157 results about "Hamming distance" patented technology

The invention belongs to the technical field of cross-modal information retrieval, and provides a quick cross-modal retrieval method and system fused with fine-grained semanteme, multi-modal data to be retrieved are obtained, a trained cross-modal retrieval model is used for learning to obtain continuous Hash features, discrete Hash codes are generated according to the continuous Hash features, and the multi-modal data to be retrieved are retrieved according to the discrete Hash codes. Performing cross-modal matching and retrieval based on Hamming distance matching, and feeding back a retrieval result; in the training process of the cross-modal retrieval model, the fusion loss function, the cross-modal alignment loss function and the shared subspace loss function are subjected to weighted fusion, and corresponding parameters are optimized with the purpose of minimizing the total loss function after weighted fusion until training requirements are met. According to the method, the cross-modal semantic consistency among different modal features is optimized, so that the generated Hash code can reflect semantic association among multi-modal data more accurately, and the precision and efficiency of cross-modal retrieval are improved.

The invention relates to the technical field of intelligent traffic, and discloses a camera state judgment method based on road surface covering and Hash comparison, and the method sequentially comprises the steps: extracting key frame pairs from a video stream at intervals; performing road surface region extraction on each frame of image to obtain a road surface mask; carrying out covering processing on the road surface area in each frame of image based on the mask to obtain a covered image; calculating a difference hash feature of each frame of covered image to obtain a hash code; calculating the Hamming distance between the Hash codes of the key frame pair; and judging whether the camera is in a rotating or stable state according to a comparison result of the Hamming distance and a threshold value. The method only depends on the video image data, does not need an external sensor, effectively eliminates the dynamic interference of the vehicle through covering the road surface, achieves the efficient and robust judgment of the state of the camera through combining the lightweight difference hash calculation, is low in calculation cost, and is suitable for edge equipment and complex road environments.

The invention relates to the technical field of data synchronization, in particular to a digestive system department clinical information acquisition and synchronization system, which comprises a multi-source heterogeneous acquisition module for generating a standardized diagnosis and treatment sequence, a characteristic spectrum construction module for mapping a text and an image into vector nodes and constructing a dynamic diagnosis and treatment characteristic spectrum, the state fingerprint verification module calculates a map fingerprint Hamming distance to position a difference feature node, and the incremental collaborative synchronization module constructs an incremental data packet and sends the incremental data packet to the central server. According to the method, the multi-dimensional characteristic spectrum based on the diagnosis and treatment time sequence is constructed, discrete images and texts are converted into topological association units, heterogeneous data semantic level alignment and integrity verification are achieved, version conflicts and information faults are eliminated, meanwhile, a dynamic hash fingerprint difference comparison strategy is adopted, accurate recognition is achieved, and only substantial change nodes are transmitted; and the network load is reduced, and high real-time consistency and zero-loss circulation of whole-flow information are ensured.

The invention provides a multi-modal Hash retrieval method and device for an agent with a body, and relates to the technical field of artificial intelligence application, and the method comprises the following steps: constructing a multi-modal Hash retrieval model for the agent with the body; the multi-modal Hash retrieval model comprises a multi-modal feature encoder and a Kolmogorov-Arnod Hash layer which are connected in sequence; constructing a joint loss function fusing multi-modal supervision optimal transmission alignment loss and self-paced learning loss, and training the multi-modal Hash retrieval model by using the joint loss function to obtain a trained multi-modal Hash retrieval model; receiving multi-modal feature retrieval data, inputting the multi-modal feature retrieval data into the trained multi-modal hash retrieval model, and outputting a hash code of the multi-modal feature retrieval data; and calculating the Hamming distance of the Hash code of the multi-modal feature retrieval data, and returning a multi-modal feature retrieval result according to a sorting result of the Hamming distance. According to the method, the retrieval effect and the retrieval efficiency can be effectively improved.

