100 results about "Drift detection" patented technology

The present leverages data hierarchies to provide a systematic means to determine data differences between equivalent data. This allows disparate data storage systems to efficiently determine divergent data locations by utilizing, for example, data signatures representative of varying degrees of data granularity. Comparative analysis can then be performed between the databases by employing an iterative approach until the desired level of data granularity is obtained. This allows, in one instance of the present invention, discrepant data to be determined without the transfer of large amounts of data and without requiring homogeneous data storage systems. Another instance of the present invention utilizes equivalent logical data views from non-identical data sets to determine data discrepancies. Yet another instance of the present invention determines discrepancies of a federated and/or integrated data system by employing reversible data statistical signatures, providing a simplistic transfer protocol and sheltering each data system from the other's complexities.

A user interest discovering method with an ontology situation blended in comprises the steps that firstly, a user interest characteristic extracting model based on a second-order hidden Markov model is constructed for complex and multi-dimensional Web user interest behavior characteristic data in an e-commerce website; secondly, situation information capable of reflecting user interests is analyzed, wherein the situation information comprises individual information, environment information, device information and the like; thirdly, a user interest model based on situation ontology is constructed, and meanwhile the interest degree of the user individual information is measured and expressed by using the ideology of fuzzy logic; and lastly, a model is established according to user browsing paths and based on a user interest drifting detection method for a hidden semi-Markov model, and the average value of the average logarithm probable probabilities of a sequence is regarded as a threshold value point which is used for judging whether the interests are drifted. According to the user interest discovering method with the ontology situation blended in, the interest model capable of meeting user demands is constructed so as to provide individualized recommendation services and provide an effective means to improve the user satisfaction degree, and the user interest discovering method has good application value.

A computer implemented method for guiding an under-actuated marine surface vessel in tracking a desired trajectory comprises expressing a predetermined vessel trajectory as a set of straight line segments, determining a desired current line segment, and determining the vessel's current position. A cross-track error and the derivative of the cross-track error are then determined. Next, a radius R of the line-of-sight (LOS) circle using a newly introduced exponential function is determined. Intersection points M and N between the LOS circle and the current desired line segment are determined. A desired heading angle is determined as the angle between the line of sight and a predetermined fixed reference line. A drift detection algorithm is built into the proposed guidance scheme to detect situations whereby the ship moves parallel to its desired trajectory for an extended period of time without being able to correct for the cross track error.

The present disclosure is for systems and methods for connecting offline machine learning training systems with online near-real time machine learning scoring systems. It is not trivial to connect an offline training environment with an online scoring environment. For example, offline training environments are usually static and contain large amounts of historical data that is needed for the initial training of models. Once trained, the model algorithms are then migrated into an online scoring environment for transactional or event based scoring. This migration effectively breaks the connection between the data in the offline environment and the model now running in the online environment. When new or shifting data occurs in the online environment, the static model running in the online environment goes unaltered to the changing inputs. The present disclosure solves the issues that are caused by the break in the offline and online environments.

The invention discloses a portal personalized recommendation service system, which introduces an interest model feedback updating mechanism and refers to that: (A) an interest model feedback updating module is added to the A output end of a privacy protection unit; (B) information connection is established between the C output end of the privacy protection unit and a recommended selector; (C) a feedback updating algorithm set is added to a data storage unit, and the feedback updating algorithm set is used for the storage of interest model updating algorithm; wherein, the interest model feedback updating model consists of an interest drifting detection unit and an interest model updating unit. The portal personalized recommendation service system which introduces the interest model feedback updating mechanism solves the problem of interest drifting in the portal personalized recommendation service system, realizes the quick, accurate and stable updating of the interest model of a portal user, and provides for the user a personalized recommendation resource service which is closer to the interests of the user. The portal personalized recommendation service system of the invention can quickly response to the interest drifting of the portal user, thus realizing the efficient and accurate updating of the interest model of the user.

This invention relates to high stable energy filtration digital microscopic imaging receives method and its equipment. It belongs to energy filtration digital microscopic imaging technique field. Drift detection feedback method is used to eliminate the drift of different energy digital space distribution to energy selecting crack caused by high voltage fluctuation or interference of electromagnetic wave. So the energy filtration receive device can keep the filtrated electron energy stable all the time during signal acquisition process. Then energy selecting crack width is adjusted to make energy segment that contain sample excitatory feature spectrum information. So the high space distinguishing and high energy distinguishing energy filtration image can be got after accumulation.

