31 results about "Average entropy" patented technology

The invention discloses a virus identification method comprising the following steps: obtaining RNA sequencing data of a to-be-tested sample; assembling the first portion of the sequencing data so as to obtain assemble sequences; comparing the first portion of the sequencing data with the assemble sequences so as to obtain a comparison result; determining mutation sites on the assemble sequences according to the comparison result, and determining at least one of the following assemble sequences a-c: a, at least one from the group formed by average entropy and median entropy, and mutation site proportion; b, at least one from the group formed by the average mutation rate and median mutation rate, and the mutation site proportion; c, mutation site proportion; comparing at least one from the assemble sequences a-c with the corresponding boundary, and determining the assemble sequence falling into the boundary to be from virus. The invention also discloses a virus identification device; the virus identification method and / or device can accurately predict whether an unknown sequence is a virus sequence or not without depending on the homologous sequence alignment.

The invention discloses a wavelet analysis post-processing method and device for a power transient signal. The method is to conduct the following one or more post-processing on the wavelet coefficients after the power transient signal is analyzed by wavelet, and the result is to send power Monitoring center processing: modulus maximum extraction, singularity detection, obtaining modulus maximum points and singularity; statistical processing or cluster analysis of wavelet coefficients; neural network classification, that is, wavelet coefficients or wavelet post-processed data as neural Network input, output neural network classification results; energy analysis, to obtain energy distribution coefficient and energy fluctuation coefficient; wavelet entropy calculation, to obtain wavelet energy spectrum entropy or wavelet time entropy or wavelet singular entropy or wavelet time-frequency entropy or wavelet average entropy or wavelet distance entropy. It can effectively extract the characteristics of power transient signals, which can be applied to power system transmission line traveling wave distance measurement and fault identification, power quality analysis and equipment fault diagnosis.

A system for storing a specification document to be checked on a storage device in a computer readable format. A computer implemented method which stores a graph structure of rules for performance of checking, and creates a rule application table of whether a rule is an application subject. For each node rule of the graph structure, average entropy of whether a specification satisfies the rules is derived by searching the graph structure of rules. While performing a depth-first searching of the graph structure of rules, a priority order index is derived from the rule application table for each node rule. Amendment candidates are then displayed according to value of the priority order index.

The invention belongs to the technical fields of information security and true random number generators, and specifically relates to a method for testing the maximum entropy rate of true random number generators. The method for testing the maximum entropy rate of a true random number generator disclosed by the invention includes four steps: parameter selection, data collection, block calculation, and analysis and fitting. The present invention utilizes the characteristic that the average entropy rate calculated after the random data flow is divided into blocks is saturated with the decrease of the data block length and the increase of the data rate, and performs linear fitting on the relationship curve between the saturated entropy rate and the data block length, and simulates The corresponding function value of the straight line when the data block length is 1 is the maximum entropy rate. The invention can effectively solve the problem that the maximum entropy rate cannot be uniquely determined in the test of the true random number generator.

A method, computer program product, and computing system for processing a data portion to divide the data portion into a plurality of data chunks; performing an entropy analysis on each of the plurality of data chunks to generate a plurality of data chunk entropies; and determining an average data chunk entropy from the plurality of data chunk entropies.

The present invention proposes an entropy-based neural machine translation dynamic decoding method and system. By analyzing the relationship between the entropy value of a sentence and the BLEU value, it is found that the average entropy value of words in a sentence with a high BLEU value is lower than that in a sentence with a lower BLEU value. The average entropy value of words is small, and the BLEU value of sentences with low entropy value is generally higher than that of sentences with high entropy value. By calculating the Pearson coefficient between the entropy value of the sentence and the BLEU value, it is found that there is a correlation between the two. Therefore, the present invention proposes that in each time step of the decoding phase of the training process, not only must a certain probability be sampled to select real words or predicted words to obtain context information, but also to calculate the entropy value according to the prediction result of the previous time step, and then according to the entropy The value dynamically adjusts the weight of contextual information. Addresses the issue of error accumulation in neural machine translation models during decoding due to differences in contextual information between training and inference.

The invention discloses a quality assessment method based on trajectory data, which includes steps: S1, expressing the trajectory data as a set of tuples in four dimensions, and obtaining a map; S2, grouping the trajectory data according to entities, and using the time dimension to The grouping is sorted, and each trajectory of the trajectory data is converted into a set of ordered line segments; S3, dividing the map into grids of equal size, and projecting the trajectory data into the grid; S4, calculating entropy of the grid, and calculate the average entropy and weighted average entropy of all the grids. The invention utilizes entropy to effectively evaluate the quality of trajectory data, so as to realize the comparison of the data quality of multiple pieces of trajectory data and analyze the feasibility of using it for data analysis, and the data quality assessment is simple, effective and low-cost.

