147 results about "Linear network coding" patented technology

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention relates to the field of artificial intelligence, and discloses an E-commerce platform commodity recommendation method based on AI intelligence, and the method comprises the steps: obtaining user environment and behavior data through multi-modal perception, generating a three-dimensional intention vector through space-time convolution network coding, and driving a dynamic strategy selector to configure a recommendation weight; generating an initial recommendation list in combination with the commodity feature library and the user portrait; collecting user interaction feedback in real time, analyzing and outputting a correction vector through a lightweight loop network, dynamically adjusting a strategy, and reordering recommendation results; after a user places an order, a scene complementation generator is started, a complementation commodity sub-list adaptive to a current transaction scene is generated based on knowledge graph multi-hop reasoning, and visual fusion presentation is carried out. According to the technical scheme, accurate intention recognition, millisecond-level strategy correction and dynamic complementary recommendation are achieved, and recommendation real-time performance, adaptability and diversity are improved.

In a blockchain, transacting a record comprises performing linear network coding on the record or on a data file corresponding to the record, to produce a plurality of coded data parts; and storing at least one of the plurality of coded data parts on the blockchain or storing a proof of knowledge on the blockchain, the proof of knowledge derived from the coded data parts. Linear network coding coefficients might be derived from cryptographic hashes of the plurality of coded data parts. An all-or-nothing transform can use the cryptographic hashes to provide the proof of knowledge.

The invention relates to a knowledge tracking model research method integrating difficulty perception and memory enhancement. According to the method, a graph attention network coding exercise-knowledge point topological relation is constructed, and a difficulty embedding layer and a gating fusion mechanism are combined to realize joint characterization of difficulty features and a topological structure. The difficulty coefficient is innovatively introduced as a dynamic regulation factor of attention weight in memory network updating, the memory intensity of high-difficulty exercises is enhanced, and the discrimination and topological consistency of knowledge representation are improved through a multi-task optimization framework integrating graph structure loss and contrast loss. According to the method, challenges of exercise difficulty perception and knowledge point topological relation modeling are effectively solved, experimental results show that the AUC of the model reaches 0.93 (improved by 7.8% compared with traditional BKT) on an ASSIST2009 data set, the AUC of the model reaches 0.90 (improved by 0.21 compared with KSGAN) on an EdNet data set, objective indexes and teaching scene verification show that the method has remarkable advantages in the aspects of knowledge point correlation modeling and knowledge tracking, and the method is suitable for popularization and application. The method provides an innovative solution for personalized education, and has technical breakthrough and industrial application values.

The invention relates to the technical field of computer vision and target detection, and particularly discloses a lightweight real-time two-wheeled vehicle helmet detection method. According to the method, firstly, a video stream is collected and preprocessed through a traffic monitoring camera, then feature extraction and fusion are carried out through a lightweight backbone network, an encoder and a neck network in sequence, finally, a detection result is output through a decoder, a StripCGLU module is introduced to reduce the calculation complexity, a Pola Former module is adopted to enhance the feature interaction capability, and finally, the detection result is output through a decoder. And a GLBiFPN network is designed to optimize multi-scale feature fusion. According to the method, the calculation complexity and parameter quantity of the model are remarkably reduced, the requirement of edge calculation equipment for efficiency is met while high detection precision is guaranteed, and an effective technical means is provided for safety monitoring in an intelligent traffic system.

The invention discloses a high-frequency perception semi-supervised electromagnetic shielding light window image segmentation method based on multitask and transformation consistency learning, and the method comprises the steps: 1, constructing an electromagnetic shielding light window OM image data set, carrying out the shooting of an electromagnetic shielding light window, selecting an original image, generating a corresponding same-name label image, and dividing the same-name label image into a training set, a test set, and a verification set; 2, constructing a semi-supervised electromagnetic shielding light window image segmentation network model HAMTC-Net; 3, a network model HAMTC-Net is trained; 4, evaluating the performance of the HAMTC-Net network model by using the verification set and optimizing parameters; and 5, inputting an electromagnetic shielding light window image to be segmented into the trained HAMTC-Net network model, and outputting a segmentation result. According to the method, the global learning ability of a network encoder is enhanced through a multi-task method while the transformation consistency is fully utilized, and the high-frequency characteristics of data are fully utilized to guide training, so that the segmentation precision and generalization ability of the model in a complex electromagnetic shielding light window image are effectively improved.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