The invention provides an SAR target screening method based on binary super-dimensional calculation, and relates to the technical field of real-time target detection. The method comprises the following steps: directly carrying out real and virtual part solution and sign function quantization on I / Q dual-channel complex data of an original echo of a synthetic aperture radar (SAR) to obtain a binary fusion matrix; constructing an abnormal profile based on a preset background statistical model, and adaptively dividing the quantized data into multiple segments according to the abnormal profile; performing super-dimensional mapping on each segment through an independent random binary projection matrix, generating segmented super-vectors, and splicing the segmented super-vectors into a complete sample super-vector; and finally, target discrimination is realized by calculating the Hamming distance between the target and a pre-stored background / target prototype super vector. The quantization logic can be dynamically switched according to the target size, and the problem that a single quantization strategy is poor in adaptability is effectively solved. And through an adaptive segmentation mechanism of energy perception, the weak target characterization capability is effectively enhanced, and the screening accuracy under a strong clutter background is improved.

A deep learning model Hash identification method based on multi-feature fusion comprises the following steps: firstly, extracting structural features (layer type sorting de-duplication), parameter statistical features (quantile normalization and histogram statistics) and functional behavior features (middle layer output quantiles) of a deep learning model; then, model structure hash, parameter hash and function hash are calculated respectively; thirdly, generating a comprehensive hash value by fusing triple hash; meanwhile, a TLSH (Trend Micro Location-Sensitive Hashing) algorithm is adopted, so that the tolerance of the TLSH algorithm to the tiny fluctuation of the parameters is improved; and finally, based on Hamming distance, calculating model similarity, and realizing efficient black box model traceability and infringement detection. The method does not need to modify a model structure or parameters, does not depend on external data, supports high robustness to attacks such as pruning and fine tuning, and is suitable for various network architectures such as CNN and Transform. The method has the advantages of lightweight calculation, high concealment and cross-platform adaptation capability, and can be widely applied to intellectual property protection and infringement tracking scenes of a deep learning model.

The invention discloses an agricultural disease hash retrieval method based on DV stabilization and adaptive feature enhancement, and belongs to the technical field of agricultural disease image retrieval and deep hash, and the method comprises the following steps: 1, obtaining an original disease image as the input of a backbone network; step 2, extracting disease image features by using a backbone network; step 3, based on a self-adaptive feature enhancement module, enhancing the disease image features to obtain self-adaptive image features; step 4, optimizing network parameters through a total loss function containing DV stabilization; step 5, obtaining a binary hash code through the hash layer; and step 6, carrying out Hamming distance sorting on the obtained Hamming codes and the Hamming codes of all the images in the retrieval set obtained by the same method, and returning a plurality of disease images with the Hamming distance smaller than a preset Hamming distance. The agricultural disease image retrieval efficiency and precision are effectively improved.

The invention discloses a defect detection method for a reticle mask, which relates to the technical field of mask defect detection and comprises the following steps of: automatically calling and solidifying detection parameters according to an instruction multiplying power and a mask name; performing quadratic polynomial fitting normalization correction on the reference image according to the gray level of the light calibration point; dividing a detection area, extracting ORB features, and obtaining sub-pixel homonymy point pairs through Hamming distance and RANSAC; and performing differential defect detection after solving an affine matrix to precisely align the Die image. According to the method, manual re-judgment can be replaced, the detection time is shortened to the second level, the defect recognition accuracy is larger than or equal to 99%, and the efficient and accurate requirements of 28 nm nodes are met.

The invention belongs to an infringement recognition method, and provides a three-dimensional model block chain copyright infringement recognition method, system and program product for solving the problems that an existing three-dimensional digital copyright infringement recognition method is low in recognition accuracy, poor in recognition effect in a complex scene and not applicable in a high-precision requirement scene. And calculating the Hamming distance between the copyright fingerprint set of the three-dimensional model to be detected and the copyright fingerprint set of the three-dimensional model registered on the given chain, and judging whether to infringer according to the relationship between the Hamming distance and a preset overall matching rate threshold. Wherein during copyright fingerprint set calculation, key point scales in all vertexes of a three-dimensional model are determined according to the geometric volume and surface complexity of the three-dimensional model, mixed feature descriptors are obtained by combining geometric structures and surface texture changes of local neighborhoods of candidate key points, and corresponding fingerprint abstract values are calculated after Hash processing. And the recognition stability under non-infringement operations such as rotation, zooming, slight editing and the like is remarkably improved.

