108 results about "Temporal complexity" patented technology

The invention relates to a computational graph processing method and device, electronic equipment and a readable storage medium. The method comprises the following steps: traversing a computational graph in the process of topological sorting of nodes, and carrying out ring formation detection on the computational graph based on in-degree of an object to obtain a ring formation detection result; the in-degree represents the forward dependency number of the object; in the process of traversing the computational graph, performing address repeated writing detection on the nodes based on the in-degree of the parameters to obtain an address repeated writing detection result; performing head node detection on the node set after topological sorting to obtain a head node detection result; and verifying the computational graph based on a ring formation detection result, an address repeated writing detection result and a head node detection result. By adopting the method, the time complexity can be reduced.

The invention discloses a semi-supervised flotation working condition recognition method based on spatial-temporal feature fusion, and belongs to the field of industrial process monitoring. The method comprises the steps that flotation froth videos are collected and preprocessed to obtain a data set of a preset proportion; constructing a student model fused with spatial-temporal characteristics to predict each working condition category; a teacher model is constructed, and index moving average of student model parameters is used for updating so as to maintain stability and guide learning of the student model; and calculating supervision loss based on the labeled training set, calculating consistency loss based on the unlabeled training set, and performing semi-supervised training on the model by minimizing the total loss. According to the method, a spatial-temporal characteristic representation model is embedded into a semi-supervised learning framework, foam state evolution characteristics are fully represented, and high-precision working condition recognition is realized by using limited labeled samples; through the spatio-temporal feature fusion method of the cross additive attention mechanism, mutual enhancement of spatio-temporal features is realized, the time complexity is reduced, and the calculation efficiency is improved.

The invention discloses a method and device for determining a connected domain and computer equipment, and belongs to the field of image processing. The method comprises the following steps: scanning run lengths in an image to be processed according to columns; when any run length is scanned, taking the scanned run length as a seed run length, and extracting run length information corresponding to the seed run length in a run length structure; determining a target connected domain to which the seed run belongs based on the run information; and gradually expanding the target connected domain by taking the seed run length as a starting point until all run lengths included in the target connected domain are obtained, returning to the first step to continue execution, and obtaining all connected domains in the to-be-processed image after all columns in the to-be-processed image are traversed. According to the method, the connected domain is extracted and merged by taking the run length as an object, so that the time complexity is remarkably reduced, and the consumption of computer processing resources can be reduced while the processing efficiency is improved.

The invention provides a depression emotion grade judgment method based on a Transform algorithm, and relates to the field of machine learning and data mining. The method comprises the following steps: preprocessing collected user related data to obtain a data coding sequence; a Transform encoder is used, based on a multi-head self-attention mechanism and a proxy token, related emotion feature extraction is carried out on the data coding sequence, and a feature vector is output; inputting the feature vectors into a decision tree, and constructing an EJ decision tree model for emotion judgment; and inputting user data needing to be judged into the constructed EJ decision tree model, and judging the emotion level of the user. According to the method, a Transform encoder and an EJ decision tree model are combined, the discriminating ability and transparency of the model in depression emotion judgment are enhanced, the discriminating ability of emotion features is improved by utilizing an agent token, the defect that a traditional model excessively depends on a complex convolutional or recurrent neural network structure is overcome, the time complexity is reduced, and the method is suitable for popularization and application. And the emotion level of the depression patient can be judged more accurately, flexibly and quickly.

The invention discloses a time sequence anomaly detection method based on multi-model fusion, and is suitable for the technical field of industrial data anomaly detection. According to the method, an anomaly detection model named DDI-Net is provided for the complex characteristics of a multivariable time sequence in an industrial scene, the advantages of a de-noising diffusion model and an Informer model are combined, and the method specifically comprises the following steps that the time complexity and the memory use efficiency of O (LlogL) are achieved through a ProbSparse self-attention mechanism, and the calculation efficiency is remarkably improved; preferentially processing the dominant attention score in the N-layer stacked structure by utilizing a distillation self-attention mechanism, and sharply reducing the total space complexity to O ((2-belongs to) LlogL); and an abnormal score is calculated by comparing the difference between the generated sequence and the original sequence, so that accurate detection is realized. According to the method, the F1 score is up to 98.26% on five industrial data sets such as SMD and SWaT, the training efficiency is improved by more than 30% compared with similar methods, meanwhile, memory consumption is reduced, the training speed is increased, and the method is suitable for industrial system real-time monitoring and high-dimensional data rapid analysis scenes.

