104 results about "Adaptive monitoring" patented technology

Methods, apparatuses and systems directed to adaptive monitoring of network application performance. In one implementation, the present invention improves processes used by enterprises to track, manage, and troubleshoot performance of network applications across distributed networks. In one implementation, the present invention allows for a network application performance monitoring scheme that tracks end-to-end performance of selected network applications on a passive basis, while selectively engaging more invasive (synthetic) approaches to tracking performance and troubleshooting issues when needed.

Compact tunable optical cavities are provided for in-situ NIR spectroscopy. MEMS-tunable VCSEL platforms represents a solid foundation for a new class of compact, sensitive and fiber compatible sensors for fieldable, real-time, multiplexed gas detection systems. Detection limits for gases with NIR cross-sections such as O₂, CH₄, CO_x and NO_x have been predicted to approximately span from 10^ths to 10s of parts per million. Exemplary oxygen detection design and a process for 760 nm continuously tunable VCSELS is provided. This technology enables in-situ self-calibrating platforms with adaptive monitoring by exploiting Photonic FPGAs.

Various embodiments provide adaptive monitoring of a wireless communication network. In one embodiment, a first set of network data generated for a wireless communication network is analyzed. The first set of network data is a set of historical network data for the wireless communication network. A baseline for at least one operating characteristic associated with the wireless communication network is determined based on the analyzing. A second set of network data generated for the wireless communication network is received. The second set of call detail records that has been received is utilized to determine if the at least one operating characteristic corresponds to the baseline. A set of monitoring operations performed by a network management system with respect to the wireless communication network is dynamically adjusted based on the at least one operating characteristic failing to correspond to the baseline.

The invention relates to a method for adaptive monitoring of a cloud computing system based on failure prediction. Characteristic vectors of monitoring data are calculated through a principal component analysis technology so as to depict a system running state, and the abnormal degree of the system is estimated by calculation of the deviation between characteristic vectors of current monitoring data and characteristic vectors of historical monitoring data. When the abnormal degree of the monitored system is high, a monitoring period is shortened so as to closely track a running state of the monitored system, and thus error prediction and detection accuracy and timeliness are improved. In contrast, when the abnormal degree of the monitored system is low, the monitoring period is prolonged, and thus the monitoring overhead is reduced.

A method for dynamically and adaptively monitoring a system based on its running behavior adjusts monitoring levels of the monitored application in real-time. A rules-based mechanism dynamically adjusts monitoring levels in real-time, based on the system's performance observed during a workload run, whether in a production or test environment.

The invention discloses an online self-adaptive working condition monitoring and fault diagnosis method for a process industrial course and belongs to the technical field of fault monitoring and diagnosis of complex industrial courses. The method comprises the following steps of firstly, analyzing historical observation data under a normal working condition, introducing an elastic regression network combining Lasso constraints with Ridge constraints to establish an industrial course fault monitoring model on the basis of sparse principal component analysis, and then obtaining a course controllimit to industrial course fault monitoring statistics; during online monitoring of industrial course faults, adopting an order-1 matrix correcting algorithm for resolving a covariance matrix of the online monitoring data, conducting recursion updating on a load matrix of the sparse monitoring model to obtain the course control limit to the course fault monitoring statistics matched with the working condition, and achieving self-adaptive fault detection in the process industry course; finally, according to the detected faults, adopting a contribution plot method for obtaining specific causes of the faults. By means of the method, the faults of the process industry course with complex and changeable working conditions can be self-adaptively monitored for a long time; the method has the advantages of low calculation complexity, high precision, a low report missing rate and the like.

Various embodiments of the present invention provide systems and method for adaptive monitoring of physical movement.

The invention relates to a human body falling-over detecting early warning device based on multiple-sensor cooperation. The human body falling-over detecting early warning device based on multiple-sensor cooperation is composed of a master machine and one or more slave machines. The master machine comprises a sensor communication module, a microprocessor, a global system for mobile (GSM) communication chip, a global position system (GPS) position chip, a light-emitting diode (LED) lamp, a button, a power source and other peripheral devices. Each slave machine includes an acceleration sensor or a pressure sensor, a microcontroller and a sensor communication chip. The master machine obtains data from the sensor of each slave machine through a sensor network, detects falling over and conducts early warning. By means of wearable-type wireless monitoring, falling-over monitoring is portable and user-friendly. Multiple-sensor cooperation and mixed positioning are adopted so as to achieve accurate and comprehensive falling-over monitoring. On the basis of a self-adapting monitoring model, falling-over monitoring is personalized and capable of catering to individuals.

The invention discloses a Web distributed software evolution and monitoring method based on a micro-service. The method comprises the following steps: step 1, constructing a demand interaction model under a micro-service architecture; step 2, providing a key component module required by an RMAE framework; step 3, providing an RMAE overall architecture and an operation process; step 4, introducingthe DYNAMICO reference model proposed by the Villegas into an RMAE framework; step 5, further providing a routing delegation method of the RMAE framework for the user demand; step 6, giving an RMAE cooperation algorithm; and step 7, giving a corresponding micro-service adaptive monitoring algorithm by combining the characteristics of the micro-service and the RMAE scene. The method ensures that the RMAE framework can effectively manage the constantly changing Web task requirements, and further ensures that Web distributed software can dynamically carry out self-adaptive adjustment evolution according to user requirements in a micro-service environment.