The invention provides a three-dimensional seismic data mixed noise suppression method based on an MSAT-Unet. The method comprises the following specific steps: constructing an MSAT-Unet network comprising a multi-scale expansion convolution residual module, a channel-space attention mechanism and a Transform convolution module; the encoder is improved into multi-scale expansion residual convolution, so that the receptive field is expanded, and the capability of capturing local details and global semantic information is enhanced; a channel-space attention mechanism is integrated behind the decoder, a direction sensitive context is extracted through multi-dimensional adaptive pooling, and details and edge recovery are enhanced; meanwhile, an improved decoder is a Transform convolution module, and the feature reconstruction and complex structure recovery capability is improved; the optimized model carries out training and reasoning on the three-dimensional seismic data, mixed noise can be effectively suppressed, a clear data basis is provided for subsequent interpretation, and the method is high in generalization and robustness and good in performance in the aspect of three-dimensional seismic data denoising.

The invention discloses a blast furnace smelting safety reinforcement learning decision optimization method based on expert strategy constraint. According to the method, an optimization strategy can be learned from an off-line expert track on the premise that operation safety is ensured. Specifically, a conditional generative adversarial mechanism is introduced to realize the alignment of strategy distribution and expert decision, and the exploration ability in a security domain is retained while the expert experience is inherited. And an independent state-action safety evaluation network is designed and a discount factor is introduced, so that the long-term accumulation risk caused by the large hysteresis characteristic of the blast furnace is effectively dealt with. In addition, memory is adopted to enhance network coding historical information, incomplete state observation is supplemented, and information deviation in decision is reduced. According to the method, effective integration of triple guidance mechanisms is realized, knowledge inheritance is realized through distributed alignment, performance improvement is driven through reward optimization, and risk prevention and control are ensured through explicit security constraints.

The invention relates to the technical field of intelligent terminal control, in particular to an intelligent management method and system for an integrated first-aid kit, and the method comprises the steps: setting a step length through a long-short-term memory network, synchronously generating a trend change curve through forward rolling and backward rolling, extracting a trend difference, and marking a periodic fluctuation material type under a sensitivity threshold value; fusion of bidirectional time sequence association and sensitivity screening is realized in trend dynamic capture and change identification, the sensitivity of material shortage prediction is improved, and the sensitivity of material shortage prediction is improved by combining resource weight priority allocation and pointer network coding, setting urgency parameters to cut low-frequency categories and reconstructing a material category sending sequence. And hierarchical scheduling and sequence planning of instruction sending are optimized. Based on remote feedback recording of continuous failure categories and reorganization of inventory synchronous update sending instructions, the actual state of the materials is dynamically incorporated into a cyclic supplement process, the continuous supply guarantee capability of emergency material use is enhanced, and the abnormal response speed and the inventory health management level are improved.

The invention discloses a reverse knowledge distillation-based cross-stage feature fusion method for industrial anomaly detection and positioning, and relates to the technical field of knowledge distillation and transfer learning. The method comprises the following steps: step 1, constructing a teacher network encoder, a student network encoder and a decoder; 2, extracting input image features through a student network encoder, and reconstructing the features through a student decoder; step 3, student network cross-stage feature fusion design; 4, locally sensing a dynamic attention module LDA, and adding a sliding window and convolution to replace original global pooling; and step 5, a self-adaptive multi-scale feature fusion module CAAMS-FF establishes a dependency relationship of a context so as to realize a fine-grained high-quality feature reconstruction effect. And step 6, inputting an anomaly graph obtained by the teacher-student network into the segmentation sub-network to obtain a final anomaly detection and positioning result. While the calculation efficiency is maintained, the spatial positioning precision and the multi-scale adaptability are significantly improved.

The invention discloses an image splicing method and device and a storage medium. The method comprises the steps of fusing paired or grouped images to be spliced into a fused image through a splicing model; the splicing model comprises a splicing network, a correlation analysis network and a fusion network which are connected in sequence; the deep semantic features and the shallow detail features of the to-be-spliced images are extracted through the splicing network, the correlation analysis network can more accurately judge the overlapping relation and the spatial position between the images by mining the context correlation information between the images, and the registration deviation is greatly reduced in combination with the dynamic determination of the size of the minimum bounding rectangle. In the fusion network coding stage, the feature expression capability is enhanced by reducing the size of a feature map, and in the decoding stage, fine fusion of deep and shallow layer features is realized by recovering the size. The three networks cooperate to effectively solve the problems of high calculation complexity, low registration precision, poor splicing quality and the like of a traditional method, and the application requirements of mobile equipment and complex scenes are met.