The invention discloses a metasurface multi-beam rapid generation method based on semi-supervision, and the operation process of the method comprises the following steps: 1, introducing an attention mechanism based on a convolutional neural network, and designing a cross entropy loss function; step 2, training the model by using a large number of samples obtained by caliber field superposition, migrating trained model parameters to a new model, and training based on the samples optimized by a traditional algorithm; step 3, semi-supervised learning is introduced, a model obtained through initial training is utilized to label unlabeled data samples, labeled sample data is added to label data samples, then the network model is retrained, the process is continuously repeated, and the number of the samples is iteratively increased; and 4, inputting required directional diagram data to obtain a corresponding coding matrix. Based on the method, a multi-beam coding problem is converted into a multi-label classification problem, the condition of repeated traversal in a codebook set in a traditional algorithm is avoided, and the time complexity is reduced.

The embodiment of the invention provides a text map generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an initial U-shaped neural network model used for image denoising processing in a text map generation process, and the initial U-shaped neural network model comprises a plurality of groups of attention structures; replacing the multiple groups of attention structures with a target model structure with linear complexity; performing model distillation on the U-shaped neural network model after the structure replacement based on the initial U-shaped neural network model to obtain a target U-shaped neural network model; and according to the target U-shaped neural network model, carrying out image denoising on the text map to obtain a target image. According to the embodiment of the invention, the target model structure with linear complexity is adopted to replace multiple groups of attention structures, the calculation time complexity of the attention module is reduced to linear complexity, and the target U-shaped neural network model is obtained, so that the text map can generate the image with the resolution greater than 2k.

A system comprising a game execution unit configured to execute a game, wherein executing the game comprises rendering a plurality of image frames for display to a player of the game, a game information obtaining unit configured to obtain information about the execution of the game, including obtaining information, corresponding to an image frame currently being rendered, a complexity estimation unit configured to estimate a spatial and / or temporal complexity of the image frame being rendered in dependence upon the obtained information, a parameter selection unit configured to select one or more encoding parameters in dependence upon the estimated spatial and / or temporal complexity, and an encoding unit configured to encode the video of the game being executed using the selected encoding parameters, the encoded video comprising the plurality of image frames for display to the player.

The invention discloses an automatic test vector generation method and system based on a decision tree, and the method comprises the steps: firstly extracting the node feature data of a to-be-tested circuit, and then obtaining and calculating the backtracking confidence coefficient of each logic gate through a trained decision tree model; the backtracking confidence is a prediction probability value of the decision tree model for successful fault propagation of the logic gate in the backtracking path; and finally, selecting a logic gate with high backtracking confidence as a node to be added into a backtracking path, and generating a test vector according to the backtracking path. According to the method, the number of test vectors can be effectively reduced, the time complexity of fault detection is reduced, and the problems of high backtracking frequency, high resource consumption, noise data interference and the like in a traditional method are solved.

This invention discloses a fast beam alignment method based on the simplex method. It achieves rapid beam alignment between communication devices operating on highly mobile platforms through two stages: preprocessing and simplex processing. The preprocessing stage introduces a hierarchical search approach, providing a more reasonable initial solution for the subsequent beam alignment algorithm, effectively reducing the likelihood of the simplex algorithm getting stuck in local optima. Compared to previous beam alignment methods, this significantly reduces the overall algorithm's time complexity and implementation complexity, while maintaining the same order of magnitude of angular error, demonstrating superior performance.

The present invention discloses a curve data compression method and system based on an autoencoder neural network, the method comprising: performing normalization operations on vector space data after preprocessing to obtain preprocessed data, wherein the vector space data is curve data on a map; constructing a compression model of the autoencoder according to the preprocessed data; using closure error correction to obtain coordinate increments from the output of the compression model, and restoring the arc curve in the curve on the map according to the coordinate increments. The model of the present invention does not need to label samples, and can include all obtained curves into the samples. All subsequent compressed curve data is also a fine-tuning of the model parameters, so the accuracy is also improved. The time complexity of the model of the present invention is not high, and training can be completed on a daily PC. The compressed file of the present invention has a confidentiality function, and the parameters of the neural network and the network model are the keys for data interpretation, which can better ensure the security of geographic information data in network transmission.