The invention provides a positioning drift detecting method and device. The method comprises the following steps of acquiring the GSV of current time output by a GNSS receiver; based on the elevationangle and the carrier-to-noise ratio of a visible satellite included in the GSV, calculating the number svnum1 of satellites whose elevation angle is greater than a preset elevation angle threshold and the carrier-to-noise ratio is greater than a preset first carrier-to-noise ratio threshold; and determining whether the svnum1 is less than a preset satellite number threshold, and if the svnum1 isless than the threshold, determine that positioning drift is generated at the current time. In the invention, through calculating the number svnum1 of the satellites whose elevation angle is greater than the preset elevation angle threshold and the carrier-to-noise ratio is greater than the preset first carrier-to-noise ratio threshold, when the svnum1 is determined to be less than the preset satellite number threshold, the number of satellites with good satellite signals participating in current positioning calculation is too small, at this time, a positioning position calculated according tothe satellite signal may be drifted, and, in the invention, positioning drift is determined to be generated at the current moment, and the accuracy of the positioning drift detection is ensured.

The invention discloses a temperature sensor drift detection method in a wind-side heat exchanger, a processor and an air conditioner. The method includes: when the air-conditioner is in a standby state, receiving the first temperature value collected by the temperature sensor in the wind-side heat exchanger, and receiving the ambient temperature The second temperature value collected by the sensor; determine the absolute value of the difference between the first temperature value and the second temperature value; determine whether the absolute value is greater than the preset drift temperature difference value, if greater, then determine the wind Drift of the temperature sensor in the side heat exchanger. It can be seen that, when the air conditioner is in the standby state, the present invention can determine whether the absolute value of the difference between the temperature value collected by the temperature sensor in the wind-side heat exchanger and the ambient temperature value is greater than the drift temperature difference by obtaining the ambient temperature value collected by the ambient temperature sensor. value, and then it can be determined whether the temperature sensor drifts, which solves the problem that the existing air conditioner cannot detect the drift of the temperature sensor.

The invention provides an automobile parking point static drift detection method and system. The method comprises the following steps: when an automobile is in a parking state, obtaining a plurality of automobile parking point information according to time sequence, and adding the parking point information into a parking point list; according to the parking point information in the parking point list, determining a logic parking center point and a logic parking range of the automobile; re-obtaining new parking point information of the automobile; according to position coordinates and vehicle speed of the new parking point of the automobile, judging whether the new automobile parking point is within the logic parking range and the vehicle speed is smaller or equal to a preset driving judgment speed; if so, connecting the new automobile parking point with the last two automobile parking points in the parking point list in the obtaining time to form a parking point included angle; and judging whether the parking point included angle is larger than or equal to a preset drift judgment angle, and if so, determining the new automobile parking point as a static drift point. The technical scheme can detect whether the positioning information has automobile parking point drift cases.

The invention discloses a double helix light beam-based sample axial drift detection and compensation method. The method comprises the following steps: performing phase modulation on a collimated laser beam to obtain a double helix illuminating light beam and projecting to a sample to be detected on a three-dimensional nano scanning platform to obtain a reflected light beam; focusing the reflected light beam into a focusing spot with two intensity peak values; receiving the focusing spot by using a photoelectric induction device to obtain spot intensity distribution information; calculating an included angle between a connecting line of the two intensity peak values and the horizontal direction according to the spot intensity distribution information; establishing a calibration function by using the relation of the included angle and axial drift of the sample; when the sample to be detected drifts in axial position, obtaining the axial drift of the current sample according to the calibration function by using the included angle measured in real time and then adjusting the axial position of the three-dimensional nano scanning platform to finish correction of the axial position of the sample to be detected. The invention also discloses a double helix light beam-based sample axial drift detection and compensation device.

The invention discloses a residual depth feature target tracking method for drift detection. Hierarchical features are extracted by using a convolutional neural network; and a residual structure is added into the convolutional neural network and different network layers are connected to realize fusion of shallow and deep features, so that manual design of a feature fusion mode is not required. With the network structure, the feature fusion function is realized automatically; the target and the background are distinguished by the deep features; and the resolution is improved by being compared with that of the traditional feature. When a target position of a current frame is predicted, a strategy of detecting a model drift is put forward and a response strength reduction counter is designed.Counting is carried out by comparing response strengths of adjacent frames; and whether a model drift occurs is determined based on the value of the counter, so that a corresponding model update scheme is used as a remedial measure to realize precise tracking.

