355 results about "Network usage" patented technology

A monocular self-supervision depth estimation method fusing multi-resolution features and global context belongs to the technical field of computer vision, and comprises the following steps: firstly, providing an encoder based on a cross-stage feature high-resolution network, and enhancing the extraction capability of multi-scale features by improving a cross-stage connection mechanism of an HRNet network; secondly, a visual Transform module is introduced to improve complex down-sampling operation, and a multi-head self-attention mechanism is utilized to establish global pixel association; then, an improved decoder network is provided, a channel attention module is used for re-weighting coding features, multi-level features of an encoder are utilized more efficiently, and the balance between segmentation precision and calculation cost is achieved; and finally, proposing an improved attitude estimation network based on lightweight ResNet18, predicting 6-DoF relative poses of adjacent frames in combination with a four-layer convolutional decoder, and accurately estimating the depth of each object. According to the invention, the accuracy of depth estimation is improved, and the visual effect of the depth view is improved.

The invention relates to a textile product defect identification method based on improved YOLOv11. The method comprises the following steps: acquiring a textile product defect image data set; performing pretreatment; dividing into a training set and a verification set; the method comprises the following steps: introducing MConv into a YOLOv11 backbone network, adding a CCIAP module behind a C2PSA module, and applying BiFPN in a path aggregation network; performing prediction through YOLO Head to obtain N prediction feature maps; the overall loss of the network is calculated, and network parameters are optimized through back propagation; predicting the verification set image through a network to output AP values of various categories; repeating the above steps to obtain a trained YOLOv11 network; and detecting the test image or video by using the trained detector to obtain a detection result. According to the method, the MConv is introduced into the YOLOv11 network to enlarge the receptive field, the CCIAP module is added behind the C2PSA to improve the feature extraction capability, and the BiFPN is applied to the Neck layer to enhance the feature fusion capability, so that the target detection precision is improved and the real-time detection of textile product flaws is realized under the condition that the reasoning speed is not influenced.

Embodiments provide a user device, UE, of a wireless communication network, the wireless communication network using one or more Artificial Intelligence / Machine Learning, AI / ML, models for one or more use cases, wherein the UE is configured or preconfigured with a plurality of AI / ML models for performing one or more certain operations, and wherein, dependent on one or more criteria, for performing the one or more certain operations, the UE is toswitch from a first AI / ML model to a second AI / ML model, ordeactivate one or more of the plurality of AI / ML models, orswitch from a current operation mode to a new operation mode.

The invention provides a continuous anomaly detection method and system for a wind power equipment state monitoring system, and the method comprises the following steps: S1, collecting original multi-dimensional time series data related to the equipment state in the operation process of wind power equipment, and dividing the original multi-dimensional time series data into n tasks according to a time window; performing standardization and random mask preprocessing on the data of each task to generate n tasks to be trained; s2, constructing a continuous learning model based on a Gaussian mixture variational auto-encoder, wherein the continuous learning model comprises an encoder, a decoder and an attention priori network; s3, performing local training on the continuous learning model by using the (1-n) th to-be-trained task; s4, performing global training on the initial global decoder by using the (2-n) th to-be-trained task, and outputting a trained continuous learning model; and S5, inputting a to-be-detected sample into the trained continuous learning model, and outputting a detection result. The problem of disastrous forgetting in an existing anomaly detection method is solved.

Apparatuses, systems, and techniques to use two or more neural networks to encode two or more different portions of a a set of information are described. In at least one embodiment, two or more neural network encoders are used to encode two or more different portions of a set of information to be used by two or more networks that each include one of the two or more neural networks respectively.

Systems and methods for object tracking are described. One or more aspects of the systems and methods include receiving a video depicting an object; generating object tracking information for the object using a student network, wherein the student network is trained in a second training phase based on a teacher network using an object tracking training set and a knowledge distillation loss that is based on an output of the student network and the teacher network, and wherein the teacher network is trained in a first training phase using an object detection training set that is augmented with object tracking supervision data; and transmitting the object tracking information in response to receiving the video.

