76 results about "Labelling algorithm" patented technology

A license plate character partitioning method based on a quick region labeling algorithm and a license plate master space location method belongs to the image processing technical field and relates to a license plate automatic recognition technique. Firstly the license plate region is converted through grey level histogram and grey level stretching conversion to realize reinforcement of the character region on the license plate; secondly a two-valued threshold value is calculated and the license plate grey level image is converted into a two-valued image; thirdly a connectivity analysis of the license plate two-valued image is carried out according to the quick region labeling algorithm and an alternate region of characters is obtain through a region growing method; fourthly a master space location is fixed from the license plate two-valued image; fifthly the final character region is obtained through mending and making up for the character region based on the feature of the license plate master space location; finally the characters are partitioned from the license plate grey level image. The license plate character partitioning method based on the quick region labeling algorithm and the license plate master space location method provided by the invention can effectively improve performances such as systematic versatility and location accuracy.

A computer-implemented system for designing a fire protection and/or piping system comprising a personal computer for loading programs into dynamic memory and storing data on a static memory device, means for providing user input, and program files comprising a process algorithm, traversing algorithm, and tagging algorithm. The process algorithm performs data validation, error checking and error resolution, saves data to a start point inserted by a user, pulls layer details into programming variables, applies processing logic to the system, and comprises a main pipes algorithm and a branch resizing algorithm. The traversing algorithm travels the system and presents objects to the process, main pipes and branch resizing algorithms in a logical order. The tagging algorithm attaches to each pipe in the system a tag with property set data pulled into and displayed in the tag. A method of using the foregoing system to design a fire protection and/or piping system.

The present invention discloses a computer system for providing an automatic selection of input language according to a content of the input text typed by a computer user. The system comprises a plurality of text input fields to receiving text characters by the input means; each the text input field is uniquely identified by Application/Document/Field triplet; a tagging algorithm for tagging each the input field with the default input language based on the data in the database related to the input field; and a language selection algorithm for automatic selection of typing language in the input field to the default input language of the input field. The default input language of each the input field is predetermined by the tagging algorithm according to the language properties of each the input field.

A multiplexer handling different types of traffic in different queue types, including in one embodiment time-stamped content such as in the form of MPEG service data, untimed content such as in the form of other variable bit rate data, and periodic tables, such as in the form of MPEG program specific information, allocates a tag to each packet, which is used to schedule packets for transmission. A tagging algorithm tags each packet in the queue, and a packet scheduling algorithm uses the tag to determine which packet is next to be transmitted, and a control algorithm determines when the scheduling algorithm will be executed. The algorithms cooperate to allow the multiplexer to handle traffic without requiring an internal clock to be synchronized with the input streams, nor stuffing of packets to pad out the contents of the variable bit rate traffic.

The invention relates to a method for extracting non-taxonomy relations between entities for Chinese patents. The method comprises following steps: S1): initializing basic relation sets of concept pairs; S2): automatically marking candidate relational words by use of a relational word marking algorithm based on domain relation intensity; S3): performing characteristic selection to obtain characteristic vectors; S4): performing classification to the characteristic data obtained in S3) by use of a support vector machine SVM. According to the invention, the extraction of non-taxonomy relations between entities for Chinese patents is defined as extraction of relations between entities which suit a SAO structure; a method of syntactic analysis characteristics and relational word dictionary characteristics in combination with traditional characteristics is brought forward, and a support vector machine is used for relation extraction so that the problem of right relation example structures with semantic errors in SAO structure relation extraction task is solved; the method is prior to traditional relation extraction method and can better satisfy practical application needs.

The invention provides a secondary encryption-based data validation and data recovery algorithm in cloud storage. The algorithm comprises the steps of firstly carrying out first encryption after data segmentation, generating a hash value of a file by adopting an asymmetric encryption technology, utilizing the hash value of an original file as a secret key for secondary encryption of a random ciphertext data block and then utilizing the hash value as the secret key of a next ciphertext data block; and integrating data integrity and possession validation into a decryption operation of data access and then defining a data recovery label algorithm to generate a recovery label of the data for recovering original data. By adopting a secondary encryption technology, the overhead of computation and the complexity of validation are reduced by integrating data validation into a decryption process, and the data integrity and possession are validated in each access, so that the loss due to the fact that a user is misguided by wrong data is avoided. The memory space occupied by the recovery label is small and the recovery is simple and accurate.

The invention discloses a method for testing a categorical data set. The method includes the steps that after the categorical data set is obtained, if the categorical data set needs to be processed in a standardization mode, the categorical data set is standardized in an absolute standard deviation mode; the categorical data set is divided into a training set and a test set, an ecological niche cultural algorithm is used for learning to obtain dual weight values of a dual weighted naive Bayes multi-label classifier, and then the training set is trained to obtain optimized weight values; the optimized weight values are substituted into the test set for prediction. A data training process is added on the basis of a traditional naive Bayes multi-label algorithm, and then the categorical data set is predicted. As traditional data classification is improved through a particle swarm optimization algorithm, the improved algorithm can improve the classification accuracy.

