635 results about "Network Convergence" patented technology

A system and charging control method of network convergence policy and charging control architecture are disclosed, based on the QoS parameter, charging policy and user subscribing information granted by the service layer, the PCRF confirms PCC rule, and provides the PCC rule to the policy distribution function PDF. As the interface between the PCRF and PCC policy performing entity, the PDF performs the protocol conversion and information distribution. Based on the PCC rule, the PCC policy performing entity performs QoS policy of the service data flow and the detecting and charging of the service flow.

A method, system, and computer program product for providing network convergence of applications and devices across multiple types of networks are provided. The method includes receiving an identifier for a device and user credentials for an account. The account specifies network access privileges for a user. The method also includes authenticating the user credentials and storing the device identifier with the user credentials within the account upon successful authentication of the user credentials. The method further includes using the user credentials and an identifier of a respective selected device specified in the account to route a sign on request from the respective selected device to a network authentication element for providing a single sign on credential for a user of the respective selected device.

The invention provides a set top box with a WIFI (Wireless Fidelity) module. The set top box comprises a set top box body, wherein an external USB (Universal Serial Bus) interface, a functional press key, a power supply indication lamp, a signal locking indication lamp and an SD (Secure Digital) card reader socket are arranged on a front panel of the set top box body, wherein heat dissipation holes are arranged in side plates of two ends of the set top box body; an output interface, a user interactive interface and a three-network fused interface are arranged on a rear terminal board of the set top box body; the set top box body is internally provided with a main control circuit; the main control circuit comprises a main chip, a WIFI module, a demodulator, a TS (Transport Stream) demultiplexing device, a multimedia decoder, a multimedia encoder, a flash memory, an inner memory and an input/output control circuit. According to the set top box disclosed by the invention, network transformation cost is saved and a plurality of networks are fused and accessed; meanwhile, a television program is played and Internet is accessed or three-screen interaction is realized; and after an intelligent terminal passes through WIFIAP (Wireless Fidelity Access Point) authentication in real time, the intelligent terminal can be subjected to intelligent interaction with the set top box; and the invention provides a more flexible and humanized operation manner for users.

Embodiments of the present invention provide a flexible system for providing wireless broadband access. The system can include a gateway that has a plurality of gateway premises equipment (“GPE”) units and a network fuser connected to the plurality of GPE units to coordinate the activities of the plurality of the GPE units. The network fuser can provide DSP configuration information and RF configuration information to the GPE units. The network fuser is also operable to connect to a high-speed network (e.g., a backhaul network) and to provision data from the high-speed network to each of the plurality of GPE units. The system can also include a plurality of customer premises equipment (“CPE”) units remote from the gateway.

The invention provides a vehicle model recognition model construction method based on depth learning. According to the method, a deep convolution neural network structure is designed for vehicle model recognition; softmax classification loss and sorting loss which is built through a vehicle model type hierarchical structure and is based on tetrad are used together to supervise the deep convolution neural network training; and intra-class differences and inter-class differences of vehicle models are constrained at the same time to learn character representation with rich semantic information and a discriminating classifier. In order to speed up network convergence, an online difficult sample mining policy and an improved gradient backtracking optimization algorithm are used, which greatly reduces the network training time. The invention further provides a vehicle model recognition method based on depth learning. The vehicle model recognition method uses the model constructed by the model construction method to recognize the vehicle type in a vehicle picture, which effectively improves the vehicle model classification accuracy.

The invention relates to a photovoltaic array fault diagnosis and early warning method comprising the following steps: combining an Elman nerve network optimized by a non-linear least square method and a decision tree with experience knowledge so as to form a fault diagnosis model; collecting present photovoltaic array operation data and meteorology data, and computing errors when compared with historical normal state data; using the fault diagnosis model to obtain the corresponding fault type and credibility when the error is bigger than a threshold; finally integrally evaluating so as to obtain the final fault type credibility, and selectively carrying out fault early warning according to the credibility values; updating a fault knowledge base according to the field actual measurement conditions. The method combines the LM-Elman nerve network and the decision tree with experience knowledge so as to built the fault diagnosis model, thus improving the history data sensitivity, providing better prediction effect when compared with a BP network, and improving the network convergence speed and training precision; the experience knowledge is supplemented, thus providing stronger robustness; the method can timely detect and diagnose, thus reducing fault incidence rate, and ensuring the photovoltaic power station to stably work.

