5249 results about "Network on" patented technology

A model engine is provided for managing transactions for one or more computing devices over a network. There is provided a machine learning model trained and tested on historical transaction data including labelled fraud data, to provide a target signal indicative of a likelihood of fraud within a given transaction, generating an ensemble of decision trees. The engine further includes extracting a set of rules by traversing each tree in the ensemble of decision trees from a root node to each leaf node of the tree, each path from the root node to a particular leaf node including splitting criterion providing a rule to form a set of rules. A proactive risk management system is also provided for applying the set of rules to a new transaction and when at least one rule is met, triggering predetermined actions on at least one computing device on the network.

An example operation may include one or more of establishing a network connection between a computing system and one or more external sources over a computer network, receiving a request from a user via a software application on a user device, collecting data about the user from the one or more external sources via the established network connection, executing a machine learning model on the collected data about the user to determine a goal of the user, and displaying an image of the goal via a user interface of the software application.

A system and method for user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the executable graph and provide deeper insight into application and user security on a given network.

A system and method for attacker interdiction using user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the dependency graphs and provide deeper insight into application and user security on a given network. Attacks may be detected by analyzing the dependency graphs, and attacker interdiction may be implemented by actively orchestrating network security and IT devices using SOAR workflows.

The invention provides a multi-platform cooperative unmanned inspection task planning method and system for a wind power scene, and relates to the technical field of wind power plant inspection, and the method comprises the steps: based on a wind power plant three-dimensional scene model, adaptively dividing an inspection region, constructing a reachability path network, calculating an inspection difficulty coefficient according to target detection point information, and calculating a target inspection point; and generating an unmanned aerial vehicle and unmanned vehicle inspection area. A sensor is deployed to collect environment and equipment state data, feature vectors are extracted to establish an inspection constraint model, and main and branch inspection paths are planned on the reachability path network. And performing multi-platform relative positioning by using ultra-wideband radio waves, and dynamically optimizing an inspection path according to the equipment health score and the inspection constraint model. Through multi-platform cooperation, adaptive path planning and intelligent state identification, the wind power plant inspection efficiency and accuracy are improved, and the inspection cost is reduced.

The invention belongs to the field of switching chips, and relates to a message processing and transmitting device on a CXL switching chip, which comprises a CXL switching interface unit module, an annular topological network on chip and a CXL management unit module, the CXL switching interface unit module is used for receiving and analyzing messages of two sub-protocols, namely. Cache and. Mem, sent by CXL equipment directly connected to a CXL switching chip, then converting and packaging the messages into a message format conforming to the standard of the network on the circular topology chip, and further transmitting the messages through the network on the circular topology chip; and meanwhile, the CXL switching interface unit module also needs to receive the message from the ring topology network-on-chip, converts and packages the message into a corresponding CXL sub-protocol message format, and then distributes the message to other CXL equipment. The method has the characteristics of high bandwidth, low delay and multi-channel concurrency, and provides an efficient transmission channel for a large amount of memory data access scene; and the method has the advantages of high expandability, friendliness to chip back-end physics and the like.

Various embodiments are disclosed for a services policy communication system and method. In some embodiments, a communications device stores a set of device credentials for activating the communications device for a service on a network; and sends an access request to the network, the access request including the set of device credentials.

The invention discloses a trajectory prediction method and device based on a time-space diagram neural network learning model, and the method comprises the steps: carrying out the data cleaning and trajectory compression based on to-be-classified original trajectory data, and outputting a compressed grid sequence; based on the grid sequence, static track information classification is executed, and track static attributes are extracted; dynamic space-time image track classification is executed, and track dynamic space feature attributes are extracted; the method comprises the following steps: pre-constructing a directed weighted network based on a driving road section connectivity attribute feature, and completing training of a trajectory prediction model based on a trajectory static attribute, a dynamic space feature attribute and the directed weighted network; and based on the trajectory prediction model, inputting spatial-temporal characteristic data of a trajectory to be predicted, and obtaining an optimal trajectory path prediction result. According to the method, uncertain track geographical background information on the road network can be effectively utilized, and meanwhile, errors caused by map matching are avoided.

The invention discloses an integrated energy system model prediction control method based on deep reinforcement learning, which is used in the field of energy prediction control, and comprises the following steps: constructing a state prediction model according to the state, output increment and disturbance quantity of each unit in integrated energy equipment in a park; predicting the output state of each device of the system at each moment in the prediction time domain within the day, establishing a rolling optimization objective function based on a prediction value and a day-ahead plan value, and calculating an intra-day scheduling optimal control quantity; and constructing an intelligent agent, and learning weight parameters in the rolling optimization objective function by using a double-delay depth deterministic strategy gradient algorithm to realize optimization of model prediction control of the integrated energy system. According to the invention, through learning of the neural network on different scenes, the optimization efficiency and quality of the algorithm on the integrated energy system are improved.

