84 results about "Network complexity" patented technology

The invention discloses an underwater target recognition method based on an improved YOLOv8 algorithm, and the method comprises the steps: inputting an underwater image into an improved YOLOv8n model for underwater target detection, and obtaining an output underwater target recognition result. The method has the advantages that the convolution blocks of the P5 layer of the backbone network and the last layer of the neck network adopt DSConv, so that the network complexity is reduced, and the reasoning speed is increased; a fourth C2f module of the backbone network adopts a C2f DiRMB module in which an inverted residual attention mechanism and dual-channel convolution are introduced, so that the capability of capturing key global information of the network is enhanced, training parameters are reduced, and the understanding of a complex scene is improved; and finally, a small target detection head for improving the small target detection capability is additionally arranged in the head network. According to the underwater target identification method, the mAP (at) is 0.5%, the mAP (at) is 0.5-0.95%, and the accuracy and the recall rate are respectively improved by 0.5%, 0.8%, 0.5% and 1.0%.

Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and / or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and / or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).

Systems, computer programs, devices, and methods that enable ML-based vision processing for low-power, embedded, and / or real-time applications. In one exemplary embodiment, smart glasses use classifiers that are based on machine-learned (ML) patch relationships. The ML patch features are determined during an offline training process. The ML patch features are grouped into weak classifiers, strong classifiers, and detectors to progressively improve prediction accuracy. An object detection architecture uses triggering logic, search management, and a classification neural network to enable event-based searching, interest-based searching, and / or dynamic search control. In some cases, pre-processing may also be used to minimize the neural network complexity (e.g., pre-processing for scaling, rotations, translations, etc.).

The invention discloses an image denoising method based on a transform blind spot network and knowledge distillation. The method comprises the following steps: obtaining a training set; constructing a blind spot denoising network model based on a transform, and carrying out the training of the blind spot denoising network model; changing the size of a blind convolution kernel of the blind spot denoising network model to obtain a group of pre-trained blind spot denoising network models; respectively inputting the noise images into the group of pre-training blind spot denoising network models to obtain pseudo clean images with different weights; constructing a lightweight denoising network model based on the improved U-net, and training the lightweight denoising network model by using the noise image and the pseudo clean images with different weights; and processing a noise image by using the trained lightweight denoising network model to obtain a final denoised image. According to the method, image details are reserved, meanwhile, the complexity of the network is reduced, and the visual performance, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) of the image are improved.

The invention relates to the technical field of communication networks, in particular to an intention-driven satellite-ground convergence network on-demand arrangement system and method, and the method comprises an intention input layer which is used for receiving a multi-mode intention of a user; the intention analysis layer is used for analyzing the intention of the user to generate a recognizable network strategy; the arrangement control layer comprises a design state and an operation state; wherein the design state is used for dynamically generating a resource arrangement strategy based on an identifiable network strategy, and verifying the feasibility of the resource arrangement strategy in the digital twin network; the running state is used for generating an executable scheme based on the verified resource arrangement strategy; and the infrastructure layer comprises a satellite-ground unified atomic power abstraction module which is used for abstracting heterogeneous resources into standardized atomic power and executing an executable scheme. The scheme aims to provide a brand-new intention-driven satellite-ground fusion network arrangement architecture with dynamic generation, thorough decoupling and active prediction capabilities, so that the technical problem that complexity and dynamics of a 6G network cannot be handled at present is solved.

The application discloses an underwater target recognition method based on an improved YOLOv8 algorithm, which comprises the following steps: inputting an underwater image into an improved YOLOv8n model for underwater target detection, and obtaining an output underwater target recognition result. The method has the following advantages: DSConv is used in the convolution block of the P5 layer of the backbone network and the last layer of the neck network, which reduces the network complexity and improves the inference speed; the C2f_DiRMB module is used in the fourth C2f module of the backbone network, which introduces an inverted residual attention mechanism and a double-channel convolution, enhances the ability of the network to capture key global information, reduces the training parameters, and thus improves the understanding of complex scenes; finally, a small target detection head is added in the head network to improve the small target detection capability; and the underwater target recognition method improves the mAP@0.5%, mAP@0.5-0.95%, precision and recall by 0.5%, 0.8%, 0.5% and 1.0% respectively.