The invention discloses a device and a method for the data interception and the time sequence drifting detection of an interface of a synchronous dynamic random access memory. The device comprises a differential signal to single-ended signal converting circuit, a first phase delay circuit and a data interception circuit, wherein the differential signal to single-ended signal converting circuit isconnected to a synchronous transmission interface to receive a differential data interception signal transmitted by the synchronous transmission interface and convert the differential data interception signal into a single-ended data interception signal; the first phase delay circuit is connected to the differential signal to single-ended signal converting circuit and regulates the phase of the single-ended data interception signal to generate a single-ended data interception signal subjected to phase delay; and the data interception circuit is connected to the first phase delay circuit to intercept synchronous data transmitted by the synchronous transmission interface according to the single-ended data interception signal subjected to the phase delay.

The invention discloses a spectrum drifting detection method for an interference type hyperspectral imager. The method comprises the following steps: (1) converting the spectroscopic data of a hyperspectral imager into radiance on entrance pupil position of each wave band to serve as spectrum radiance to be detected; (2) obtaining the radiance on entrance pupil position of satellite height; (3) obtaining radiance on entrance pupil position of each wave band by the response function of the hyperspectral imager to serve as reference spectrum radiance; (4) comparing the positions of the spectrumradiance to be detected and the reference spectrum radiance on an atmospheric absorption peak, determining whether the offset of the spectrum radiance to be detected and the reference spectrum radiance by a judgment function; turning to the step (5) if yes; otherwise, regulating the regulation amount of wave band and bandwidth in the response function of the hyperspectral imager, and circularly judging from step (3); and (5) carrying out inverse fast Fourier transform to the interference data of the hyperspectral imager for the hyperspectral imager spectrum drifting amount determined when a constraint condition is satisfied to obtain a spectrum data graph of the hyperspectral imager, which removes spectrum drifting.

The invention discloses a drifting detection underwater robot device and a control method, and belongs to the technical field of detection underwater robots. A robot main body is of a torpedo-shaped structure; hydrologic detection equipment ADCP (Acoustic Doppler Current Profiler), ranging sonar and an emergency load rejection device are carried at the fore body of a robot; a pressure-resistant cabin is arranged at a middle cabin section; the pressure-resistant cabin is divided into an energy source cabin and a control cabin; two high-energy density lithium batteries are arranged in the energysource cabin and are correspondingly used for supplying power to a power system and a control system; a conformal antenna (comprising Beidou positioning and communication, radio and WiFi), DVL, a depthometer and ranging sonar are carried at the tail section; high-efficiency propellers are arranged on the left side and the right side of the tail of the robot; and vertical channel propeller is arranged at each of the front and rear of the robot. According to the surrounding environment and task instructions of the robot, the effect of selecting to start or close a drifting mode is intelligentlyrealized, so that low-power-consumption, long-voyage and long-time detection monitoring tasks are realized.

The invention relates to a novel real-time detection method and a device of beam displacement drift and angle drift. The invention combines defocus grating, lenses and an image sensor, integrates angle drift detection and displacement drift detection and isolates the angle drift information and the displacement drift information of incident beams through the position changes of light spots of the image sensor. According to relative changes of positive and negative first-order light spots which form images on an imaging plane, the invention calculates the displacement drift information of the incident beams by computer programming and isolates the angle drift information of the beams according to the changes of the entire imaging light spots. The invention has the advantages of convenient operation, high measuring accuracy, wide application range and low cost, etc.