The invention discloses a ship abnormal behavior detection method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent maritime affair monitoring, and the method comprises the steps: obtaining original ship trajectory data, carrying out the augmentation processing and hybrid standardization processing of the original ship trajectory data, and obtaining a standard trajectory sequence; based on the standard trajectory sequence, pseudo labels are generated through unsupervised clustering, and the pseudo labels are used for representing different normal navigation modes; taking the standard trajectory sequence and the pseudo tag as supervision signals, and training a neural network encoder through comparative learning to obtain a target trajectory encoder; based on a target trajectory encoder, constructing a plurality of normal behavior mode reference models according to the trajectory in each cluster corresponding to the pseudo tag; and inputting a to-be-detected new track into the target track encoder to generate a to-be-detected embedded vector, and performing anomaly judgment according to the distance between the to-be-detected embedded vector and each normal behavior mode reference model. According to the invention, the accuracy of ship abnormal behavior detection is improved.

A method is performed by a network node for precoding of downlink communications predicting downlink channel state. The method receives a compressed-dimensional representation of a channel state that is encoded through an encoder neural network of a UE. The method maps the compressed-dimensional representation through a forward-prediction neural network to generate a compressed-dimensional predicted representation of a forward channel state at least one step forward in time k+Δ, wherein Δ is a number of steps forward in time. The method decodes the compressed-dimensional predicted representation of the forward channel state through a decoding neural network to generate an increased-dimensional predicted representation of the forward channel state, where the increased-dimensional predicted representation is a higher dimensional representation than the compressed-dimensional predicted representation. The method precodes signals for transmission through the downlink channel to the UE based on the increased-dimensional predicted representation of the forward channel state.

According to disclosed embodiments, methods and systems of data transmission are provided. An aspect of the present disclosure is a method comprising receiving an audio stream, parsing the audio stream into packets, encoding each packet using Alphabet Linear Network Coding (ALNC), and transmitting the encoded packets.

The invention provides an encrypted network flow detection method and device based on improved multiple graphs, and a program product. The method comprises the following steps: splitting a to-be-detected encrypted network flow into at least one bidirectional network flow; extracting network flow features in each piece of bidirectional network flow information to construct a multi-graph comprising at least one node pair; each node is constructed through connection information features in the network flow features; each node pair is connected through a first type edge and a second type edge; inputting the multiple graphs into a pre-trained encrypted network flow detection model to obtain a detection result corresponding to the encrypted network flow; the encrypted network flow detection model comprises a relational graph convolutional network encoder and a multi-layer perceptron classifier. The method can solve the problems that an existing encrypted network flow detection method is large in consumption of computing resources and storage resources and poor in practicability. Accuracy and practicability of encrypted network flow detection are enhanced, and possibility is provided for real-time monitoring and quick response to potential security threats.

The invention discloses a knowledge tracking method and system based on group and individual dual-path collaborative evolution, which are used for modeling the knowledge mastering condition of a learner from a problem state, a concept state and an overall knowledge state and comprehensively capturing the dynamic evolution process of the knowledge state. According to the method, the complex interaction relationship among different knowledge dimensions is effectively captured by fusing the LSTM network, the Transform encoder, the multi-head attention mechanism and the MALA linear attention module. Multi-dimensional feature representation is constructed by using question embedding, concept embedding and answer embedding, and fine adjustment of knowledge states is realized through a gating mechanism and a state updating module. The linear attention calculation and rotation position coding technology of amplitude perception is adopted, so that the capturing capability of the model on the long-term dependency relationship is enhanced while the calculation efficiency is ensured. A knowledge forgetting rule is dynamically reflected through a time decay factor and a state updating mechanism, the ability and problem characteristics of a learner are quantified in combination with an IRT model idea, and accurate modeling and performance prediction of the learning process of the student are achieved.