The invention discloses a near infrared spectrum reconstruction method based on stage perception mixed prior and a two-channel optical imaging system, and the method comprises the steps: constructing a deep expansion network model of stage perception mixed prior based on RGB guidance, and the network model comprises k cascade reconstruction stages, each reconstruction stage comprises a degradation perception residual gradient descent module and a near-end mapping module. According to the method, a degradation sensing residual error gradient descent module is introduced, the difference between a sensing matrix and an actual degradation process is effectively reduced through degradation learning, and the iteration process of a deep expansion network is divided into two stages of spectral space preliminary feature extraction and space detail deep extraction by applying a segmented optimization strategy; the network reconstruction precision is guaranteed, the optimization of the calculation efficiency is improved by adjusting the network complexity in stages, and the completeness and quality of the reconstructed image are guaranteed while the calculation overhead is reduced.

The invention discloses a water chilling unit small sample fault detection method and system based on transfer learning, and the method carries out the fault detection through an expansion causal convolution module, a dense neural network module and a classification layer which are connected in sequence. A sparse connection strategy is introduced into dense blocks of the fault detection model, and representative connection between far and near layers is only reserved in each dense block, so that the network complexity is reduced, the feature redundancy is reduced, and the feature multiplexing advantage is kept; meanwhile, the method utilizes a transfer learning strategy to transfer labeled data knowledge of a source domain to a target domain, and introduces a meta-learning thought in a fine tuning stage to carry out gradient updating. In addition, feature subsets with high discrimination ability are screened out from an original high-dimensional feature space in combination with importance scores and correlation analysis, so that the detection precision of model training is improved.

The invention provides an OCTA image retinal vessel segmentation method based on a full-resolution network, and the method comprises the steps: carrying out the preprocessing of an input OCTA image through a data preprocessing module, wherein the preprocessing comprises the optical transformation and geometric transformation, and obtaining the preprocessed data; the feature coding module uses five feature extraction units which are arranged in sequence to extract image features of five levels and outputs the image features to the cascade feature enhancement module; a cascade feature enhancement module CFEM combines low-level detail information and high-level semantic information through cross-layer guidance for feature fusion, and then outputs a segmentation result; calculating loss and optimizing the OCTA image retinal vessel segmentation model based on the full-resolution network to obtain an optimized segmentation model; using the optimized segmentation model to obtain a retinal blood vessel image; according to the method, high-precision and high-efficiency segmentation of the retinal blood vessel can be realized, the network complexity is relatively low, and the computing resource consumption is relatively low.

The invention discloses a communication network complexity evaluation and optimization method and device, equipment, a medium and a product. The method comprises the following steps: acquiring communication network complexity original information of each segmented network in an end-to-end communication network; the original information comprises evaluation data and weight of each preset first evaluation element and evaluation data of a preset second evaluation element; the first evaluation element is an evaluation element related to network complexity, and the second evaluation element is an evaluation element related to communication service volume; respectively calculating the network complexity of each segmented network according to the original information; and according to the segmented network weight and the network complexity of each segmented network in the end-to-end communication network, calculating the network complexity of the end-to-end communication network for optimizing the communication network. By adopting the method and the device, the cross-domain coordination, scheduling and optimization requirements of the end-to-end network are considered, the network complexity evaluation of the end-to-end communication network is realized, and the optimization of the communication network is facilitated.

The present invention relates to a communication signal recognition method and system based on an adaptive feedforward neural network, belonging to the technical field of signal recognition, and solves the problems of inaccurate recognition of existing small-sample signals and difficult adjustment of model parameters. It includes randomly generating the activation states of each hidden-layer neuron of binary type, the input weights of each hidden layer of real number type, and the hidden-layer bias based on a single-hidden-layer feedforward neural network, and forming multiple initial network individuals in a hybrid coding form as the parental population; using the root mean square error and network complexity as the objective function, and based on the preprocessed signal sample set, an improved NSGA-III algorithm is used to obtain the Pareto optimal population; according to the root mean square error, multiple network individuals are obtained from the Pareto optimal population as base classifiers, and the preprocessed communication signals collected in real time are input into the base classifiers for ensemble learning. After weighted summation of the output results, the category corresponding to the maximum value is taken as the communication signal recognition result. Accurate signal recognition is achieved.

The application discloses a billet detection method based on deep learning, and belongs to the technical field of billet detection, which is mainly used for the identification and positioning of billets in the process of discharging from a hot continuous rolling heating furnace. The multilayer feature extraction structure of the method is embedded with a CSA module, a SPPELAN network and an improved PANET network, the complex environment in which the billets are located is fully considered, end-to-end network construction from input of a detection picture to output of a prediction result is realized, and the problems of low precision and poor real-time performance of traditional methods are solved. Moreover, a residual network is used to extract image features, the network complexity and feature effectiveness are well balanced, and the model attention to the target and the prediction accuracy are improved. Meanwhile, the improved path aggregation network also makes the model pay more attention to billet features of different scales, and improves the detection precision. Therefore, the automation and intelligence degree of the method is high, and the industrial application prospect is good.