The invention discloses a pipe network on-line monitoring system, which relates to the field of urban capital construction and comprises a sensing deployment module, a signal acquisition module, a signal processing module, a pipe network modeling module, a leakage positioning module, an edge calculation module, an energy scheduling module, an anomaly prediction module, a deposition evaluation module, an early warning visual module and a center management module. According to the method, the recognition capability of the system on a hidden and complex pipeline structure is improved, accurate and efficient three-dimensional modeling can be carried out, the path deviation resistance capability is improved, and the more comprehensive and robust leakage sensing capability is realized; the method is advantaged in that communication burden and response time delay are substantially reduced, sensitivity and foresight of abnormal evolution trend identification are improved, overall identification accuracy of the system is improved, a global optimal risk solution can be efficiently output, a high-risk area is visually displayed, and rapid pre-judgment and decision making of operation and maintenance personnel are facilitated.

A method of unlearning a training example from a neural network, comprising: during training of the neural network on a training dataset, recording a plurality of recordings in a recording dataset, wherein a recording includes weight values of the neural network at the time at which the recording is recorded, selecting an unlearning training example to unlearn from the neural network, computing a total-loss value of a change in a loss function for each of plurality of training examples induced by a change of weights of the neural network in response to the unlearning training example, determining a certain recording to use to remove the unlearning training example according to the total-loss values, and re-training the neural network from the determined certain recording using an adapted training dataset excluding the unlearning training example; and producing an unlearned neural network.

The invention relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out sensor network deployment on a plastic mold to obtain a mold parameter sensing network; operating parameters of the plastic mold are collected in real time through the mold parameter sensing network, and mold operating state data flow is obtained; performing process state feature extraction on the mold operation state data stream to obtain mold process state feature data; performing spatio-temporal data hierarchical analysis on the mold process state feature data to obtain mold state multi-scale feature data; and performing layered thermal field dynamic modeling on the plastic mold based on the mold state multi-scale characteristic data to obtain a mold thermal field evolution model. The key process parameters of the mold can be identified, and the stability and reliability of the mold performance are enhanced.

The invention discloses a multi-modal industrial data exception collaborative detection method and system. Multi-modal data in the industrial production process are collected in real time through a data collection module. The data preprocessing module is used for cleaning, integrating and standardizing the acquired data and eliminating noise and abnormal data; and the multi-modal data fusion module fuses the preprocessed data and constructs a multi-modal data fusion graph. And the anomaly detection module applies a graph attention mechanism network on the fusion graph, captures the complex mutual relation of different modes on the global sequence level in time and space, and performs anomaly detection by using a dichotomy model. The root cause positioning module performs root cause positioning in the fusion image by using a random walk algorithm after detecting an anomaly, and determines the first several abnormal root causes by comparing weights of different root causes. According to the system, high efficiency and high quality of industrial anomaly detection can be improved in various complex industrial application scenes.

Security registration and authentication systems and methods are disclosed herein for extending OpenID security standards for registration of OpenID client devices for passkey authentication. A client device comprises client frontend application (app). A registration and authentication server comprises a client backend app for receiving an authorization request of the client device comprising a prompt parameter, and implementing one of: (a) a secure registration algorithm if the prompt parameter defines a create value; or (b) an authentication algorithm if the prompt parameter does not define the create value. The security registration and authentication systems and methods provide an authentication token to the client device, the authentication token authenticating the client device to initiate a secure session with a secure resource on a computer network.

A deep reinforcement learning-based vehicle platoon control method and system. The method comprises: on the basis of a vehicle dynamics model, constructing a motion simulation model for vehicle platoon operation; on the basis of the motion simulation model for vehicle platoon operation and vehicle operating states, by using a constant time headway strategy, obtaining a state space and an action space for vehicle platoon control; and establishing a DDPG network on the basis of the state space and the action space for vehicle platoon control, using a reward function mechanism to train the DDPG network, stopping training when the total reward obtained per episode during the training process no longer increases, so as to obtain a trained control strategy network, and by using the trained control strategy network, performing platoon control.