The invention relates to a high-sensitivity strip drift detection device based on an array infrared technology. The high-sensitivity strip drift detection device comprises an infrared emission device, an infrared receiving device and auxiliary calibrating devices, wherein the infrared emission device and the infrared receiving device are fixed at the two ends of an edge of a conveyed strip respectively; the fixing positions of the infrared emission device and the infrared receiving device correspond to each other; and the auxiliary calibrating devices are positioned on the two sides of each of the infrared emission device and the infrared receiving device. Due to the introduction of the infrared calibrating devices, the mounting process is simple and easy to implement, the calibrating operation is simple, convenient and intuitive, and the working load and the working strength for mounting and calibrating are greatly reduced; due to the introduction of a differential array, the measurement resolution ratio, the response speed and the sensitivity level of a system can be greatly increased with lower cost; and a pulse drive unit based on a complex frequency is designed in the infrared emission device, and an automatic gain control AGC unit is introduced into the infrared receiving device, so that the robustness, the reliability, the stability, the precision, the sensitivity and the anti-jamming capability of the device are greatly improved and the purpose of zero drift is achieved.

The invention discloses a drifting detection method for a temperature sensor in an outdoor unit heat exchanger, a processor and an air conditioner. The method comprises the steps that when the air conditioner is in a startup state, the pressure values, collected by pressure sensors, of refrigerating fluid in an air conditioner pipeline are received, and the temperature value collected by the temperature sensor in the outdoor unit heat exchanger is received; based on the received pressure values, the saturation temperature values corresponding to the pressure values are determined, and the absolute value of the difference between the received temperature value and the saturation temperature values is determined; and whether the absolute value is larger than a preset drifting temperature difference value or not is judged, and if yes, it is determined that the temperature sensor in the outdoor unit heat exchanger drifts. When the air conditioner is in the startup state, the pressure values of the refrigerating fluid in the air conditioner pipeline are obtained, and therefore whether the absolute value of the difference between the temperature value collected by the temperature sensor in the outdoor unit heat exchanger and the saturation temperature values corresponding to the pressure values is larger than the drifting temperature difference value or not can be judged, then whether the temperature sensor drifts or not can be determined, and the problem that an existing air conditioner cannot detect drifting of a temperature sensor is solved.

The invention provides an user interest drift detection method and system based on network structure, comprising the following steps: constructing a global item network GIN; detecting whether the user is noise user or not; using the cutting algorithm based on consistency after removing the noise user and detecting whether the user interest drift generates or not and the searching the drift position. The detection method greatly reduces the calculation amount and realizes the quick detection of the user interest drift and prevents the noise user from influencing the stability of a recommendation system.

The invention provides a threshold voltage drift detection method and a threshold voltage drift detection device. The threshold voltage drift detection method is applied to a pixel driving circuit, and the pixel driving circuit is electrically and separately connected with a control line, a voltage line and a detection node. The detection period comprises a setting stage and a detection stage, andthe threshold voltage drift detection method comprises the following steps of at the setting stage, controlling a transistor included in the pixel driving circuit to be in a bias state; and at the detection stage, providing a predetermined control voltage signal for the control line to provide a predetermined voltage signal for the voltage line, and judging the threshold voltage drift state of the transistor according to the potential of the detection node. According to the present invention, the bias temperature stress test can be directly carried out, and the threshold voltage drift state of the transistor included in the pixel driving circuit can be detected.

The invention provides a dynamic intuitionistic fuzzy cognitive map construction method, a time sequence prediction method and a time sequence prediction system. The dynamic intuitionistic fuzzy cognitive map construction method comprises the following steps: clustering standardized time sequence data into nodes of an intuitionistic fuzzy cognitive map by utilizing fuzzy C-means clustering; training a weight matrix of the intuitionistic fuzzy cognitive map to obtain an initial intuitionistic fuzzy cognitive map; receiving new standardized time sequence data and inputting the new standardized time sequence data into the initial intuitionistic fuzzy cognitive map; adjusting the position of a clustering center of the intuitionistic fuzzy cognitive map by utilizing dynamic fuzzy C-means clustering to obtain a dynamic intuitionistic fuzzy cognitive map; in the process of adjusting the position of the cluster center of the intuitionistic fuzzy cognitive map, judging whether the data at the current moment has concept drift or not by utilizing drift detection if the data at the current moment successfully fall into an existing cluster and an error between a real value solved by utilizing the adjusted intuitionistic fuzzy cognitive map and a predicted value is greater than a preset error threshold value and if so, adjusting a weight matrix of the intuitionistic fuzzy cognitive map; otherwise, not changing the intuitionistic fuzzy cognitive map.