The invention relates to a nano tackifying fluid and supercritical CO2 fluid synergistic fracturing method. In the target stratum development area, nano tackifying fluid and supercritical CO2 fluid are adopted for collaborative fracturing, sand-carrying fluid and displacing fluid are combined to form a fracture network, and the corresponding relation between the construction parameter combination and the fracture flow conductivity and the corresponding relation between the construction parameter combination and the fracturing construction fluid amount are established; based on the corresponding relation, a multi-target optimization model with the fracture conductivity maximization and the fracturing construction liquid amount minimization as the target is constructed, and through population initialization, non-dominated sorting, crowding distance calculation, crossover variation selection and iterative updating, a construction parameter optimization result is output. The shale reservoir fracture flow conductivity and the fracture network complexity can be improved, the construction liquid amount is reduced, and the fracturing transformation effect and economical efficiency are improved.

The invention relates to the technical field of multi-node ranging, and particularly discloses a multi-node ranging method and system based on code division multiple access, and the method comprises the steps: S1, calculating the network complexity, obtaining the bandwidth when the network complexity exceeds a certain range, and calculating the channel capacity; s2, calculating transmission time, calculating a transmission time ratio, correcting the bandwidth based on the transmission time ratio to obtain a corrected bandwidth, and obtaining corrected time; and S3, setting the bandwidth as a modified bandwidth, sending bytes with the same number to the test nodes, obtaining actual transmission time, screening qualified nodes, presetting a byte number gradient, obtaining a ladder coefficient when the requirement is met, sorting the test nodes, retaining efficient nodes and adjusting the corresponding bandwidth as the modified bandwidth. According to the invention, the byte data transmission frequency in the multi-node ranging process can be reduced, the positioning delay is reduced, and the response speed is improved.

Aspects of the disclosure are directed to a randomly determined first hop for network routing in an N-dimensional grid network. The first hop for a packet traversing the network is randomly determined based on a probability distribution for potential first hops. Based on a destination for the packet, a router in the network can search for potential paths and corresponding probabilities for the first hop, select a path based on the first hop, and forward the packet along that path to its destination. Network routing with a randomly determined first hop can improve fault tolerance without adding network complexities to the N-dimensional grid network.

This disclosure relates generally to image coding and particularly to methods and systems for neural image compression (NIC). The disclosed NIC decoder / encoder may include various neural network components that are configured to achieve a balance between network complexity and coding efficiency. Such a NIC decoder / encoder implementation particularly include a core decoder and a hyper decoder each including a neural network architecture adapted for achieving a lightweight decoder / encoder.

The invention provides a cross-network http calling method and device, electronic equipment and a readable storage medium, and the method comprises the steps: receiving an access request which is sent by a service requester and aims at a target intranet service, and the access request comprises an access domain name; the access domain name is a domain name created based on the IP address of the target intranet service, the target intranet port number and a preset domain name generation rule; extracting an IP address field and a port number field in the access domain name based on a pre-configured domain name matching rule; proxy the access request to the target intranet service based on the extracted IP address field and port number field, and obtaining a response result; and returning a response result to the service requester. According to the invention, for any newly-added intranet service, as long as the domain name of the access request of the intranet service accords with the domain name generation rule, automatic access can be realized through a dynamic proxy method without modifying the configuration of a reverse proxy server or restarting the service. The cross-network complexity is reduced, and the requirements of automation and rapid iteration are met.

The invention provides a communication method, terminal equipment and a core network element. The method comprises the following steps: a first terminal device sends first information to a first core network element through a user plane message; wherein the first information is used for indicating the information of the second terminal equipment. Under the condition that the first core network element collects the information of the second terminal device, the network side can obtain the information of the second terminal device so as to perceive the second terminal device. In this case, the first terminal device can obtain the information of the second terminal device without a private interface, thereby realizing unbinding of the first terminal device and the second terminal device. For a consumer, the consumer can purchase the first terminal device of any manufacturer, so that the network side can sense the information of the second terminal device. And for a manufacturer or a supplier of the second terminal equipment, the manufacturer or the supplier does not need to deploy the server, so that the cost and the network complexity are reduced.

The invention discloses a method-level test case sorting method based on change measurement and defect severity prediction, which comprises the following steps of: constructing an abstract syntax tree of each method, and analyzing differences in different software versions; the method comprises the following steps: abstracting a calling relation of a software runtime method into a software network MSN, extracting a network complexity index and a source code level measurement index of each method, constructing a defect severity prediction data set by combining the defect severity in the method, and constructing a defect severity prediction model by using a machine learning classifier to predict the defect severity in the method; and evaluating the test importance of the method based on the change measurement of the method and the predicted value of the defect severity, evaluating the priority of the test cases in combination with the coverage information of the test cases, and executing the test cases according to the priority sequence. According to the method, the change size of the method and the severity of the defect are considered, the method is of great significance in improving the regression test efficiency and improving the software quality, and technical support is provided for developing high-credibility software.