An authentication method includes: computing, in a secret state, a hash value from a first feature value of the biometric information, using a hash function; converting the hash value from a first integer share to a binary share; computing, in a secret state, an XOR bit sequence by performing an exclusive OR operation between the hash value and a registered hash value; converting the XOR bit sequence from the binary share to a second integer share; computing, in a secret state, a total of a plurality of bit values present in the XOR bit sequence; and determining, using the Hamming distance, whether a registered feature value is to be used in authentication processing.

The invention discloses a network fast security verification method and system. The method comprises the steps that an initial device generates a verification request and records a corresponding request timestamp; collecting a first device feature to perform migration feature calculation to obtain a migration feature classification result, generating a first feature hash code based on the migration feature classification result, and generating an initial feature hash code set in combination with the request timestamp; determining a migration frequency and a migration state according to the initial feature hash code set, selecting a corresponding verification process based on the migration frequency and the migration state, and when the migration frequency and the migration state are not changed, performing first verification based on the Hamming distance between the first feature hash code and the initial feature hash code set; and when the migration times and the migration state change, generating a second feature hash code by using the migration token, the second device feature and the verification times, and performing Hamming distance comparison on the second feature hash code and a third feature hash code generated according to the initial feature hash code set and the verification times to realize efficient device identity verification.

Methods for analyzing a nucleic acid sequence are described, including methods for generating a sequencing coding scheme that accommodates for errors during the sequencing process. The methods may comprise, for example, generating different codewords; assigning a different codeword to each one of four different bases, wherein the different codewords adhere to designated Hamming distance rules; contacting different polynucleotide templates with nucleotides of the four different bases; allowing binding and optional incorporation of the nucleotides; imaging the different polynucleotide templates to determine recorded signal code sequences; and comparing recorded signal code sequences to the different codewords to identify bases corresponding to the recorded signal code sequences and / or checking for errors in the recorded signal code sequences.

The application belongs to the technical field of new energy sending-out line protection, and provides a new energy station sending-out line pilot protection method and system based on Hamming distance. The new energy station sending-out line pilot protection method comprises obtaining current sampling values of the station side and the system side of the sending-out line after protection starting; data preprocessing is performed on the current sampling values of the station side and the system side of the sending-out line, and a first numerical sequence and a second numerical sequence containing 0 and 1 are obtained respectively; the Hamming distance is calculated according to the ratio of the different sampling point numbers between the first numerical sequence and the second numerical sequence to the sampling point number of the sampling window length; the fault type is judged according to the size of the Hamming distance, and whether to issue a trip command to the protection devices of the station side and the system side of the sending-out line is judged in combination with the comparison result of the Hamming distance and the setting value of the Hamming distance.

To improve the tamper resistance of semiconductor devices against physical attacks. [Solution] The semiconductor device according to this disclosure includes a transition circuit that transitions a state at a predetermined Hamming distance according to the result of comparing a value to be authenticated with a reference value, and a determination circuit that determines the validity of the authentication by determining whether the Hamming distance between the state before the transition and the state after the transition matches the predetermined Hamming distance.

The application discloses a keyword extraction method, system, device and storage medium. The method comprises the following steps: obtaining a sensitive word group list, removing stop words and splicing words in each sensitive word group, and obtaining preprocessed text; based on the preprocessed text, a word vector is constructed, and a mapping relationship between a word group in the preprocessed text and the splicing words is established; an initial cluster center vector is selected in the word vector, a Hamming distance between a remaining word vector and the initial cluster center vector is calculated, the word vector is divided into clusters according to the Hamming distance, and a preliminarily divided keyword cluster is obtained; based on the word vector and the preliminarily divided keyword cluster, an adaptive clustering algorithm is adopted to obtain a similar word vector cluster; and the similar word vector cluster is spliced with the splicing words according to the mapping relationship, so that a keyword list is generated; and it is ensured that the obtained keywords can accurately reflect the characteristics of the text content.

