382 results about "Gaussian process regression gpr" patented technology

The invention provides a fuzzy curve analysis based soft sensor modeling method using time difference Gaussian process regression, it is suitable for application in chemical process with time delay characteristics. This method can extract stable delay information from the historical database of process and introduce more relevant modeling data sequence to the dominant variable sequence. First of all, the method of fuzzy curve analysis (FCA) can intuitively judge the importance of the input sequence to the output sequence, estimate the time-delay parameters of process, and such offline time-delay parameter set can be utilized to restructure the modeling data. For the new input data, based on the historical variable value before a certain time, the current dominant value can be predicted by time difference Gaussian Process Regression (TDGPR) model. This method does not encounter the problem of model updating and can effectively track the drift between input and output data. Compared with steady-state modeling methods, this invention can achieve more accurate predictions of the key variable, thus improving product quality and reducing production costs.

The invention relates to a condition monitoring data stream anomaly detection method, in particular to a condition monitoring data stream anomaly detection method based on an improved gaussian process regression model. The problem that an existing method for processing monitoring data stream anomaly detection is poor in effect is solved. The method comprises the steps that firstly, the historical data sliding window size is determined; secondly, the types of a mean value function and a covariance function are determined; thirdly, the hyper-parameter initial value is set to be the random number from 0 to 1; fourthly, q data closest to the current time t are extracted; fifthly, the gaussian process regression model is determined; sixthly, prediction is conducted by means of the nature of the gaussian process regression model; seventhly, PI of normal data at the time t+1; eighthly, monitoring data are compared with the PI; ninthly, whether the real monitoring data need to be marked to be abnormal or not is judged; tenthly, beta (xt+1) corresponding to the monitoring value at the time t+1 is calculated; eleventhly, the real value or prediction value and the t+1 are added into DT; twelfthly, new DT is created. The condition monitoring data stream anomaly detection method based on the improved gaussian process regression model is applied in the field of network communication.

The invention discloses a mobile robot autonomous cruise method for reliable WIFI connection. The method comprises the following steps that 1, a robot traverses the whole environment through autonomous exploration navigation, and a WIFI two-dimensional distribution probabilistic model is built according to WIFI signal intensity data of limited measuring points at the access position by means of a Gaussian process regression model; 2, environmental grid maps are built at the same time and mixed with WIFI signal intensity distribution to generate a mixed map, namely a WIFI map; 3, the built WIFI map is utilized for carrying out obstacle avoidance navigation, and therefore it is ensured that the path through which a robot passes bypasses the WIFI signal weak area while the optimal path obstacle avoidance navigation is achieved. The WIFI signal distribution of the whole indoor room can be estimated only based on the data of the limited WIFI signal intensity measuring points through a machine learning algorithm, and the method is applicable to application occasions with high requirements for real-time wireless network connection in remote mobile robot cruise monitoring.

The invention discloses a Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries, relates to a method for predicting the SOH of the lithium batteries, belongs to the fields of electrochemistry and analytic chemistry and aims at the problem that the traditional lithium batteries are bad in health condition prediction adaptability. The method provided by the invention is realized according to the following steps of: I. drawing a relation curve of the SOH of a lithium battery and a charge-discharge period; II, selecting a covariance function according to a degenerated curve with a regeneration phenomenon and a constraint condition; III, carrying out iteration according to a conjugate gradient method, then determining the optimal value of a hyper-parameter and bringing initial value thereof into prior distribution; IV, obtaining posterior distribution according to the prior part; V, obtaining the mean value and variance of predicted output f' without Gaussian white noise; and VI, together bringing the practically predicted SOH of the battery and the predicted SOH obtained in the step V into training data y to obtain the f', then determining the prediction confidence interval and predicting the SOH of the lithium battery. The method provided by the invention is used for detecting lithium batteries.

