Abstract: A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: A system for testing a Nakagami fading channel and a verification method thereof are provided. The testing system includes a signal generator, a Nakagami fading channel simulator, and a computer. The signal generator is used to output a sine wave signal whose frequency is f and transmit the sine wave signal to the Nakagami fading channel simulator and the computer. The Nakagami fading channel simulator is used to generate a Nakagami fading channel. The computer is used to perform data processing and analysis. In the verification method, time domain fading characteristics, first-order statistics characteristics, and second-order statistics characteristics of the Nakagami fading channel are respectively verified. Verifying the time domain fading characteristics is verifying a waveform fluctuation rate and a fluctuation range on a time domain under different Nakagami fading factors.

Abstract: An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data.

Abstract: A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate

Abstract: An electromagnetic interference objective complexity evaluation method based on a fast S-transformation time-frequency space model comprises the following steps: (1) carrying out fast S-transformation on space electromagnetic signals acquired in real time to obtain a fast speed S-transformation two-dimensional time-frequency matrix of the signal; (2) calculating time domain occupancy rate TP, frequency occupancy rate FP, and energy occupancy rate EP of an evaluation object and interference signals in the fast speed S-transformation two-dimensional time-frequency matrix; (3) calculating electromagnetic interference objective complexity C=TP*FP*EP according to the time-frequency space model; and (4) finding a grading standard according to an objective complexity value so as to determine an electromagnetic interference objective complexity type.

Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

Abstract: A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose. The present invention may effectively suppress a magnitude and frequency of the common-mode voltage of the multiphase motor without increasing calculation complexity or reducing other performance indexes.

Abstract: An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data.

Abstract: A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate

Abstract: An electromagnetic interference objective complexity evaluation method based on a fast S-transformation time-frequency space model comprises the following steps: (1) carrying out fast S-transformation on space electromagnetic signals acquired in real time to obtain a fast speed S-transformation two-dimensional time-frequency matrix of the signal; (2) calculating time domain occupancy rate TP, frequency occupancy rate FP, and energy occupancy rate EP of an evaluation object and interference signals in the fast speed S-transformation two-dimensional time-frequency matrix; (3) calculating electromagnetic interference objective complexity C=TP*FP*EP according to the time-frequency space model; and (4) finding a grading standard according to an objective complexity value so as to determine an electromagnetic interference objective complexity type.

Abstract: A system for testing a Nakagami fading channel and a verification method thereof are provided. The testing system includes a signal generator, a Nakagami fading channel simulator, and a computer. The signal generator is used to output a sine wave signal whose frequency is f and transmit the sine wave signal to the Nakagami fading channel simulator and the computer. The Nakagami fading channel simulator is used to generate a Nakagami fading channel. The computer is used to perform data processing and analysis. In the verification method, time domain fading characteristics, first-order statistics characteristics, and second-order statistics characteristics of the Nakagami fading channel are respectively verified. Verifying the time domain fading characteristics is verifying a waveform fluctuation rate and a fluctuation range on a time domain under different Nakagami fading factors.

Abstract: A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.

Abstract: A space vector pulse width modulation (SVPWM) method for suppressing a common-mode voltage of a multiphase motor includes the following steps: (1) dividing all basic vectors of the multiphase motor into q types, and selecting therefrom x types having equal common-mode voltage magnitude of which an absolute value is smallest; (2) for each type in the x types of basic vectors, structuring y classes of auxiliary vectors according to an optimization model; (3) synthesizing reference vectors by virtue of the auxiliary vectors to obtain functioning time of basic vectors functioning in each switching period; and (4) obtaining an optimal functioning sequence of the basic vectors functioning in each switching period with fewest switching operations of a converter as a purpose.

Abstract: A 5G-oriented multidimensional adaptive MIMO system including a transmitting antenna array, a first rotary table, a broadband vector signal generator, a receiving antenna array, a second rotary table, a broadband vector signal analyzer and a data acquisition terminal is provided. The broadband vector signal generator and the broadband vector signal analyzer are respectively connected with the transmitting antenna array and the receiving antenna array. The data acquisition terminal is connected with the broadband vector signal generator and the broadband vector signal analyzer. In addition, a method for adjusting radiating modes of antenna ports by using the 5G-oriented multidimensional adaptive MIMO system is also provided.

Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

Abstract: A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.

Abstract: A quantum evolution method includes steps of: according to the quantum evolution method, initializing a generation number t=0, and initializing a population Q(t)={q1t, q2t, . . . , qnt}; observing Q(t) and generating P(t)={x 1t, x2t, . . . , xnt}, wherein represents strings comprising 0 or 1 with a length of m; evaluating each xit with an evaluation function, and inputting evaluating results into a fitness function F(t), F(t)={f1t, f2t. . . , fnt}, wherein fit represents a fitness of each individual; selecting an elite group E(t) from P(t) according to the fitness; evolving Q(t) through U(??ijt); inputting an optimal solution b of P(t) into B(t), wherein if the optimal solution is better than an original optimal solution in B(t), then replacing the original optimal solution; otherwise remaining the original optimal solution; and judging a shutdown condition, if satisfied, outputting the optimal solution; otherwise returning to the step (2) for further evolution.