67 results about "Sequence segmentation" patented technology

The invention discloses an automatic segmentation method for a lung-area CT sequence. The method is characterized by comprising the steps of 1) an image I of the lung-area CT sequence is input; 2) the image I is segmented via interactive region growth; 3) according to a segmentation result, an initial contour is obtained, and seed-point coordinates of adjacent images are calculated; 4) based on the seed-point coordinates of the image I, a present image II in the sequence is segmented via the interactive region growth in step 2; and 5) step 2, 3, 4 are repeated to determine whether all images in the CT sequence are segmented, and if no, the step 3 is turned to. The automatic segmentation method automatically calculates mapping of the seed-point areas by combining the image context object characteristic continuity of the sequence to realize sequence segmentation, thereby obtaining complete three-dimensional area data of the lung, and providing basis for VOI extraction and classification of a suspected lung tubercle for a CAD system.

The invention discloses a stomach computed tomography (CT) sequence image segmentation method based on interactive region growth, which mainly solves the problems that in the prior art, CT sequence segmentation speed is slow, and poor segmentation is easy to occur. The method includes: firstly, a seed point is selected manually in a target area to be segmented in a first image, the interactive region growth is used for performing segmentation, a center of a segmentation result and eight neighborhoods of the center are projected into a next CT image to serve as seed points, the interactive region growth is continuously used for performing segmentation to obtain the target area of the current image, and the segmentation result of the previous image is projected into a next image repeatedly to serve as a seed point to be segmented continuously until segmentation of a whole sequence is completed. Compared with a traditional serial region growth, the stomach CT sequence image segmentation method based on the interactive region growth has the advantages of being rapid in speed, good in effect and the like, can be used for segmenting stomach CT sequence images, and can well segment target areas which may occur in stomach lymph gland in the sequence.

A cell search timing synchronization method includes: performing cross-correlation operation for main synchronous signals kept by UE (user equipment) and received signals so as to obtain correlation sequences; segmenting the correlation sequences to obtain sequence segments corresponding to the correlation sequences; and computing a peak-to-average value of the sequence segment corresponding to each correlation sequence, and obtaining a timing synchronization position according to the maximum peak-to-average value of the correlation sequences. After application of the cell search timing synchronization method in the embodiment, cell search is accurately timed and completed in the presence of strong uplink interference signals.

The invention provides a correlation time series issuing method and system based on differential privacy. The correlation time series issuing method comprises segmenting an original sequence to be issued through an observation window with a preset length, and processing a subsequence section in the observation window as a short-time stationary process; calculating the autocorrelation function of the subsequence in the window first, then generating a correlation Laplacian noise sequence being consistent with the autocorrelation function of the subsequence, overlay noise onto the subsequence, and issuing a sequence containing noise after the original sequence is processed. According to the invention, a sliding window mechanism and a correlation Laplacian noise mechanism are employed, and the problem that in an existing correlation time series issuing method by means of a differential privacy mechanism, the actual effective privacy protection intensity cannot reach the preset value and the data availability is low is solved.

The embodiment of the invention provides a Polar coding method and coding device. The coding method comprises the following steps: dividing an information bit sequence to be encoded into a plurality of segments when the coding parameter satisfies a preset segmentation condition; and performing Polar coding on the plurality of segments to obtain a plurality of encoded bit sequences. By adoption ofthe segmentation coding method, the number of times of repeated rate matching schemes under certain conditions can be reduced, and the performance loss caused by repetition can be reduced.

The invention discloses a polar code encoding method, especially relates to a polar code encoding method of a sectional-cascaded Hash sequence, and belongs to the field of the channel encoding. The encoding method comprises the following steps: performing Hash check on information bit sequence sections, and orderly cascading the information bit sequences with checked bit sequences to construct a new bit sequence to perform the polar code encoding. The core of the method is that the check accuracy is improved by using the independent random feature of the Hash sequence, the probability of generating the same check bit by different information is lowered; and meanwhile, the sectional checking method not only can be used for checking the information bit, but also can be used for checking the former check bit.

The invention discloses a phonetic segmentation method and device for speech synthesis, wherein the method comprises: acquiring corpus text and converting it into a pinyin sequence, wherein the pinyin sequence comprises a plurality of phones, each having multiple states; segmenting speech data corresponding to the corpus text into multiple speech frames, and acquiring acoustic features of the speech frames; with respect to each state, clustering the speech frames according to the acoustic features, and generating multiple nodes corresponding to the state; calculating an optimal path corresponding to the pinyin sequence based on a dynamic programming algorithm and a two-dimensional state network, and segmenting the pinyin sequence according to the optimal path. The phonetic segmentation method and device for speech synthesis in embodiments of the invention enables the accuracy of pinyin sequence segmentation, thus improving the reliability of a speech synthesis acoustic model and enable speech in text-to-speech conversion to be smoother and more natural.