The present invention discloses a short-term load prediction method based on variable selection and Gaussian process regression. The method includes the following steps that: 1) bad data elimination, supplementation and normalization pre-processing are performed on sample data; 2) candidate input variables are selected from the perspectives of historical load, temperature and humidity, and the date type of a prediction date, and the scores of the importance of the variables are calculated through a random forest algorithm, and the scores of the importance of the variables are sequenced; 3) an optimal variable set is determined through adopting a sequence forward search strategy and based on a Gaussian process regression model; 4) the Gaussian process regression model is trained based on the determined optimal variable set, and the parameters of the model are optimized based on improved particle swarm optimization; and 5) the predictive performance of the model is verified in a test set. With the method provided by the invention adopted, prediction accuracy can be effectively improved, and the load prediction problem of a power system can be solved.

The invention discloses a Gaussian process modeling based wind turbine shafting state monitoring method in the field of wind turbine state monitoring. The technical scheme includes: collecting values of normal temperature of a bearing to be monitored and of correlated variables of the bearing temperature from historical data of a wind turbine SCADA system to form a bearing temperature vector set; building a bearing temperature model by the aid of a Gaussian process regression method; using the bearing temperature model for monitoring the bearing in real time, and using difference between the measured bearing temperature and the predicated temperature outputted by the model as predicated model residual; comparing the predicated model residual with a set residual threshold, and when the predicated model residual is larger than the residual threshold, judging the bearing to be abnormal; and otherwise, judging the bearing to be in a normal state. The method has the advantages that under the operation conditions of random changing of wind speed and time varying of rotating speed of a wind turbine shafting, states of bearings on the wind turbine shafting are analyzed and judged accurately, bearing fault alarm is sent timely, and maintenance complexity and cost are lowered.

Described herein is a method and system for characterizing in-ground rock types from measurement-while-drilling data in a mining environment. The method includes the steps of drilling holes at a plurality of selected locations within a region of interest; collecting measurements while drilling to obtain an array of data samples (162) indicative of rock hardness at various drilling depths in the drill hole locations; obtaining a characteristic measure (163) of the array of data samples; performing Gaussian Process regression (164) on the characteristic measure; and applying boundary detection (166) to the rock hardness output data obtained from the Gaussian process model to identify the distribution (280) of at least one cluster of rock type within the region of interest.

The invention discloses a JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression). The method is used for a complex and changeful multi-stage chemical process and is a multi-model strategy which is continuously updated online; a Gaussian mixture model is adopted to identify different stages of the process, and a self-adaptive learning method is adopted to continuously update an established GPR model; when new data arrive, partially similar data are selected based on Euclidean distance and angle principle at each stage and used for establishing a partial GPR model; finally, new data obtained through calculation belong to posterior probability of each stage, and the partial model is subjected to fusion output. According to the method, key variables can be predicated accurately, so that the product quality is improved, and the production cost is reduced.

The invention discloses a gaussian process regression method for predicting a network security situation in the technical field of network information security. According to the invnetion, a hierarchical network security situation evaluation index system is structured by using an analytic hierarchy process; the damage degree of various network security threats to the network security situation is analyzed by the system so as to calculate a network security situation value of each time monitoring point and structure a time sequence and then structure into a training sample set; the training sample set is subjected to iterative training by utilizing gaussian process regression so as to obtain a prediction model meeting an error requirement; an optimal training parameter of the gaussian process regression is dynamically searched by utilizing an particle swarm optimization in the training process so as to reduce a prediction error, and finally the prediction of the network security situation value of the time monitoring point in the future is finished by utilizing the prediction mode. The gaussian process regression method provided by the invnetion has the beneficial effects of better adaptability and lower prediction error in the respect of reducing the prediction error of the network security situation.