The present application relates to the technical field of cross-modal retrieval, and particularly relates to a strong correlation unsupervised cross-modal retrieval method guided by information quantity, which is realized through the following steps: firstly, image local features and global features, and text features are extracted; the image local features and global features are enhanced; the enhanced local features are subjected to regularization processing; then, the image global features and local features are subjected to orthogonal fusion using an image feature fusion network; next, the image features and text features are fused according to a different modal feature information quantity conversion ratio principle using a multi-modal fusion network; finally, different modal features are mapped into hash codes, and Hamming distance is used for similarity sorting, so as to obtain a retrieval result. The present application focuses on the enhancement and fusion of data features, can obtain more semantic information, and improves the retrieval efficiency.

The invention provides an automatic component classification method oriented to multi-level category labels and related equipment. Comprising the following steps: preprocessing to-be-identified component data with a plurality of fields; extracting a text feature in the to-be-identified component data with the first format, and fusing the hierarchical structure information of the category label into the text feature to obtain a fused feature vector; loading a preset coding matrix based on the DECOC algorithm; the coding matrix is provided with a plurality of corresponding binary classifiers; fusing prediction probabilities of a plurality of pre-trained base classifiers based on a soft voting mechanism in each binary classifier to obtain corresponding weighted prediction probabilities so as to obtain a plurality of predicted weighted probabilities corresponding to the plurality of binary classifiers; combining the plurality of weighted prediction probabilities into a prediction code word; and determining a prediction category label group according to the prediction code word and the Hamming distance of each row in a preset coding matrix. And the types of the components can be quickly and accurately predicted.

The invention provides a ciphertext classification model anti-attack method introducing error correction codes. The method comprises the following steps: performing redundant information addition processing on original ciphertext data through an ECC (Error Correction Code) scheme; inputting the current adversarial sample into the target ciphertext classification model for forward propagation, and then calculating a model output result and a loss value of a real encryption algorithm label; solving the embedding gradient of the loss value relative to the current adversarial sample through back propagation; combining gradient income, cost and ECC residual error correction quota to construct a comprehensive evaluation index, and screening an optimal character for replacement to generate a candidate confrontation sample; the disturbance amplitude of the candidate adversarial sample is verified through the Hamming distance, and if the disturbance amplitude exceeds the maximum ECC error correction capability, discrete ECC safety projection is executed for constraint, and a new adversarial sample is obtained; and the confrontation sample with successful deception model is verified at the receiving end. According to the method, the integrity and the concealment of communication data are kept while the attack is effectively resisted.

The invention discloses a wireless VPDN service fault positioning method and related equipment, and the method comprises the steps: obtaining case knowledge base data, and constructing a knowledge graph of a wireless VPDN fault through the case knowledge base data; wherein the knowledge graph comprises a plurality of fault vector nodes; performing weighted clustering on all fault vector nodes in the knowledge graph, and determining a preset number of centroids and corresponding fault clusters in the knowledge graph; and in response to a retrieval fault vector input by a target object, matching a target fault cluster in the knowledge graph, and further performing intra-cluster neighbor search on the target fault cluster through weighted Hamming distance and locality sensitive hashing to obtain a target feature attribute vector so as to locate a wireless VPDN service fault. The method can significantly reduce the dependence on the personal experience of operation and maintenance personnel, greatly improves the efficiency and accuracy of fault positioning, shortens the service recovery time, and can be widely applied to the technical field of fault positioning.

The invention discloses a power equipment deformation detection method and system, equipment and a medium, and the method comprises the steps: obtaining detection data and contrast data of to-be-detected power equipment, carrying out the image synthesis according to the detection data and the contrast data, and obtaining a to-be-detected operation image and a contrast operation image; inputting the running image to be detected and the contrast running image into the Hash model, and outputting a detection Hash code and a contrast Hash code; calculating the Hamming distance between the detection hash code and the contrast hash code, and judging the deformation degree of the to-be-detected power equipment according to the Hamming distance; and when the deformation degree is greater than a preset deformation threshold value, taking the to-be-detected power equipment as a starting point, and establishing a deformation propagation path to judge the deformation influence on the adjacent power equipment. The technical problems that an existing power equipment deformation detection method is low in efficiency, poor in real-time performance, insufficient in feature extraction, large in noise interference and incapable of effectively evaluating the influence of deformation propagation are solved.

