207 results about "Self-organizing map" patented technology

A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps. Exposing threats and intrusions within packet payload at or near real-time rates enhances network security from both external and internal sources while ensuring security policy is rigorously applied to data and system resources. Intrusion Detection and Protection (IDP) is provided by a flow processing facility that processes a data flow to address patterns relevant to a variety of types of network and data integrity threats that relate to computer systems, including computer networks.

A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps. Exposing threats and intrusions within packet payload at or near real-time rates enhances network security from both external and internal sources while ensuring security policy is rigorously applied to data and system resources. Intrusion Detection and Protection (IDP) is provided by a flow processing facility that processes a data flow to address patterns relevant to a variety of types of network and data integrity threats that relate to computer systems, including computer networks.

A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps. Exposing threats and intrusions within packet payload at or near real-time rates enhances network security from both external and internal sources while ensuring security policy is rigorously applied to data and system resources. Intrusion Detection and Protection (IDP) is provided by a flow processing facility that processes a data flow to address patterns relevant to a variety of types of network and data integrity threats that relate to computer systems, including computer networks.

The disclosed embodiments provide a system and method for using modified Latent Semantic Analysis techniques to structure data for efficient search and display. The present invention creates a hierarchy of clustered documents, representing the topics of a domain corpus, through a process of optimal agglomerative clustering. The output from a search query is displayed in a fisheye view corresponding to the hierarchy of clustered documents. The fisheye view may link to a two-dimensional self-organizing map that represents semantic relationships between documents.

A data processing device for processing time-sequence data includes a learning unit for performing self-organizing learning of a SOM (self-organization map) making up a hierarchical SOM in which a plurality of SOMs are connected so as to construct a hierarchical structure, using, as SOM input data which is input to the SOM, a time-sequence of node information representing a winning node of a lower-order SOM which is at a lower hierarchical level from the SOM.

Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.

The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering. The results, similar for all three methods, were encouraging, exceeding the quality of classification achieved by the trained experts. Thus, a device and associated method for classifying cardiography data is disclosed, comprising applying a kernel transform to sensed data acquired from sensors sensing electromagnetic heart activity, resulting in transformed data, prior to classifying the transformed data using machine learning.

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

The invention provides a cloud cluster extraction method of network information. The cloud cluster extraction method comprises the following steps of: performing file writing, data storage and access to network information by a distributed file system; performing seamless combination on calculation models Map/Reduce of SOM (Self-Organizing Maps), a Kmeans clustering algorithm and cloud calculation to obtain a Map/Reduce SOM and Kmeans clustering algorithm based on the cloud calculation; performing control on the whole Map/Reduce by JobTracker, and distributing Map tasks or Reduce tasks by free TaskTracker; executing an instruction sent from the JobTracker and processing movement of data between Map and Reduce phases at the same time by the TaskTracker; periodically reporting finished work and state updating by each TaskTracker node; and if one TaskTracker node keeps silent for longer than a pre-set time interval, recording that the state of the node is dead and sending data distributed to the node to the other nodes by the JobTracker. The cloud cluster extraction method of the network information has good characteristic extracting performance and overcomes the disadvantage of too strong subjectivity in the existing network flow time sequence analyzing and predicating algorithm.

The invention relates to an equipment health state evaluation and recession prediction method based on multi-channel sensing signals. The operation steps of the method are as follows: the first step: joint noise filtration of the multiple sensing signals: adopting a multi-scale wavelet filtering algorithm to perform the joint noise filtration on the multi-channel sensing signal collected from equipment so as to improve the quality of the multiple sensing signals; the second step: generation of an original characteristic set; the third step: extraction of local characteristics of the signals; the fourth step: modeling of a reference self-organizing mapping model; and the fifth step: health quantitative evaluation and residual life prediction. By adopting the method, the quantitative evaluation and the life prediction of performance recession of the equipment can be realized, the operation reliability of the equipment can be further improved and the maintenance cost can be reduced.

