208 results about "Web modeling" patented technology

The present invention provides systems and methods for risk detection and analysis in a computer network. Computerized, automated systems and methods can be provided. Raw vulnerability information and network information can be utilized in determining actual vulnerability information associated with network nodes. Methods are provided in which computer networks are modeled, and the models utilized in performing attack simulations and determining risks associated with vulnerabilities. Risks can be evaluated and prioritized, and fix information can be provided.

A system and method of modelling a communications network using a computer system is disclosed, the method including generating a network representation using computer-readable code that represents structured information; parsing the network representation; generating a network model using the parsed network representation, the network model including a plurality of network objects and relationships between the plurality of network objects; and storing the network model in memory. Any type of network may be modeled. The computer-readable code may be any suitable language or instructions for representing structured information such as, for example, extensible mark-up language (XML). A network inventory adapter receives the network representation from the network. The network inventory adapter is a software component that may be used to connect applications to the network. The network inventory adapter receives the network representation from the network and reads and parses the network representation to determine which network objects are to be operated on and the order of operation.

To provide an improved approach to traffic data evaluation in a network using dynamic routing there is provided a traffic data evaluation apparatus for a network using dynamic routing comprising traffic data collection means (12) to collect data with respect to a real traffic flow in the network. Further, the traffic data evaluation apparatus comprises a network modelling unit (14, 16) to model the network through a virtual network having virtual links without capacity restrictions imposed thereon. Still further, there is provided a network load evaluation means (18) to map the real traffic flow onto the virtual network assuming optimal routing and to compare the capacity used for each virtual link with the capacity assigned thereto. Thus, it is possible to draw conclusions on the network load by real network measurements also for a network using a dynamic routing protocol.

The present invention is a system and method for coverage analysis in a wireless network. The invention seeks to evaluate the location and characteristics of a proposed base station in a wireless communication network, prior to the base station being erected. Data is collected from both: a test transmitter of known characteristics, erected at the proposed location for the proposed base station; and from a plurality of wireless channels of the existing network. The data may be collected at a plurality of measurement locations. The collected data is used to model and subsequently evaluate the behaviour of the network in the presence of channels that would be introduced into the network if the new base station were erected. Modeled data may include estimates of signal to noise ratios for various wireless channels for both existing base stations and the proposed base station, at the measurement location. The model data may be displayed on a GUI, depicting modeled signal characteristics in a geographical area surrounding the proposed base station, to estimate overall acceptability of a proposed base station at a proposed location before incurring the cost of installing the base station.

A system and method for extracting and managing latent social networks is described. The system generally comprises a network modeling component, a digital information component coupled to the network modeling component, and at least one third party computer system coupled to the network modeling component over a first network. The method operates to process user data to identify and extract at least one latent social network, and identify user needs within the network. The method also allows communications between a first entity (such as a brand or advertiser) and the user, such that information relating to the identified user needs may be delivered directly to the user.

An apparatus for modeling a contact network may include a processor. The processor may be configured to store a plurality of contacts lists, which collectively comprise a contact network. Each contacts list may be comprised of a plurality of contact entries and may be associated with a user of a remote device. The processor may further be configured to model the contact network using one or more modeling parameters. The processor may be configured to generate a plurality of suggested contact entries for a user based at least in part upon the one or more modeling parameters used to model the contact network. The suggested contact entries may be extracted from contact entries stored in the contacts network. Corresponding methods, systems, and computer program products are also provided.

A Path Generator connects to a communication network and uses genetic algorithms to assign flows to paths. Genotypes encode flow to path assignments for working and protection paths. Genotype fitness functions are computed as a weighted sum of constraint fitness functions. Each constraint fitness function evaluates the degrees to which the genotype is a satisfactory solution. The system can be used for network modeling. It can also receive requests for on-demand assignment of flows and on-demand rerouting of flows.

The invention provides a cooperative control method for multi-intersection signal lamp based on Q value migration depth reinforcement learning, and belongs to the crossing field of machine learning and intelligent traffic. A multi-intersection traffic network of an area is modeled into a multi-Agent system firstly. Each Agent simultaneously considers the influence of adjacent Agent actions at themost recent moment in the learning strategy process, so that multiple Agents can cooperatively conduct signal lamp control of multi-intersection. Each Agent adaptively controls one intersection through a deep Q network, and a network input is a discrete traffic state code of the original state information of the corresponding intersection. An optimal action Q value of the adjacent Agent at the most recent moment is transferred to the loss function of the network in the process of learning. The cooperative control method for multi-intersection signal lamp based on the Q value migration depth reinforcement learning can improve the traffic flow of the regional road network and the utilization rate of the road and can reduce the queuing length of the vehicle to relieve the traffic jam, and hasno limitation on the structure of each intersection.

