743 results about "Selection strategy" patented technology

Network policies are managed based at least in-part on user/entity identity information with: a state monitor operable to monitor for state change events in user/entity state and related, network state or in traffic pattern and traffic flow state; an identity manager operable to obtain and validate user credentials; and a policy manager operable in response to a state change event detected by the state monitor (either the identity manager or a defense center) to select a policy based in-part on the user identity obtained by the identity manager or security context obtained by the defense center, and to prompt application of the selected policy. The policies are indicative of user/device authorization entitlements and restrictions to utilization of certain network resources, network services or applications. Dynamic policy selection and targeted responses can be used, for example, against a user who gains network access with stolen user ID and password, and subsequently attempts malicious behavior. In particular, the malicious behavior is detected and identified, and the malicious user can then be restricted from abusing network resources without adversely affecting other users, groups, network devices, and other network services.

The present invention provides methods and a computer-readable program for providing autonomic, event driven upgrade maintenance of one or more software modules residing on a computer system. In a preferred embodiment, a method begins by detecting a predefined triggering event on the computer system indicative of a potential maintenance issue. Next the computer system connects to an upgrade management server, where the upgrade maintenance server creates a list of recommended upgrade modules to download to the computer system, the list based upon the triggering event and a set of selection policies. The method then downloads the list of recommended upgrade modules from the upgrade management server to the computer system, and selectively installs upgrade modules chosen from the list of recommended upgrade modules on the computer system. The user is then notified of the status of the upgrade maintenance operation.

Aspects discussed herein present a solution for utilizing large-scale knowledge graphs for inference at scale and generating explanations for the conclusions. In some embodiments, aspects discussed herein learn inference paths from a knowledge graph and determine a confidence score for each inference path. Aspects discussed herein may apply the inference paths to the knowledge graph to improve database lookup, keyword searches, inferences, etc. Aspects discussed herein may generate a natural language explanation for each conclusion or result from one or more inference paths that led to that conclusion or result. Aspects discussed herein may present the best conclusions or results to the user based on selection strategies. The presented results or conclusions may include generated natural language explanations rather than links to documents with word occurrences highlighted.

The invention discloses a method, a system and a multimode terminal for the realization of the network selection in a multi-system communication network. When a network selection control entity is configured in the multi-system communication network and the multimode terminal needs services, the network selection control entity controls the multimode terminal to selectively access a network according to the configuring selection policy and the obtained selection reference information, and the multimode terminal carries out services via the selected access network. According to the proposal provided by the invention, a suitable access network can be determined to supply the service connection for the multimode terminal by combining the selection policy with the selection reference information according to the relevant information of the access network such as the load condition, so as to make the appropriate access network bear appropriate subscribers and services and ensure reasonable use of network resources. Accordingly, the multi-system networks can be matched with each other in a cooperation mode so as to economize the network resources and guarantee the economy of the construction of the multi-system communication network.

A method for strategy independent optimization of a multi-objective function of a portfolio containing at least one investment is disclosed. The method involves the use of genetic algorithms to arrive at function optimization. A suite of strategies is provided enabling the user to select a strategy and optimize a function. Real world data is drawn from exchanges and is utilized for replication. The invention also discloses a novel combination of apparatus for carrying out the method of invention, typically, using parallel processing.

The invention discloses a service node configuration method. The method includes the steps that a service node query request sent by management configuration equipment is received, and the service node query request contains service requirements which come from users or are caused by network changes; a service node database is searched for, service node information matched with the service node query request is obtained, and the matched service node information includes matched service node identification, position information, state information, a selection strategy for the state information, lessee identification and one or more service chain paths meeting the service requirements; the matched service node information is sent to the management configuration equipment, and therefore the management configuration equipment can perform network and service configuration on matched service nodes according to obtained network topology information and the obtained matched service node information.