The invention provides an SOC online measurement method for a lithium battery based on the Gaussian mixture process and the dynamic OCV correction. According to the invention, the Gaussian mixture regression (GMR) process is integrated with a Gaussian mixture model and a Gaussian process regression model, so that the time series of dynamic non-linearity can be effectively represented. The dynamic OCV correction method can calibrate an OCV-SOC curve according to external factors, so that the accurate OCV is obtained. Therefore, the SOC is corrected, and the accumulative error is eliminated. In this way, a battery model can be updated in real time according to the appropriate algorithm difficulty under the complex working condition of an automobile. Meanwhile, battery characteristics can be accurately tracked and the accumulated estimation error is corrected. The long-term precision is guaranteed.

The invention discloses a method for predicting a bearing fault based on Gaussian process regression. The method comprises the following five steps of: step 1, setting prediction system parameters, initializing a Gaussian process regression model; step 2, collecting a bearing vibration signal regularly, extracting characteristics of a vibration signal to obtain time domain characteristic parameters of the bearing vibration signal, and carrying out fault symptom judgment; step 3, judging whether a fault symptom exists; step 4, calculating and storing the characteristic parameters, and carrying out dynamic updating of the Gaussian process regression model; and step 5, predicting the fault of a bearing. According to an actual use condition of a product, small amount of data is collected, time that the product possibly has the fault is given quantificationally, a calculation speed and prediction accuracy are improved by using the Gaussian process regression, a whole life cycle of the bearing is divided into three time ranges, such as a health time range, a sub-health time range and a fault time range by use of an idea of health management, fault prediction is carried out in the sub-health state, usage management capacity of the bearing is improved.

The present invention discloses an auto building method and system of a Wi-Fi position fingerprint map. The method comprises the steps of acquiring crowdsourcing data, building the Wi-Fi position fingerprint map by using a pedestrian navigation reckoning method and a machine learning method, and screening according to an indoor map to obtain Wi-Fi strength information with a position tag; and using the Wi-Fi strength information with the position tag as a training sample of a Gaussian process, so as to obtain a function relationship between the signal strength and the position information, resolving a super parameter in the Gaussian process, and predicting according to the super parameter so as to obtain Wi-Fi strength information of a poor constraint area of the indoor map. Regression is performed by using the Gaussian process, the Wi-Fi position fingerprint of a wide area is predicted based on the Wi-Fi position fingerprint information of a high constraint area of the map, so that the Wi-Fi position fingerprint map of the whole indoor area is built automatically.

The invention discloses a short-term wind speed prediction method of Gaussian process regression and particle filtering, thereby realizing on-line dynamic detection and correction of abnormal values and improving wind speed prediction accuracy. According to the method, an input variable set having the highest correlation with a wind speed value at a to-be-predicted time is determined by using a partial autocorrelation function, a state vector is determined, and a proper training sample set is constructed; a Gaussian-process-regression-based short-term wind speed prediction model is establishedin the training sample set and a fitting residue during the training process is given; on the basis of combination of the state vector and the Gaussian process regression model, a particle filteringstate space equation is established and state estimation is carried out on a current measurement value by using a particle filtering algorithm; and the estimation value and the measurement value residual of particle filtering are analyzed, determination is carried out based on a 3 sigma principle, and an abnormal value is corrected. According to the method provided by the invention, the abnormal value can be detected and corrected effectively; the short-term wind speed prediction precision is improved; and a wind speed prediction problem of the power system is solved.

The invention discloses an estimation method and device of a lithium battery health status, and a storage medium. The method comprises the following steps: serving multiple health indicators (HI) corresponding to multiple effective charging cycles as input variables, and multiple health statuses (SOH) corresponding to multiple effective charge cycles as output variables, and training to obtain a Gaussian process regression GPR model, wherein the GPR model is obtained by using multiple groups of data through the machine learning training, each group of data in multiple groups of data comprisesthe HI of each effective charge cycle and the SOH corresponding to the HI; acquiring the HI of the to-be-detected charge cycle, inputting the HI into a GPR model, and enabling the GPR model to outputthe SOH corresponding to the HI. The technical problem that the method for detecting the health status of the lithium battery is comparatively complex and hard to adapt to the collection data with badquality in the related technology is solved.