The present invention relates to the technical field of railway inspection, and relates in particular to a railway foreign-object intrusion detection method and system based on deep learning. The method comprises: S10, acquiring image data to undergo detection; and S20, inputting the image data into a trained attention semantic segmentation network to obtain a foreign-object detection result. The attention semantic segmentation network is obtained by first training a preset attention semantic segmentation network architecture using a preconfigured data set, and then re-training the attention semantic segmentation network on specified image data using an adaptive correction algorithm. A backbone network architecture is obtained by inserting a specified attention mechanism at a specified position within a pre-selected residual neural network, and using three parallel dilated convolutions as an initial convolutional layer in the residual neural network. A dual-branch decoder combines an edge recognition branch and a semantic segmentation branch. The method is applicable to various scenarios, achieves improved detection accuracy and maintains a lightweight design.

Apparatus for data communication includes a network interface for connection to a packet data network and a host interface for connection to a host computer, which includes a central processing unit (CPU) and a host memory. Packet processing circuitry receives, via the host interface, from a kernel running on the CPU, associations between multiple remote direct memory access (RDMA) sessions and multiple different User Datagram Protocol (UDP) 5-tuple, which are assigned respectively to the RDMA sessions, and receives from an application running on the CPU a request to send an RDMA message, using a selected group of one or more of the RDMA sessions, to a peer application over the packet data network, and in response to the request, transmits, via the network interface, one or more data packets using a UDP 5-tuple that is assigned to one of the RDMA sessions in the selected group.

An entropy value is generated for a data packet to be transmitted on a computing network. The entropy value is usable to select or change a network path for the data packet. In response to receiving an acknowledgement message for the data packet, the entropy value is saved in a storage structure if the entropy value is acknowledged as not congested. When transmitting an additional data packet, the oldest saved entropy from the storage structure is reused and the oldest saved entropy value is invalidated.

The invention discloses an improved YOLOv8-based penicillin bottle body defect detection method, and belongs to the technical field of computer vision, and the method comprises the following steps: obtaining and preprocessing penicillin bottle body defect data to obtain a training set and a test set; establishing a defect target detection model of the improved YOLOv8; training a defect target detection model by adopting the training set, optimizing a loss function, and updating a model weight parameter until the loss function is converged; and testing the defect target detection model by adopting the test set. According to the invention, a space separable pooling attention module SSPA is introduced, so that the perception capability of the network on subtle features such as bottle body scratches can be enhanced; a feature enhancement module MMFF is introduced, convolution kernels of different sizes are adopted to extract defect features of different sizes, such as smudginess, scratches and loading quantity anomaly, under a complex background, and the feature fusion effect is improved by module stacking.

Aspects of the subject disclosure may include, for example, obtaining a group of threat research reports, obtaining a group of indicators of compromise (IOCs), and monitoring a network of Internet of Things (IoT) devices. Further embodiments can include determining a group of possible malware on the network based on the monitoring of the network, and generating a network security recommendation based on the group of threat research reports based on the group of threat research reports, the group of IOCs, and the group of possible malware. Other embodiments are disclosed.

The invention discloses a remote sensing target detection method based on a convolutional neural network, and the method comprises the steps: 1, constructing a full-scale enhanced alignment detection network model OSEADet comprising Backbone, Neck and Head, the Neck is an enhanced feature pyramid network EFPN, adding a P2 detection layer on the PAFPN network, enabling a feature map P2 to pass through SPDConv, outputting a feature map rich in small target information, fusing the feature map with a feature map P3, and obtaining a feature map P3; carrying out feature integration by using a CSP thought and a CSPF module; the Head comprises three dynamic task alignment sharing detection heads DSTA, the DSTA comprises a bounding box regression branch and a classification branch which are respectively used for predicting a target position and a target category, the tasks of the three DSTA are combined through group convolution, task interaction features are learned from a plurality of convolution layers, joint features are obtained, interaction among the tasks is increased, and the tasks are dynamically adjusted and aligned through a TaskDecomposition layer; 2, carrying out training and verification on the OSEADet; and 3, the trained OSEADet is used for predicting the position and category of the remote sensing target, so that the detection precision is high, and the remote sensing target can be detected in real time.

The invention discloses an intelligent storage equipment health state assessment and predictive maintenance method and system, and the method comprises the steps: arranging a multi-mode sensor network on equipment, collecting vibration, stress, temperature, current, voltage and image data in real time, carrying out the collection and preprocessing of edge data, and transmitting the data to an analysis layer; and the analysis layer performs online self-learning calibration by using a digital twin model, fuses the data in combination with a fuzzy neural network for dynamic weight adjustment, and calculates a health index and a residual life prediction value of the equipment. The system can give out an early warning before a fault symptom occurs, and automatically generates a maintenance decision and a task scheduling scheme, thereby achieving the real-time monitoring and predictive maintenance of the equipment state, and remarkably improving the operation efficiency of a warehousing system and the reliability of the equipment.