The invention provides a road intersection control period dividing method and system based on multidimensional time sequence segmentation, and the method comprises the steps: configuring a road intersection stationary information table; taking parameters or indexes for reflecting the traffic demand features as the period dividing basis, and obtaining a data time sequence; gathering the traffic flow data at different spatial levels according to the road intersection stationary information table; determining a road intersection traffic flow time sequence dimension d through road intersection canalization conditions; carrying out the time-dimension gathering of the traffic flow detection data; constructing a T*d basis matrix C; employing a dynamic planning and correction algorithm of non-parameter multi-change-point analysis for determining a dividing number and a specific dividing scheme of the basis matrix C. According to the invention, an automatic period dividing mechanism reduces manual intervention, and the traffic demand features are taken as the dividing basis, thereby reducing the technical requirements for a signal configuration link in an application, and improving the period dividing reliability and stability.

The invention provides a segment parallel coding method of a feedforward convolutional code. The method is characterized in that: carrying out parallel coding on input information bit sequence by a plurality of serial feedforward convolution encoders, carrying out segmentation on an input information bit sequence to be coded, respectively applying a serial feedforward convolution encoder to code each segment of the information bit sequence, wherein, each segment of the information bit sequence is named as a block, in segmenting, overlapping bits, or after coding, inputting full 0 bits for supplement, generating overlap, according to an overlapped part, connecting code results of each serial feedforward convolution encoder in series, and taking the results as final output of parallel feedforward convolution coding. According to the invention, based on information sequence blocking, a characteristic of a feedforward convolution encoder is fully utilized, resource occupation and power consumption are reduced, and coding throughput is raised.

The invention relates to word segmentation technology and provides a word input method and a word input device aiming at the defects of large amount of computation and low identification accuracy of ambiguous words in the existing word segmentation methods. The word input method comprises the steps of receiving the input word-building element sequence and searching and displaying at least a word string matched with the word-building element sequence; and receiving the input word string selection command and generating and outputting corresponding word segmentation packages based on the selected word string in the displayed word strings. The invention also provides a word input system. The method and system provided by the invention can automatically finish the word sequence segmentation in the input process; therefore, the technical scheme provided by the invention can greatly reduce the amount of computation of existing word segmentation operation and improve the word segmentation efficiency. In addition, the real segmentation intention of the user can be accurately reflected according to the segmentation on the word sequence conducted by the word segmentation packages, thus greatly improving the word segmentation accuracy of the word sequence.

The invention discloses a trill identification method and a trill identification device. The trill identification method comprises steps that target audio data corresponding to a target song comprising at least one identification note is acquired; the audio data fragment of the target audio data corresponding to the identification note is extracted, and the first fundamental frequency of the audio data fragment is extracted, and then a corresponding first note value sequence is acquired; the first note value sequence is divided into at least two note value sequence fragments according to at least one preset periodic value, and by aiming at every preset periodic value, a note distance between the two adjacent note value sequence fragments of the at least two note value sequence fragments is calculated, and is used as the periodic note distance corresponding to the periodic value; the minimum value of the periodic note distance is determined, and a target period corresponding to the minimum value is acquired; when the target period is smaller than a preset periodic threshold value, the target audio data is determined to be trill audio data. By adopting the trill identification method and the trill identification device, the identification of the trills of the audio data is realized.

The invention provides a method and device for predicting epidemic situation data in a segmented mode, a computer readable medium and electronic equipment, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring an effective regeneration number corresponding to each unit time of a target disease in N continuous unit periods to obtain a first sequence containing N effective regeneration numbers, N being an integer greater than 2; segmenting the first sequence to obtain a plurality of sub-sequences, and determining a fitting function corresponding to each sub-sequence according to the segmented sub-sequences; and predicting epidemic data of the target disease based on the plurality of fitting functions. According to the technical scheme, the epidemic situation data can be subjected to related prediction on the basis of the effective regeneration number, so that the method has more practical application significance; meanwhile, the effective regeneration number is fitted in a segmented mode, prediction is achieved on the basis of multiple fitting functions, the prediction accuracy can be improved, and the epidemic situation data prediction accuracy is improved.

The invention discloses an HLA sequencing peak graph identification method, which comprises the following steps of: (1) constructing a peak graph identification model: (1) collecting machine peak graph data under the existing HLA, and (2) pre-processing to complete information extraction, original sequence comparison, sequence segmentation and dislocation repair work of a binary ab1 file; 3, feature extraction; (4) collecting a large amount of artificially recognized data, training the data by using a random forest algorithm, and constructing a peak graph recognition model; (2) carrying out base recognition on the to-be-detected HLA first-generation sequencing original off-machine data by utilizing a peak diagram recognition model; (3) arranging the recognized base sequence, and reassembling the single-chain and double-chain part sequences; and (4) outputting an identification result. According to the recognition method, the peak diagram sequence information can be accurately obtained,the overall accuracy is 99.5% or above, and the working efficiency of HLA data interpretation personnel is greatly improved.