A system and method for identifying a monitoring point in an electrical and electronic system (EES) in a vehicle. The method includes defining a network model of the EES where potential monitoring point locations in the model are identified as targets, such as nodes. The method then computes a betweenness centrality metric for each target in the model as a summation of a ratio of a total number of shortest paths between each pair of targets and a number of shortest paths that pass through the target whose betweenness centrality metric is being determined. The method identifies which of the betweenness centrality metrics are greater than a threshold that defines a minimum acceptable metric and determines which of those targets meets a predetermined model coverage. The monitoring point is selected as the target that best satisfies the minimum metric and the desired coverage.

A propylene polymerization production process optimal soft-measurement meter based on genetic algorithm optimized BP neural network comprises a propylene polymerization production process, a site intelligent meter, a control station, a DCS databank used for storing data, an optimal soft measurement model based on genetic algorithm optimized BP neural network, and a melting index soft-measurement value indicator. The site intelligent meter and the control station are connected with the propylene polymerization production process and the DCS databank; the optimal soft-measurement model is connected with the DCS databank and the soft-measurement value indicator. The optimal soft measurement model based on genetic algorithm optimized BP neural network comprises a data pre-processing module, an ICA dependent-component analysis module, a BP neural network modeling module and a genetic algorithm optimized BP neural network module. The invention also provides a soft measurement method adopting the soft measurement meter. The invention can realize on-line measurement and on-line automatic parameter optimization, with quick calculation, automatic model updating, strong anti-interference capability and high accuracy.

A method, apparatus, computer product and structure is presented for representing and managing large amounts of information concerning networks of elements. While being useful for communication networks, it can be also usefully deployed in the context of other networks such as distribution and transportation networks. The method uses a hierarchical construct called “catalog”—a set of elements (which could be “atomic” elements or catalogs themselves)—to organize information about physical or abstract entities relevant for modeling the network. A matrix construct whose rows and columns constitute such elements are used to model connections at different levels of abstraction. A common framework and representation provided using these two constructs is shown to be useful for visualization, administration, configuration, modeling, monitoring and manipulation of the network.

A computer system protection method monitors and evaluates per process network communications activity to determine whether the process has been compromised. In one embodiment, a network modeling scheme gathers data to build a model and then compares networking activities to the model as they occur. In an alternate embodiment, modeling is not required and the comparison is done of network data collected at one layer of a communication system to network-related data collected at another layer. As a result of a comparison and an indication of compromise, a given remedial action is taken.

A Path Generator connects to a communication network and uses genetic algorithms to assign flows to paths. Genotypes encode flow to path assignments for working and protection paths. Genotype fitness functions are computed as a weighted sum of constraint fitness functions. Each constraint fitness function evaluates the degrees to which the genotype is a satisfactory solution. The system can be used for network modeling. It can also receive requests for on-demand assignment of flows and on-demand rerouting of flows.

The invention discloses a traditional Chinese medicinal material production place determination method based on principal component analysis and a BP neural network. The traditional Chinese medicinalmaterial production place determination method comprises the following steps of sample preparation, spectroscopic data acquisition of samples, principal component analysis, BP neural network modelingand production place determination. A novel LIBS detecting technology is used for detecting traditional Chinese medicinal material samples, and the detecting process is simplified. Dimensionality reduction is conducted on full spectrum data through principal component analysis, the principal component number having a certain accumulative contribution rate is extracted, and unnecessary noise background signals and the like can be removed. After BP neural network training, quick determination of traditional Chinese medicinal materials can be achieved, and the quality and safety of the traditional Chinese medicinal materials can be effectively ensured.

The invention discloses an algorithm for collaborative filtering recommendation based on interest changes and trust relations. The algorithm mainly comprises the steps that (1) users' interest similarity degrees are computed by a time-fusion attenuation function; (2) a trust network of the users are established, and the users' trust degrees are computed; (3) the users' similarity degrees are computed in combination of the users' interest similarity degrees and the users' trust degree; and (4) a score of a target user aiming at a project is predicted. According to the invention, based on computation of the users' interest similarity degrees with application of the time-fusion attenuation function, network modeling is conducted for evaluation relations between the users and the project, and trust relations between the users are analyzed and excavated. Finally, the user's interest and trust relations are synthesized in the collaborative filtering recommendation. In this way, recommendation accuracy is enhanced.