The invention discloses a watermark embedding method of relational database based on (t, n) threshold, which sets the following parameters according to relational database: A watermark picture, a big prime number p in Lagrange interpolation polynomial, a user key in marker algorithm, t and n values in (t, n) threshold, and a data changing range len. The data changing range len is the tth after the radix point; the system makes value treatment on the watermark picture to obtain the value expression form bigint; the system completes the embedding of watermark via (t, n) threshold algorithm and Lagrange interpolation polynomial. The invention also provides the extracting method of the watermark. Therefore, the invention has the advantages of good robustness, stable watermark pictures, high calculation efficiency, and high practice.

The invention discloses a similarity based semantic Web service clustering labeling method. The method is characterized by comprising two parts of realizing semantic Web service similarity calculation and realizing a semantic Web service clustering labeling algorithm. During the semantic Web service similarity calculation, in combination with results of input/output (I/O) parameter mixed similarity calculation and service description keyword similarity calculation, a calculation result of semantic Web service similarity is comprehensively obtained, and the difference and similarity between service functions are reflected; and I/O parameters can directly describe functions of corresponding service modules and serve as measurement standards for calculating the semantic Web service similarity from a functional perspective. According to the method, the accuracy of similarity calculation can be improved and the performance of a service discovery system is further improved.

The invention discloses an image labeling algorithm based on a visual sense and semantics and the algorithm can be used to realize automatic labeling of an image area. An image area labeling algorithmbased on a visual semantic relation graph comprises two portions: construction of the visual semantic relation graph and image labeling based on the visual semantic relation graph. In the visual semantic relation graph construction, a global similarity and an area similarity among images, semantic association of image area labels and association among levels are considered. On the constructed visual semantic relation graph, semi-supervised learning is performed, an association degree between an unlabeled image area and the image area labels is acquired and label prediction is performed on theunlabeled image area.

The invention discloses a multi-instance multi-label learning based area monitoring method used in a wireless sensor network. Fisher Kernel is utilized to compress multiple sensor node data at the same moment; after compression, the whole wireless sensor network corresponds to a single sample data rather than multiple sample data at a certain moment; an MMD is utilized to compress historical packet data, and most representative packet data is picked out to form a data subset. Training of the compressed data and assessment on a monitoring region state are completed by utilizing a rapid multi-instance multi-label algorithm. Rapid network state prediction can be achieved by establishing a sensor network state prediction model on sensing datasets subjected to two-layer compression.

The invention provides a safe development method for application middleware of a mobile internet intelligent terminal. The safe development method includes: A, modeling: A1, determining the safety level of a program through a multi-pattern matching algorithm and screening out a subfunction containing sensitive application program interfaces (API), A2, analyzing the subfunction, and A3, constructing a system migration picture of the subfunction to complete modeling of the system; B, specification: using standard language complementary transistor logic (CTL) to describe malicious actions of a program to construct a syntax tree of a CTL formula, and performing translation, analyzing, EF replacing and variable binding on the CTL formula; and C, verification: using a non-recursive marker algorithm to achieve model verification. The safe development method for the application middleware of the mobile internet intelligent terminal remedies shortcomings of traditional Android systems in authority endowment and can effectively protect safety of user private data, actual active defense is achieved, and all access to the private data needs to be chosen by users.

The invention provides a labeling algorithm for a biomedical image based on LDA (Lejeune Dirichlet Allocation), mainly aiming at that the biomedical image is labeled, each image has a corresponding text file in a biomedical image language database and the particularity is combined. The LDA is used for extracting a subject term from captions of the image; then a context is extracted from the corresponding text file of the image according to the captions; and finally, the LDA is used for modeling the context; an obtained subject term is used as a final label of the biomedical image. The labeling algorithm has the beneficial effects that the biomedical image is labeled and the captions and the text file which are related to the image in a data set are sufficiently utilized to dig label words of the image; the accuracy is high and the plurality of label words can be generated at one time. After the biomedical image is accurately labeled, the related image is searched by using keyword index; the labeling algorithm is convenient and rapid and meets a text retrieval habit of people.

The invention discloses a fast connected region marking algorithm for binary image streams, which can quickly identify the connected region according to the continuous image streams formed by a lineararray image sensor, and proposes a fast connected region marking algorithm for binary image which is suitable for FPGA parallel implementation. The algorithm scans the image line by line from left toright and from top to bottom, tags the target pixels, calculates the number of connected regions, and computes the maximum number of horizontal and vertical target pixels of each connected region. Only two adjacent lines of data are needed to make the tagging judgment, and only one scan is needed for the image. The algorithm can not only mark the connected region of binary continuous image stream, but also mark the connected region of static binary image.

