156 results about "Functional network" patented technology

A communications passthrough mechanism for high availability network communications between a shared system resource and clients of the system resource. The system resource includes a control/processing sub-system including multiple peer blade processors. A port of each blade processor is connected to each client/server network path and each client is connected to a corresponding port of each blade processor. Each blade processor includes a network fault detector exchanging beacon transmissions with other blade processors through corresponding blade processor ports and network paths. Each blade processor includes response generator responsive to a failure to receive a beacon transmission from a failed port of an other blade processor for redirecting the client communications to the failed port on the other blade processor to the corresponding port of the blade processor. A path manager in the blade processor is responsive to operation of the response generator for modifying the communications routing table to correspond with the redirection message to route the client communications to the failed port of the other blade processor to the other blade processor through the inter-processor communications link. Each blade processor may also include an inter-blade communications monitor for detecting a failure in the inter-processor communications link between the blade processor and another blade processor, reading the communications routing table to select a functional network communications path to a port of the other blade processor, and modifying the communications routing table to redirect inter-processor communications to the selected functional network communications path.

The present invention combines a data processing structure with a graphical user interface (GUI) to create an information analysis tool wherein multiple functions are combined in a network to extract information from multiple data sources. The functional network is created, and graphically represented to the user, by linking individual operations together. The combination of individual operations is not limited by the input or output characteristic of any single operation. The form of the input to or output from a by individual operation, whether from a database or from another operation, is the same. That is, both the input to and the output from an analysis function is a list of document identifiers and corresponding document characteristics. Because the form of the input and output from each operation is the same, arbitrary combinations into of operations may be created. Moreover, functional networks of individual operations can then be used for database retrieval as well as to filter data streams. Furthermore, the user is able to create a visual representation of the structure forming a functional network which may be dynamically updated as new data is added or functions switched in or out. Because, inter alia, the network structure dynamically responds to information as it is presented to the network, the visual representation of the network conveniently provides the user with information concerning the characteristics of the database or stream of data that are substantially unavailable through conventional search, filtering, or clustering techniques alone.

A network-type multifunctional autoamtic irrigation method features that the irrigation is quantitatively and timely performed according to the moisture of soil and its management system software module is designed, which contains initial parameters, autoamtic operation, random operation, operation state, history data, and failure alarm and treatment. The system is composed of PC, central controller, communication repeater, and sampling controller. It can be used in the-range of 1-5 km.

The invention provides a brain-like coprocessor based on a neuromorphic circuit. The brain-like coprocessor comprises a storage module storing training characteristic information, a processing module of the neuromorphic circuit based on a hierarchical structure, an encoder and a decoder which are respectively connected with the input end and the output end of the processing module, and a comparison module which is respectively connected with the output end of the storage module and the output end of the decoder. The storage module of the brain-like coprocessor comprises a training characteristic database and/or a configurable training characteristic database. The processing module comprises a solidification function network module and/or a configurable function network module and has a quite good expansion capability. According to the invention, the brain-like coprocessor employs a distributed storage and parallel cooperative processing mode, is especially suitable for processing non-formal problems and unstructured information and can also process formal problems and structured information, such that the speed of a computer in processing such problems as brain-like calculation, artificial intelligence and the like is substantially accelerated, the energy consumption is reduced, the fault tolerance capability is greatly improved, the programming complexity is reduced, and the computer performance is enhanced.

Network functional components made up of a set of network elements (routers, switches and other infrastructure devices) communicating and cooperating to implement a network service such as a routing protocol or the spanning tree used to implement VLANs, are assigned performance and/or health metrics, which are communicated through a communications device, such as a display or speaker. A “Correct” performance metric for each network functional component of the network indicates that functional network component's conformance to a set of programmed configuration standards, which typically represent best practices for the network. A “Stable” performance metric for each network functional component indicates the degree to which that network functional component is operating efficiently and effectively. By combining these performance metrics for the individual network functional components, for instance by averaging them, one can arrive at a single performance metric for the entire network. The metric scales are is arbitrary, for example a scale of 0-10 can be used, and can accommodate weighting of the values based on the seriousness of performance issues identified on the network.