A method of scheduling data transmission in a radio access network is provided. The radio access network comprises a scheduler configured to orchestrate communication of data between one or more base stations and a plurality of User Equipments, UEs, according to a set of instructions. The method comprises receiving real-time network usage data. The method further comprises dynamically modifying the set of instructions based on the network usage data. The method further comprises implementing the modified set of instructions so that the scheduler is configured to orchestrate communication of data between the one or more base stations and the plurality of UEs according to the modified set of instructions.

SYSTEMS AND METHODS IN THE FIELD OF SELF-SUPERVISED DETECTION OF FACIAL BOUNDARIES. Systems and methods for self-supervised learning (SSL) in facial detection networks are proposed. In one embodiment, a facial detection network comprises encoder components configured to encode facial features, the encoder components including trained components of a masked image modeling (MIM) network configured to process non-overlapping patches determined from the input image, the MIM network trained with an SSL objective; and decoder components configured by training to determine local matches between features to determine estimates for facial landmarks. In one embodiment, the MIM network is an MAE network.In one embodiment, the decoder components are derived from those of a second trained network comprising the encoder components as trained but fixed, wherein the decoder components of the second network are trained using locality constraint repulsion loss (LCR). Methods are proposed for SSL training of the encoder and decoder components. Figure for abstract: none.

The invention relates to the technical field of network advertisement processing, and discloses a method for intelligently identifying and automatically closing website advertisements based on a multi-modal large model. The method comprises the following steps: acquiring text data, image data and interactive behavior data of a website page in real time, and performing format standardization processing on each modal data to obtain a multi-modal original data set; extracting text features, image features and behavior features of the advertisement candidate area according to the data set, and establishing a feature association index; performing cross-modal semantic fusion on the feature association index through a pre-trained multi-modal large model to generate an advertisement recognition probability value; comparing the advertisement recognition probability value with a preset threshold value, judging whether the advertisement candidate area is a target advertisement or not, and generating a recognition result; and triggering a page element operation instruction based on the identification result, executing an automatic closing operation on the target advertisement area, and recording an operation log. According to the method, interference of advertisements on user browsing is effectively reduced, and network use experience is improved.

Cybersecurity reconnaissance, analysis, and scoring uses distributed, cloud or edge-based pools of computing services to provide sufficient scalability for analysis of IT / OT networks using only publicly available characterizations. An in-memory associative array manages a queue of configuration and vulnerability search tasks through at least one public-facing proxy network which uses configurable search nodes to approach the target network with search tools in a desired manner to control certain aspects of the search in order to obtain the desired results, especially when target network behavior adjusts based on counterparty characteristics. A data packet modifier reveals IP addresses of threat actors behind port scans and subsequently block the threat actors.

The invention discloses an urban electric vehicle charging station site selection planning method based on reinforcement learning, and the method comprises the steps: obtaining the road network and vehicle track data of a city, and obtaining node information; defining a reinforcement learning framework according to the node information; based on a-greedy attenuation strategy, selecting and executing an action to form a new state and a reward value, and expanding the new state and the reward value to an experience playback buffer pool; using a state value function and an action dominant function to improve the DQN network, using a priority-based experience playback mechanism to sample from an experience playback buffer pool to train the improved DQN network, constructing a target function by taking the score maximization of the scheme and the total cost not exceeding the budget as targets, and generating a plurality of candidate schemes; the score of each scheme is calculated, and the scheme meeting the objective function and having the highest score is the optimal scheme; the method can improve the sampling efficiency of high-value experience, shortens the convergence time, gives consideration to the balanced layout of multiple regions in a city, and remarkably improves the overall score and coverage effect of a charging station site selection scheme.

