286 results about "Offline analysis" patented technology

A digital computer and method for processing data indicative of images of facial and head movements of a subject to recognize at least one of said movements and to determine at least one mental state of said subject is provided. The outputting instructions for providing to a user information relating to at least one said mental state. A further processing data reflective of input from a user, and based at least in part on said input, confirming or modifying said determination and generating with a transducer an output of humanly perceptible stimuli indicative of said at least one mental state.

An implantable medical device and method are provided for assessing autonomic tone and risk factors associated with arrhythmias and, based on this assessment, an early recurrence of ventricular tachycardia or ventricular fibrillation is predicted. Specifically, changes in R—R interval, heart rate variability, patient activity, and myocardial ischemia are measured prior to and after a detected an arrhythmia episode. A recurrence score is calculated as a weighted sum of measured parameters and compared to a prediction criterion. The prediction criterion may be a preset threshold score or an individualized episode template based on previously calculated recurrence scores associated with recurring episodes. Stored parameters and episode-related data may be downloaded for offline analyses for optimizing prediction criteria and monitoring patient status.

A system and method for analyzing Java thread deadlocks. A snapshot of threads in a Java Virtual Machine is generated, producing a thread dump file which can be analyzed offline. The thread dump file is optimistically parsed to identify threads which are deadlocked. A user is provided with an interface to select filtering rules for the threads, resulting in a set of filtered threads which are candidates for being in infinite wait conditions.

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to “clean” the training data.

The invention discloses a state monitoring and fault diagnosis system of a gas turbine generating unit and a use method of the state monitoring and fault diagnosis system. The state monitoring and fault diagnosis system comprises a vibration monitoring module and a network module of a control system of the unit, a signal acquisition module and a computer which is provided with a LabVIEW platform, wherein a state performance signal is transmitted to the computer by the network module; the signal acquisition device is provided with a signal acquisition circuit and a signal processing circuit; a vibration signal output by the vibration monitoring module is acquired by the signal acquisition circuit, is processed by the signal processing circuit and is then transmitted to the computer; data acquisition is performed after an acquisition channel of the signal acquisition circuit is set by the LabVIEW platform; the LabVIEW platform comprises a real-time analysis module, an off-line analysis module and a fault diagnosis module; on-line monitoring and analysis are performed on the state performance signal and the vibration signal by the real-time analysis module; off-line analysis is performed on recorded data by the off-line analysis module; and fault diagnosis is performed on the unit by the fault diagnosis module. The state monitoring and fault diagnosis system is easy to operate and high in diagnosis accuracy and efficiency and can be applied to state monitoring and diagnosis of each turbine generating unit.

The invention discloses a workpiece grabbing method for a robot based on the integration of vision and an environmental attraction domain. The method comprises two phases of offline analysis and online grabbing; in the offline analysis phase, environmental constraint domains are established based on a gripper and the CAD model of a workpiece, the high-dimensional environmental constraint domains are decomposed into a plurality of subspaces with three dimensions or less, and the local lowest point of each constraint domain in the subspaces, so that the grabbing state can be stabilized; in the online grabbing phase, a camera is utilized for capturing a single image, the position and posture of the workpiece are recognized, routes from the initial state to the high-dimensional environmental constraint domains are planned through computing, and a computer outputs track points and controls the robot the move and stably grab the workpiece. The invention further discloses a robot grabbing system for implementing the method.

The invention relates to a low-frequency oscillation monitoring technology of a large-scale electrical network on the basis of a wide area measurement system (WAMS). The invention combines the advantages of various real-time analysis algorithms in allusion to the limitation that the low-frequency oscillation monitoring system in prior art only can perform offline analysis, so as to disclose an online low-frequency oscillation monitoring system of an electrical network to realize the online monitoring and analysis of the low-frequency oscillation of the electrical network. The low-frequency oscillation monitoring system of the electrical network of the invention is a software function module based on a traditional WAMS system, and comprises a system management module, a data communication module, a real-time monitoring module, a data analysis module and a database module. The invention realizes the real-time recording, real-time displaying and stable analysis functions of the low-frequency oscillation data in the electrical network, and is mainly used for the monitoring and analysis of the large-scale electrical network.