The present invention relates to a fast image clustering method based on bipartite graph embedding and discriminant information. Aiming at the problems of high spatio-temporal complexity required for eigenvalue decomposition and similarity matrix construction in spectral clustering algorithms, and the independence between low-dimensional feature space embedding and label learning and the lack of discriminant information, the present invention proposes a fast image clustering method based on bipartite graph embedding and discriminant information. A bipartite graph is constructed in combination with anchor points, and the coordinate descent optimization method is used to replace eigenvalue decomposition and post-processing to reduce the difficulty of data storage and improve the data processing speed. In addition, the present invention simultaneously performs subspace learning and label learning, which can preserve the local features of data and make full use of discriminant information. Therefore, fast and efficient clustering analysis of large-scale and high-dimensional image data can be realized.

The application provides a Petri net supervisory control method and a controller applied to a complex manufacturing system, and the method comprises the following steps: obtaining system information of the complex manufacturing system, and constructing a Petri net model of the complex manufacturing system according to the system information; constructing a reachable graph of the Petri net model according to the Petri net model; taking the current state and resources of each device as input, and predicting transition enabling values of each device in all reachable states through a trained neural network model; and performing supervisory control on the complex manufacturing system according to the transition enabling values of each device in all reachable states. For the complex manufacturing system, the application can avoid the state explosion problem caused by the calculation of the reachable graph of the complex manufacturing system, greatly reduce the time complexity and calculation complexity caused by the calculation of the reachable graph, and directly realize the deadlock control of the complex Petri net and has high behavior permissibility.

The application relates to a fine image classification method, which is characterized by the following steps: constructing a decomposition dictionary learning model, constructing a self-adaptive local order preserving constraint model, constructing a classifier model, constructing a target function, solving the target function and constructing a classification method model. The application can not only adaptively capture the local geometric structure information of atoms and the high-order rank information features of the neighborhood of each decomposition atom in the decomposition dictionary learning process, but also can measure the contribution of multi-view data to the representation of an object through the distance between atoms, enhance the discrimination performance of the decomposition coefficients and the decomposition dictionary. Furthermore, the decomposition coefficients and the class label matrix of the fine image are used to construct a classifier model, the classifier parameters are constrained by a norm, the robustness of the classifier learning model is enhanced, the time complexity of the algorithm is reduced, and the classification performance of the adaptive local order preserving constraint multi-view decomposition dictionary learning algorithm is improved.

The invention provides an aircraft cockpit man-machine interaction task complexity obtaining method and device. The method comprises the steps that man-machine interaction tasks needing complexity evaluation are determined; performing step decomposition on the task according to a flight manual; drawing a task action flow chart according to the specific steps of the task; determining input and output nodes, the number of the input and output nodes, an action type, a time type and a decision type of each node; calculating task step logic complexity; calculating task step scale complexity; calculating task step operation complexity; calculating task step time complexity; task step decision complexity is calculated; calculating a weight factor of each complexity in the total complexity; and calculating the total complexity. According to the method, the task complexity and the expected man-machine interaction behavior in the flight process can be quantitatively described, and the problem of relatively high subjectivity in the previous model workload evaluation work is solved.

This invention relates to the field of game data processing technology, specifically to a method and system for determining the validity of grids in a match-3 game. The method includes the following steps: parsing the acquired game layout configuration data to determine geometric boundary features; traversing each scan line using a scan line algorithm to extract continuous valid intervals; constructing a compact index list containing only valid units based on the valid intervals; and generating interactive objects based on this index list. This invention introduces a discrete topology mapping mechanism to directly locate valid interactive areas, eliminating redundant calculations for invalid empty areas in the traditional full matrix traversal mode. This significantly reduces the time complexity and memory space consumption of logic initialization, solves the technical problem of low efficiency in constructing irregularly shaped layouts, ensures rapid construction and accurate response of game logic in complex terrain scenes, and effectively improves the frame rate stability and user interaction smoothness.

The invention discloses a Chinese character braille conversion method and system oriented to embedded equipment. The method comprises the following steps: firstly, linearly storing a Chinese character pinyin library according to a GB2312 coding sequence, wherein each Chinese character pinyin data record adopts a fixed-length L byte; secondly, analyzing an area code byte Qh and a bit number byte Q1 from the GB2312 code; calculating a physical offset address of the Chinese character pinyin data according to the area code byte Qh and the bit number byte Q1, and adding the physical offset address with the initial physical address to obtain a final physical address; directly reading the Chinese character pinyin data with the fixed length of L from the final physical address; and finally, converting the Chinese character pinyin data into 6-bit braille codes, converting the 6-bit braille codes into 8-bit data through bit splicing and regrouping, and outputting the 8-bit data to an 8-bit data latch to drive braille dot matrix display. According to the method, traversal searching is replaced by direct address calculation, the algorithm time complexity is improved, and the method is suitable for an embedded environment and has the advantages of being high in efficiency and low in resource occupation.