The invention discloses a rainfall prediction method and system based on an artificial intelligence algorithm and a knowledge graph, and belongs to the technical field of weather forecast, and the method comprises the following steps: constructing a multi-modal data container, inputting different types of meteorological data into the multi-modal data container according to the structural characteristics of the data, and obtaining multi-modal data; performing space-time alignment, data cleaning and preprocessing and data sequence segmentation on the multi-modal data; constructing a knowledge graph about rainfall prediction; and creating a multi-modal rainfall prediction model based on an artificial intelligence algorithm, and carrying out intelligent correction on predicted rainfall data. According to the method, different types of multi-modal meteorological data are fully utilized, the rainfall prediction precision is improved through the multi-modal rainfall prediction method, a rainfall prediction knowledge graph is constructed, a model prediction result can be intelligently corrected, the uncertainty of an artificial intelligence algorithm is reduced, and the rainfall prediction accuracy is improved. And the accuracy and reliability of model prediction are improved.

The embodiment of the invention provides a character recognition method and device, electronic equipment and a readable storage medium, and aims to improve the recognition accuracy of character recognition. The method comprises the steps of obtaining N character sequence segmentation images of a to-be-recognized image, wherein the pixel value of each pixel point in the nth character sequence segmentation image in the N character sequence segmentation images represents the possibility that a character corresponding to the pixel point is the nth character; obtaining M character category segmentation images of the to-be-identified image, wherein the pixel value of each pixel point in the mth character category segmentation image in the M character category segmentation images represents the possibility that the pixel point belongs to the mth character; and determining a character sequence and a character category of each character in the to-be-identified image according to the pixel valueof each pixel point in each character sequence segmentation image and the pixel value of each pixel point in each character category segmentation image.

The invention provides a rib instance segmentation, counting and positioning method and system, and the method comprises the following steps: carrying out the semantic segmentation of ribs in a chest CT, and obtaining a rib semantic segmentation binary mask; collecting a manual label corresponding to the rib semantic segmentation binary mask to obtain a rib instance segmentation mask; processing the rib semantic segmentation binary mask and the rib instance segmentation mask, and making a sequence segmentation training sample set; establishing a deep convolutional neural network model; training the deep convolutional neural network model by using the training sample set to obtain a deep convolutional neural network capable of predicting the topmost ribs; inputting the rib semantic segmentation binary mask into the trained deep convolutional neural network, obtaining a prediction mask of the topmost layer of ribs from top to bottom in sequence, and further obtaining a predicted rib instance segmentation mask. According to the method, explicit modeling is carried out on the rib counting problem, the ribs are segmented one by one from the lung top to the lung bottom, and rib counting and positioning are achieved.

The invention relates to an industrial control protocol analysis method, in particular to an efficient industrial control protocol analysis method based on deep learning, which comprises the following steps: acquiring flow data messages of an industrial control system from a simulation platform and an open source platform, analyzing protocol field change characteristics, and adopting an unsupervised learning method to analyze the flow data messages of the industrial control system. A voting expert algorithm (VE) performs sequence segmentation and format feature inference on protocol fields. And taking the processed field sequence features as input, building a bidirectional long-short-term memory neural network model (BiLSTM-AM) added with an attention mechanism, performing training, using softmax as a protocol field classifier, and realizing industrial protocol field classification result prediction according to a classification result. According to the invention, based on the bidirectional long-short-term memory neural network model added with the attention mechanism, a good detection result is achieved in unknown industrial protocol prediction and classification; the problem that most network protocol reverse tools in the current market cannot efficiently and accurately analyze the industrial protocol is solved.

The invention provides a transformer DGA online monitoring data processing method, which comprises the following steps: according to the characteristics of returned data, enabling online data to be equivalent to a time sequence; in the first stage, the thought of a sliding window algorithm is introduced, an improved sequence piecewise linearization algorithm is provided, sequence data is divided into a plurality of line segments represented by slopes and spans, then online monitoring data is symbolized by using improved K-means clustering, and finally the relevance among different indexes in DGA is mined by using an APRIORI algorithm, so that the DGA is subjected to online monitoring. An abnormal value existing in the data is explored; and in the second stage, according to the screened abnormal numerical sampling points, an improved particle swarm optimization support vector regression algorithm is used, the solving speed and solving diversity of the algorithm are guaranteed, key parameters in the support vector regression algorithm are optimized, and the sampling points are repaired, so that the processing of the online DGA monitoring data of the transformer is completed.

The invention relates to a polarization code decoding method, in particular to a polarization code decoding method based on a segmented Hash sequence, and belongs to the field of channel encoding. According to the polarization code decoding method, the Hash sequence is inserted in an information bit sequence in segments, the information bit sequence is checked in segments by using the Hash sequence in a decoding process, and a decoding search path is reduced or the decoding is terminated in advance by using the check result, thereby reducing the decoding complexity and reducing the decoding delay.