3935 results about "Correlation analysis" patented technology

A tracking system uses RFID (radio frequency identification) tag technology to facilitate the identification and tracking of items in an environment through a technique known as shadowing. As an object or target moves within a pre-described detection zone with communicating antenna and RFID sensors, the object or target blocks the line of sight between respective antenna and sensors, preventing electromagnetic coupling between the sensor and the antenna and thus casting an electromagnetic shadow along the line of sight. One approach of this invention uses this shadow technique to perform functions such as theft (shrink) deterrence/ detection; tracking the motion of objects through an environment by monitoring the shadow; and correlation analysis of people shadows to tagged items (e.g., merchandise, articles) to foster marketing and merchandising effectiveness.

Platforms, systems, media, and methods for providing virtual reality (VR) tour builder and editor applications, multi-modal VR tour applications, and VR tour analytics applications useful in real estate sales and marketing, advertising, entertainment, education, healthcare scenarios.

Replica correlation processing, and associated representative signal-data reduction and reconstruction techniques, are used to detect signals of interest and obtain robust measures of received-signal parameters, such as time differences of signal arrival and directional angles of arrival, that can be used to estimate the location of a cellularized-communications signal source. The new use in the present invention of signal-correlation processing for locating communications transmitters. This enables accurate and efficient extraction of parameters for a particular signal even in a frequency band that contains multiple received transmissions, such as occurs with code-division-multiple-access (CDMA) communications. Correlation processing as disclosed herein further enables extended processing integration times to facilitate the effective detection of desired communications-signal effects and replication measurement of their location-related parameters, even for the communications signals modulated to convey voice conversations or those weakened through propagation effects. Using prior, constructed, signal replicas in the correlation processing enables elimination of the inter-site communications of the signal representations that support the correlation analyses. Reduced-data representations of the modulated signals for voiced conversation, or for the variable components of data communications, are used to significantly reduce the inter-site communications that support the correlation analyses.

The present invention is a method for detecting an abnormal event for process units of a Delayed Coking Unit. The method compares the operation of the process units to statistical and engineering models. The statistical models are developed by principal components analysis of the normal operation for these units. The engineering models are based statistical and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

A signal detector is provided in which correlation analyses of multiple segments of a received signal from separate and distinct periods of time are combined to improve the detection capability of the signal detector. In one embodiment, the signal detector is part of a GPS receiver.

The invention discloses a network safety situation awareness early-warning method and system based big data, and the method comprises the steps: collecting intelligence information from the Internet, non-governmental organizations, governmental agencies and interiors of companies; obtaining log and network flow generated by safety equipment, network equipment, a host and other safety protection systems, carrying out the real-time preprocessing of the collected original data, carrying out the standardization of the data after preprocessing, and converting the data into safety data with the unified standard; carrying out scene modeling according to different attack behaviors, carrying out correlation analysis through combining with the intelligence information and the safety data, and generating early-warning information; carrying out the check processing of the early-warning information, and carrying out the visualized display of the early-warning information after check. The method can achieve the complete sensing of a safety situation, the early warning of safety threats and the capability of timely handling and responding of a safety event, and improves the overall safety protection capability of a power system.

The invention discloses a high-interaction honeypot based network security system and an implementation method thereof. The system comprises a honeypot monitoring module, a website correlation analysis module, a summarizing analysis/display module, wherein the honeypot monitoring module is used for carrying out classified acquisition on the attack incidents network behavior logs and host behavior logs; the website correlation analysis module is used for carrying out summarization and automatic correlation analysis on the attack incidents network behavior logs and the host behavior logs, and filtering invalid network alarms, capturing known attack incidents, automatically finding out unknown attack incidents, and extracting concealed Internet malicious resource information according to the automatic correlation analysis results; and the summarizing analysis/display module is used for summarizing and accounting known attack incidents and/or unknown attack incidents captured on all honeypot websites, displaying and reducing selected attack incidents for users, and carrying out automatic detection on concealed Internet malicious resources according to the concealed Internet malicious resource information.

The invention discloses a method for vibration online monitoring and fault diagnosis of a power transformer. The surface vibration of an oil tank of a transformer, the voltage, the current and the oil temperature of the transformer, and the switching values reflecting the on-states of a vibration source fan and the oil tank of the transformer are taken as basic monitoring parameters to be monitored; after signal conditioning is performed on the monitoring parameters, continuous synchronous waveform data sampling, processing and saving are performed; then the processed waveform data are continuously displayed in real time; signal decomposition, multi-parameter correlation analysis and characteristic vector magnitude extraction are performed on the waveform data; and then various characteristic vector thresholds of a specific transformer are compared to analyze and diagnose the operating state and the fault of the transformer. With the method, the operating state of the power transformer can be monitored in real time, and early warning as well as the analysis and the diagnosis of the fault condition of the transformer are provided, thus guaranteeing the secure and stable operation of the transformer.