The invention provides an automatic Parkinson's disease identification method based on multimode hyperlinks network modeling. The method includes: the DTI structure connection is used as the constraint and fused into the building process of an fMRI brain function network to build a multimode hyperlinks network model; node degree, edge degree and fit degree are extracted according hypernet featuresto serve as the original feature set, a multitask feature selection method (semi-M2TFS) is used to perform optimal feature subset screening on the original feature set to obtain the feature subset indicating the maximum difference degree between a Parkinson's disease patient and a normal person; a multi-core support vector machine pattern classifier is trained according to the optimal feature setand applied to Parkinson's disease patient classification diagnosis. Compared with an existing single-mode hyperlinks network modeling method, the method has the advantages that the multimode hyperlinks network can truly reflect the brain function connection mechanism and is excellent in classification identification accuracy and significant to the assisting of Parkinson's disease clinical diagnosis and automatic identification.

Method and apparatus for modeling a network are described in which a 1 port measurement is made on the network by inputting an excitation signal at one port of the network and recording the results reflected back to the port. A 1 port parametric model of the network is generated whereby as much information about the topology of the network is included in calculating initial values of the parameters. Then the values of the parameters of the 1 port parametric model are optimized by reducing the difference between the results of the measurement step and results calculated using the 1-port parametric model and the excitation signal.

The invention relates to a method for predicating gas concentration in real time based on a local decomposition-evolution neural network. The method comprises the following steps that 1), the data of the gas concentration in a coal mine work face are acquired through a mine gas sensor, and the collected data of the gas concentration are stored in a historical database; 2), data in the gas concentration historical database are processed as a time sequence to obtain the data x (t) of the gas concentration time sequence, wherein time is the real time of collecting gas data, and the gas concentration is used as the dependent variable of the time; 3), LMD decomposition is carried out on the data x (t) of the gas concentration time sequence through a local decomposition algorithm to obtain a plurality of PF components; 4), neutral network modeling predication is respectively carried out on the obtained PF components; 5), the predication values of all the PF components are cumulated to obtain a gas emission amount predication result; 6), whether the gas monitor data of other monitor points need to be predicated or not is judged, if the gas monitor data of other monitor points need to be predicated, the step 3), the step 4) and the step 5) need to be repeated for predication, and if the gas monitor data of other monitor points do not need to be predicated, the predication is finished. The method can be widely applied to predicating the gas concentration in real time.

The present invention relates to a rockburst dynamic prediction method based on BP neural network modeling. The method comprises the steps of: determining and acquiring rockburst influence factors; performing quantification processing on qualitative description parts in influence factor indexes, and obtaining an initial population; performing BP neural network training on the eight acquired influence factors separately; optimizing a number of neurons, an algorithm learning rate and momentum factors by using a genetic algorithm, and obtaining an optimal hidden layer node number; and performing prediction on rockburst of a mine by using a BP neural network algorithm model obtained through training, and obtaining a risk level of the rockburst of the mine. The method provided by the present invention has relatively high reliability, overcomes the defect of no association between the rockburst and the influence factors of the rockburst in the current rockburst prediction process, implements middle and short term dynamic prediction on the rockburst, and can be widely applied to the field of mine rockburst prediction.

A system (800) improves a network designer's ability to analyze a data network having several routers. The system (800) accesses static routing information and / or open shortest path first route summarization information, determines an identity of a network prefix using the accessed information, and analyzes the data network using the determined identity. The network designer can use this determined identity for traffic analysis or modeling of the data network.

The invention discloses a real-time yield predicting method for a catalytic cracking device. According to the real-time yield predicting method for the catalytic cracking device, kinetic parameters and device parameters of a catalytic cracking reaction are corrected in real time by processing field real-time data by adopting a data reconciliation technology, and combining an improved differential evolution algorithm, so that the actual operating situations of the device can be described accurately by using a catalytic cracking device mechanism model. The method comprises the following steps: on the basis of a corrected model, analyzing the influence on the yield of a catalytic cracking product caused by key operation / process conditions, such as an operating temperature, a feeding load, a raw material preheating temperature, a reaction pressure, a residue adding ratio, a regenerator temperature, a catalyst-to-oil ratio and the like; performing piecewise linearization according to an influence trend, solving a linear equation to obtain corresponding Delta-Base yield data, associating the operating conditions and the Delta-Base yield data by combining a neural network modeling technology, and establishing a yield agent model, so that the yield data calculating speed is improved; the real-time yield predicting of a continuous catalytic cracking device is realized; a theoretical support is provided for establishing an accurate plan optimization PIMS model.