Techniques for protection scheme configuration in a communication network are disclosed. For example, a home communication network of given user equipment sends protection scheme configuration data to the given user equipment using a user equipment parameter update procedure, or a steering of roaming procedure in a visited communication network scenario.

The present application provides systems and methods for adaptive network resource allocation using application prioritization and deprioritization. An application prioritization manager (APM) receives data about an application, or other contextual data, from various components, and prioritizes the application's network resources among other applications that are also prioritized. The APM generates scores for different factors that are derived from the data, such as application type, application network usage, whether an application is minimized, or whether an application is in focus, among others. The APM combines the scores for the different factors into a combined score for the application, and prioritizes the application among other applications based on their combined scores. The APM selects a prioritization mechanism for the application based on the combined score, such as traffic shaping, traffic policing, traffic scheduling, or other techniques that control aspects of the network traffic.

There is provided systems and methods for self-supervised learning (SSL) in face detection networks. In an embodiment a face detection network comprises encoder components configured for encoding features of the face, the encoder components comprising trained components of a Masked Image Modeling (MIM) network configured to processes non-overlapping patches determined from the input image, the MIM network trained with a SSL objective; and decoder components configured through training for determining local correspondences between the features for determining estimates for the facial landmarks. In an embodiment, the MIM network is an MAE network. In an embodiment the decoder components are derived from those of a trained second network comprising the encoder components as trained but frozen, wherein the decoder components of the second network are trained using a locality constrained repellence (LCR) loss. Methods are provided for SSL training of the encoder and decoder components.

A system and method for artificial intelligence enhanced mobile energy and data and network control, planning and optimization. The present invention relates to a system and method for optimizing energy and data and network use in mobile platforms, such as facilities, vehicles, tools, and devices. The system leverages AI, data analytics, and context-aware techniques to collect, process, and analyze data from various sources, including sensors, weather data, and spatial and temporal data with locality aware computing, transport, storage and networking across assets that may be owned or operated by multiple stakeholders. By considering a variety of factors, the system generates optimized recommendations for data storage, compute, transmission, device settings, fleet management, and physical and virtual route planning and logic locality planning. The invention offers benefits, including improved energy efficiency, enhanced data and network management, increased operational efficiency, and cost savings.

Embodiments of the present disclosure provide enhanced systems and methods for predicting optimal design flow parameters for optimized output targets for physical design synthesis of a given IC design. A Variational Autoencoder (VAE) along with a regression network are trained using a dataset comprising synthesis design construction flows from historical IC designs to provide a training data representation of the dataset constrained to a latent space of the VAE. The system generates feature vectors based on the training data representation of the dataset and updates the feature vectors with initial design characteristics of the given IC design. The system iteratively performs an input gradient search of the updated feature vectors to optimize an objective function of the design targets to identify locally optimal design parameters. The system identifies globally optimal design flow parameters for optimized design targets based on locally optimal design parameters.

The invention relates to the technical field of industrial equipment intelligent monitoring, in particular to a power transmission cable monitoring image enhanced recognition and false alarm prevention method under ice and snow conditions, which comprises the following steps: S1, constructing a training data set, and preprocessing images in the training data set; the training data set comprises an image pair composed of a degraded image and a corresponding real clear image, namely a degraded-clear image pair; s2, constructing an improved SRGAN model for image enhancement, wherein the improved SRGAN model comprises a generative network and a discrimination network; s3, training the improved SRGAN model by using the training data set constructed in the step S1, and performing parameter optimization by using an RMSProp optimizer in network training; s4, performing enhancement processing on the input degraded image through the network model obtained through training in the S3; the problems that the efficiency is low and potential faults cannot be found in time in two existing modes for checking freezing of glaze, rime or wet snow of the camera are solved.