The present application belongs to the technical field of image processing, and particularly relates to a non-Lambertian surface photometric stereo model and method based on three-dimensional convolution. The present application discloses a non-Lambertian surface photometric stereo model based on three-dimensional convolution, which comprises an information fusion layer, an inter-frame feature extraction layer, a spatial feature extraction layer, a maximum pooling layer and a regression layer. The information fusion layer is used for fusing each image and corresponding light source information, so as to ensure one-to-one correspondence between the image and the light source in subsequent processing. The inter-frame feature extraction layer is used for extracting inter-frame information, obtaining information between input image frames, and estimating the normal map. The spatial feature extraction layer extracts structural information inside a single image for normal map recovery. The maximum pooling layer is used for dimension reduction, removal of redundant information, compression of features, simplification of network complexity, reduction of calculation amount and reduction of memory consumption.

The application discloses a lightweight three-dimensional model classification system based on multi-view grouping. A view sampling module projects a three-dimensional model into N views and sequentially renders N two-dimensional depth maps. A view grouping module divides the two-dimensional depth maps into multiple groups according to the geometric semantics of the three-dimensional model. A feature extraction module extracts features from each group of two-dimensional depth maps and outputs multiple feature maps. A feature fusion module inputs the multiple feature maps into a MobileViT Block module for further analysis to obtain a final prediction result. The two-dimensional depth maps are divided into multiple groups through good geometric semantics, and then multiple groups of views use an improved MobileNet V2 network to extract effective features. Finally, multi-view feature fusion obtains the feature recognition of the three-dimensional model. The network reduces the network complexity while ensuring the effectiveness of three-dimensional model classification.

This invention discloses a fault location method and related equipment for optical networks. It reduces the network complexity of fault location and improves the accuracy and efficiency of fault location in optical networks. The method shown in this invention includes: a network management device acquiring a first sampling set from a first optical network device, the first sampling set including multiple optical powers obtained by the first optical network device sequentially sampling a first optical signal multiple times during a first fault location time period, wherein at least one optical power included in the first sampling set is less than or equal to an optical power threshold; the network management device determining the fault type of the optical network based on the changing trend of the multiple optical powers.

The application discloses a fruit recognition and positioning method for crop fruit picking. The application introduces a Ghostnet lightweight module to obtain a GN-YOLOv5s model, replaces original convolution layers with depth separable convolution to extract features and reduce network calculation amount; secondly, to further reduce the model size, a regularization term about a scaling coefficient is introduced in the BN layer for sparse training, channels with a scaling factor gamma of 0-0.005 are screened out, pruning processing is performed to obtain a GS-YOLOv5s model; finally, to make the pruned model maintain high detection accuracy, a knowledge distillation method is adopted, a teacher network is used to assist the fine-tuning of the pruned model to obtain a PD-yolov5s, the hardware cost is reduced, and the model is convenient to deploy on a low-computing platform. The application can reduce network complexity, improve detection efficiency, make it be deployed on an embedded platform with small computing power, and obtain positioning information of dense crop fruits, which lays a foundation for subsequent action planning of a picking robot.

The invention discloses a hyperspectral image compression network and compression method based on multi-scale spectrum and spatial feature enhancement, and mainly solves the problems of insufficient hyperspectral image spectrum modeling, insufficient spatial feature extraction and high network complexity in the prior art. The network comprises a main encoder, a main decoder, a super-prior encoder, a super-prior decoder and an entropy model. The main encoder comprises a spectral attention gating data unit, a convolution unit and a multi-scale spatial adaptive feature attention enhancement unit, and is used for converting an input image into potential representation and removing spatial and spectral redundancy; the main decoder and the main encoder are symmetrical in structure; the super-prior encoder comprises a convolution layer and an activation layer and is used for extracting auxiliary information from the output of the main encoder; the super-prior decoder and the super-prior encoder are symmetrical in structure; the entropy model is Gaussian distribution based on output parameters of a super-prior decoder. After the network is trained, lossy compression of a hyperspectral image can be realized. The method reduces the network complexity and spectral distortion, improves the reconstruction quality of complex ground feature details, and is suitable for earth observation, meteorological monitoring and the like.