The illustrative embodiment of the present invention is a unmanned aerial vehicle that includes a sensing system for the detection and limited identification of biological agents. The system is small, light weight, requires little power to operate and uses few consumables. The system incorporates elements that enable it to obtain an air sample, extract particulates from the air sample and retain them on a stationary-phase collection media, exposes the particulates to electromagnetic radiation, and monitor the particulates for fluorescent emissions. To the extent that fluorescent emissions are detected and exceed a predetermined value, an alarm is triggered. In some embodiments, in addition to performing real-time analyses on the extracted particulates, the collection media is removed from the system and the sample is subjected to more detailed analysis via additional equipment. Various sample-collecting regions on the collection media are “time stamped” or “location stamped” so that it can determined when and/or where each sample that is being analyzed “off-line” was obtained.

The invention relates to an online steelmaking continuous casting control method, belonging to the technical field of steelmaking continuous casting; the method comprises the following steps: an interface management server is in network connection with a site controller, and stores the acquired various state information and process data in a database after data processing and format conversion, and also stores the production method delivered by the upper system in the database; a database server stores the production site real-time process data, planning data and various system configuration data in the database; an application server executes the steelmaking continuous casting production online control method, acquires data from the database server and stores the regulation result in the database; a client workstation displays various data, conducts the query of routine data, stores the data in a local computer and accordingly realizes the offline analysis of data; the online steelmaking continuous casting control method has high solving speed, higher automation degree, can preciously adjust to minutes, reduce the manual adjusting times and molten steel redundant waiting time.

The invention relates to a panoramic data analysis scheme of an intelligent transformer substation. In the scheme, network message recording, fault recording and synchronous phasor measuring equipment is integrally designed; in the aspect of message recording, the network online monitoring and message offline analysis of the intelligent transformer substation are realized, and various abnormal conditions of the network of the transformer substation are accurately positioned in real time; in the aspect of fault recording, the fault transient and regular recording of a power system is realized, and an important basis is provided for the judgment of the action behavior of grid fault analyzing, protecting and breaker equipment; and in the aspect of synchronous phasor measurement, the scheme carries out real-time dynamic safe monitoring on the power system, obtains a synchronous phasor by rapid and accurate measurement and provides basic data support for a wide area measurement system. The integrated design scheme realizes one-time configuration, and a running person maintains a set of system according to a set of standard, thereby fully meeting the new requirement on the intelligent transformer substation. The method of the invention is suitable for the panoramic data analysis of the intelligent transformer substation of the power system.

The invention discloses a method for optimizing an audio-visual cognitive event-related potential experimental paradigm. The method comprises the following steps of: on the basis of two first optimal Oddball sequences, performing combined stimulation of different matching modes of target stimulation and background stimulation modes on each subject respectively to induce a cognitive electroencephalographic experiment, establishing a plurality of second Oddball sequences, synchronously recording a plurality of second electroencephalographic signals, performing off-line analysis, and acquiring a plurality of second event-related potential data; acquiring the second Oddball sequence which corresponds to the second event-related potential data with the highest identification correct rate, wherein the second Oddball sequence is taken as a second optimal Oddball sequence of each subject; and gradually changing the appearance proportion of target stimulation, the duration and the stimulation interval of each subject in the second optimal Oddball sequence to induce cognitive electroencephalographic signals, acquiring a plurality of event-related potential data respectively, performing off-line analysis, and constructing an effective Oddball sequence scheme. The method is used for the auxiliary identification of the target stimulation, and can accurately position the target stimulation.