The invention discloses a field spraying droplet drift test method and a drift droplet receiving device. The test method is characterized by comprising steps: multiple test points are distributed in a downwind direction area in a plant protection machinery pesticide spraying target area; more than one hanging ropes in a straight pull state is arranged in each test point for absorbing droplets; an aqueous solution containing a fluorescent tracer of a certain condensation is used for replacing pesticide to be put in a pesticide spraying box of the plant protection machinery; after the plant protection machinery completes spraying on the target area, the hanging rope is got back; and through detecting the information of the fluorescent tracer on the hanging rope in each test point, the drift deposition amount of the sprayed droplets is calculated and analyzed. The field spraying droplet drift test method is easy to execute, the operation is convenient, the droplet drift test working efficiency can be effectively improved, and promotion and use are facilitated.

The invention discloses an industrial big data-based equipment residual life prediction method and system and a medium, and the method comprises the following steps: collecting the real-time operation data of equipment, carrying out the feature extraction and feature selection of the real-time operation data, and obtaining the data features; performing data drift detection according to the data features, and determining the health stage of the equipment; and obtaining a corresponding residual life prediction model according to the health stage, constructing a health factor according to the data features, inputting the health factor into the obtained residual life prediction model, and outputting an equipment residual life prediction result. According to the operation data generated in real time in industrial actual production, the health factor of the equipment is acquired, and the residual service life of the equipment at the current moment is predicted by using the health factor, so that the complete life cycle of the equipment can be more utilized, the service life waste and the time waste caused by sudden shutdown are reduced, the equipment health management is automatically carried out. The method can be widely applied to the field of prediction of the residual life of the equipment.

The invention relates to the technical field of optical communication, and discloses a method for OLT (optical line terminal) loopback detection in a GPON (Gigabit-Capable PON) system. The method comprises the following steps: S1, initializing a receiving and packaging processing task of a CPU of OLT equipment, performing message receiving, recognizing whether a received message is a two-layer study mac address drifting message, going to S2 if yes, and finishing if not; S2, initializing a two-layer study processing task of the OLT equipment, and copying the message to the CPU of the OLT equipment under the situation of enabling the two-layer study mac (Media Access Control) address drifting detection loopback; S3, performing polling whether the two-layer study mac address drifting message is received, going to S4 if yes and repeating S3 if not; S4, performing alarm processing. The invention further discloses a system for OLT loopback detection in the GPON system. The method and the system disclosed by the invention can be used for effectively detecting loopback in the GPON system.

The invention relates to a distributed type node drift detection method and a distributed type node drift detection device and belongs to the technical field of wireless sensor network. The distributed type node drift detection method includes: firstly, collecting RSSI (received signal strength indicator) data of communication of beacon nodes and the rest beacon nodes according to set time intervals; calculating RSSI variation of the neighboring sampled beacon nodes to acquire a time sequence G of the RSSI variation of the beacon nodes and then performing linear fitting on the time sequence G of the RSSI of the beacon nodes to acquire a fitted regression line of the timing sequence of the RSSI variation; finally, judging whether difference value between the fitted value on the fitted regression line of the timing sequence of the RSSI variation of the beacon nodes is greater than the set threshold and corresponding actual value is greater than a set threshold or not, if yes, determining that the corresponding beacon nodes shift. The beacon nodes possibly shift can be judged out automatically by calculating the RSSI variation at different time, simpleness is achieved, and high practicability is realized.

The invention discloses a drifting detection method of distributed beacon nodes in a wireless sensor network. The drifting detection method of the distributed beacon nodes in the wireless sensor network comprises a scoring mechanism and a negotiation mechanism which are adopted by each beacon node, wherein the scoring mechanism observes based on signal strength among the beacon nodes, and the negotiation mechanism detects drifting among the beacon nodes. In the aspect of the scoring mechanism, by mutually observing changes of received signal strength indication (RSSI) between each beacon node through the each beacon node, changed row vectors of the RSSI and unchanged row vectors of the RSSI are calculated by the scoring mechanism, and then scoring is conducted by the scoring mechanism; and in the aspect of the negotiation mechanism, scoring results are mutually informed by neighbor beacon nodes, each beacon node adjusts self-scoring-results according to scoring results of the neighbor beacon nodes, and finally adjusts whether the each beacon node drifts or not according to final scoring results. The drifting detection method of the distributed beacon nodes in the wireless sensor network is low in communication overhead, and gives consideration to arithmetic speeds and result precision.