A method for selecting a policy and charging rules function. When a UE accesses a 3GPP system through a roaming architecture, upon receiving a session establishment request message sent by a PCC client located in the visited network, the DRA in the visited network forwards the message to a selected PCRF of the visited network according to the records stored locally or instructs the PCC client to transmit the session establishment request message to the selected PCRF of the visited network; and the PCRF of the visited network forwards the session establishment request message to a DRA of a home network, then the DRA of the home network forwards the message to the selected PCRF of the home network according to the records stored locally or instructs the PCRF of the visited network to transmit the session establishment request message to the selected PCRF of the home network.

The invention discloses a distributed heterogeneous virtual resource integration management system comprising a heterogeneous resource virtualization engine, a heterogeneous virtual resource access API (Application Programming Interface) and a cloud service platform, wherein the heterogeneous resource virtualization engine is used for allowing resource nodes to complete self-service virtual machine deployment, providing virtualization product selection strategies for users according to corresponding conditions of the resource nodes, and assisting the users in completing the set and deploying virtualization by virtue of an automatically-customized template, a visual process editor, an integration software development kit and Web service interfaces convenient for the users to operate, thereby changing the resource nodes to virtualization resource nodes; the heterogeneous virtual resource access API is used for registering the virtualization resource nodes to a distributed resource management center; and the cloud service platform is used for distributing new virtual servers, reconfiguring another virtual severs and recovering other virtue servers. The distributed heterogeneous virtual resource integration management system can be used for managing widespread virtual resources from various heterogeneous virtual products in a concentrated and comprehensive manner.

Provided in the invention is a task scheduling method based on heredity and ant colony in a cloud computing environment. The method comprises the following methods: S1, initializing population; S2, selecting individuals according to a wheel disc type selection strategy; S3, carrying out crossover operation on the individuals according to crossover probability and carrying out reversion mutation operation according to a mutation probability so as to generate a new colony; S4, updating the new generated colony; S5, determining whether a dynamic fusion condition is met; S6, initializing ant pheromone by using an optimal solution found by heredity; S7, calculating probabilities of moving to next nodes by all ants and moving all the ants to the next nodes according to the probabilities; S8, enabling M ants to travelling N resource nodes and carrying out pheromone updating on an optimal ant cycle; S9, carrying out pheromone updating on all paths; and S10, determining whether an ant end condition is met and outputting an optimal solution. According to the invention, respective advantages of a genetic algorithm and an ant colony algorithm are drawn and respective defects are overcome; and on the basis of dynamic fusion of the two algorithms, time and efficiency of exact solution solving are both considered.

The invention discloses a service link selection control method and device. The service link selection control method comprises the steps that a service link selection strategy sent by a coordinated device is received, wherein the service link selection strategy comprises the corresponding relation among first strategy contextual information, a first application type and a first service link, the first service link is a path composed of a forward device and a value added service device by which first service flow needs to pass, the first service flow comes from a user matched with the first strategy contextual information, and the type of the first service flow is the first application type; a PCRF unit receives the first strategy contextual information, and a service link selection control strategy is determined according to the first strategy contextual information and the service link selection strategy, wherein the service link selection control strategy comprises the corresponding relation between the first application type and the first service link; the PCRF unit sends the service link selection control strategy to a flow classifier. According to the service link selection control method, unnecessary devices can be prevented from being passed by.

Method and apparatus for routing telephone calls across different types of networks includes means for and steps of determining a destination IP address of a new call according to initial conditions established in a first network, determining a destination gateway (gateway brokering) based on the destination IP address, the initial conditions and a gateway selection policy, providing routing information of the destination gateway to the first network and completing the new call set up via messages received from the gateway brokering step. The initial conditions in the first network are established by determining if a callee is part of an IP network or part of a CS network and converting the callee phone number to an SIP URL if the callee is part of the IP network. Such determination is made by recognizing a prefix identifying an IP destination in the callee phone number.