The invention discloses a dynamic emotion recognition method based on unsupervised contrast graph learning electroencephalogram enhancement, which comprises the following steps: carrying out preprocessing and segmentation processing on unmarked electroencephalogram signals, and segmenting the unmarked electroencephalogram signals into overlapped segments with fixed lengths; differential entropy features of each segment of signal are extracted; establishing enhanced graph data by taking electroencephalogram channels as nodes, differential entropy features as node attributes and Euclidean distances between the channels as edge weights; constructing a pre-training model containing graph convolution and long and short term memory network encoders and projectors; executing pre-training: inputting enhanced graph data into an encoder to obtain spatial-temporal characteristics, and mapping the spatial-temporal characteristics through a projector; constructing a dynamically updated feature queue; optimizing the model by comparing loss functions; migrating the pre-trained encoder to a classification network, and performing fine tuning by using label data; and finally realizing electroencephalogram signal emotion classification. According to the method, through comparison learning pre-training, the classifier can learn rich feature representation from the unlabeled data, and dependence on large-scale manual labeling data is effectively reduced.

The invention relates to the technical field of artificial intelligence and power systems, and discloses a power system sample generation method, system and device based on a graph attention network, and a medium, and the method comprises the steps: obtaining historical operation data of a power system, and constructing a graph structure; encoding the graph structure by using a graph attention network encoder to obtain a node embedding representation; performing graph-level aggregation on the node embedded representation to obtain a global representation vector, inputting the global representation vector into a variational auto-encoder, and generating a mean vector and a logarithmic variance vector of potential variables; potential variables are obtained through re-parameterization skill sampling, and model parameters are trained; and based on the trained model, generating a potential vector in a potential space through a sphere center sampling mechanism, inputting the potential vector into a decoder, and generating a new power system operation sample. According to the method, a sample generation scheme with high reliability and high robustness is provided for uncertainty modeling and data-driven optimization scheduling of a power system.

This invention discloses an adaptive intrusion detection method based on CDIVAE and a bidirectional temporal model, belonging to the field of network attack detection technology. It uses the GWR algorithm to filter and cluster source domain data, removing a large amount of duplicate data and extracting a clearly distributed and relatively small subset of source domain data. A Gaussian mixture conditional domain-invariant variational autoencoder (GMI) is used to reduce the difference in posterior distribution between the source and target domains. Finally, an intrusion detection is performed using a fusion neural network BRN-BiLSTM, which combines a bidirectional preservative network encoder and a bidirectional long short-term memory network. The BRN first applies bidirectional temporal weighting to the data, and then the BiLSTM identifies and classifies the time-weighted data. The memory unit and gating unit effectively capture data dependencies. This invention achieves better domain-invariant feature extraction and data distribution alignment, and also exhibits better cross-domain intrusion detection performance.

This application discloses a method, apparatus, equipment, and storage medium for locating remaining oil. The method includes: acquiring segmented displacement state data of well groups and boundary transition trajectory data of the development stage; obtaining a continuous potential displacement state sequence by boundary interception, trajectory insertion, and time concatenation; obtaining a continuous displacement state sequence over all time periods by using a three-layer fully connected decoding method mirroring the encoding layer of the preceding depth Koopman autoencoder network; extracting the state at six consecutive sampling times at the boundary to form a local continuous change sequence; obtaining displacement obstruction judgment parameters through two-layer fully connected compression mapping; generating two types of markers through threshold comparison, cross-screening contradictory well groups, associating spatial locations, and merging to obtain the location result. This application can accurately distinguish two types of easily confused areas, and the location result can directly support the selection of sites and potential tapping decisions for later-stage oilfield development measures.

The invention discloses a single-cell multi-omics translation method based on comparative learning, and relates to the technical field of cell sequencing, and the method comprises the following steps: judging whether a first mode and a second mode are paired modes or not; if yes, generating a feature matrix; if not, obtaining a feature matrix through an auxiliary network encoder, a core network encoder and a clustering module in sequence; the feature matrix sequentially passes through data enhancement and an auxiliary network encoder to obtain a first hidden layer feature; judging whether the first hidden layer feature contains a space coordinate or not; if not, sequentially performing data enhancement, a core network encoder and a translator on the first hidden layer feature to obtain a second hidden layer feature; if yes, feature coding is carried out on the first hidden layer feature, and then a second hidden layer feature is obtained; and translating the second hidden layer feature through the core network decoder and the auxiliary network decoder in sequence to obtain a first mode and a second mode. According to the method, the translation model can learn real cell type characterization, so that counterfeit elimination, true storage and interpolation supplement are realized.

