setting a decision controller, when the mobile application is subjected to fine-grained unloading, the application is decoupled, and only a task in a ready state is assigned, allocated and allocated to target computing equipment every time, and the time delay of multi-application fine-grained computing unloading under the cloud-edge collaborative network is effectively reduced.

The invention relates to an instruction disassembling method, a processor, an instruction disassembling device and a storage medium. The instruction disassembling method comprises: according to the analyzed data size of the first source operand and a preset splitting granularity, calculating to obtain a target cycle index and a data reading capacity; reading first sub-data from a first storage device, storing currently read first sub-data into a second storage device, and executing an operation according to an operation instruction; and storing the obtained current operation result into the first storage device, and continuing to determine the data reading capacity until the current cycle index is equal to the initial value or the current cycle index is equal to the target cycle index, thereby finishing the operation corresponding to the operation instruction. By splitting and circularly reading the first source operand, splitting big data into small data for circular operation processing, and circularly reading the operand according to the data reading capacity, the size of data capable of being accommodated during operation processing meets the requirement, and the operation speed is increased.

The invention discloses a question and answer task downstream task processing method and model, and the method comprises the steps: obtaining a context representation HCKey perceived by key information and a question representation HQKey perceived by key information, and generating a context representation G perceived by a question; calculating an update vector z and a memory weight g based on G, and updating G to obtain an output vector Gg; generating a context granularity vector GC and a sequence granularity vector GCLS, generating an output vector Cout, using softmax to calculate the probability that each character in the context serves as an answer starting and ending position, and extracting a continuous subsequence with the maximum probability to serve as an answer. The invention provides a two-way cascading attention mechanism, and constructs a mechanism taking fine reading and slight reading as a whole and a multi-granularity module based on a granular computing thought, so that the model effectively pays attention to and screens effective information, better understands texts under various granularities, gives out more accurate answers, and improves the accuracy of text analysis. And the performance is improved on the basis of a baseline model.

The invention discloses a traditional Chinese medicine case data mining method. According to the method, a traditional Chinese medicine case knowledge base based on granular computing is constructed on the basis of collecting a large number of traditional Chinese medicine cases and an association rule mining algorithm and a clustering algorithm are utilized to carry out efficient data mining on traditional Chinese medicine medical cases, so that the problems that term expression is fuzzy and complex in massive traditional Chinese medicine medical cases, and a data mining algorithm is low in efficiency and not wide in application due to the fuzzy and complext term expression are solved, application of the data mining technology in the field of traditional Chinese medicine is broadened, andtheoretical and technical support is provided for promoting development of related industries.

The invention relates to a text feature selection method based on full-coverage granular computing, which comprises the following steps: 1) segmenting a sample text set, stopping the use of words, andpart-of-speech tagging; 2) extending the position and part of speech factors to the document of feature words in a TFIDF algorithm with different weight coefficients To calculate the text-word frequency probability of the feature words; 3) using a bLDA topic model to generate the probability of the feature words to calculate the semantic information of the feature word; 4) granulating the featurewords, reducing the feature words by using the knowledge reduction algorithm of full coverage granulating computation, and obtaining the document of the text-word frequency probability of the reducedfeature word set; 5) combining bLDA and the improved TFIDF algorithm to calculate the weight of feature words to obtain the text-word frequency probability of the reduced feature word set. Through the invention, the part of speech, position and semantic factors of the feature words are considered, and meanwhile, the feature words which are not strong in meaning to the text are removed, so the more representative feature word set is selected and the precision of clustering is improved.