The invention discloses an optical network self-adapting route system for a low/middle orbit double-layer satellite. The system is connected with a satellite-ground access segment by a space segment route, a middle orbit satellite managing layer consists of at least four manager satellites and a manager satellite, the manager satellites and the manager satellite manage a low orbit satellite route layer directly, and the low orbit satellite route layer consists of one main proxy satellite, four secondary proxy satellites and at least 8 ordinary satellites which are connected. The calculating method comprises topological parameters; determination of the life span report of the access to the middle orbit satellite by the low orbit satellite; main proxy node and link; link time delay among satellites; time delay report of the low orbit satellite; time delay report of the middle orbit satellite; wavelength usage rate business load; and the calculation of route cost function. The optical network of the double-layer satellite is divided into an access network with highly dynamic change and a space segment core network of periodic change, the access network takes charge of network convergence between the space segment and the ground network, and the space segment uses a laser link among satellites to relay until reaching a service target satellite.

A method of utilizing packet domain to provide service of circuit domain user includes expanding network node function of MGCF, S ¿C CSCF and AS to make IMS not only support interworking call of firm network circuit domain but also provide service for firm network domain user, furthermore expanding HSS function to store and manage IMS contracted service data related to firm network circuit domain user.

The invention discloses a method of deep neural network based on a discriminable region for dish image classification. The method relates to the field of image processing, integrates a significant spectrum pooling operation, and fuses low-level features and high-level features in a network. The method adopts a convolution kernel filling operation, effectively preserves important information on characteristic spectra, and is matched with data dimensions of a full connection layer, so that the full connection layer can utilize a VGG-16 pre-training model at a training state, thereby improving the training efficiency and network convergence speed. Each image to be classified is subjected to normalization processing based on the model which is learned in a constructed database, the image is tested by using a trained convolutional neural network, the classification precision is measured by using Softmax loss, a classification result of the image is obtained, real categories and predicted categories of targets in all test images are compared, and a classification accuracy rate is obtained through calculation. The method is used for testing on a self-established data set CFOOD90, and theeffectiveness and the real-time performance of the method are verified.

The invention discloses an Ethernet communication method and system and an SDN exchanger. The method comprises the steps that the SDN exchanger reports the MAC address learned locally to an integrated controller, and the integrated controller issues forwarding tables of forwarding ports corresponding to target MAC addresses to all SDN exchangers under a management domain based on whole network topology automatic finding and route computing based on strategies; and the SDN exchangers achieve Ethernet frame forwarding according to the MAC address forwarding tables learned locally and the MAC address forwarding tables issued by the integrated controller. The integrated controller issues the MAC address forwarding tables to the SCN exchangers in the management domain, the boundedness that a traditional Ethernet exchanger learns whole network MAC addresses through a data forwarding plane is avoided, and the problems that broadcast storm and spanning tree protocols exist in the traditional Ethernet, so that a large amount of redundancy chain blocking is caused, network convergence speed is slow, and traffic engineering cannot be achieved are solved.

The invention discloses a general service platform supporting multiple services and based on multi-network integration. The platform comprises a service access unit, a message processing unit, a service engine unit and a management unit, wherein the service access unit, which is connected with the communication network of each network operator and is used for the access of various communication services, provides protocol support and carries out data interaction with the message processing unit; the message processing unit, which receives the service request of the service access unit and judges the type of the request, calls the service instance inside the service engine unit; moreover, the message processing unit receives the service instruction sent by the service engine unit and transmits the service instruction to the service access unit; the service engine unit, which is used to provide operation support for each communication service, executes service according to the service call request sent by the message processing unit, and triggers a new service request according to the execution result; moreover, the service engine unit returns the new request to the message processing unit; the management unit is respectively connected with each unit to complete coordination management of each unit. The general service platform provides a common platform for each network operator to realize the integration of different network services; moreover, the platform is convenient for update and maintenance.

