Abstract: A transformer fault diagnosis and positioning system based on a digital twin is provided and includes the following. A communication sensing module, which is configured to transmit bottom-level monitoring data obtained from a device entity to a system support module. The system support module, which is configured to receive and preprocess bottom-level monitoring data, and store various data, models and expert systems. A dynamic twin module, which is configured to analyze a multi-dimensional probability status of a device fault, construct a dynamic degradation model of different health states, and realize model correction through human-computer interaction, real-time measurement, and dynamic update subsequently. A decision-making diagnosis module, which is configured to construct a digital twin of data to be diagnosed, to realize diagnosis and positioning of the health state.

Abstract: A low-complexity precoding method for large-scale MIMO fast convergence is provided. The method includes the following steps: in step 1, starting a precoding procedure to initialize relevant parameters of an MIMO transmitting antenna; in step 2, accelerating a symmetric successive over-relaxation algorithm through a Chebyshev semi-iterative algorithm to complete a precoding inversion process according to the initialized parameters; and in step 3, generating a signal to be transmitted of the MIMO transmitting antenna according to a result of the iterative algorithm and ending the precoding procedure. Herein, the step of initializing the parameters includes parameter initialization and setting a number of transmitting antennas, a number of single-antenna users, and a channel transmission matrix.

Abstract: A method and a system for selecting important delay taps of channel impulse response are provided, which belong to the field of adaptive transmission technology of wireless communication. The method performs local predictability estimation on all delay taps in the channel impulse response in wireless communication, thereby selecting the important delay taps in the channel impulse response. The disclosure uses recurrence rates of the delay taps to estimate local predictability of the delay taps. Noise delay taps are distinguished from the important delay taps through the local predictability of the delay taps, which improves the identification rate of the important delay taps of the channel impulse response in wireless communication. The disclosure may implement the selection of the important delay taps of the channel impulse response, which provides guarantee for implementing adaptive transmission and adaptive coding of wireless communication.

Abstract: An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification.

Abstract: A circuit health state prediction method and system based on an integrated deep neural network are provided and relates to a technique for predicting a power electronic circuit failure. The invention serves to identify and diagnose a health state of a simulation circuit based on historical data by using an integrated deep neural network, and the method includes: carrying out parameter aging simulation experiments for different devices; extracting a series of time domain features of output signals through a temporal transformation method, and establishing health indices of the devices based on an improved angular similarity; predicting a health state of the simulation circuit in degeneration by using CAE and LSTM-RNN; and predicting validity of the circuit health state prediction method by referring to relevant evaluation indices. The invention is capable of effectively predicting the health state of the simulation circuit and is highly accurate and easy to implement.

Abstract: The disclosure discloses an IGBT module reliability evaluation method and device based on bonding wire degradation, which belong to the field of IGBT reliability evaluation. The realization of the method includes: obtaining a relationship between a IGBT chip conduction voltage drop Uces and an operating current Ic along with a chip junction temperature Tc; for an IGBT module under test, obtaining the conduction voltage drop Uces-c of the IGBT chip through the operating current Ic and the chip junction temperature Tc; obtaining an external conduction voltage drop Uces-m of the IGBT module by using a voltmeter; performing subtraction to obtain a voltage drop at a junction of a IGBT chip and a bonding wire, and combining the operating current to obtain a resistance at the junction; determining that the IGBT module has failed when the resistance at the junction increases to 5% of an equivalent impedance of the IGBT module.

Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.

Abstract: The disclosure provides a wireless channel scenario identification method and system. The method includes: simulating different wireless channel scenarios to obtain a channel scenario baseband signal y(t)pq; extracting a feature parameter of y(t)pq, extracting an autocorrelation function Ah(t)pq and performing a Fourier transform thereon to obtain a power spectral density function S(t)pq; normalizing S(t)pq to obtain a normalized channel scenario power spectral density function S(t)pq; designing a deep learning network and inputting S(t)pq and a category label pair to train the deep learning network; and for a system with a channel scenario to be identified, collecting a passband signal at its receiving end, obtaining the normalized scenario power spectral density function ?(t)pq, and using ?(t)pq as an input of the trained classifier, the output of the classifier being a label sequence of the channel scenario, and the channel scenario is effectively determined.

Abstract: The disclosure provides an adaptive inversion method of Internet-of-things environmental parameters based on an RFID multi-feature fusion sensing model, including the following steps. Space-medium-interference is proposed as an overall concept, from the multipath propagation mechanism of electromagnetic waves, the electromagnetic wave transmission mechanism is considered. Combining with the joint characteristics of the generalized time domain, frequency domain, energy domain, and spatial domain, a global signal transfer function of RFID sensing is analyzed and derived to complete extraction of RFID sensing main features.

Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Abstract: A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage.

Abstract: A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

Abstract: A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Abstract: Provided are a time domain channel prediction method and a time domain channel prediction system for an OFDM wireless communication system, which relate to the technical field of adaptive transmission in wireless communication. Frequency domain channel information is converted into time domain tap information by inverse Fourier transform. With respect to each time domain tap information, tap information prediction based on an extreme learning machine is realized, and finally predicted tap information is converted into frequency domain channel information by Fourier transform. To improve a generalization ability of a channel predictor, an output weight of the extreme learning machine is punished by a combination of l2 regularization and l1/2 regularization. The disclosure may provide satisfactory prediction performance and may output a sparse output weight, which reduces the requirement for memory storage. The disclosure ensures adaptive transmission and adaptive coding of wireless communication.

Abstract: A method and a system for selecting important delay taps of channel impulse response are provided, which belong to the field of adaptive transmission technology of wireless communication. The method performs local predictability estimation on all delay taps in the channel impulse response in wireless communication, thereby selecting the important delay taps in the channel impulse response. The disclosure uses recurrence rates of the delay taps to estimate local predictability of the delay taps. Noise delay taps are distinguished from the important delay taps through the local predictability of the delay taps, which improves the identification rate of the important delay taps of the channel impulse response in wireless communication. The disclosure may implement the selection of the important delay taps of the channel impulse response, which provides guarantee for implementing adaptive transmission and adaptive coding of wireless communication.

Abstract: A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.

Abstract: The disclosure discloses a channel prediction method and system for a MIMO wireless communication system. The method includes the following steps: obtaining frequency domain channel information of each antenna pair of the MIMO wireless communication system through channel estimation; processing, by inverse Fourier transform, frequency domain channel information of each antenna pair to obtain information of each effective delay path of the MIMO wireless communication system; training the width learning system; utilizing the trained width learning system to predict each effective delay path of each antenna pair, so as to obtain the information of the next moment of each effective delay path of each antenna pair; after summarizing the information of the next moment of each effective delay path of each antenna pair obtained through prediction, the Fourier transform is utilized to convert the information into predicted frequency domain channel information.