838 results about "Component analysis" patented technology

The invention discloses a near infrared spectrum analyzing method based on the isolated component analysis and genetic neural network, which comprises the following steps for the acquired near infrared spectrum: firstly, effectively compressing spectrum data by using wavelet transform; secondly, extracting an independent component and a corresponding mixed coefficient matrix of a near infrared spectrum data matrix by using an isolated component analysis method; thirdly, building a three-layer BP neutral network, using the mixed coefficient matrix of a training sample as the input and correspondingly measured component concentration matrix as the output, and optimizing a neutral network structure by adopting a genetic algorithm, and obtaining a GA-BP neutral network by the training of the training sample; fourthly, predicting and analyzing the measured component concentration of the predicted set sample by using the GA-BA neutral network. The method enriches the chemical measurement method, widens the application range of the isolated component analysis and has favorable application prospect.

The invention discloses a BP neural network construction and identification method for identifying different brown rice grains, adopting the following technical scheme: including the following steps: 1) acquiring images: 2) image preprocessing: 3) extracting image feature information of different brown rice grains: 4) main Component analysis reduces dimensionality of image feature information; 5) Design BP neural network structure: 6) Train neural network, and use the BP network neural network constructed by any of the above-mentioned construction methods to identify different brown rice grains. The method proposed by the present invention obtains images of different types of brown rice grains through black as the background, uses image processing technology to obtain its characteristic information, and uses principal component analysis to reduce the dimensionality of the characteristic information, and finally uses BP neural network to perform different types of brown rice grains. identify. The method can identify different types of grains objectively, accurately and quickly, and overcomes the disadvantages of traditional manual detection.

The invention discloses a flood forecasting method based on cluster analysis and real time correction, which comprises the following steps: 1) using PCA(Principal Component Analysis) to perform dimensionality reduction to the input of a model; 2) using the K-means clustering method to conduct clustering analysis on original data; dividing the flood data into different classifications; and then training different SVM models; when a testing sample is inputted, using the clustering center to determine the classification of the test sample and predicting the corresponding model to obtain a predicted value q; and 3) using a BP neural network for real time correction; calculating the error sequence between the predicated value and the actual value; using the error sequence data to train the BP neural network error correction model to obtain the error correction value qe. The final forecasting result is the model predicted value q plus the error correction value qe. According to the invention, the original hydrological data are divided into several classifications by cluster analysis, and through the training of the models, forecasting can be available by the multiple models. Then, real-time correction is achieved by the BP neural network to improve the forecasting accuracy for the time of flood peak.

The invention provides an industrial park atmospheric pollutant diffusion simulating and tracing method. The industrial park atmospheric pollutant diffusion simulating and tracing method specificallycomprises the following steps: (1) based on multi-point pollutant component analysis and concentration real-time monitoring data, analyzing atmospheric pollutant emission characteristics of an emission source, and calculating dynamic emission source intensity; (2) constructing an atmospheric pollutant diffusion model based on a Gaussian smoke plume diffusion theory and a smoke mass diffusion theory, calculating pollutant diffusion distribution characteristics of an emission source according to real-time meteorological data, and predicting the pollutant concentration of sensitive points; and (3) simulating and calculating the influence of the low-rise building within the factory boundary distance of the industrial park on airflow movement, determining a transmission path of emission sourcepollutants, and performing tracing analysis on sensitive point pollutants. The theories of Gaussian diffusion, smoke mass diffusion and computational fluid mechanics are integrated, the problem that the industrial park atmospheric pollutant emission traceability is difficult is solved, and the effective supervision of the industrial park ambient air quality is realized.