The invention discloses a layered integrated Gaussian process regression soft measurement modeling method used for a complex changeable multi-stage chemical process. The layered integrated Gaussian process regression soft measurement modeling method is an on-line multi-model strategy. A Gaussian mixture model is employed to identify different stages of the process, principal component analysis is carried out on data in each stage, on the basis of the contribution degree of each auxiliary variable in the principal element space, data in each mode is divided into several subspaces, and a corresponding Gaussian process regression soft measurement model is established. When new data comes around, variable selection is carried out by means of subspace PCA, and on the basis of the soft measurement model which is established off line, the prediction output of each model can be obtained. By carrying out mean value fusion on outputs of subspace models, first layer integrated output, i.e., local prediction output in each mode can be obtained, finally new data obtained according to calculation is attached to the posterior probability of each different stage, and local prediction in each mode is fused by means of the posterior probability to obtain second layer integrated output. Key variables can be accurately predicted, and therefore the product quality is improved, and the production cost is reduced.

The invention discloses a partial least squares-based Gaussian regression soft measurement modeling method. The method can be applied to industrial processes with relatively strong time-varying characteristic, coupling, nonlinearity, hysteresis and other complex characteristics. The method comprises the following steps of: firstly, carrying out dimensionality reduction on multi-element input dataon the basis of a partial least squares method, and selecting proper score vectors as input of a Gaussian process regression model; secondly, selecting and combining covariance functions, and constructing different types of Gaussian process regression soft measurement models to predict output data; and finally, evaluating prediction ability of the models by using test set data. Modeling results ofpaper-making wastewater treatment process data prove that a partial least squares-based dimensionality reduction technology for measured variables can improve the prediction ability of the Gaussian process regression model; and the Gaussian process regression models constructed by different covariance functions provide multiple options for effluent indexes, so that the method is more suitable forcomplex and changeable paper-making wastewater treatment environment.

The invention relates to a qualitative and quantitative combination water quality monitoring method which can quickly and effectively analyze the reflectivity of a remote sensing image spectrum. By the use of an abnormity detection method for a support vector data description, pixel points of polluted water in a high-resolution image can be quickly identified to judge the distribution of the polluted water from the qualitative angle so as to obtain a polluted water quality analysis result; furthermore, compared with a conventional empirical method, a band difference value Gaussian process regression method is higher in model forecasting precision; by the use of the method, overproof water quality parameters can be automatically analyzed from the qualitative angle to supply reliable basis to water pollution treatment; meanwhile, homemade GF-1 WFV data and HJ-1A HSI data which are used in the monitoring method disclosed by the invention are low in cost and short in interval period and can meet the requirement for continuous and stable operation of development of environmental remote sensing service.

The invention provides a background repairing method of a road scene video image sequence. The method comprises the steps that first, an optical flow diagram of a current frame to the adjacent frame is calculated, then based on Gaussian process regression, splashes in a lost optical flow diagram area are calculated, and the splashes are used for initialization of optical flow prediction; after the initialization of the optical flow prediction, then three layers of BP neural networks are used for achieving row-by-row optical flow prediction; then image repairing is conducted, in the image repairing stage, the optical flow initialization and optical flow prediction are used for enabling the pixels in a current frame lost area to correspond to an adjacent frame background area, and a Gaussian mixture model is used for achieving image repairing. According to the video image sequence under a movement background condition, corresponding of the current frame lost foreground area pixels to the adjacent frame background pixels is achieved based on optical flow information, image repairing of the movement foreground lost areas in the road scene video image sequence is effectively achieved, and the method is simple and effective.

A method and system for suppressing bone structures based on a single x-ray image is disclosed. The bone structure suppressing method predicts a soft-tissue image without bone structures from an input x-ray image. A set of features is extracted for each pixel of the input x-ray image. A soft-tissue image is then generated from the input x-ray image using a trained regression function to determine an intensity value for the soft-tissue image corresponding to each pixel of the input x-ray image based on the set of features extracted for each pixel of the input x-ray image. The extracted features can be wavelet features and the regression function can be trained using Bayesian Committee Machine (BCM) to approximate Gaussian process regression (GPR).