When a state machine of the time sequence circuit is influenced by a single event upset (SEU) effect, a state code of the state machine is very easy to change, so that the state machine is in an invalid or error state and cannot be recovered, and the function of the time sequence circuit is failed. Aiming at a state machine based on Hamming distance-3 coding, the invention provides the low-overhead error detection circuit for the Hamming distance-3 five-bit state code, and the resource overhead in the error detection process is greatly reduced by simplifying the judgment logic of a detection unit.

The application discloses a machine learning-based retrospective clinical data management method. The method comprises the following steps: obtaining an electronic medical record log file and parsing unstructured text to generate a word vector; calculating the Hamming distance between the word vector and the knowledge graph standard entity vector to extract standardized test indicators; establishing a two-dimensional physiological correlation mask matrix according to the pathology dependent edge; truncating the time series data into a sequence feature matrix and performing linear transformation to generate a query matrix, a key matrix and a value matrix; calculating the dot product value of the query matrix and the transpose of the key matrix, adding the scaling factor and the mask matrix after exponentiation, and then multiplying the value matrix to obtain the context feature representation; and generating a dense feature tensor data through a feedforward neural network to predict the interpolation value. The application can effectively curb the overfitting of the time series model under sparse data, improve the physiological authenticity of the feature array, and reduce the generalization error of the downstream prediction model.

The invention relates to a metal membrane piece position correction method and system based on improved template matching. The method comprises the steps that an adaptive Retinex algorithm is combined with a DnCNN denoising network to carry out image preprocessing; fusing an ASIFT algorithm and a SuperPoint network to extract feature points and a confidence map; initial matching is realized by using the FREAK descriptor and the weighted Hamming distance, and initial mismatching is filtered based on a bidirectional nearest neighbor criterion; designing a double-layer filtering mechanism in which Guided-PROSAC and local topological structure verification are combined to eliminate mismatching; mapping angular points of the template image to a target image to calculate a pose error vector; the pose error vector is transmitted to a mechanical arm controller for metal diaphragm piece position correction; according to the method, the real-time requirement of a production line is met through reasoning on edge equipment, multiple defects of the traditional technology in the aspects of robustness, precision and automatic integration are overcome, and reliable technical support is provided for a precise assembly task in intelligent manufacturing.

The invention discloses a two-dimensional texture positioning method and device based on RANSAC (Random Sample Consensus) and ICP (Inductively Coupled Plasma), relates to the technical field of two-dimensional texture positioning, and solves the problems of insufficient robustness and low positioning accuracy of two-dimensional texture positioning in the prior art. Through Hamming distance and distance ratio testing, the stability of feature matching is improved, the RANSAC algorithm is used for detecting and eliminating mismatching points, the positioning robustness is improved, the ICP algorithm is used for refined positioning based on an inner point set, the positioning precision is improved, and the reliability of a positioning result is ensured through re-projection error and confidence evaluation. According to the method, abnormal values are effectively detected and eliminated through the RANSAC algorithm, the positioning robustness is improved, refined positioning is carried out in combination with the ICP algorithm, and the positioning precision is remarkably improved.

The application relates to the technical field of big data processing and network security, and discloses a ubiquitous network data deduplication system supporting similarity data ownership verification, which comprises an encryption module, a label generation module, a deduplication decision module, a challenge generation module, a proof calculation module, a verification module and an integrity evidence module; the system derives a high-entropy session key through two-dimensional chaotic mapping to encrypt plaintext data blocks, generates a similarity label with a threshold, and realizes efficient deduplication based on three-stage Hamming distance determination; the dynamic challenge-response mechanism is used to provide an auditable ownership proof for each download request, and the tree and block chain evidence technology are combined to realize multi-granularity checking of data integrity and rapid positioning of damaged blocks, so that the storage efficiency, security and usability are considered.