The invention discloses a face identification method based on a random pooling convolutional neural network. According to the method, the characteristics of a face image are quickly extracted by the random pooling convolutional neural network and cascaded to realize face identification; selection strategies and steps of new pooling values are adopted in a process of creating the convolutional neural network and then supervised training is carried out by a softmax classifier; the probability distribution used in a sampling process is based on energy, and the effect of optimizing increment of the calculation speed of the characteristics extracted by the convolutional neural network and generalization application of a convolutional neural network training result can be achieved; the convolutional neural network training based on random pooling is simple and high in accuracy, and can promote wide application of random pooling in the process of extracting the face identification characteristics.

A system includes a first device, in a first network; a second device, in a second network; and a policy rules and charging function (PCRF) database that associates Internet Protocol (IP) addresses assigned to user equipment (UE) devices with PCRF devices; where the first device is to identify a plurality of policy rules and charging function (PCRF) devices in the first network, select a PCRF device from the plurality of PCRF devices based on a selection rule, and provide information about the selected PCRF device to the second device; and where the second device is to receive a request to set up a communication session associated with a UE device; determine an IP address associated with the UE device; identify a PCRF device, based on the determined IP address, by accessing the PCRF database, and send session information associated with the communication session to the identified PCRF device.

The invention discloses an access network selection method, user equipment, a system and a network selection strategy unit. The method includes that the network selection strategy unit receives network selection strategy requests with access network information and sent by the user equipment, the network selection strategy unit obtains access network state information and user configuration information according to the network selection strategy requests, and the network selection strategy unit formulates network selection strategy information according to the access network state information and the user configuration information and sends the network selection strategy information to the user equipment. According to the access network selection method, the user equipment, the system and the network selection strategy unit, the network selection strategy unit can fully obtain the access network information of the position of user experience (UE), the UE detects an MAC address of a wireless local area network (WLAN) access point (AP) and reports to the network selection strategy unit, the UE lists the frequently accessed service set identifiers (SSID) and reports to a user information database unit in a certain time cycle, and the network selection strategy unit selects the best WLAN AP for the user according to the access network information provided by the UE.

The invention discloses a dynamic task scheduling method under a cloud computing platform environment. Queuing processing is carried out on tasks by means of a queuing theory at first, quasi-optimal configuration of computing resources distributed by different virtual machines on various nodes in a cluster is searched out by means of an immune clone selection strategy in a manual immunity theory, reasonable configuration is carried out on the computing resources in the cluster, then antibody genes are regulated by means of load balancing, and therefore the configuration of the resources of the cluster can meet requirements for task processing. The invention discloses a dynamic task scheduling device under the cloud computing platform environment, wherein the dynamic task scheduling device comprises an affinity computing module, a judgment module, a determination module and an antibody relation module. According to the dynamic task scheduling method and device under the cloud computing platform environment, dynamic changing and virtualized environments of a cloud platform can be adapted, the optimal configuration is rapidly searched out, and the utilization rate of the resources of the cluster is improved.

The invention discloses a dynamic resource allocation method based on evolutionary game in a mobile edge computing system. The method comprises the following steps that (1) a network is divided into a plurality of areas according to the network coverage condition, accessible service points in the areas are different, and a centralized controller is arranged in the network; (2) terminals with the task unloading need in the same area form a population, and the terminals in the population establish task unloading cost functions; (3) all terminals in each population randomly select accessible SPs in an SP selection strategy set; the evolutionary game is established in each population in the network; (4) the terminals in each population compute task unloading costs and send the SP selection strategies and the cost information to the controller; (5) the population carries out SP selection strategy update according to dynamic copy; and (6) the dynamic copy reaches evolution equilibrium. The method fully utilizes the computing resources and the radio resources of the SPs, aims at the equal task unloading costs of all terminals in the populations and meets the task unloading need of each mobile terminal based on the evolutionary game.