The invention discloses an image super-resolution reconstruction method based on a multi-core gaussian process regression and mainly solves the problems that the current super-resolution reconstruction method generates edge sawtooth effect and the reconstruction texture is not rich. The image super-resolution reconstruction method based on the multi-core gaussian process regression comprises the following steps: (1), obtaining a low-resolution luminance image and an interpolation image and blocking the low-resolution luminance image and the interpolation image; (2), extracting central pixels and eight neighborhoods of low-resolution luminance image blocks to train an upper sampling model of the gaussian process regression; (3), forecasting pixel values of initial high-resolution luminance image blocks by using the upper sampling model; (4), combining all the initial high-resolution luminance image blocks to obtain an initial high-resolution luminance image; (5), obtaining an analog low-resolution image and blocking the analog low-resolution image; (6), extracting central pixels of the analog low-resolution image blocks to train a deblurring model of the gaussian process regression; (7), forecasting pixel values of the high-resolution luminance image blocks by using the deblurring model; and (8), combining all the high-resolution luminance image blocks to obtain a high-resolution luminance image. The image super-resolution reconstruction method based on the multi-core gaussian process regression is applicable to video monitoring and imaging of high-definition televisions.

The invention discloses a semi-supervised Gaussian process regression soft measurement modeling method on the basis of improving a self-training algorithm. The method is used for chemical process soft measurement modeling of a training dataset with a deletion primary variable. The method comprises the steps that deleted primary variable samples are estimated through the self-training algorithm, according to the influence of obtained estimation samples on original training data, samples high in generalization capacity are screened out and added into an original sample set, and then a new training sample set is formed to conduct modeling. By means of the method, on one hand, effective screening of estimation samples is achieved, and the semi-supervised model precision is improved; on the other hand, screening rules are simple, an entire data set does not need to be divided, and limitation of the model structure does not exist. Accordingly, the product quality can be improved, and the production cost can be lowered.

The invention discloses a Bayes Regression-based Radio Map correction method in WiFi (wireless fidelity) indoor location. The method comprises the following steps of A, performing a location request: WiFi equipment sends a location request, searches a power fingerprint and sends to a location server; B, performing position estimation: the location server compares the current power fingerprint with the power stored in the Radio Map, and gives the current WiFi power fingerprint value to predict the position of the current node; C, performing precision adjustment: performing online dynamic correction on the Radio Map by using a Bayes Regression algorithm, reducing a power standard value to the precision of the meter level by Gaussian process regression and iteration and converting to the standard difference of the position error; D, performing location reply: the location server sends the standard difference of the prediction position and the position error to a location requesting party by a WiFi network. According to the method, the hardware investment and location delay are reduced, and a more reliable prediction result is provided for the location object.

The invention discloses a terrain-aided inertial integrated navigational positioning method of a low-cost underwater vehicle. The method comprises the steps that 1, an integrated navigational positioning system is initialized; 2, a linear discrete state equation taking underwater vehicle position errors as state variables is established; 3, a nonlinear discrete measurement equation taking a water depth value as an observed variable is established through Gaussian process regression; 4, a particle filtering algorithm of a nonlinear integrated navigation system is established; 5, a position error is calculated, the position of a strapdown inertial navigation system is corrected, position parameter update of integrated navigation is completed, and accurate positioning of underwater vehicle integrated navigation is achieved. The terrain-aided inertial integrated navigational positioning method of the low-cost underwater vehicle has the advantages of being simple in algorithm, accurate in modeling, high in positioning accuracy and the like, and a novel integrated navigational positioning scheme which takes the strapdown inertial navigation system as the primary role and takes terrain navigation as the subsidiary role is provided for equipping the low-cost underwater vehicle for a low-resolution nautical chart and a single-beam depth finder.