The invention discloses an intelligent cognitive wireless network system for realizing heterogeneous network convergence. New function entities with multi-domain active cognition, high-efficiency heterogeneous network resource management, and network or terminal reconstruction are added in the network elements of the existing wireless network system, namely, a cognitive information management module, an intelligent management module and a heterogeneous network convergence management module are added on a network side; a reconstruction management unit, a united wireless resource management & self-optimization unit, a cognitive information/knowledge base and cognitive information management module are added on a terminal side; four interactive interfaces, namely interfaces among operators, interfaces between the network side and the terminal side, interfaces among respective modules and interfaces among units inside the modules are arranged. Modules or units inside the modules realize information exchange via interfaces, and realize corresponding network functions jointly, so that the cognitive capacity, autonomous capacity and adaptive capacity of the network are extended, the intellectualization of network management is promoted, the efficient utilization of heterogeneous network convergence and system frequency spectrum is realized, and overall performance of the network is promoted.

The invention discloses a system, a device and a method for multi-network integration. The system comprises an application gateway server and a unified account management server, wherein the application gateway server is used for resolving a domain name in an access request sent from a client to an application system, acquiring an authentication notification based on the acquired resolved address, and establishing a communication connection between the client and the application system based on the notification; the unified account management server is used for performing authentication on anaccount corresponding to the resolved address, and notifying the application gateway server if the authentication is passed. The method comprises the following steps of: resolving the domain name in the access request sent from the client to the application system, acquiring the authentication notification based on the acquired resolved address, and establishing the communication connection between the client and the application system. The system, the device and the method for the multi-network integration can overcome the defects of incapability of uniformly accessing and managing multiple networks, complex account application procedure, poor safety, low efficiency and the like to realize uniform access and management of the multiple networks, simplify the account application procedure,and improve the safety and the efficiency.

The embodiment of the invention discloses a network selection method based on multi-network convergence and a multi-mode terminal device. The multi-model terminal device stores and compares network data transmission rate information and network load information of a first network and second network; types of services to be launched or received by the terminal device are judged, wherein the services comprise the real-time services and the non-real-time services; as for the real-time services, networks with high data transmission rates are optimally selected by the multi-mode terminal, and if communication quality of the current optimally-selected networks can not meet requirements of the real-time services, the current optimally-selected network is switched to another network, and the real-time services are carried by the other network; as for the non-real-time services, networks with small loads are optimally selected by UE. By means of the method, the quality of the real-time services can be guaranteed, network loads can be balanced, and user experience can be improved.

The invention provides a travel time predicting method based on a network convergence and taking account of a traffic state time-space relation. The method obtains high-precision data by subjecting floating car data to refined processing. In particular, the process for subjecting the floating car data to refined processing ingeniously uses a convolutional neural network and a recurrent neural network which are mainly used in the field of artificial intelligence currently, fuses the networks, fully takes account of the time-space relation of a traffic state, deeply excavates a traffic state evolution rule, and ultimately achieves precise travel time prediction. Compared with various conventional travel time prediction models, the method fuses the time-space relations of the traffic state, fully excavates historical data characteristics, overcomes a difficulty that other methods cannot cope with the high time-varying characteristics and non-linear change characteristics of the traffic flow, does not need priori knowledge and parameter identification, and has good fault tolerance and robustness, high precision, and good stability.

The invention belongs to the technical field of automatic identification and discloses a gesture estimation method based on a parallel convolution neural network. The method comprises the steps of (S1) using an RGB-D camera to obtain a color image and a depth image, (S2) identifying a position of a hand in the color image according to a hand classification module obtained through training in advance, (S3) finding a corresponding position of the hand in the depth image according to the corresponding position of the hand in the color image, (S4) converting a separated depth image into a gray image, and then inputting the gray image into a parallel convolution neural network gesture estimation model to carry out identification so as to obtain multiple joint coordinates which can express hand gestures, and (S5) outputting a gesture result. The invention provides an end-to-end gesture estimation network architecture, the complexity of an algorithm is reduced, the network convergence speed is greatly improved by a parallel structure, the training time is greatly saved, and improves the efficiency is improved.