The invention provides a real-time functional magnetic resonance data processing system based on brain functional network component detection, which realizes real-time network analysis of brain functional magnetic resonance images obtained on line. The system comprises a data preprocessing module, a functional network detection and display module, a region-of-interest selection module, a data feedback module and a parameter configuration module, wherein the data preprocessing module is used for improving the signal to noise ratio of the data by image denoising, artifacts removing, etc after carrying out online reading and format conversion on the brain functional magnetic resonance images and for reducing the interference of noises and other factors in the magnetic resonance images; then the functional network detection and display module is used for carrying out real-time network analysis and extracting the functional network components of brain activity in specific state; the region-of-interest selection module is used for recording and saving the activity conditions of one or more node regions of interest in the network; the data feedback module is used for feeding the result data of the regions of interest back to the persons to be tested in real time according to different application requirements or by various ways or judging the result data by categories; and the parameter configuration module is used for providing parameter setting, reading and saving functions for each module and each unit contained by the invention. The system has important application values in multiple fields such as online estimation of quality of data, mind reading, brain function adjustment, clinical treatment, etc.

An integrated distributed control system incorporates a plurality of micro electromechanical system (MEMS) modules formed into a functional network which is in turn integrated into a management network. The functional network is configured to sense a condition such as an operating characteristic of a machine or the temperature within an area and to provide control functions to effect changes in the condition.

A system and method for scalable multifunctional network communication between presentation devices and service providers are disclosed. A group of consumer premise equipment (CPE) units are coupled to the presentation devices, and a headend control computer receives upstream messages from the CPE units and for sending downstream messages to the CPE units. A group of service provider control subsystems interface between the headend control computer and the service providers. The head end control computer receives messages from the CPE units and transports them to the service provider control subsystems, and the headend control computer receives messages from the service provider control subsystems and transports them to the CPE units.

The invention discloses a complex network clustering method based on the key influence of nodes, which comprises the following steps: sequencing nodes in a complex network according to the size of degree, wherein the subordinate community of each initial node is not determined; taking a node (the subordinate community thereof is not determined) with the largest degree as a core node of a community; beginning to build the community; determining the subordinate community of a node adjacent to the core node; after the community is completely built, taking a node (the subordinate community thereof is not determined) with the largest degree as a core node of a community; beginning to build a community; and repeatedly performing a process of community building until the subordinate communities of all nodes in the network are determined, thereby obtaining a final network cluster structure. By using the method disclosed by the invention, a clustering precision in the complex network is superior to that obtained by using a FN (functional network) clustering method; and the method plays a positive role in carrying out fine-grained reveal on a real cluster structure of the complex network.

The invention relates to an apoplexy recovery degree index calculation method based on brain electrical signals. The method includes the following steps of firstly, enabling a preferred examinee to execute a left/right hand motor imagery task according to a prompt, utilizing multi-channel brain electrical signal collecting equipment to collect the brain electrical signals produced when the examinee executes the motor imagery task, then, utilizing a public average eliminating reference method to lower the level of public noise, utilizing an independent component analysis method to eliminate ocular artifacts, improving the signal to noise ratio of the brain electrical signals, utilizing a Butterworth filter to draw data of frequency ranges which are closely relevant to the motor imagery task for later analysis, finally, calculating a coefficient of polymerization and an overall efficiency property of a brain function network, calculating accuracy of the execution of the motor imagery task, utilizing the brain function network property and motor imagery task accuracy as recovery degree indexes, and storing calculation results in a special data base. The apoplexy recovery degree index calculation method based on the brain electrical signals has the advantages of being free of will interference and strong in objectivity. An objective assessing basis can be obtained through the method.

A method of generating a molecule-function network including bio-events by carrying out a connect search using a biomolecule-linkage database including information on the bio-events, and a method of predicting a pathway between an arbitrary biomolecule and an arbitrary bio-event in said network or a method of predicting the bio-events to which an arbitrary biomolecule in said network is related.