A control system for an internal combustion engine is disclosed. In the control system, an engine state parameter is calculated using a self-organizing map for calculating a predetermined output parameter according to at least one engine operating parameter which indicates an operating condition of said engine. The engine state parameter indicates an engine state which is relevant to the predetermined output parameter.

The invention discloses a fault detection, diagnosis and performance evaluation method for a redundant aileron actuator. According to the method, fault detection, diagnosis, evaluation and real-time detection of the actuator are performed by means of an input order signal, an output displacement signal, a force motor current signal and aerodynamic loading data of the actuator; the fault detection is realized by a two-stage neural network, a first neural network is used as a system observer and is matched with actual output to acquire a residual error, and a second neural network outputs a self-adaptive threshold value synchronously; the fault detection is realized by the system observer and a force motor current observer; a time domain feature is extracted from a residual error signal and output to a self-organizing mapping neural network, and a minimum quantization error is acquired and normalized to a health degree, so that the actuator performance is evaluated; and on the basis of fault detection, the aerodynamic loading data is introduced, by means of a specific input order spectrum, the system observer and the neural network with the self-adaptive threshold value are trained, and the real-time fault detection is realized.

A method for fusing and diagnosing fault information of a circuit of an electric meter on the basis of SOM (self-organized mapping) and D-S (Dempster-Shafer) theories includes firstly, creating a fault mode set for the faulty circuit according to circuit analysis of the faulty circuit of the electric energy meter and fault modes specified by GJB299C; secondly, selecting to-be-observed fault signal points corresponding to fault modes in the set according to the fault mode set created in the first step and using the to-be-observed fault signal points as test points for functions and states of the circuit; thirdly, preprocessing fault signals acquired at the fault signal points selected in the second step; fourthly, fusing fault information by the aid of the SOM theory, outputting fault conclusions, selecting 70% of data for training and selecting 30% of the data for testing; and fifthly, fusing the fault conclusions by the aid of the D-S theory and making a decision for faults. By the aid of the method, confidence degree of a fault diagnostic result is further increased, integral uncertainty caused by errors is reduced, accuracy of fault diagnosis is greatly improved, and the method is an extremely important means in the field of information fusion.

The invention belongs to the remote sensing image processing technical field, in particular to a remote sensing image mixed pixel decomposition method based on self-organization mapping neural network. The method integrates the self-organization mapping neural network and the fuzzy grade of membership in the fuzzy theory to work out abundance values after decomposition. Simultaneously, as the self-organization mapping neural network does not has the competitive learning characteristic of an objective function, the method is free from the problem of local extremum. In addition, the invention automatically meets the two bindings required by the mixed pixel decomposition problem, wherein the two binds are respectively the binding of non-negative abundance values and the binding of the sum of the abundance values being 1. The invention has a better mixed pixel decomposition effect and a higher anti-noise capacity. The new method has a particularly important application value concerning the high-accuracy ground object separation and ground target detection and identification based on multi-spectrum and high spectrum remote sensing image.

The invention provides a health evaluation method for a hydraulic system based on a fault observer and SOM (self-organized mapping). The method includes: firstly, training the fault observer by the aid of input and output signals of the hydraulic system in a normal operation state to acquire residual signals in the normal state; secondly, acquiring residual signals of a certain component in a degenerate state through the fault observer by the aid of input and output signals of the degenerate component in the system; thirdly, extracting time-domain features of the residual signals in the two states and building a self-organized mapping model by the aid of residual features in the normal state; and finally, inputting residual features of the degenerate component to the self-organized mapping model to acquire the health degree of the hydraulic system in the corresponding states, and judging whether the current hydraulic system can continue running or not by the aid of the lowest health threshold value. The health evaluation method is used for evaluating health of the hydraulic control system in real time, is fine in evaluation effect, reduces dependency on historical data, and has engineering practicability.

A temporary self-organizing map in which classes are associated with respective vector points is first derived by a first learning section of a data learning apparatus. Thereafter, by use of, for example, an improved method incorporating concept of vicinity learning intp learning vector quantization, a second learning section modifies the temporary self-organizing map and obtains a final self-organizing map. By use of the final self-organizing map thus derived, image meaning determining processing is performed.