125results about How to "Improve classification ability" patented technology

The invention discloses a wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method. The method comprises the following steps: a rolling bearing vibration signal is obtained, and the acquired vibration signal is subjected to continuous wavelet transformation to obtain a time-frequency diagram; autocorrelation operation is performed on wavelet coefficients corresponding to each frequency on the time-frequency diagram to filter out noise interference and extract periodic fault components; the Hilbert transformation is used to perform envelope demodulation to obtain a fault characteristic frequency; a processed time-frequency diagram is input as a feature diagram, and categories of early faults are determined by training a deep learning classification model. Theoretical and experimental results show that fault categories can be determined accurately at an early stage when weak fault of a rolling bearing occur based on an improved wavelet time-frequency diagram used as the classification model for input training, right determination results can be given when the method is applied to different bearings, high training speed can be realized, and high actual application value can be achieved.

The invention discloses a face recognition method of a deep convolutional neural network, which reduces the time complexity, and enables a weight in the network to still have a high classification capacity under the condition of reducing the number of training samples. The face recognition method comprises a training stage and a classification stage. The training stage comprises the steps of (1) randomly generating a weight wj between an input unit and a hidden unit and an offset bj of the hidden unit, wherein j equals to 1,...,L and represents the number of the weight and the offset, and the total number is L; (2) inputting a training image Y and a label thereof, by using a forward conduction formula hw, b(x)=f(W<T>x), wherein hw, b(x) is an output value, x is input, and an output value hw, b(x<(i)>) of each layer is calculated; (3) calculating the offset of the last layer according to a label value and an output value of the last layer; (4) calculating the offset of each layer according to the offset of the last layer, and acquiring the gradient direction; and (5) updating the weight. The classification stage comprises the steps of (a) keeping all parameters in the network to be unchanged, and recording a category vector outputted by the network of each training sample; (b) calculating a residual error delta, wherein delta=||hw, b(x<(i)>)-y<(i)>||<2>; and (c) classifying a tested image according to the minimum residual error.

The invention discloses a radar scanning-mode sea clutter local multi-fractal target detector, belonging to the field of processing radar signals. The working of the radar in a residing mode is supposed in the existing sea clutter local multi-fractal target detection method, thus having larger dimensionality calculation and computation workload and being lack of instantaneity. The invention can be used for carrying out multi-fractal calculation on the basis that the radar works in a scanning mode, and a primary scanning process is not needed to acquire sufficient sampling points; the calculation of each scanning period dimensionality is updated by an iterative approach, thus ensuring the instantaneity; and the fuzzy characteristics of a local multi-fractal spectrum is extracted to form detection statistic. The detector has favorable capabilities of classifying the sea clutter and the target and detecting weak targets in strong sea clutter, is suitable for tracking target signals into fractal unstable clutter, and has generalization and application value.

The invention discloses a method for structuring a text classifier by carrying out characteristic expansion treatment on text information objects in a training set and a text classifying device applied to the method. Through carrying out the characteristic expansion treatment on texts to classify the texts and carrying out the characteristic expansion treatment on training texts to structure the classifier, the performance of the classifier is improved and harmful information in short text information can be intercepted and filtered. The invention has favorable identification ability and classification ability on short texts and is particularly suitable for the processing of the texts in instant messaging systems such as QQ, MSN and cellphone short messages and the texts in network comments.

The invention provides a micro-expression recognition method based on a difference slice energy diagram and Gabor transformation. The micro-expression recognition method comprises the following steps: constructing a micro-expression sequence difference slice energy diagram, calculating micro-expression frames containing change regions in a micro-expression periodic sequence, graying the micro-expression sequence to extract micro-expression difference slices, and overlapping micro-expression difference slice frame sequences to obtain a micro-expression difference energy diagram; extracting the characteristics of the difference slice energy diagram, constructing a Gabor filter kernel function, performing Gabor characteristic extraction on the difference slice energy diagram, sampling the extracted characteristics, writing the characteristics of each sample in a column vector form, maximizing the quotient of inter-class divergence and intra-class divergence through linear discriminant analysis with supervisory information, and further extracting the characteristics of each sample; performing classification and identification, training a model according to training data, and predicting and classifying test samples according to the model. The method provided by the invention has general applicability. Compared with an existing method, the method provided by the invention has a higher identification rate.

The invention discloses a method for quickly recognizing a front face through video monitoring, which is used for recognizing the most front image frame in one segment of video monitoring. The method comprises the following steps of firstly, extracting front face images from a standard video base and monitoring videos to use as a training positive sample, extracting side face images as a training negative sample, then extracting features of an integral channel, and utilizing an Adaboost algorithm to train a strong classifier from the features. When the front face is recognized, in the inputted monitoring videos, a face is detected by the existing matured face detection algorithm, a detection window is recognized, the images are scored, and the image with highest score is selected, namely the most front face frame. The method has the advantages that the recognizing speed and recognizing precision of the face gesture are improved, the front face is accurately recognized, and the calculation workload of face gesture recognizing is reduced.