The invention relates to a network attack scene generating method based on multi-source alarm logs. The network attack scene generating method includes the steps that firstly, the alarm logs generated by multiple network security protection devices are collected, effective alarm log data are extracted through preprocessing, and noise, redundancy or ineffective logs are removed; for the effective alarm logs acquired by one device, log format differences of different devices are shielded through single-source log aggregation and mapping, and attack event information is analyzed and extracted; attack events extracted from different sources are fused and analyzed, and network attack events with high credibility are generated; then through correlation analysis of the attack events, a network attack scene graph is generated, and the whole attack process of one attack operation is analyzed. Due to the fact that the multi-source logs are fused, the analyzed attack event information can more completely and more reliably depict attacks to which a network is subject, attack scenes are acquired through correlation analysis of the attack events, the intentions of attackers can be more clearly shown, and the security threat state of the network can be more clearly reflected.

The invention discloses a log management and fault diagnosis method and device under multiple hosts. The method comprises fault log collection, fault log analysis and fault log correlation analysis. Fault log collection comprises collecting fault logs of all hardware and software in the cluster and storing the logs in a log server uniformly. Fault log analysis comprises filtering the fault logs, extracting log template information and classifying the logs according to log types. Fault log correlation analysis comprises performing fault reason analysis by using log analysis results and combining time windows, clustering the related faults caused by the same fault into a category and trying to find the root of the type of the faults. The method and the device are capable of analyzing the system operation condition effectively, assisting administrators to process system faults and improving the fault type determination accuracy.

Systems and techniques are provided for tracking inventory events in an area of real space. A plurality of sensors produce respective sequences of frames in corresponding fields of view in the real space. The field of view of each sensor overlaps with the field of view of at least one other sensor. The processing system uses the sequences of frames produced by sensors in the plurality of sensors to identify gestures by detected subjects in the area of real space and produce inventory events. The inventory events include a subject identifier identifying a detected subject, a gesture type of the identified gesture by the detected subject, an item identifier identifying an inventory item linked to the gesture by the detected subject, a location of the gesture represented by positions in three dimensions of the area of real space and a timestamp.

A portable communication apparatus for match-making with a plurality of remote communication apparatuses has memory means adapted to store match-making data, including local match-making data associated with the portable communication apparatus. A transceiver is adapted for wireless communication with a first remote communication apparatus, so as to transmit the local match-making data to the first remote communication apparatus, as well as to receive first remote match-making data associated with the first remote communication apparatus. A processing device is adapted to perform a correlation analysis between the local match-making data and the first remote match-making data and-in case a result of the correlation analysis indicates a match between the portable communication apparatus and the first remote communication apparatus-provide an alert to a user of the portable communication apparatus. The memory means is adapted to store the first remote match-making data, and the transceiver is adapted to transmit the first remote match-making data, in addition to the local match-making data, to a second remote communication apparatus.

The invention relates to a multiple-template matching identity recognition method based on an ECG (Electrocardiogram) under an electrocardiogram abnormality state, and belongs to the technical field of biological characteristic identity recognition. The ECG data of a user to be recognized is compared with the data of a registered user in a template library to obtain an identity recognition result. The key technology of the method comprises the following steps: carrying out electrocardiosignal preprocessing for eliminating noise interference; carrying out electrocardiosignal decomposition to separate an electrocardiogram waveform of each period; carrying out standardized processing for independently achieving standardization on time and amplitude scales; carrying out characteristic extraction: in the step, characteristics are extracted by wavelet transform, and clustering analysis is carried out by an ISODATA (Iterative Self-organizing Data Analysis Techniques Algorithm) so as to construct an ECC template library; and carrying out correlation analysis: in the step, correlation between ECG test data and each template is calculated, an optimal matching template is selected, and finally, an identity recognition result is obtained. The multiple-template matching identity recognition method provided by the invention utilizes the intrinsic electrocardiosignal of a human body to recognize an identity, and the ECG data under the abnormality state is considered.

Computer-implemented methods are described for, first, characterizing a specific category of information content—pornography, for example—and then accurately identifying instances of that category of content within a real-time media stream, such as a web page, e-mail or other digital dataset. This content-recognition technology enables a new class of highly scalable applications to manage such content, including filtering, classifying, prioritizing, tracking, etc. An illustrative application of the invention is a software product for use in conjunction with web-browser client software for screening access to web pages that contain pornography or other potentially harmful or offensive content. A target attribute set of regular expression, such as natural language words and/or phrases, is formed by statistical analysis of a number of samples of datasets characterized as “containing,” and another set of samples characterized as “not containing,” the selected category of information content. This list of expressions is refined by applying correlation analysis to the samples or “training data.” Neural-network feed-forward techniques are then applied, again using a substantial training dataset, for adaptively assigning relative weights to each of the expressions in the target attribute set, thereby forming an awaited list that is highly predictive of the information content category of interest.