Disclosed in the present invention are a knowledge-distillation-based dynamic fusion method, system and apparatus for missing multi-modal data. The method comprises: on the basis of a classification network, constructing a plurality of single-modal teacher models, and respectively preforming training on the classification network by means of each type of single-modal data to obtain corresponding single-modal teacher models; on the basis of a threshold network and a series of expert networks, constructing a multi-modal dynamic fusion network, wherein the threshold network is used for determining which expert networks are activated and outputting one one-hot vector, the length of the vector is the number of expert networks, the data used by each expert network is a subset of a plurality of modalities for feature fusion, and the multi-modal dynamic fusion network, on which training performed using data including complete modalities has been completed, is used as a student model; and using the teacher models to perform distillation training on the student model, and inputting actually acquired multi-modal data into the multi-modal dynamic fusion network to obtain a category prediction result. The present invention can increase the effective utilization rate of data and improve the prediction accuracy of multi-modal models.

A method for establishing a cross-issuer chargeback fraud detection system includes receiving a fraud analysis request for one or more chargebacks from a payment processor using an Application Programming Interface (API) over a computer network, extracting identifying information of transactions associated with the one or more chargebacks from the fraud analysis request, searching for a fraud analysis profile linked to the extracted identifying information in a profile database, determining whether the fraud analysis profile linked to the extracted identifying information exists in the profile database, upon determining that the fraud analysis profile linked to the extracted identifying information does not exist in the profile database, retrieving historical transaction data associated with the extracted identifying information from a historical transaction database, retrieving reported fraudulent activities from one or more financial institutions associated with the extracted identifying information, and aggregating the retrieved historical transaction data and reported fraudulent activities.

The invention provides a multi-modal large language model construction method for automatic accounting document auditing, and belongs to the technical field of large language models.The method includes the steps that table graph structure data are constructed, a graph convolutional network is input for node feature propagation, and a sequential dependency relationship is modeled in combination with a bidirectional long-short-term memory network; a cross-modal semantic alignment encoder is constructed to map a visual token sequence and a language token sequence to a unified semantic space, a conditional generative adversarial network is adopted to carry out data enhancement, a time sequence modeling framework of a dynamic graph neural network is constructed to process time-varying graph structure data, an active learning framework and a noise adaptation network are designed, and time-varying graph structure data processing is carried out. A knowledge distillation technology and a model optimization method are used, a multi-task uncertainty weighted loss balance algorithm is adopted for training to obtain a multi-modal large language model for accounting document automatic auditing, and the problem that in the prior art, it is difficult to accurately understand complex table structures and cross-modal semantic association is solved.

The application provides a kind of road surface crack detection method based on unsupervised learning, including collecting normal road surface data and generating normal road surface data set, also including the following steps: download vgg16 network model pre-trained on Imagenet data set as expert network, and respectively design identification clone network and positioning clone network;Identification clone network and positioning clone network are trained using the data in normal road surface data set;Get actual road surface image;Actual road surface image is respectively imported into expert network and identification clone network, it is judged whether there is crack, if there is crack, then execute next step;Actual road surface image is respectively imported into expert network and positioning clone network, and the positioning of specific position of crack is realized.The application is used for road surface crack identification and positioning, only needs to provide training set constructed by normal road surface picture, does not need to carry out pixel level road surface crack annotation, saves a lot of manpower cost, and also can have higher detection precision and real-time performance.

1. The name of the design product: graphical user interface for network usage management and monitoring of electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: display interface for network usage management and monitoring. 7. The human-computer interaction mode of the graphical user interface: design 1 front view is a statistical interface for network usage, sliding the screen upwards to enter the design 1 change state diagram; design 2 front view is a statistical interface for network usage, sliding the screen upwards to enter the design 2 change state diagram; design 3 front view is a management interface for network usage, sliding the screen upwards to enter the design 3 change state diagram; design 4 front view is a management interface for network usage, sliding the screen upwards to enter the design 4 change state diagram; design 5 front view is a management interface for network usage, sliding the screen upwards to enter the design 5 change state diagram; design 6 front view is a management interface for network usage, sliding the screen upwards to enter the design 6 change state diagram; design 7 front view is a statistical interface for network usage, sliding the screen upwards to enter the design 7 change state diagram; design 8 front view is a statistical interface for network usage, clicking the "management" button in the lower part of the interface to enter design 8 change state diagram 1, sliding the screen upwards to enter design 8 change state diagram 2; design 9 front view is a statistical interface for network usage, clicking the "management" button in the lower part of the interface to enter design 9 change state diagram 1, sliding the screen upwards to enter design 9 change state diagram 2. 8. Other circumstances that need to be explained: omit other views. The "X" in each view represents replaceable or changeable text, numbers or punctuation marks.