The invention provides a verifiable cross-modal retrieval method, and belongs to the technical field of Internet of Things security. The method comprises the steps that a data owner constructs a data set, a 1-norm image vector, a 1-norm text vector, an index table, a prime number table, a ciphertext vector and a Merkel prime number tree, sends the ciphertext vector, the index table and the Merkel prime number tree to a cloud server, and sends a root abstract of the Merkel prime number tree, the prime number table, the 1-norm image vector and the 1-norm text vector to a query client; the query client sends the cross-modal query request to the cloud server; the cloud server receives the cross-modal query request and sends the inner product vector, the group number interval and the verification object set to the query client; and the query client performs integrity verification, correctness verification and Hamming distance calculation according to the inner product vector, the group number interval and the verification object set to find the most similar data. According to the method, the prime number is fused into the traditional Merkel tree to construct the Merkel prime number tree to ensure the integrity and correctness of a cross-modal retrieval result.

This invention relates to the field of disaster prediction technology in the energy industry, and discloses a method for disaster prediction and assessment in the energy industry based on multi-source data. The method includes: generating binary status words through distributed edge nodes based on fixed rules; calculating the Hamming distance of the status word sequence on the central platform to obtain the consensus volatility and its first-order difference as the collapse acceleration; and triggering an early warning when the acceleration continuously exceeds a threshold. This invention transforms traditional probabilistic early warning into deterministic action instructions through the collaborative monitoring of binary status and collapse acceleration. It also combines topology propagation coherence verification to eliminate false alarms and outputs diagnostic-level composite early warnings based on micro-rule classification, thus realizing a closed loop from disaster identification to precise handling. This method and its simplified architecture enable real-time stability monitoring of large-scale energy networks, significantly improving the reliability of early warnings and response efficiency.

The invention relates to the technical field of energy industry disaster prediction, and discloses an energy industry disaster prediction and evaluation method based on multi-source data, and the method comprises the steps: generating a binary state word based on a solidification rule through a distributed edge node, the central platform calculates the Hamming distance of the status word sequence to obtain a consensus fluctuation rate and a first-order difference of the consensus fluctuation rate as collapse acceleration, and when the acceleration continuously exceeds a threshold value, early warning is triggered. According to the method, false alarms are eliminated in combination with topology propagation coherence verification, diagnosis-level composite early warning is output based on micro-rule classification, closed loop from catastrophe identification to accurate disposal is achieved, real-time stability monitoring of a large-scale energy network is achieved through the method and a simple framework, and early warning credibility and response efficiency are remarkably improved.

The invention discloses a two-dimensional texture mapping method based on multiple sub-graphs and a storage medium, relates to the technical field of two-dimensional mapping, and solves the problems that in the prior art, texture slam implementation cost is high, and robustness to low-texture scenes is low. The method comprises the steps that a front end carries out feature point extraction through ORB features and quadtree uniform distribution; the Hamming distance of a descriptor is adopted for feature matching, the stability of feature matching is improved through a distance ratio test, a key frame is inserted into a texture feature sub-graph when pose transformation is larger than a certain threshold value, and a new texture feature sub-graph is constructed when positioning is accidentally lost in mapping or the key frame in the sub-graph reaches a preset threshold value. According to the method, the texture feature sub-graphs serve as map units, feature matching results are stored, loopback detection and global optimization are carried out at the rear end, the robustness in a low-texture scene is remarkably improved, the calculation complexity is reduced through a multi-sub-graph mechanism, the mapping cost is low, and the positioning error is small.

The invention discloses a communication method of a power domain non-orthogonal multiple access system based on a symmetric Hamming distance constellation and a related device, and the method comprises the steps: carrying out the coding and bit interleaving processing of information bit sequences of two users, and obtaining corresponding bit interleaving sequences; modulating the bit interleaving sequence to obtain a corresponding modulation symbol sequence, and determining transmission power; based on the symmetric Hamming distance, determining a mutual constellation arrangement function according to the modulation orders of the modulation symbol sequences of the two users; based on a mutual constellation arrangement function, performing mapping superposition according to the modulation symbol sequence and the transmission power through a joint constellation mapper to obtain a superposed symbol sequence; transmitting the superposed symbol sequence through a transmission channel to obtain a received signal; and decoding the received signal by adopting an iterative soft demodulation and decoding mode, and recovering the decoding bit sequences of the first user and the second user. According to the method, the correlation between the users is established based on the mutual constellation arrangement function to reduce the interference between different user constellations.