The invention discloses a noise diagnosis algorithm for rolling bearing faults of rotary equipment. Firstly, a sound pick-up device collects running noise signals of a rolling bearing, and the signalsare subjected to preliminary fault judgment through a bearing normality and anomaly pre-classification model based on an anomaly detection algorithm; secondly, according to a fault pre-judgment result, the abnormal signals (the faults occur) pass through a neural network filter to filter normal components in the signals of the bearing, the output net abnormal signals are connected to a subsequentfeature extraction module, and the normal signals (no faults occur) are directly connected to the feature extraction module; the feature extraction module extracts Mel-cepstrum coefficients (MFCC) ofthe signals to serve as eigenvectors, feature reconstruction is carried out by utilizing a gradient boosted decision tree (GBDT) to form composite eigenvectors, and principal component analysis (PCA)is used for carrying out dimensionality reduction on features; and finally, feature signals are input into an improved two-stage support vector machine (SVM) ensemble classifier for training and testing, and at last, high-accuracy fault type diagnosis is achieved. According to the algorithm, the bearing faults can be effectively detected and relatively high fault identification accuracy is kept;and the algorithm has relatively high effectiveness and robustness for detection and classification of the bearing faults.

Change in a torque waveform is monitored while removing continuously-varied periodic noise in real time, and the change in the torque waveform caused purely by the wafer state is detected by separating noise components while removing the noise not caused by the wafer state such as drift noise cased by dressing conditions and the polishing pad state, thereby reliably detecting a polishing end point at high precision in the wafer state when polishing is finished. A polishing end point detection device utilizing torque change having a torque measurement means for measuring torque, a periodic component analysis means for analyzing periodic components in data by subjecting Fourier transformation to the measured data, an average processing time calculation means for calculating moving average processing time for removing periodic noise components based on the analyzed periodic components, an average processing means for correcting the waveform by performing averaging process based on the moving average processing time calculated in real time for the data, and an end point detection means for detecting the polishing end point of a predetermined film based on change in the corrected torque waveform is provided.

A method of isolating sources of variance in parametric data includes steps of: (a) cleaning a data set of measurements for a plurality of parameters; (b) generating a principal component analysis basis from the cleaned data set; (c) estimating an independent component analysis model from the principal component analysis basis; (d) calculating percentages of variance for the plurality of parameters explained by each component in the estimated independent component analysis model; (e) if the calculated percentages of variance indicate that a component is a minor component, then transferring control to step (f), else transferring control to step (g); (f) removing the minor component from the principal component analysis basis and transferring control to step (c); and (g) generating as output the estimated independent component analysis model wherein no component of the independent component analysis model is a minor component.

The invention relates to a method for measuring a particle accelerator beam position. The method for measuring the particle accelerator beam position includes: step S1, sampling N electrode signals of N electrodes of a stripe beam position probe through N channels when the beam passes through the probe; step S2, making up an M*N matrix X for the N electrode signals, and analyzing the principal components of the matrix X; step S3, calculating to obtain beam signal induction amplitudes which are corresponding to the N electrodes one by one, and outputting the N beam signal induction amplitudes through the N channels; step S4, comparing the N beam signal induction amplitudes output from the N channels and calculating to obtain a position signal of the beam. According to the method for measuring the particle accelerator beam position, the electrode signals of the electrode probes are formed into the matrix and are processed simultaneously through a principal component analysis method, and accordingly the noise of each electrode signal is eliminated effectively, and the beam position measuring precision is effectively improved.

The invention relates to an ultrasonic scattering coefficient optimal computation method for crack direction recognition, belonging to the field of nondestructive examination. According to the method, an ultrasonic phased array detection system is used for acquiring full-matrix data of a crack defect; firstly, the acquired data are used for performing full-focusing imaging on the defect to determine the position of the defect and then a scattering coefficient space distribution of the crack defect is computed to determine an angle of the crack; the quantity of wafers included in a sub array and the quantity of wafers between adjacent sub arrays greatly affect the measurement precision of the crack angle. A plurality of evaluation indexes are used for evaluating the quantity of a crack angle measurement result according to the quantities of the wafers included in different sub arrays and the wafers between the adjacent sub arrays, and the measurement result is comprehensively evaluated by a main component analysis method to obtain an optimal measurement result; corresponding parameters, namely the quantities of the wafers included in the sub arrays and the quantity of the wafers between the adjacent sub arrays, are optimal detection parameters.

