Zhejiang University

Jiming Chen

Zhiguo Shi

Chengwei Zhou

Hang Zheng

Shibo He

Yong Wang

Peng Cheng

Yuhao Chen

Zijun Que

Zhengguo Xu

Chao Li

Qinmin Yang

Wenhai Wang

Yu Liu

Youxian Sun

Junfeng Wu

Yunong Bao

Guangyang Zeng

Yong Xiong

Jieqiang Wei

Guozhen Gao

Mincheng Wu

Ruiwei Yu

The present invention provides a method for predicting remaining useful life of bearings based on a gated recurrent neural network, comprising the following steps: S1. obtaining full life cycle vibration signals of bearings, extracting the vibration distribution features and creating a training set of gated recurrent neural network; S2. constructing a gated recurrent neural network model, and introducing an attention mechanism that directly calculates weights to improve the integrity of extracting temporal information; S3. adding a Bayesian layer to construct a nonlinear mapping relationship between temporal information and remaining useful life; S4. taking vibration signals of a test bearing as input, the output result of the gated recurrent neural network model is the remaining useful life of the test bearing at the current time. The present invention does not need to add additional neural network layers, which avoids the problem of increasing the complexity of the model. The integrity of extracted information is improved through weighted fusion of temporal information extracted at different time. Moreover, by adding a Bayesian layer, the traditional point prediction results are converted into interval predictions, to consider the prediction uncertainty of remaining useful life of bearings.

Method for predicting remaining useful life of bearings based on a gated recurrent neural network

Disclosed in the present invention is a two-dimensional direction-of-arrival estimation method for a coprime surface array based on virtual domain tensor filling, which mainly solves the problems of the loss of multi-dimensional signal structural information and the inability to fully utilize virtual domain statistics in the existing method. The steps thereof are as follows: constructing a coprime surface array; modeling a tensor of a received signal of the coprime surface array; constructing an augmented non-continuous virtual surface array based on cross-correlation tensor transformation of the coprime surface array; deriving a virtual domain tensor based on mirror extension of the non-continuous virtual surface array; dispersing contiguous missing elements by reconstructing the virtual domain tensor; filling the virtual domain tensor based on the minimization of a tensor kernel norm; and decomposing a filled virtual domain tensor to obtain a direction-of-arrival estimation result.

Two-dimensional direction-of-arrival estimation method for coprime surface array based on virtual domain tensor filling

The disclosure provides a method for estimating a direction of arrival of an L-type coprime array based on coupled tensor decomposition. The method includes: constructing an L-type coprime array with separated sub-arrays and modeling a received signal; deriving a fourth-order covariance tensor of the received signal of the L-type coprime array; deriving a fourth-order virtual domain signal corresponding to an augmented virtual uniform cross array; dividing the virtual uniform cross array by translation; constructing a coupled virtual domain tensor by stacking a translation virtual domain signal; and obtaining a direction of arrival estimation result by coupled virtual domain tensor decomposition. The present invention makes full use of the spatial correlation property of the virtual domain tensor statistics of the constructed L-type coprime array with the separated sub-arrays, and realizes high-precision two-dimensional direction of arrival estimation by coupling the virtual domain tensor processing, which can be used for target positioning.

Method for estimating direction of arrival of an L-type coprime array based on coupled tensor decomposition

Disclosed in the present invention is a contact tracing pre-screening method for infectious disease susceptible people based on WiFi matching. In the method, based on WiFi connection records of a user and a list of anonymous identification codes of confirmed users collected by a mobile device, a judgment on whether a user has an infection risk is given through the steps of record matching, information compression, dangerous WiFi database construction, coincidence rate calculation between the user and a dangerous WiFi, etc. Data required by this method is easy to be obtained for general smart mobile devices, and no special application program is required. Compared with traditional contact tracing methods based on GPS and Bluetooth, this method provides another dimension of information without using an additional device and sensitive data, has higher operating efficiency, and can help to carry out contact tracing more comprehensively and efficiently.

Contact tracing pre-screening method for infectious disease susceptible people based on WiFi matching

The present disclosure discloses a three-dimensional co-prime cubic array direction-of-arrival estimation method based on a cross-correlation tensor, mainly solving the problems of multi-dimensional signal structured information loss and Nyquist mismatch in existing methods and comprising the following implementing steps: constructing a three-dimensional co-prime cubic array; carrying out tensor modeling on a receiving signal of the three-dimensional co-prime cubic array; calculating six-dimensional second-order cross-correlation tensor statistics; deducing a three-dimensional virtual uniform cubic array equivalent signal tensor based on cross-correlation tensor dimension merging transformation; constructing a four-dimensional virtual domain signal tensor based on mirror image augmentation of the three-dimensional virtual uniform cubic array; constructing a signal and noise subspace in a Kronecker product form through virtual domain signal tensor decomposition; and acquiring a direction-of-arrival estimation result based on three-dimensional spatial spectrum search.