The present invention is a method and system for detecting an abnormal event for process units of a Polymers Unit. The method compares the operation of the process units to statistical, engineering or heuristic models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on correlation analysis between variables or simple engineering calculations. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

The invention discloses a dos attacking and defending method based on the data mining technique and a system thereof. The system comprises an anomaly detection module, a data mining engine module, a trust IP list filter module and a flow control module. The anomaly detection module adopts an efficient analysis covariance method to analyze the current network flow so as to judge the current system is abnormal or not, the data mining engine module uses a correlation analysis method to pick up a trust IP list to be used for filtering a data packet. The attacking system overcomes the shortcomings of the traditional system based on the trust IP list filtration, and can effectively differentiate normal flow and abnormal flow when defending the attack. The data mining engine module of the system picks up and transmits the IP trust list and the attribute value list with high efficiency so as to cause the system to defend the dos attack (Dos/DDoS) in real time.

The invention relates to the technical field of movement behavioral analysis and prediction in a sense network environment, and specifically to a movement space-time trajectory analysis method in the sense network environment. The movement space-time trajectory analysis method in the sense network environment comprises data reception of receiving trajectory movement position data generated by a positioning device and resolving the data format into a data format applicable to data treatment; semantic treatment of performing clustering operation on the semantic trajectory data; space-time correlation of performing characteristic analysis and statistics on clustered semantic trajectory data in a time domain and a space domain, and performing time-space correlation analysis in combination with the time domain and the space domain; correlation similarity analysis of calculating space-time correlation similarity of the semantic trajectory and performing analysis and calculation on the correlation among different space domains and different movement objects; outputting a result. The movement space-time trajectory analysis method in the sense network environment solves the problem of continuous treatment and mutual correlation of time and space dimensions in a traditional transactional database, and meets the need from a sense network application service to real-time analysis of trajectory movement data.

The invention discloses a combination forecast modeling method of wind farm power by using gray correlation analysis, belonging to the technical field of wind power generation modeling. In particular, the invention is related to a weighted combination forecast method of wind power based on a least square support vector machine and an error back propagation neural network. The forecast method comprises that forecasted values of wind speed and wind direction are acquired in advance from meteorological departments while real-time output power is acquired from a wind farm data acquiring system; that the forecasted values of wind speed and wind direction and the real-time output power are inputted into a data processing module for data analyzing extraction and data normalization, and then normalized data is loaded to a database server; processed data in the database server is extracted by a combination forecast algorithm server to carry out model training and power forecast, and the wind farm sends running data to the data processing module in real time to realize rolling forecasting. The method of the invention achieves the goal of combination forecast of wind farm output in a short time. The method not only maximally utilizes advantages of two algorithms but also increases forecast efficiency by saving computing resources and shortening computing time.

The invention relates to a network security situation assessment method, in particular, a Bayesian network-based multi-step attack security situation assessment method. The method includes the following steps that: multi-step attack generating patterns are mined through association analysis, so that an attack graph can be constructed; a Bayesian network is established according to the multi-step attack graph, attack wills, probability of success of attacks and the accuracy of event monitoring are defined as the probability attributes of the Bayesian network; based on the event monitoring, a multi-step attack risk is calculated according to the posterior reasoning and cumulative probability of the Bayesian network; and the security situations of a host and the whole network are quantitatively assessed according to a hierarchical quantitative assessment method. With the method of the invention adopted, the problem of lack of correlation analysis in a network security situation assessment process can be solved. According to the method of the invention, monitoring events are taken into risk assessment, and a network security situation assessment model is accurately established, and therefore, the effectiveness and real-time performance of the method of the invention can be enhanced.

The invention relates to a transformer state evaluation method based on correlation analysis and variable weight coefficients, which includes collecting data of transformer states; classifying and processing the collected data as a basis of the transformer state evaluating; evaluating the transformer states; applying a correlation analysis method to establish a comprehensive state quantity evaluation system of the transformer; utilizing a single state quantity score calculating method and a transformer overall score calculating method to grade the transformer states; extracting constant weight coefficients of each of single state quantities and comprehensive state quantities; calculating constant weight coefficients of each of the comprehensive state quantities; calculating an overall score of the transformer states based on scores of each of the comprehensive state quantities and the constant weight coefficients; and determining a transformer operation state based on the overall score of the transformer states. The transformer state evaluation method has the advantages that evaluation models are simple and easy to program, no abundant state evaluation samples are needed, operation states of transformers can be determined rapidly, and operation states and conditions of transformers are truly reflected.