Various embodiments of the present invention provide systems and method for adaptive monitoring of physical movement. As one example, a method for adaptive initialization of a monitoring system is discussed. The method includes associating a monitor device with a monitor target; receiving information indicating movement of the monitor target from the monitor device; plotting or defining one or more paths corresponding to the movement of the monitor target during a learning period; and based at least in part on the one or more paths, identifying one or more exclusion zones where movement of the monitor target is restricted.

Embodiments disclosed herein provide a system, method, and computer readable storage medium storing computer instructions for implementing a Socialware architecture encompassing a suite of applications for continuously and adaptively monitoring and filtering traffic to and from social networking sites, particularly useful in an enterprise computing environment. In some embodiments, an appliance may be coupled to a proxy server for providing a plurality of Socialware services, including analyzing, logging, and reporting on traffic to and from social networking sites. Some embodiments may allow a user to report, identify, and prevent malicious and potentially malicious content and/or activity by another user. Some embodiments may encrypt outgoing traffic to and decrypt incoming traffic from social networking sites. Some embodiments may provide an enterprise user to define and restrict certain social networking activities outside of the enterprise computing environment.

An embodiment of the invention discloses a method and a device for monitoring a business server and belongs to the field of monitoring. The method includes that corresponding data acquiring components of various areas transmit business monitoring data to data gathering components arranged on the business server, the data gathering components generate monitoring information by the received business monitoring data, the business server transmits the generated monitoring information to a monitoring server, and the monitoring server stores the received monitoring information, calculates the monitoring information by grades according to statistical time intervals and generates gathering data to display. Various acquiring modules of the monitoring system are deployed in various areas to acquire data centrally, and according to analytic statistics of the monitoring data subjected to calculated by grades and gathered and according to corresponding monitoring commands of automatic prediction of the gather data, monitoring strategies of the various acquiring modules are adjusted again, and accordingly, an adaptive monitoring system is formed.

The invention discloses a self-adaptive monitor matching anti-interference method of a wireless sensor network. The self-adaptive monitor matching anti-interference method adopts a 'self-adaptive monitor matching window' flexibly adjustable in receiving window size to receive data information transmitted from a source node. A receiving monitor window is maximum in size in an initial state, and a received signal strength indicator measures the strength of wireless signals. When the signal strength is lower than a set threshold value, a cycle rate counter adds 1. When the signal strength is higher than the set threshold value, signal receiving starts. When signal receiving succeeds, a communication window is adjusted to be maximum in size and returns to the initial state. If the receiving fails, the window size is decreased. In the monitoring process, if no interference signal or normal signal is found in M cycles, the size of the receiving monitor window is minimum. When the monitor window is in a minimum state, interference signals always exist in the M cycles, and the window is adjusted to be in an intermediate state. By the adoption of the self-adaptive monitor matching anti-interference method, monitor errors between a transmitting node and a receiving node in a wireless interference environment is effectively decreased, and unnecessary energy consumption and lost data packets are reduced.

A new energy grid connected system subsynchronous/supersynchronous oscillation adaptive monitoring method comprises the following steps: 1, preprocessing acquired signals; 2, using a subsynchronous/supersynchronous oscillation monitoring algorithm to fast determine whether subsynchronous oscillation has happened or not; 3, sending an early warning; 4, using a subsynchronous/supersynchronous oscillation identification algorithm to identify a subsynchronous oscillation frequency and calculate a supersynchronous signal frequency; 5, using mode filtering to extract fundamental wave, subsynchronousharmonic wave and supersynchronous harmonic wave signals; 6, using a DFT phasor algorithm to respectively calculate a fundamental wave phasor, a subsynchronous harmonic wave phasor, and a supersynchronous harmonic wave phasor for the step 5; 7, making amplitude and phase compensation after phasor calculation in step 6. The method can fast monitor whether the power system has subsynchronous oscillations or not, can provide information that shows a frequency range to which the oscillation frequency belongs, and can send early warning information to a control center; the method can further accurately identify the oscillation frequency, and can adaptively adjust a filter characteristic frequency according to the detected oscillation frequency, thus accurately calculating the fundamental wavephasor, the subsynchronous harmonic wave phasor, and the supersynchronous harmonic wave phasor.

The invention discloses a working condition self-adaptation high-speed milling machining process tool monitoring method. The method comprises the following steps of firstly, original sensing signal obtaining, and tool wear amount measuring; secondly, signal pretreatment and characteristic extracting; thirdly, characteristic dimensionality reduction; fourthly, wear stage division; fifthly, model parameter training; and sixthly, wear state identifying. Meanwhile, the invention discloses a system achieving the above method. Online working condition self-adaptation monitoring of the tool wear state can be achieved, the tool wear state monitoring accuracy rate is improved, production cost is effectively reduced, and production efficiency and production quality are improved.