The invention discloses a multi-source self-adaptive fault-tolerant federated filtering integrated navigation system and a navigation method. The system comprises a strapdown inertial navigation system, a satellite navigation system, a Doppler velocity measurement system, a celestial navigation system, and a main filter and three sub-filters that are in information connection with the systems respectively; the three sub-filters are all in information connection with the strapdown inertial navigation system, and are connected to the main filter through a fault detection and isolation module, the strapdown inertial navigation system is connected to the fault detection and isolation module through a state transmitter, and an output result of the fault detection and isolation module passes through an information sharing factor calculation module, then the calculation result is input into the main filter; and the main filter outputs the fused information and synchronizes a fusion result tothe three sub-filters and the state transmitter. According to the system and the method disclosed by the invention, the state of each sub-filter can be tracked more accurately, and a more precise fusion result can be obtained.

The invention provides a quality control chart pattern recognition method based on improved genetic algorithm optimization. The quality control chart pattern recognition method comprises the followingsteps: simulating various pattern characteristics of a control chart by using a Monte Carlo method; generating data of a corresponding mode through the parameter values; adopting the PCA principal component analysis method to carry out dimension reduction and denoising on the original data, main features of the data are extracted, shortening the training time of the model and improvintg the recognition accuracy; establishing a probabilistic neural network model, and carrying out pattern classification recognition by utilizing the characteristics of simple structure and convenient training; optimizing a main parameter smoothing factor of the probabilistic neural network by virtue of an improved single-objective optimization genetic algorithm; searching possible abnormal reasons from different aspects according to the identification result.The method solves the problems that all abnormal conditions cannot be monitored and recognized when an existing enterprise carries out quality control, effective abnormal information is difficult to find from a control chart, and appropriate measures cannot be taken to correct the abnormal conditions in the production process.

The invention relates to a multi-label text data feature selection method and device, and belongs to the technical field of text data processing. The multi-label text data feature selection method comprises the steps of firstly considering second-order correlation between marks in a text data set, grouping the marks to enable the marks to be better suitable for a multi-mark data set, determining afinal score of each feature according to scores calculated by the features in each mark group, and selecting a set number of features with higher scores from the final scores to form a feature set; and then, based on the obtained feature set, determining the neighborhood granularity of each sample according to the classification interval of each sample in the text data set for the marks to obtaina multi-mark neighborhood decision-making system, calculating importance by utilizing the dependency degree of the improved neighborhood rough set, and screening the obtained feature set, thereby realizing feature selection of the multi-mark text data. Compared with an original neighborhood rough set feature selection method for all attributes, the multi-label text data feature selection method is lower in time complexity, and the optimal feature subset is more accurate.

The invention provides a heart sound classification method and system based on CNN combined with improved frequency wavelet slice transformation, and the method comprises the steps: carrying out the preprocessing of an obtained heart sound signal, finding the position of each cardiac cycle through a hidden half Markov model, and carrying out the interception of each cardiac cycle signal; converting each intercepted one-dimensional cardiac cycle signal into a two-dimensional time-frequency image by using improved frequency slice wavelet transform; calculating sample entropies of the acquired heart sound signals respectively; comparing the two-dimensional time-frequency image with a preset sample entropy threshold, when the sample entropy of the heart sound signal is greater than the presetsample entropy threshold, performing network training and classification according to the two-dimensional time-frequency image by using a first convolutional neural network, otherwise, performing network training and classification according to the two-dimensional time-frequency image by using a second convolutional neural network; firstly, signals with different interference degrees are distinguished by using sample entropy, and then classification is performed by using different convolutional neural network models for different signals, so that the accuracy of heart sound signal classification is greatly improved.

The invention is suitable for the mode identification technology field and provides a supervised learning optimization method under a tensor mode and a system thereof. The method comprises the following steps of receiving an input training tensor data set; introducing a within-class scattering matrix into a target function so that the target function maximizes a between-class distance and simultaneously minimizes a within-class distance; constructing an optimization framework of a target function of an OPSTM subproblem; constructing an optimization framework of a target function of an OPSTM problem; solving a modified dual problem and outputting a Lagrangian optimal combination and an offset scalar b; calculating a projection tensor w*; calculating an optimal projection tensor w; according to the w and the b, constructing a decision function; carrying out rank decomposition on tensor data to be predicted and then inputting into the decision function so as to carry out prediction. In the invention, problems of a dimension curse, over learning, a small sample and the like generated when a vector mode algorithm is used to process the tensor data are overcome and a time-consuming alternative projection iteration process in an existing tensor mode algorithm is effectively avoided.

The invention discloses an air pollution monitoring system based on NB-IoT technology and edge computing. The air pollution monitoring system comprises a sensing layer, a transmission and processing layer and a platform layer. The sensing layer takes an STM32 processor as a main control chip, collects atmospheric pollutant information by using a laser dust sensor and a gas sensor, and sends monitoring data to an edge computing server through an NB-IoT network base station to be cooperatively processed with a cloud server, so that real-time analysis and processing of atmospheric pollutant data are realized; and the processed data are uploaded to an upper computer, so that the position of a pollution source on a map can be positioned on line, and an early warning function can be realized through a short message mode or a mode of checking pollution indexes in real time through a Web page. According to the invention, a mode of cooperative processing of a plurality of edge computing servers and the cloud server is implemented through NB-IoT wireless communication network transmission, the load of a cloud platform is reduced, the data transmission efficiency and the accuracy of atmospheric pollution data monitoring are improved, acquisition, transmission, analysis processing, early warning and positioning of atmospheric pollution monitoring are realized in an all-around manner, and it is convenient for a user to know the air pollution condition in time.