A node model of the present invention is employed by a network modeling and simulation system and includes a communication protocol stack with a plurality of protocol layers. Each protocol layer includes communication functions. The protocol layer functions are each represented by one or more distinct software implementations that are optimized for different simulation purposes. A configurable module switch is disposed between protocol layers within the stack to selectively control information flow between functions within adjacent protocol layers. The node model enables a user to perform network simulation or analysis of varying detail or granularity.

The invention relates to an RBF neural network modeling method based on feature clustering, which belongs to the field of automatic control, information technology and advanced manufacture. The invention particularly relates to an RBF neural network modeling method based on feature extraction function clustering, which can solve the modeling problem that data can be scattered. The method is characterized by comprising the following steps: defining a feature extraction function based on existing mechanism knowledge, determining an RBF network center in a clustering algorithm based on the feature extraction function, and determining a weight value from the hidden layer to the output layer of the RBF network in a least square method. The invention also provides a clustering algorithm based on the feature extraction function, which is not used for directly clustering data, but is used for clustering data with scattering features through introduction of the feature extraction function based on the mechanism knowledge. The obtained clustering center is used as the RBF network center, and the weight value from the hidden layer to the output layer of the RBF network can be obtained with a linear interpolation method. The invention can effectively solve the modeling problem that the data has scattering features, and can achieve high modeling accuracy.

The invention comprises a method and apparatus for determining an expected impact of a disaster on a network. In particular, one embodiment of the method includes modeling the network as a plurality of geographical regions associated with respective pluralities of network elements and network element interconnectivities, generating a disaster model associated with the disaster by adjusting a disaster framework using a disaster parameter, generating a disaster analysis model using the network model and the disaster model, wherein the disaster analysis model includes a disaster probability parameter and a disaster impact parameter, and determining the expected impact of the disaster on at least a portion of the network using the disaster analysis model.

The invention relates to the field of petroleum reservoir and image processing, in particular to a core CT image processing-based remaining oil micro-occurrence representing method. The method comprises the following steps: pre-processing of a CT image: carrying out CT image interpolation on the basis of three Lagrange interpolations; image segmentation and modification; CT image-based pore and throat network modeling and quantitative characterization of remaining oil micro-occurrence. According to the core CT image processing-based remaining oil micro-occurrence representing method, the steps of CT image pre-processing, image interpolation, image-based medium segmentation, core model three-dimensional reconstruction, pore / throat segmentation and pore / throat topological structure reconstruction are carried out to obtain the three-dimensional configurations of all the pores and throats as well as the topological connection relation among the three-dimensional configurations, and finally obtain the quantitative characterization of the remaining oil micro-occurrence.

The invention discloses a real-time yield prediction method for a hydrocracking device. Field real-time data are processed with a data reconciliation technology, and hydrocracking reaction kinetics parameters are corrected in real time in combination with an improved differential evolution algorithm, so that a mechanism model can accurately describe the actual running condition of the device. On the basis of the corrected model, effects caused by key operation / process conditions such as the raw material density, the sulfur content, the nitrogen content, the reaction temperature, the pressure, the hydrogen-to-oil volume ratio and the like on hydrocracked products are analyzed. Piecewise linearization is performed according to the effect trend, a linear equation is solved, corresponding Delta-Base yield data are acquired, the operation condition is associated with the Delta-Base data with a neutral network modeling technology, a yield surrogate model is established, the yield data calculation speed is increased, real-time prediction of the yield of products of the hydrocracking device is realized, and theoretical support is provided for establishing an accurate plan optimization PIMS (process industry modeling system) model.

A Path Generator connects to a communication network and uses genetic algorithms to assign flows to paths. Genotypes encode flow to path assignments for working and protection paths. Genotype fitness functions are computed as a weighted sum of constraint fitness functions. Each constraint fitness function evaluates the degrees to which the genotype is a satisfactory solution. The system can be used for network modeling. It can also receive requests for on-demand assignment of flows and on-demand rerouting of flows.

The invention discloses an image segmentation system based on deep neural network modeling. The system comprises an image acquisition module, a pixel classification module configured to manually obtain two types of pixels in the crop image in a clicking manner, namely, crop pixels and background pixels, namely, positive samples and negative samples corresponding to class labels respectively, and select the same number of positive samples and negative samples as training samples of a deep convolution neural network; a color space conversion module configured to convert the training sample froman RGB color space to a standardized rgb and Lab color space, and convert the Lab color value of the sample into an unsigned 8-bit integer form according to an ICC specification to form a color feature of the training sample; a neural network training module; and a model test module. The system of the invention has the advantages of higher crop image segmentation processing speed and segmentationaccuracy, can be better adapted to the outdoor complex and changeable illumination environment, and can effectively segment and extract crops in crop growth observation.