The present invention belongs to the technical field of fault diagnosis of aero-engines, and provides a method for fault diagnosis of an aero-engine rolling bearing based on random forest of power spectrum entropy. Aiming at the above-mentioned defects existing in the prior art, a method for fault diagnosis of an aero-engine rolling bearing based on random forest is provided, wherein test measured data for an aero-engine rolling bearing provided by a research institute are used for establishing a training dataset and a test dataset first; and based on an idea of fault feature extraction, time domain statistical analysis and frequency domain analysis are conducted on original collection data by adopting wavelet analysis; thereby realizing effective fault diagnosis from the perspective of engineering application.

A data processing apparatus includes at least one memory, and at least one processor. The at least one processor is configured to obtain, in a first coordinate system, two-dimensional data relating to a position of a predetermined part of an object in an image, calculate three-dimensional data relating to the position of the predetermined part in a second coordinate system based on the two-dimensional data relating to the position of the predetermined part in the first coordinate system, and obtain information indicating an orientation of the object based on the calculated three-dimensional data relating to the position of the predetermined part in the second coordinate system.

The present disclosure provides a distortion-free boundary extension method for online wavelet denoising. The method includes: S1: acquiring a signal segment x.sub.n, and performing a distortion-free boundary extension on the signal segment to obtain M+N+L data; S2: decomposing a lifting wavelet of the N data to be denoised into j layers to acquire approximation coefficients and detail coefficients; S3: calculating a threshold of each layer of the lifting wavelet; S4: thresholding the detail coefficients of each layer to obtain estimated values of the detail coefficients; S5: performing wavelet reconstruction by the approximation coefficients and the estimated values of the detail coefficients obtained by thresholding to obtain a reconstructed signal after denoising; and S6: outputting data.

In a method for the recognition of an object by means of a radar sensor system, a primary radar signal is transmitted into an observation space, a secondary radar signal reflected by the object is received, a Micro-Doppler spectrogram of the secondary radar signal is generated, and at least one periodicity quantity relating to an at least essentially periodic motion of a part of the object is determined based on the Micro-Doppler spectrogram. The determining of the at least one periodicity quantity includes the following steps: (i) determining the course of at least one periodic signal component corresponding to an at least essentially periodic pattern of the Micro-Doppler spectrogram, (ii) fitting a smoothed curve to the periodic signal component, (iii) determining the positions of a plurality of peaks and/or valleys of the smoothed curve, and (iv) determining the periodicity quantity based on the determined positions of peaks and/or valleys.

A method for creating a mother wavelet function. The method includes preparing a plurality of vectors, extracting a kernel from the plurality of vectors, and extracting the mother wavelet function from the kernel. The kernel includes a mode value of a vector of the plurality of vectors.

A method for detecting and predicting an epileptic seizure. The method includes preparing a plurality of electrical signals, extracting a plurality of patterns from the plurality of electrical signals, extracting a plurality of features from the plurality of electrical signals by applying the plurality of patterns on the plurality of electrical signals, optimizing the plurality of patterns and the plurality of features, and classifying each of the plurality of electrical signals in a plurality of classes by applying a plurality of classifiers on the plurality of features. The plurality of electrical signals include a plurality of samples. The plurality of classes include a seizure class and a non-seizure class, and the plurality of classifiers include a plurality of cascaded AdaBoost classifiers.

Aspects of the present disclosure describe systems, methods and structures employing time-space de-noising for distributed sensor.

An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data

Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.

A GIS mechanical fault diagnosis method and the device are disclosed. The method includes: collecting vibration signals to be measured of various excitation sources of GIS in mechanical operation; performing wavelet packet-feature entropy vector extraction on the vibration signals to be measured, when it is determined that the vibration signals to be measured are abnormal according to standard vibration signals in the normal state; inputting the extracted wavelet packet-feature entropy vectors into the pre-trained BP neural network for GIS mechanical fault identification, and outputting the corresponding fault. The disclosure integrates the vibration signals under the action of various excitation sources, extracts the feature entropy vectors according to the entropy theory, and constructs and trains a BP neural network that can classify and recognize various GIS mechanical faults, so as to perform comprehensive and effective GIS mechanical faults diagnose.