The described technology is generally directed towards conflict management of applications implemented on a radio access network (RAN) network. Various embodiments are presented to enable identification of a potential conflict (e.g., where an application is being onboarded) as well as a present conflict (e.g., where two or more applications are already operational). Conflicts may directly affect a control target, e.g., two applications are trying to control the same target. Conflicts may indirectly affect a control target, two applications affect a first metric and a second metric, but a third metric is affected. Conflict resolution includes preventing an application onboarding, terminating operation of an application, identifying causes unrelated to the application, and suchlike. Conflict resolution can be conducted at a RAN intelligent controller (RIC).

Systems, methods and computer-readable storage media utilized to complete a cardless transaction on a distributed ledger network. One method includes receiving, by a point-of-sale (POS) computing device, a transaction request including a biometric sample from an individual associated with a payment account at a financial institution. The method further includes authenticating, by the POS computing device, the biometric sample by cross-referencing the biometric sample with a biometric dataset stored on the distributed ledger network. The method further includes, in response to authenticating the biometric sample, generating, by the POS computing device, a cryptogram associated with the biometric sample and processing, by the POS computing device, the transaction request utilizing the cryptogram.

A server device is provided for authenticating client devices on a communication network. The server device includes a transceiver configured for operable communication with at least one client of the communication network, and a processor including a memory configured to store computer-executable instructions. When executed by the processor, the instructions cause the server device to receive an authentication request from a client device, generate a seed for a first key for the client device if the client device authenticates, transmit the seed for the first key to the client device, receive a hash of the first key from the client device, and validate the first key based on the hash of the first key.

Apparatuses, systems, and techniques to train a neural network on multiple graphics processing units (GPUs). In at least one embodiment, the neural network may be trained in parallel based, at least in part, on two or more randomly selected, similarly-sized portions of one or more datasets.

The invention discloses a polyp image segmentation method based on boundary clue depth fusion, and the method comprises the steps: inputting an intestinal polyp image into a backbone network through constructing a boundary clue depth fusion network, and extracting different levels of feature information, so as to guide the extraction of a polyp region; a boundary clue mining module is constructed to obtain boundary detail information by means of partial hierarchical features, and more accurate positioning of a polyp area is realized; constructing a foreground target enhancement module to enhance the learning of the network on the overall structure characteristics of the foreground target in combination with the reverse attention branch, so that the network focuses on the foreground target; and constructing a depth feature fusion module to integrate boundary information and foreground information, further improving the segmentation performance through cascade feature fusion and boundary information guidance, and finally obtaining a polyp segmentation result by using the trained model. According to the method, the limitation of the polyp in boundary uncertainty and morphological diversity is overcome, the weighted joint scale difference loss optimization model is adopted in training, and high-precision recognition and segmentation of the polyp region are realized.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

The invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and belongs to the technical field of data processing, and the method comprises the steps: 1, importing a preset BIM model into a preset intelligent monitoring platform, and carrying out the analysis of the preset BIM model; 2, creating a monitoring project in a preset intelligent monitoring platform based on a model analysis result, and configuring monitoring project information; step 3, acquiring a monitoring demand matched with the monitoring project, determining the type of a monitoring point, analyzing the information of the monitoring project, and creating a plurality of monitoring points; 4, the monitoring equipment based on the monitoring points collects monitoring data in real time, uploads the monitoring data to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring points; and 5, analyzing the real-time data, and optimizing the deployment of the monitoring points and the data of the monitoring points. And an efficient and intelligent solution is provided for application scenes such as engineering monitoring and environment monitoring.

The invention belongs to the technical field of finite element simulation optimization, and provides a hot upsetting process multi-target collaborative optimization method and system based on finite element simulation, and the method comprises the steps: firstly constructing a hot upsetting process multi-field coupling finite element model, and then deploying a sensor network in hot upsetting equipment to collect actual process data. Actual data and simulation data are matched through a space-time registration algorithm, then a parameter optimization model is constructed based on a dual-channel depth deterministic strategy gradient algorithm, and a technological parameter solution set is obtained through calculation; and finally, a multi-objective optimization function is defined according to a preset optimization index, and optimal hot upsetting process parameters are screened out in combination with an entropy weight method and a reference point-based non-dominated genetic algorithm. According to the method, high-precision simulation, real-time monitoring and multi-target collaborative optimization of the hot upsetting process are achieved, and the production efficiency and the product quality of the hot upsetting process are remarkably improved.