The process of System Reconstruction is used to integrate sequence data, clinical data, experimental data, and literature into functional models of disease pathways. System Reconstruction models serve as informational skeletons for integrating various types of high-throughput data. The present invention provides the first metabolic reconstruction study of a eukaryotic organism based solely on expressed sequence tag (EST) data. System Reconstruction also provides a method for the identification of novel therapeutic targets and biomarkers using network analysis. The initial seed networks are built from the lists of novel targets for diseases with the high-throughput experimental data being superimposed on the seed networks to identify specific targets.

A method and system for data link layer packet processing which unidirectionaly captures, filters, enqueues, processes and forwards packets between multiple network interfaces are introduced. Fast and intelligent data link layer network applications or equipment can implemented by programming the invention. Each direction of packet flow in this system is independently processed from others. This feature provides very flexible packet processing and very fast packet forwarding since each flow can be implemented in an isolated process, application or a device. As the system operates at OSI model's data link layer (e.g. Ethernet), installing the system into a functional network does not require any change in the configuration of network applications or equipment. This system can be utilized for employing various networking functions such as network emulation, bridging, firewall, virus detection, bandwidth management, traffic monitoring and in-line intrusion detection, etc.

The invention relates to a human-computer command interaction method based on a graphical interface in a network management client. The method comprises the following steps that: data configuration files are defined; according to the data configuration files, the graphical interface is generated; user operation is received by a graphical interface port and a corresponding human-compute command is generated according to the data configuration files; or data in a server terminal is received or responded, converted according to the data configuration files and provided to a user in an image mode by the graphical interface port. The interaction method is based on the data configuration files; the interface is automatically generated by a layout manger; the command is automatically generated by a human-computer command resolver; the heaviest graphical interface part in the network management development can be relieved; a majority of the interfaces can be automatically completed by the layout manager; a few interfaces can be expanded by the port; the human-computer command interaction method greatly reduces the risk of fault introduction, saves the development cost of network management software and can be widely suitable for various functional network management graphical interfaces.

The invention discloses a vibration fault detection system and method for wind turbine generator units. Vibration data, including vibration data of front and rear bearings of a gearbox and vibration data of front and rear bearings of a generator, of all parts of the wind turbine generator units are collected through vibration collection nodes by setting up a wireless sensor network, routing nodes passing through all the units are transmitted to a network coordinator node in a multi-hopping mode and transmitted to a monitoring center in the distance through 3G, optical fibers and an etheric multifunctional network, and a negative selection algorithm based on mahalanobis distance is adopted for the monitoring center to achieve remote detection early warning of fault units and fault parts. The vibration fault detection system for the wind turbine generator units comprises the monitoring center, a detection terminal, a database server, the multifunctional network, the network coordinator node, the routing nodes and the vibration collection nodes. According to the system and method, efficiency of the algorithm is improved, the accuracy rate of fault diagnosis is improved, a large amount of time is saved, and the larger the data size is, the more apparent superiority of the algorithm is.

The invention relates to the field of driving fatigue detection, in particular to a driving fatigue detecting method based on a phase-locked value established brain function network. The method includes the steps of collecting EEG signals during driving within the wake time and test time, denoising, decomposing and reestablishing the EEG signals, calculating the phase-locked value (PLV) in the sequence of the wake time and test time for every two channels, forming a functional connection matrix of the channels in the sequence of the wake time and test time according to the PLV, setting a connecting strength threshold, comparing the connecting strength threshold with each element value in the functional connection matrix to obtain the connection relation between the channels in the sequenceof the wake time and test time and form a brain function network of a testee in the sequence of the wake time and test time, and analyzing the difference of the brain functional network topological structure in the sequence of the wake time and test time on three sub-bands through comparison to judge whether the driving fatigue state is realized in the sequence of the test time or not. The detection reliability and accuracy are high.

A method for anticipating the tipping point of traffic resilience based on percolation analysis, includes steps of: step (I) based on the topology structure of the urban road network and daily traffic operation data, constructing the urban traffic dynamical network; step (II): based on the urban traffic dynamical network constructed, analyzing the relationship between the system performance of urban traffic functional network and the congestion rate by percolation analysis; and step (III): based on the relationship between the system performance and the congestion rate, exploring the resilience function of the urban traffic functional network, and analyzing its resilience pattern and its resilience tipping point.