Three-dimensional co-prime cubic array direction-of-arrival estimation method based on a cross-correlation tensor

The present invention belongs to the field of array signal processing and relates to a composite tensor beamforming method for an electromagnetic vector coprime planar array. The method includes: building an electromagnetic vector coprime planar array; performing tensor modeling of an electromagnetic vector coprime planar array receiving signal; designing a three-dimensional weight tensor corresponding to a coprime sparse uniform sub-planar array; forming a tensor beam power pattern of the coprime sparse uniform sub-planar array; and performing electromagnetic vector coprime planar array tensor beamforming based on coprime composite processing of the sparse uniform sub-planar array. Starting from the principles of receiving signal tensor spatial filtering of two sparse uniform sub-planar arrays that compose the electromagnetic vector coprime planar array, the present invention forms a coprime composite processing method based on a sparse uniform sub-planar array output signal.

Composite tensor beamforming method for electromagnetic vector coprime planar array

Disclosed is a slot-type ultra-wideband depolarized chipless RFID tag. The tag comprises a tag slot unit, a metal plate and a dielectric substrate, wherein the tag slot unit is formed by means of etching the metal plate, and is located on the upper surface of the dielectric substrate; the tag slot unit is composed of at least four annular slot groups, and tags are distributed in a centrosymmetric manner; and each annular slot group is composed of four concentric annular slots that are distributed in a nested manner; a transceiver antenna TX transmits a horizontally polarized electromagnetic wave; a scattered wave obtained; a transceiver acquires a frequency spectrum of the scattered wave; the spectrum is converted into a time-domain signal; a response of the tag is extracted; and an MFCC feature of the time-domain signal is extracted.

Slot-type ultra-wideband depolarized chipless RFID tag

The present disclosure discloses a method for capturing a trip sign of a turbine due to a high bearing temperature based on correlation and a device therefor. By combining a temperature of a target bearing and related operating parameters thereof, this method can capture possible abnormal trip online. According to the present disclosure, it is not necessary to add additional detection equipment, and it does not need to establish a complex physical model for turbine bearings, and only the historical data of the operating parameters of the temperature of the target bearing and generator set operating parameters related to the temperature of the target bearing are required to complete the establishment of the model for capturing abnormal sign before the trip, which is convenient for popularization and application.

Method and device for capturing trip sign of turbine due to high bearing temperature based on correlation

Provided is a method of identification and compensation of an inherent deviation of a yaw error of a wind turbine based on a true power curve. The method, based on a wind turbine data acquisition and monitoring control (SCADA) system includes a wind speed, an active power, and a yaw error and so on, runs data in real-time, first pre-processes the data to a certain degree, and then divides a power curve data according to a certain yaw error interval, fits the power curves according to different yaw error intervals through a true power curve fitting flow in connection with an outlier discrimination method, further quantitatively analyzes the different power curves and determines an interval scope of the yaw error inherent deviation value based on an interval determination criterion, and finally compensates the identified inherent deviation value to a yaw error measurement value.

Method of identification and compensation of inherent deviation of yaw error of wind turbine based on true power curve

Discloses a combined ultra-wideband cross-polarized chipless RFID tag based on MFCC feature coding, which comprises a tag patch unit, a dielectric substrate and a grounding layer, wherein the tag patch unit comprises a barcode-type resonant unit and a double L-type resonant unit; the barcode-type resonant unit consists of five identical rectangular patches arranged in parallel and rotated counterclockwise; the double L-type resonant unit is formed by reversely combining two L-type patches composed of four identical rectangular patches; a transmitting antenna transmits horizontally polarized electromagnetic waves as interrogation signals, the scattered waves reflected by the tag are acquired by a receiving antenna, a receiver acquires the spectrum of the scattered waves to convert the spectrum into time domain signals by inverse Fourier transform, the response of the tag is extracted through a window, and MFCC features are extracted by pre-emphasis and short-time Fourier transform.