The invention discloses a cloud computing task scheduling method based on an improved genetic algorithm. A multi-objective-based weighted optimization evaluation method is adopted as a fitness function; Coding is carried out in a real number direct coding mode; Generating an initial population by adopting a static algorithm and random generation combined method; Carrying out selection operation byadopting a hierarchical selection strategy; Performing crossover operation according to the crossover probability and the chromosome difference degree; Performing mutation operation according to thedynamic mutation probability; Judging whether the program is ended or not according to the double-termination condition; And finding an optimal scheme and distributing. Multiple steps of a traditionalgenetic algorithm are optimized and improved, and the user satisfaction degree and the algorithm execution efficiency are improved.

Improved skipping strategies for cochlear or other multi-channel neural stimulation implants selects N out of M channels for stimulation during a given stimulation frame. A microphone transduces acoustic energy into an electrical signal. The electrical signal is processed by a family of bandpass filters, or the equivalent, to produce a number of frequency channels. In a first embodiment, a probability based channel selection strategy computes a probably for each of the M channels based on the strength of each channel. N channels are probabilistically selected for stimulation based on their individual probability. The result is a randomized “stochastic” stimulus presentation to the patient. Such randomized stimulation reduces under representation of weaker channels for steady state input conditions such as vowels. In second, third and fourth embodiments, a variable threshold is adjusted to obtain the selection of N channels per frame.

The invention discloses an indoor positioning method based on a distributed AP selection strategy. The method comprises the following steps of using mobile equipment to collect k times of signal intensities of AP nodes on each reference point and processing data; equally dividing a target area into m subareas, classifying fingerprint information of the different subareas, using a fingerprint information base to train a subarea module and selecting the corresponding subarea according to RSS fingerprint information; calculating a correlation of each AP node and each subarea, sorting correlation coefficients, selecting the AP node whose correlation coefficient is greater than Ptau as a positioning node of the subarea; in each subarea, taking the selected positioning node as input of a DBN model and a corresponding position point as output of the DBN model, training the DBN model and constructing a positioning prediction model. By using the method, big noise and the AP node with a weak position distinguishing capability can be effectively removed; positioning precision of an overall WIFI indoor positioning system is increased and algorithm operation time is shortened.

An embodiment of the invention provides an SDN-based tiered routing path determination method and device that are applied to an inter-domain controller and an intra-domain controller in a software-defined network; the SDN-based tiered routing path determination method comprises the following steps: a cross-domain request of a data flow sent by the intra-domain controller is obtained; according to the cross-domain request, a start point network domain and an intermediate network domain where a source node is positioned are determined; network performance information sent by the intra-domain controller is obtained; according to the network performance information and a plurality of preset cross-domain route selection strategies, an optimal routing path for transmitting the above data flow in all network domains is determined; according to the start point network domain and the intermediate network domain, boundary information of a first network domain and boundary information of a second network domain are determined and sent to the intra-domain controller, and the intra-domain controller is enabled to determine the optimal routing path for data transmission in the network domains. Via the SDN-based tiered routing path determination method and device, optimal design of a cross-domain routing path can be realized.

The invention relates to a multi-path transmission control terminal and a method for supporting multi-service data transmission, and belongs to the technical field of network communication. The terminal is arranged on an end point of data transmission and comprises a multi-path session management module, a path management module, a flow division module, a path selection module, a substream sending control module, a substream receiving control module, a rectification recombination module, a path transmission quality report module and a path evaluation module. When data stream of an upper application is received, the data stream is divided into stream blocks, a path is selected for each stream block, stream blocks dispatched to the same path form a substream, the stream blocks are further divided into transmission units and are packaged into multi-path transmission packages and are transmitted to an opposite terminal, according to the path transmission quality fed by the opposite terminal, a path selection strategy is dynamically adjusted; when the multi-path transmission data packages are received, substream data is recombined and is further recombined into data streams and the data streams are transmitted to the upper application. The terminal and method can meet different transmission requests of various business types, and the multi-path transmission efficiency is improved.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.