The invention relates to the technical field of computers, in particular to a quantum heuristic combinatorial optimization solution method and system, and the method comprises the steps: generating an initial population through quantum probability distribution, carrying out the population updating through quantum behavior simulation, and calculating the quality score of a solution in combination with multi-target quality evaluation. Through quantum crossover mutation operation, efficient parallel search is realized, the time complexity is reduced to O (nlogn), and the convergence speed is increased by 40%-60%. And meanwhile, the global search capability is enhanced by utilizing quantum characteristics, local optimum is effectively avoided, the solution quality is averagely improved by 30%, and the solution efficiency and the global search performance are remarkably improved.

The invention provides an image processing method and device, equipment and a medium, and relates to the technical field of computers.The method comprises the steps that iterative segmentation operation is executed on multiple sample points of a to-be-processed image, and the process of one-time iterative segmentation operation comprises the steps that a first sample point is selected from the multiple sample points, determining a second sample point farthest away from the first sample point, determining a third sample point farthest away from the second sample point, taking the distance between the second sample point and the third sample point as a segmentation diameter, segmenting the plurality of sample points to obtain two segmented point clusters, and performing iterative segmentation in this way to obtain two segmented point clusters; when the number of the sample points in each point cluster is smaller than a preset number, segmentation is completed, clustering processing is carried out on the plurality of point clusters, and a clustering result of the to-be-processed image is obtained. According to the method, the time complexity can be reduced in a sample point segmentation mode, the clustering time of the sample points in the to-be-processed image is shortened, and the efficiency is improved.

The invention discloses a weighted integrated neural network key prediction method, a weighted integrated neural network key prediction device, a medium and equipment, and relates to the cross technical field of cryptographic analysis and machine learning. Respectively inputting each group of preset number of sample keys and the initialization parameter vectors into a sequence key algorithm to obtain multiple groups of preset number of sample key streams; using the first bit value of the sample key as a label of a corresponding sample key stream, and using each group of preset number of sample key streams and the corresponding label as training data to train the residual neural network to obtain a plurality of key prediction models; then respectively inputting the key stream of the to-be-broken text data into a plurality of key prediction models to obtain a plurality of first bit prediction values of the key of the to-be-broken text data; and performing weighted integration processing on the plurality of first bit prediction values to obtain a key prediction result of the to-be-broken text data. According to the method, the time complexity of key prediction can be reduced.

The present application relates to the technical field of surveying and mapping geographic information, and discloses a method and system for detecting error continuous surfaces based on geographic areas, which comprises the following steps: obtaining a plurality of interest surface sets of the same category in a target geographic area; obtaining the minimum circumscribed rectangle of all interest surfaces in the interest surface set; performing a primary judgment of error continuous surfaces on the circumscribed rectangle by comparing the distance from the midpoint of the circumscribed rectangle to the corner point with the distance from the midpoint of the circumscribed rectangle to the origin point, extracting all error continuous surfaces to construct an initial error continuous surface set; performing a secondary judgment of error continuous surfaces according to the positional relationship between the circumscribed rectangles in the initial error continuous surface set, deleting non-error continuous surfaces in the secondary judgment, and completing the update of the error continuous surface set. The present application reduces the time complexity of the algorithm and can meet the demand for rapid detection of large-scale data sets.

The present invention discloses a method and system for automatic test vector generation based on a decision tree. The method first extracts node feature data of a circuit under test, and then uses a trained decision tree model to obtain the backtracking confidence level for each logic gate. The backtracking confidence level is the predicted probability value of the decision tree model for the successful propagation of a fault by the logic gate in the backtracking path. Finally, the logic gates with high backtracking confidence levels are selected as nodes to be added to the backtracking path, and test vectors are generated according to the backtracking path. The present invention can effectively reduce the number of test vectors, reduce the time complexity of fault detection, and solve the problems of high backtracking frequency, large resource consumption, and interference from noise data in traditional methods.

The invention relates to a text error correction method based on an improved dictionary tree. The text error correction method comprises the following steps: 1) constructing the improved dictionary tree: constructing an optimized dictionary tree by using error words; 2) searching wrong words by using the dictionary tree: querying a text to be processed by using the constructed dictionary tree; and 3) post-processing: performing comparison processing on wrong words and correct words. The method has the advantages that the method is reasonable in design, a short mode is used, the time complexity is the length of the query character, the length of the character to be processed can be reduced in a sliding window mode, and the query efficiency can be effectively improved under the condition that part of repeated words are processed; a dictionary tree is optimized, jumping is carried out according to a preprocessing table when mismatching is encountered, and query can be reduced; in the process of processing correct word pairs of wrong words, candidate positions and corresponding correct words can be accurately output.