The invention relates to an intrusion detection method and device for an industrial control system, computer equipment and a computer-readable storage medium. The method comprises the steps: extracting an original data set of the industrial control system from a network data set of a communication protocol of the industrial control system; obtaining a training data set and a test data set from theoriginal data set; performing feature dimension reduction on the training data set and the test data set by using a principal component analysis method to obtain a training data set subjected to feature dimension reduction and a test data set subjected to feature dimension reduction; training the training data set subjected to dimension reduction based on an intrusion detection model to obtain aclassification model; and inputting the test data set subjected to feature dimension reduction into the classification model for classification processing to obtain an intrusion detection result of the industrial control system. According to the scheme, the feature dimension reduction is carried out through a principal component analysis method, redundant information is removed, the calculated amount is reduced, and therefore the technical problem that a traditional technology industrial control system intrusion detection method has a long training time is solved.

The invention provides a rapid nondestructive apple wormhole testing method based on a hyperspectral imaging technology. The rapid nondestructive testing method comprises the following steps: collecting information of an apple spectrum by using a hyperspectral imaging system; analyzing the characteristics of an apple spectrum curve; carrying out main component analysis on spectrum data; meanwhile, determining a single-waveband characteristic image according to a characteristic wavelength; separating to extract apples and wormholes in sequence by using a maximum entropy classification threshold value separation method; extracting an interested area of a PC1 (Principal Component 1) image in sequence; extracting textural features and spectrum characteristics of the interested area; and finally, rapidly and nondestructively detecting apple wormholes by using a BP (Back Propagation) neural network. The method has the advantages of high detection speed, and simplicity and convenience in operation and the precision of a detection result is high; furthermore, the apples can be prevented from being damaged.

A multicomponent fluid composition monitoring and compositional control system, in which a component analysis is effected by titration or other analytical procedure, for one or more components of interest, and a computational means then is employed to determine and responsively adjust the relative amount or proportion of the one or more components in the multicomponent fluid composition, to maintain a predetermined compositional character of the multicomponent fluid composition. The system is usefully employed in semiconductor manufacturing photoresist and post-etch residue removal, in which the cleaning medium is a semi-aqueous solvent composition, and water is the monitored and responsively adjusted component.

The invention provides an earthquake early warning method based on Internet of Things multi-sensor information fusion and a neutral network technology, which comprises the concrete steps of firstly performing networking fusion on the multi-sensor information of multiple earthquake parameter monitoring points by the Internet of Things technology, wherein the information comprises soil radon alpha energy spectrum, well radon alpha energy spectrum, temperature in soil, humidity in the soil, air pressure in the soil, surface temperature, surface humidity, surface air pressure, surface wind speed, surface rain fall, well water level, well water temperature, well water turbidity, well water mercury, well water carbon dioxide, earthquake sound, earthquake tilting and the like; then obtaining abnormal information feature vectors by wavelet analysis, a correlational analysis method, a feature tree search method, a non-linear quantifying and processing method and a main component analysis method; and finally obtaining the relation between the multi-sensor information abnormality and an earthquake by the neutral network technology, thus achieving the aim of predicting the earthquake. The technical scheme of earthquake early warning method based on the Internet of Things multi-sensor information fusion and the neutral network technology is feasible and convenient to implement and can achieve the aim of more accurately predicting the earthquake.

The invention discloses a planar array electrode capacitance sensor. A bottom shielding plate, edge shielding frames and inter-electrode shielding strips are arranged at the bottom so that influences, caused by environment noises, on a measurement result are greatly reduced, and the measurement precision is easy to improve. The invention further discloses a planar array electrode capacitance imaging system and imaging method based on the planar array electrode capacitance sensor; a data fusion technology is applied to image processing of a reconstructed image; a detected material is divided into 8 layers by utilizing ANSYS finite element simulation software aiming at the 3*4 planar array electrode capacitance sensor and a sensitivity matrix of each layer is calculated; image reconstruction is carried out by adopting a Landwerb iterative algorithm; in order to fuse and reconstruct a layered image, a main component analysis method is used for calculating main component vectors of the reconstructed layered image and main components are fused according to percentage of a contribution rate; finally, the fused image is obtained; image errors and related coefficients of the image obtained by data fusion are improved when being compared with that of an original image.