The invention discloses a user behavior analysis method and servers. The method comprises the steps of after booting a real-time analysis server, receiving historical user behavior data sent by an offline analysis server according to a first analysis result, wherein the first analysis result is an analysis result formed according to a first preset analysis strategy before the real-time analysis server boots; acquiring current user behavior data corresponding to current user behavior, wherein both the historical user behavior data and current user behavior data comprise user operations and operation objects which the user operations point at; and according to the first preset analysis strategy, analyzing the historical user behavior data and current user behavior data so as to obtain a second analysis result, wherein both the first analysis result and the second analysis result comprise at least one set of the operation objects, one operation object corresponds to at least two pieces of multimedia information, and the information content of the multimedia information corresponding to the same operation object has time correlation.

The invention provides an offline automobile gearbox NVH (noise, vibration and harshness) detecting method. A system for the offline automobile gearbox NVH detecting method comprises an offline analyzer, a control unit, a data acquisition system, a loading system, a driving system and a gearbox. The offline automobile gearbox NVH detecting method is characterized in that the offline analyzer performs offline analysis via an advanced order analysis algorithm, and the gearbox is the object for the offline analysis and provided with a vibration noise pickup sensor. The offline automobile gearbox NVH detecting method has the advantages that defects of the prior art are overcome, and efficiency in NVH detection in an offline process of the gearbox can be improved effectively.

The invention relates to a remote monitoring system for implanted medical equipment. The remote monitoring system comprises a doctor terminal, a remote monitoring server, an audio and video communication server, a patient client, a patient external controller, and the implanted medical equipment, and further comprises an electrophysiological signal sensor, a signal acquisition controller and a data analysis server which is connected with the remote monitoring server, wherein the electrophysiological signal sensor is connected with the signal acquisition controller, and is respectively connected with the patient client and the remote monitoring server through the signal acquisition controller; the data analysis server is used for carrying out online or offline analysis on the electrophysiological data of a patient acquired by the electrophysiological signal sensor. According to the remote monitoring system provided by the invention, the electrophysiological signals of the patient are acquired by the electrophysiological signal sensor, and the monitoring ways are varied, so that more comprehensive monitoring for the patient can be realized.

The invention provides a distributed information tracking system, and an information processing method and apparatus. An information collection device collects call information of a business processing stage and sends the call information to a message channel; an information analysis device obtains the call information from the message channel and performs real-time analysis on the call information to obtain the call information with same identifier information; and a corresponding call chain is obtained according to the call information with the same identifier information. Therefore, the call chain can be obtained by depending on the call information; compared with the prior art, a process of converting the call information to be in a corresponding log format and writing the call information in a local file is removed, so that the analysis timeliness is improved; the information analysis device can obtain the call chain by performing the real-time analysis on the call information; and compared with an existing mode of obtaining the call chain by three sub-steps of the real-time analysis, offline analysis and combination analysis, the analysis timeliness is further improved and the analysis complexity is lowered.

The invention provides a user information identification and classification method based on an internet application and a system thereof, and aims at compensating limitation of conventional data collection and identification modes. The user information identification and classification method based on the internet application comprises the steps that S1 use information of an application user is acquired; S2 real-time data analysis is performed; S3 offline storage is performed; S4 offline data analysis is performed; and S5 the analysis results are summarized. The method has extremely high compatibility, can realize real-time data collection and has great stability; storage pressure is low and rate of data analysis is high; and the data are stored by adopting an offline mode so that the data are ensured to be complete and errorless and historical data are ensured not to lose. The user information identification and classification system based on the internet application comprises a data collection module, a log analysis module, a storage module, an offline analysis module and a model database. The system has high precision, flexibility and stability and adopts two-stage analysis modes of real-time analysis and offline analysis so that computation pressure of the system can be reduced.

The invention provides a framework for collecting, storing, and analyzing system metrics concerning a computing system or a computer component. A configuration module is provided to configure settings specific to a metric. A data collection module is provided to collect metric data according to the settings in the configuration module and in one or more component specific plug-ins that extend and customize the framework according to specific needs of the component. The data collection module collects metrics at specified time intervals and periodically updates metric data stored in a central metrics storage module. An analysis module is provided to analyze metric data stored in the central metrics storage module online or offline. The analysis module may analyze a metric statistically or graphically, individually or combined with other metrics.