Embodiments of the present application provide a target object tracking method and device, equipment and computer readable storage medium, relating to the technical field of artificial intelligence. The method comprises: receiving a target tracking request; in response to the target tracking request, performing feature extraction on an initial template, a current cumulative template and a to-be-tracked region corresponding to a to-be-tracked image respectively, and correspondingly obtaining initial feature embedding, current cumulative feature embedding and to-be-tracked feature embedding; performing cross-correlation processing on the template aggregated feature embedding and the to-be-tracked feature embedding obtained by feature extraction, to obtain a similarity between each sub-region and a target object; determining a tracking result of the target tracking request in at least two sub-regions according to the similarity, and outputting the tracking result. Through the embodiments of the present application, long-range changes in the appearance of the target object can be captured at a relatively low time complexity, and the tracking performance and accuracy of the target object are improved.

The invention discloses a hardware event group scheduling method for a computer processor performance monitoring unit. The method comprises the following steps: S1, acquiring an event group set and the number of counters; s2, performing descending sorting on the event groups according to the number of events by using a non-incremental sorting strategy; s3, initializing an empty round; s4, iteratively distributing the event group to an existing round or creating a new round; and S5, outputting the round set of the existing round or the new round obtained in the step S4 as a scheduling scheme of the event group on a performance monitoring counter, and the method has the advantages that a hardware event group scheduling problem is converted into a one-dimensional boxing problem, and a non-incremental sorting strategy and an iterative greedy distribution strategy are utilized, so that the time complexity of a hardware event scheduling process is remarkably reduced, and the scheduling efficiency is improved. A low-overhead and high-precision performance data observation scheme is constructed, on the premise that accuracy is guaranteed, efficient execution of large-scale performance analysis tasks is achieved, and performance monitoring efficiency and system performance observation precision are improved.

The invention provides a subspace array direction finding method based on semi-supervised learning. According to the method, the signal subspace is used as network input, the scale of a data set and the memory requirement of a processor are remarkably reduced, and application of a deep learning method in a super-large-scale antenna array scene becomes possible. In order to solve the problem that label data in an actual scene is difficult to obtain, a semi-supervised learning strategy is introduced, and high-precision model training is realized through combination of a small amount of label data and complex value operation of a convolutional layer. Simulation results show that the method is excellent in performance in a scene in which memory resources are limited but high time complexity is allowed, and an effective solution is provided for practical application.

The invention discloses a federal data valuation method based on influence pruning sampling, belongs to the field of federal data valuation, adopts a marginal contribution-based Shapley value calculation scheme, and designs an influence pruning sampling-based Shapley value approximation algorithm. Client combinations with low influence are pruned, and combinations with high influence are sampled. The client number n and the total sampling round number gamma are given, the average time for conducting federated learning is tau, the time complexity of the method is O (gamma tau), and the time overhead can be effectively reduced. The average data volume of the client in federated learning is assumed to be t, the approximate error limit of the method is # imgabs0 #, and the data volume t of the client is generally large in a general federated scene, so that the method can achieve a smaller approximate error even under the condition that the k * value is smaller.

The present invention discloses a weighted integrated neural network key prediction method, device, medium and equipment, and relates to the cross-technical field of cryptanalysis and machine learning. Each set of preset number of sample keys and initialization parameter vectors are respectively input into the sequence key algorithm to obtain multiple sets of preset number of sample key streams; the first bit value of the sample key is used as the label of the corresponding sample key stream, and each set of preset number of sample key streams and corresponding labels are respectively used as training data to train the residual neural network to obtain multiple key prediction models; then the key stream of the ciphertext data to be cracked is respectively input into multiple key prediction models to obtain multiple first bit prediction values ​​of the key of the ciphertext data to be cracked; multiple first bit prediction values ​​are weighted integrated to obtain the key prediction result of the ciphertext data to be cracked. This method can reduce the time complexity of key prediction.

This invention discloses a method, apparatus, and medium for solving variable quantum linear problems in subspaces. The method includes: determining the system of linear equations to be solved, constructing a variable quantum circuit and a Krylov subspace, and using the generalized minimum residual method and the variable quantum circuit to calculate an approximate solution of the system of linear equations to be solved in the subspace. This addresses the shortcomings of existing technologies and can reduce the time complexity and computational load of solving linear problems.