Combined ultra-wideband cross-polarized chipless RFID tag based on MFCC feature coding

The invention discloses a method for estimating the direction-of-arrival of a coprime array based on virtual domain statistics reconstruction of single-bit quantized signal. The realization steps are as follows: arranging a coprime array and a single-bit analog-to-digital converter at a receiving end; calculating equivalent virtual signal corresponding to a single-bit receipt signal of the coprime array; constructing a virtual domain augmented covariance matrix of an initialized single-bit quantized signal; designing, based on statistical correlation analysis between statistics of the single-bit quantized signal and the original unquantized signal, an optimization problem based on virtual domain statistics reconstruction of quantized signal; and performing direction-of-arrival estimation by utilizing the virtual domain augmented covariance matrix corresponding to the optimized single-bit quantized signal.

Method for estimating the direction-of-arrival of a coprime array based on virtual domain statistics reconstruction of single-bit quantized signal

Disclosed is a spatial spectrum estimation method with enhanced degree-of-freedom based on block sampling tensor construction for coprime planar array, which mainly solves the multi-dimensional information loss in signals and degree-of-freedom limitation in the existing methods and which is implemented by the following steps: constructing a coprime planar array; modeling block sampling tensors of the coprime planar array; deducing coarray statistics based on the block sampling cross-correlation tensor; obtaining block sampling coarray signals of a virtual uniform array; constructing a three-dimensional block sampling coarray tensor and its fourth-order auto-correlation statistics; constructing signal and noise subspaces based on fourth-order auto-correlation tensor decomposition; estimating a tensor spatial spectrum with enhanced degrees-of-freedom. In the present disclosure, the block sampling tensors of the coprime planar array is constructed, where a coarray tensor is deduced, to realize tensor spatial spectrum estimation with enhanced degrees-of-freedom by extracting signal-to-signal subspace features from the four-order self-correlation tensor.

Spatial spectrum estimation method with enhanced degree-of-freedom based on block sampling tensor construction for coprime planar array

A two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, the method includes: deploying a coprime planar array; modeling a tensor of the received signals; deriving the second-order equivalent signals of an augmented virtual array based on cross-correlation tensor transformation; deploying a three-dimensional coarray tensor of the virtual array; deploying a five-dimensional coarray tensor based on a coarray tensor dimension extension strategy; forming a structured coarray tensor including three-dimensional spatial information; and achieving two-dimensional direction-of-arrival estimation through CANDECOMP/PARACFAC decomposition. The present disclosure constructs a processing framework of a structured coarray tensor based on statistical analysis of coprime planar array tensor signals, to achieve multi-source two-dimensional direction-of-arrival estimation in the underdetermined case on the basis of ensuring the performance such as resolution and estimation accuracy, and can be used for multi-target positioning.

Two-dimensional direction-of-arrival estimation method for coprime planar array based on structured coarray tensor processing

In a general aspect, a target is localized based on measurements from a measurement device array. In some aspects, range difference values (d) and coordinate vectors () of devices in the measurement device array are obtained. The range difference values are generated based on time difference of arrival measurements of wireless signals between the target device and each of the devices in the measurement device array. A first matrix (A) and a first vector (b) are constructed. The first matrix (A) and the first vector (b) each includes the range difference values and the coordinate vectors. Whether a second vector (y) satisfies a condition set is determined. The condition set includes a first condition (AA+&#x3bb;D)y=Ab and a second condition v(AA+&#x3bb;D)v&#x2265;0. A numerical approximation of an optimal solution of the second vector is generated. The target device is localized according to the numerical approximation of the optimal solution.

Localizing a target device based on measurements from a measurement device array

Disclosed is a high-resolution accurate two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with coprime planar array, which solves the problem of multi-dimensional signal loss and limited spatial spectrum resolution and accuracy in existing methods. The implementation steps are: constructing a coprime planar array; tensor signal modeling for the coprime planar array; deriving coarray statistics based on coprime planar array cross-correlation tensor; constructing the equivalent signals of a virtual uniform array; deriving a spatially smoothed fourth-order auto-correlation coarray tensor; realizing signal and noise subspace classification through coarray tensor feature extraction; performing high-resolution accurate two-dimensional direction-of-arrival estimation based on coarray tensor spatial spectrum searching. In the present method, multi-dimensional feature extraction based on coarray tensor statistics for coprime planar array is used to implement high-resolution, accurate two-dimensional direction-of-arrival estimation based on tensor spatial spectrum searching, and the method can be used for passive detection and target positioning.

High-resolution, accurate, two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with co-prime planar array

Growing community of inventors

Hangzhou, China

Jiming Chen