The invention discloses a seed cotton mulching film hyperspectral visual label algorithm for deep learning, and belongs to the technical field of hyperspectral imaging and deep learning. The method comprises the following steps: analyzing a characteristic spectrum section of a spectrum by utilizing an adjacent alternative algorithm, performing dimensionality reduction on image data by combining adichotomy, generating a hyperspectral pseudo-color image of the seed cotton mulching film, marking the pseudo-color image in a frame selection marking form, and correspondingly generating an image label of a high-dimensional spectrum through two-dimensional image marking. The hyperspectral technology is applied to the field of seed cotton mulching film recognition, for residual transparent mulching films which cannot be recognized by a color camera and a black-white camera, hyperspectral images of the seed cotton mulching films at 1000-2500 nm are collected through a hyperspectral imager, andthen the residual films with different spectral characteristics from seed cotton are recognized and classified.

The invention relates to the technical field of image analysis, and discloses an image analysis method based on big data. The method comprises the following steps: obtaining massive images, and converting the images into grey-scale maps by using a grey-scale map conversion method; performing binarization processing on the gray-scale maps by using a local maximum between-cluster variance method toobtain binarized images; performing enhancement processing on the binarized images by using an image enhancement algorithm based on red channel compensation to obtain enhanced images; utilizing an image labeling algorithm based on deep learning to label image labels, and storing the images and the image labels into a distributed database; and caching the images and the image labels in the distributed database into the system by utilizing a path-based caching algorithm, thereby realizing more efficient image analysis and retrieval. The invention also provides an image analysis system based on big data. According to the invention, image analysis based on big data is realized.

The invention discloses an automatic video annotation method based on multi-modal private features, and the method comprises the steps: carrying out the preprocessing and manual annotation of a videofile, and filtering a manual annotation result; utilizing a generative adversarial network to extract common features among different modal features; stripping common features in the original featuresto obtain private features of different modes; integrating the extracted common features and modal private features to form new features of the video, and learning by using a multi-marking algorithmto obtain an automatic video marking classifier; sending the to-be-labeled video sample into a classifier to obtain a classification result, and realizing automatic labeling; and performing sampling inspection on the labeling result. By adopting the method, the classification model for automatic video annotation can be trained, the video features are integrated again by utilizing the private features of different modes of unknown annotated videos, the annotation task is automatically completed, and the manual annotation time and cost can be remarkably reduced.

The invention discloses a new multi-label algorithm analysis method combining infrared spectroscopy and chemometrics. Four components of (octogen (HMX), hexogen(RDX), triamino trinitrobenzene (TATB) and trinitrotoluene (TNT)) in plastic bonded explosives (PBX) can be fast and quantitatively recognized at the same time, and measurable samples comprise single-component, double-component and three-component PBX. The method includes the following main steps that PBX samples in different concentrations are prepared, wherein 118 training sets exist, 28 independent testing sets exist, four kinds of explosives in the training sets are recognized through a support vector machine to be subjected to modeling and optimization, an optimal model is applied to the training sets and five true samples, and whether unknown mixed explosives contain HMX, RDX, TATB and TNT or not can be directly predicted. Compared with a traditional experimental method, no pre-separation needs to be performed on PBX, only the sample infrared spectrum needs to be measured, operation is easy, analysis speed is high, and accuracy is high.

The invention relates to an e-commerce user portrait data generation method and apparatus, and a computing device, wherein the method comprises the steps of collecting user basic data; processing theuser basic data to generate user-defined label data; calculating the user basic data and the user-defined label data by using a user label algorithm model to obtain a user model label; and generatinguser portrait data according to the user-defined label and the user model label. According to the embodiment of the invention, the user tag comprises the basic definition tag, and the user model tag is expanded, and the user tag type is expanded, so that more complete and accurate user portrait data can be generated, and more accurate user commodity recommendation, advertisement recommendation andchannel selection can be made for an e-commerce platform.

The invention discloses a rapid water pollution tracing method based on big data analysis in the technical field of environmental protection detection. The rapid water pollution tracing method comprises the following steps: a) acquiring data through water quality acquisition equipment of a park or a riverway; b) applying a label algorithm to the data acquired by the big data computing platform, and labeling each device with a water quality label; c) performing region and time domain analysis on the water quality label of each water quality acquisition device, judging the water flow direction, and drawing a water quality map; d) performing nonlinear data operation through the water quality label of each device and the qualified parameter of the artificially defined water quality, and providing early warning for an area where water quality pollution may exist; e) when the pollution source of the water area needs to be positioned, automatically and quickly caclculating the possible pollution source according to the water quality map; and f) according to the water quality map, judging the change period of water quality, dynamically reflecting the water quality of the water area, and achieving real-time supervision and precise positioning of pollution sources and pollution occurrence time.