Disclosed is a routing selection method, which is applied to a converged network of a WLAN access network and a 3GPP core network. A functional network element is arranged between the WLAN access network and the 3GPP core network. The method comprises: when UE accesses through a WLAN access network, a functional network element forwarding to an AAA an authentication request message sent by the UE, and forwarding to an access network element of the WLAN access network the authentication response message sent by the AAA; and/or the functional network element receiving an IP address request message sent by the access network element of the WLAN access network. Also disclosed at the same time is a functional network element. The present invention can conduct routing selection for 3GPP UE according to the type of the accessing UE and the service type, thereby reducing the access pressure of a 3GPP network.

The embodiment of the invention discloses a traffic processing method, a user plane device and terminal equipment, which are used for accurately identifying abnormal traffic and improving the trafficmanagement effect. The embodiment of the invention provides a traffic processing method, which comprises the following steps that: a user plane functional network element receives hotspot indication information sent by terminal equipment, and the hotspot indication information comprises hotspot switch opening; The user plane function network element obtains characteristic parameters of a first data packet and sends the characteristic parameters of the first data packet to a data analysis network element, and the first data packet is data obtained by the user plane function network element during the opening period of a hotspot switch of the terminal equipment; And the user plane function network element determines that the flow corresponding to the first data packet belongs to abnormal flow.

The invention discloses a brain electrical classification method for an entropy value based on a dynamic function connection. The method comprises the steps of firstly preprocessing an acquired original brain electrical signal, and then filtering the preprocessed brain electrical signal; calculating a phase relation, of the brain electrical signal of each frequency band, between every two channelsat each time point by use of a phase synchronization analysis method to obtain a dynamic function connection matrix; then, calculating a time domain entropy of a phase relation value between two channels one by one, and obtaining an information entropy of each edge to measure the complexity of the time domain of each edge of a brain electrical functional network; respectively taking a dynamic functional connection entropy of each frequency band as a classification feature of the brain electrical functional network, training an adaptive boosting classifier to obtain multiple adaptive boostingclassifiers and corresponding classification correct rates; and performing combined classification on a sample in a voting manner. With the brain electrical classification method for the entropy valuebased on the dynamic function connection, the problem of low classification accuracy rate in an existing brain electrical signal classification method is solved.

The invention discloses an electroencephalography combined VR device for intervention treatment of a children with hyperactivity and attention defect. The device includes VR glasses, a portable intelligent terminal and a wireless electroencephalography collecting cap. The wireless electroencephalography collecting cap comprises an electrode cap body, a plurality of removable dry electrodes, an electroencephalography amplification device, an electroencephalography filtering device, an analog-to-digital conversion device, a wireless transmission device and a processor. According to the device, acorresponding intervention treatment program is developed according to the activation degree of a key brain area of a functional network obtained by the electroencephalography and the connection strength of brain areas, and the VR glasses are utilized to provide sensory stimulation such as visual and auditory sense for a user, so that the user has an immersion sense and better focuses on treatment and rehabilitation.

The invention discloses a resting state complex fMRI data ICA-CNN classification framework of patients and healthy people, and belongs to the field of biomedical signal processing. According to the method, an interested functional network separated from resting state complex fMRI data by ICA is taken as a research object, and classification of patients and healthy people is realized by utilizing 2D CNN learning characteristics with few parameters; data augmentation is carried out by utilizing ICA results obtained under multiple groups of model orders, and the problem of fMRI data shortage is solved. Compared with an existing 3D CNN network, the training amount is reduced, and the accuracy is improved. For example, aiming at a plurality of fMRI data collected in an 82 tested resting state,by applying the DMN component extracted by ICA, the slice identification accuracy is higher than that of 3DCNN by (0.728 vs 0.701), and the tested identification accuracy obtained by a tested decisionis further improved by (0.914 vs 0.701).