A machine learning-based proxy model may generate measurements of quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. Once generated, the proxy model may be applied to observed operational parameters of the on-board network to quantify the user experience for any user in any given instance. User experience measurements (e.g., trends identified therein) may be utilized to identify and implement adjustments to hardware, firmware, software, and / or service procedures associated with implementation of the on-board network. These adjustments may be implemented between transits of the vehicle, or in some cases, during transit of the vehicle to improve the user experience over the duration of use of the vehicle-based communication network.

A computer-implemented method for a time-controlled delivery of updatable services to on-board systems of vehicles which use the services. The method includes analyzing the detected data to identify delivery time periods for the updatable services being optimal for each vehicle which uses the services, wherein the network usage of the backend server is optimally allotted to the specified time period on the basis of the availability of the data connection of the vehicles using the services to the backend server. A system for a time-controlled delivery of updatable services to on-board systems of vehicles that use the services is also disclosed.

This invention discloses a network management method, device, electronic device, and storage medium, relating to the field of data processing technology. It includes training an initial classification model using a sample traffic feature dataset and corresponding classification targets. The model can fuse traffic features according to the classification targets and iterate model parameters using the model classification results and the actual classification results corresponding to the training data to obtain an actual classification model. This model then outputs the traffic classification result for any server network, enabling network resource allocation. This invention solves the technical problems in related technologies, such as the use of dynamic ports breaking the fixed mapping relationship between port numbers and applications, encryption techniques hiding payload content making it difficult to directly extract traffic features, affecting classification accuracy, and thus impacting network management effectiveness. It improves traffic classification accuracy in complex network environments, facilitating effective network management and ultimately enhancing the network user experience.

A Position Calculation Unit, e.g., a PPU for a wireless communication network is provided, wherein the position calculation unit comprises an input, e.g., comprising an interface, adapted for receiving information related to a dynamic cooperation cluster of access points (DCC devices, APs, TRPs), DCC, and adapted for receiving a channel estimation result related to a channel between devices of the DCC, such as APs, and a device to be located; and a calculator configured for determining a location of the device based on the information related to the DCC and the channel estimation result. The Position Calculation Unit is configured for providing information related to the location, e.g., for a location based service and / or for use by the network.

The present disclosure provides systems and methods for systems and methods for generating potential flight plans to be used by a ride sharing network, including dynamic and / or automated changes to flight plans that have been engaged with passengers based on real-time information. In particular, the systems and methods of the present disclosure can operate to generate a fleet-level set of potential flight plans which comply with one or more constraints for a fleet of aircraft. The potential flight plans can be exposed into and used by a ride sharing network to provide transportation to users.

On-demand private network creation and management can include detecting that the managed device has entered into a defined private network area that defines an area in which the managed device should be connected to a private network. The private network can be defined by an anchor point and boundaries around the anchor point, the boundaries defining the defined private network area. Using a bootstrap account stored in a non-volatile memory of the managed device and a managed device profile associated with the managed device, the managed device can be authenticated. The managed device profile can define networks with which the managed device can communicate. Using a private network profile, a private network can be created. The private network profile can include data defining the anchor point and the boundaries around the anchor point. The managed device can be added to the private network to communicate with the private network.