The application provides a feeding network, an antenna module and an apparatus. In the feeding network, the feeding network comprises M radio frequency interfaces, N antenna interfaces, at least M first distribution units, N second distribution units and Mx(N-1) phase shift units, M is less than or equal to N, and M and N are integers greater than 1; each radio frequency interface is configured to receive a first radio frequency signal; the first distribution unit is configured to process the first radio frequency signal into N second radio frequency signals; an input end of each second distribution unit is connected with at least one first distribution unit and / or at least one phase shift unit, and an output end of the second distribution unit is connected with the antenna interface; each phase shift unit is connected between the first distribution unit and the second distribution unit, and each phase shift unit is configured to adjust the phase of the received second radio frequency signal. In this way, the network complexity of the feeding network is low, the insertion loss is small, and the isolation is good.

This invention relates to a low-light adjustable brightness enhancement method guided by a reference brightness index, belonging to the field of computer vision image technology. The first step involves extracting features from the input low-light image and the reference brightness index using methods such as convolution, downsampling, and global average pooling to obtain a feature vector. The second step decomposes the feature vector into brightness and content feature components, combining the content component of the low-light image with the brightness component of the reference brightness index to achieve feature recombination. The third step reconstructs the recombinated feature vector using methods such as transposed convolution, upsampling, and skip connections. This invention is rationally designed, fully considering the different lighting needs of various application scenarios or users. It efficiently utilizes and preserves the brightness and content information of the image. Furthermore, the network complexity is low, achieving good brightness enhancement while maintaining a fast running speed, resulting in good overall performance in adjustable brightness enhancement of low-light images.

The application discloses a large-scale MIMO downlink CSI feedback method based on deep learning enhancement, modifies an encoder network, a decoder network and feedback content on the basis of an existing self-encoding network CsiNet, enhances the feature extraction capability of a CSI matrix, reduces network complexity, and makes the input of a deep learning network and the CSI matrix be in the same order of magnitude. A puzzle solving training strategy is added to balance the relationship between compression tasks and identification tasks in deep learning, retains the integrity of physical information of the CSI matrix, does not lose the distinguishability, and enhances the downlink CSI feedback performance.

The embodiment of the invention provides a social network feature-based team communication complexity analysis method and system, and the method comprises the steps: constructing a command and control social network model, carrying out the fuzzy evaluation of a task unit, determining the overall fuzziness of a task, carrying out the multi-dimensional evaluation of a communication medium, determining a comprehensive value of the richness of the medium, and carrying out the analysis of the richness of the medium. Constructing a task fuzziness-communication medium matching matrix, coupling the task overall fuzziness with a medium richness comprehensive value, determining a medium communication efficiency parameter, and performing complexity analysis on a command and control social network model according to the medium communication efficiency parameter to obtain a command and control social network model; the average path length complexity feature, the network diameter complexity feature and the network density complexity feature of the network are obtained, weighted optimization is carried out on the network edge according to the network complexity feature, an optimized command and control social network model is obtained, and the efficiency of team communication and task execution can be improved.

The application discloses an ultrasonic video recognition method and system based on diffusion attention. The method comprises the following steps: acquiring an ultrasonic video; inputting the ultrasonic video into a trained recognition model to obtain a classification prediction result; wherein the recognition model uses multiple stages to obtain the overall representation of the ultrasonic video, each stage comprises a space-time block representation module, a diffusion window attention module and a space-time block merging module, the space-time block representation module is used for space-time division of the input ultrasonic video to obtain a discrete space-time block set; the diffusion window attention module establishes the distance dependence relationship between the space-time blocks by alternately using a fixed window attention mechanism and a diffusion window attention mechanism; and the space-time block merging module is used for down-sampling the features output by the diffusion window attention module. The application designs a diffusion window attention mechanism, which can enhance the perception field of the features, thereby quickly obtaining the overall information of the video without increasing the network complexity.

The application relates to an image enhancement method, a vehicle snapshot method, equipment and a medium, and belongs to the technical field of image processing. The method comprises the following steps: performing preprocessing on original image data collected by an image collection component; inputting the preprocessed image data into an image enhancement model to obtain image data after brightness enhancement; the calculation efficiency of the image enhancement network can be improved by performing down-sampling processing on the image input into the student network. The image input into the teacher network is not subjected to down-sampling processing, the performance of the teacher network in image enhancement can be ensured, and the calculation speed of the student network can also be ensured, and the network complexity of the student network is lower than that of the teacher network. Then, the teacher network distills the learned knowledge to the student network, so that the student network can ensure the image enhancement performance while ensuring the calculation speed. Sharing the intermediate feature map output by the teacher network to the student network can also improve the training efficiency of the student network.