The invention discloses a recommendation system attack detection method based on time series data. The method includes the steps that a user-project preference degree data set and a frequent item set excavation technology are utilized to acquire user groups and project groups; group preference degree value proportional characteristics are calculated for the pairs of user sets and the pairs of project sets; all preference degrees of projects in the project groups form time series preference degree data according to operating time; group preference degree time interval characteristics are calculated for the pairs of user sets and the project sets; group average entropy characteristics are calculated for the user groups; for the user groups, maximum group preference degree value proportional characteristics corresponding to the user groups and maximum group preference degree time interval characteristics corresponding to the user groups are selected, and the user groups are sequentially ranked through the three characteristics, and then three ordered user group sequences are acquired; the three ordered user group sequences are integrated to acquire a wholly ordered user group sequence, and the most probably attack user groups are acquired; the most probably target project groups are acquired through the group preference degree value proportional characteristics.

The invention relates to the technical field of image processing and discloses a complex wavelet domain semi-blind image quality evaluation method and system based on entropies. The method comprises the steps that subblocks of an input original reference images and an image to be evaluated are divided, the image information entropies of all the subblocks are calculated, and the subblocks of which the entropies are greater than an average entropy of all the subblocks are selected; the phase equalization value of the subblocks of the original reference image and the image to be evaluated is calculated, and the visual weight factor of quality evaluation is set; complex wavelet transformation is carried out on the subblocks selected from the original reference image and the image to be evaluated; the amplitude and the phase information of the original reference image and the image to be evaluated are extracted respectively, and the quality of the semi-blind image is evaluated in combination with the visual weight factor. The area with the rich information in the image is selected through the information entropies to serve as a space for extracting the feature information, the amplitude and the phase information of the images are utilized comprehensively, the objective evaluation based on the main features of the images are achieved, and the efficiency of evaluating the image quality is improved.

The invention relates to a maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method and belongs to the image matching field. Scale-invariant?feature?transform (SIFT) operators have strong matching ability, while, the scale-invariant?feature?transform (SIFT) operators will bring a huge amount of data, and therefore, binaryzation should be performed on the scale-invariant?feature?transform (SIFT) operators, however, if unified binaryzation is performed on all the operators, data redundancy or information loss will be brought about. The maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method of the invention comprises the following steps that: binaryzation is performed on the scale-invariant?feature?transform (SIFT) operators; average entropy calculation is performed on each layer of binaryzation results, such that different numbers of binaryzation layers are selected adaptively; new binaryzation descriptors are provided; the Hamming distance is utilized to replace the Euclidean distance so as to calculate the distance between two descriptors; and the distance between the two descriptors is compared with a set threshold value. With the maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method of the invention adopted, information of original features is reserved; the amount of data storage can be greatly reduced; computational complexity can be reduced; a requirement for a real-time property can be realized better; the matching results equivalent to original scale-invariant?feature?transform (SIFT) descriptors can be obtained and are far superior to results of matching by using the unified binaryzation.

The invention discloses a fast detecting method and device for a static vehicle target. The fast detecting method includes the following steps that images are collected, wherein vehicle images are obtained; the images are preprocessed, wherein normalization is conducted on the sizes of the vehicle images, and the vehicle images are converted into a grey-scale map; entropy information is extracted, wherein pixel level traversal is conducted on the images through search boxes, and pulse iteration average entropy information of the partial images in the search boxes is extracted; rough positioning is conducted on the vehicle target, wherein the images with the pulse iteration average entropy value larger than that the target entropy value are taken as vehicle target rough positioning images; the vehicle target is determined, wherein the vehicle image with the largest pulse iteration average entropy value in the same picture area is reserved as a target vehicle search result; the vehicle target is presented in an original large image, and positioning of the vehicle target is completed. According to the fast detecting method and device for the static vehicle target, robustness, rapidness and transportability of a pulse coupling neural network feature extraction method are used, the feature of the pulse iteration average entropy is selected as the judgment basis, and the speed of detecting the static vehicle target is greatly increased.

The invention relates to a burst traffic detection method based on dynamic threshold. By means of the method, the burst traffic in a high-speed network environment can be effectively detected on line in real time, and the problem that a fixed threshold method cannot well adapt to the dynamically changing network environment is solved. According to the technical scheme, a threshold is dynamically adjusted according to the source IP entropy value of the normal historical traffic, a sliding window mechanism is introduced, and burst traffic is detected by comparing whether the difference value between the current detection value and the average entropy value of a sliding window exceeds the dynamic threshold; the method comprises steps as follows: generation of an initial sliding window, generation of a reference value, generation of the current detection value, abnormality judgment of the current detection value, processing of normal traffic and processing of burst traffic. The detection method is rapid, simple, high in detection rate and low in false rate, the network safety is effectively guaranteed, and the detection method has high practicability and huge economic and social benefits.