This invention discloses a dual-stream point cloud compression method and system based on semantic scene graphs, relating to the field of point cloud compression. It constructs a point cloud compression network comprising an encoder network and a decoder network: In the encoder network, semantic labels are mapped onto the input point cloud, and scene graphs and octree representations are constructed respectively. Semantic modulation parameters are generated based on the scene graph and encoded to obtain a semantic bitstream. The octree representation, under the influence of semantic modulation parameters, undergoes feature enhancement and attention probability prediction to obtain node occupancy probabilities, and a geometric bitstream is generated through arithmetic encoding. These two bitstreams serve as compressed data. In the decoder network, the bitstreams are decoded, and the octree structure and scene graph information are reconstructed. Node feature representations are recovered by combining the semantic modulation parameters. The reconstructed octree structure is mapped back to the point cloud space through spatial querying and indexing to reconstruct the point cloud. This invention solves the problem of local geometric distortion in low-bitrate octree point cloud compression, ensuring reconstruction quality while reducing the transmission bitrate.

A method for encoding data includes: selecting a sequence of pivot candidate positions from a sequence of g encoded vectors to encode blocks of g data symbols in a round of encoding by the following steps: providing a set of g pivot candidate positions; selecting pivot candidate positions for the sequence from the set of pivot candidate positions; removing the selected pivot candidate positions from the set of pivot candidate positions; and repeating until the set of pivot candidate positions is empty and the sequence of selected pivot candidate positions in the round is non-linear. A set of encoded vectors is generated based on the sequence of selected pivot candidate positions, each encoded vector including zero-valued coefficients at positions within the encoded vector preceding the pivot candidate positions and non-zero-valued coefficients at least at the pivot candidate positions.

The invention relates to the technical field of medical image processing, in particular to a PL medical image segmentation method combining deep supervision and a mixed loss function. The method comprises the following steps: carrying out cutting, registration, normalization, enhancement and dicing preprocessing on an input three-dimensional medical image; inputting the image blocks into an encoder-bottleneck layer-decoder network; an encoder extracts multi-scale features through pooling of a residual VGG block and a dual-channel spatial pyramid; performing depth feature abstraction and context modeling on the bottleneck layer; the decoder performs up-sampling through transposition convolution, fuses encoder features transmitted by jump connection, utilizes space attention to gate and focus a target, and optimizes the features through an ASPP module and a residual block; and setting output in a middle layer and a final layer of the network, and training the network through a mixed loss function combining Dice loss and cross entropy loss and a deep supervision mechanism. The precision and robustness of medical image segmentation can be effectively improved.

A communication device may include: a processor configured to perform a procedure comprising: receiving a first consensus check code from a first communication device; receiving a second consensus check code from a second communication device; generating a combination code by network encoding the first consensus check code and the second consensus check code; and instructing the transmission of the combination code to the first communication device and to the second communication device.

The present invention relates to the fields of computer application technology and network coding, and more particularly to an IP core architecture for implementing triple-redundant MDS array encoding and decoding based on a systolic array. The architecture comprises a PE module, a Weight FIFO module, an encoding and decoding module, and a driver module. L PE modules are arranged horizontally to form a one-dimensional systolic array. The one-dimensional systolic array outputs R redundant packets at intervals of K*L cycles. The PE modules include registers, accumulators, and XOR units. The calculation results and valid signals of the previous PE module are transmitted to the next PE module for calculation after passing through the registers. The Weight FIFO module is used to temporarily store coefficient matrix information and periodically fan out the input ports of all PE modules corresponding to the one-dimensional systolic array. The present invention implements cyclic shift matrix multiplication operations through the systolic array, reducing computational complexity while ensuring a fixed encoding and decoding delay, and improving hardware scalability, throughput, and energy efficiency.

This invention provides a collaborative optimization method for titanium plate annealing process parameters using multi-agent reinforcement learning, belonging to the field of reinforcement learning technology. The method includes collecting and standardizing annealing process data, encoding process parameters through a multi-layer feature extraction network, and constructing a parameter coupling perception matrix to quantify the collaborative strength. A dual-branch encoding structure is used to extract single-parameter features and interaction features, and the fusion weights are dynamically adjusted based on the coupling perception matrix to complete the state collaborative representation. The collaborative representation is input into a constraint-aware policy network to generate adjustment decisions, which are then executed in a real-world scenario and feedback is collected. Based on the feedback, a reward value is calculated, and training samples are sampled with priority to construct a composite loss function. A gradient projection method is used to update the policy network parameters to within the process feasible region, achieving continuous optimization of the policy network.