A method for providing an adaptive monitoring process for distributed system containing a plurality of detected events, each of which is associated with at least one causing event. The method comprises the steps of receiving an indication of an occurrence of at least one detected event, enabling the detection of at least one second detected event, the second detected events associated with the detected events are nominally disabled, initiating at least one process for generating at least one second causing event associated with each of the enabled second detected events and identifying selected ones of the at least one second causing events based on the occurrence of at least one corresponding one of the second detected events.

An information monitoring method is provided which may track and monitor specific events of changing input data, such as stock market information, and notify venture capitalists or investors in real time of the occurrence of identified events of interest. According to the method to train or learn the quantitative patterns inherent in data sets, such as correlation between MAP and MAV, the relationship based on rules is built. A gray coefficient, trained by neural network under the specific events occurred in the historical data, is obtained for tracking and monitoring the present input data in real time. Artificial intelligence is therefore provided permitting adaptive monitoring of the input data in present invention.

The invention discloses a self-adaptive detect method of the gas dissolved in the oil of a drawing transformer, and a relative device, wherein the invention uses a high-density gas sensor array to check the object gas to obtain a first check value, when the first check value is higher than a preset threshold value, using the first check value as the check result, or else, uses a low-density gas sensor array to check the object gas again, using the second check value as the check result. And the invention judges if the ratio between the average value and mean square difference of the check values of all sensors in the array is over a preset value to check the condition of the array. The invention has high measuring accuracy, better repeatability, long service life, and low check cost.

An exemplary method of performing a brake pad wear check includes providing a brake assembly, including a brake pad and a brake rotor, providing a controller electronically connected to the brake assembly, the controller configured to calculate an expected wear of the brake pad, determine a brake pad wear threshold, compare the expected wear of the brake pad to the brake pad wear threshold, and if a first condition is satisfied, perform the brake pad wear check.

The invention relates to a multi-space-scale self-adaptive monitoring method and device for the population growth of crops. The method includes the following steps that surface parameters of the population morphological structure and the population physiological activity of the crops are monitored; remote-sensing parameters capable of estimating the growth are established according to the surface parameters and used for comprehensively and quantitatively representing the population morphological structure and the population physiological activity of the crops; the population growth of the crops is estimated according to the remote-sensing parameters capable of estimating the growth. The remote-sensing parameters capable of estimating the growth are established by combining agricultural knowledge and a vegetation remote sensing response mechanism and can be used for comprehensively and quantitatively representing the population morphological structure and the population physiological activity of the crops, a threshold valve division strategy with the space scale self-adaptive capacity is made according to the remote-sensing parameters capable of estimating the growth, space scale universality and transferability of the quantitative threshold value division method are expanded, and the advantage of comprehensively and quantitatively classifying the population growth conditions of the crops is achieved.

The invention relates to the field of power load monitoring, and particularly relates to a non-intrusive electric energy quality interference source online adaptive monitoring system and method. The non-intrusive electric energy quality interference source online adaptive monitoring system and method are put forward by using the advanced technology achievements of the field of artificial intelligence based on the idea of the pattern recognition technology from the perspective of data mining for aiming at solving the problem of automatic identification of the electric energy quality interference source. According to the method, different types of electric energy quality interferences existing in the system can be automatically monitored and the generation source of the electric energy quality interferences can be identified, i.e. the certain or multiple types of electrical equipment and the condition of generating the certain type of electric energy quality interference can be judged and detected. The beneficial effects of the non-intrusive electric energy quality interference source online adaptive monitoring system and method are that the power system electric energy quality management level can be actually enhanced, the influence of the electric energy quality disturbance on the power system can be reduced and the power utilization satisfaction of the user can be enhanced especially in the level of the power distribution network.

The invention provides an energy utilization abnormity dynamic adaptive monitoring method. The energy utilization abnormity dynamic adaptive monitoring method comprises the steps of setting N deviation value ranges and N monitoring levels in a one-to-one correspondence manner; and reporting an equipment energy utilization condition based on a reporting period corresponding to one of the monitoringlevels: 1), when it is responded to that the deviation value between the monitored actual measurement value and the standard value is within a deviation value range higher than the current monitoringlevel, switching to the monitoring level corresponding to the deviation value; and 2), when it is responded to that the deviation value between the monitored actual measurement value and the standardvalue is within a deviation value range lower than the current monitoring level, and when the monitoring continuous number of times reaches a stability coefficient corresponding to the current monitoring level, switching to the monitoring level which is one level lower than the current monitoring level. By virtue of the energy utilization abnormity dynamic adaptive monitoring method, a short-period high-frequency monitoring reporting mode can be switched directly and quickly when abnormity occurs; and when energy utilization recovers to the normal state, a long-period low-frequency monitoringreporting mode is recovered step by step gradually.