In this paper, the basic principle and characteristics of a phase-locked loop (PLL) in a single phase grid-connected system are analyzed, and this paper introduces one type virtual orthogonal voltage vector method based on a third order generalized integrator (TOGI) to construct an alpha and beta static coordinate system. The TOGI structure can eliminate the DC offset in a voltage signal or zero offset in the sampling process, and ensure the amplitude of the virtual orthogonal signal is consistent. At the same time, the adaptive frequency estimation unit is introduced, which can effectively deal with the power grid voltage frequency changes and ensure the accuracy of PLL. MATLAB (R2012a, MathWorks, Natick, MA, USA) is used to simulate the variation of power grid voltage frequency, DC component injection, harmonics injection and other parameters, and the performance of PLL is adequately verified. In addition, a 5kW single-phase energy router experimental platform is built to verify the proposed PLL. The experimental results show that the PLL can well track the frequency change of the grid voltage and eliminate the DC offset, so as to achieve accurate phase tracking.

Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

Background and Objectives: Due to the soft-field effect of the electromagnetic field and the limit of detection, image reconstruction of magnetic induction tomography has to recover more complex electrical characteristics from very few signals. These cause a problem which have underdetermination, nonlinearity, and ill-posed characteristics, and therefore lead to many difficulties in finding its solution. Although many regularization image reconstruction methods exist, they are not suitable for MIT applications due to regularization parameter selection. The purpose of this paper is to study the principle of particle swarm optimization with simulated annealing, and to propose a regularization method for reconstruction, which will provide a new way to solve the MIT image problems. Methods and Models: Firstly, the regularization principle of image reconstruction of MIT will be analyzed. Then the hybrid regularization algorithm, including Tikhonov and NOSER regularization, will be developed, using the dimension of the Hessian matrix as a penalty term respecting the prior knowledge. PSO-SA algorithm will be applied to obtain an optimal solution for regularization parameters. Finally, six typical numerical models and approximately symmetrical cerebral hemorrhage models by COMSOL will be carried out, and the voltage signals obtained from the simulation will be used to verify the proposed reconstruction method. Results: Through the simulation results, the proposed imaging method has the average CC values of 0.9932, 0.8286 and the average RE values of 0.4982, 0.8320 for simple and complex models, respectively. Moreover, when the SNR changes from 55 dB to 35 dB, the CC value of the cerebral hemorrhage model reduced by 0.1034. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in MIT image reconstruction. Conclusions: This study indicates the potential application of PSO-SA algorithm in regularization imaging problem. Compared with traditional regularization imaging methods, the proposed method has the advantages of better accuracy, robustness and noise resistance, showing the certain application value in other similar ill-ness imaging problems.

This paper proposes a source-level electromagnetic interference suppression strategy for high-voltage inverters that uses a series-connected IGBT topology and discrete staircase voltage shaping. From an information-theoretic perspective, the staircase shaping transforms chaotic wideband switching noise into a deterministic harmonic structure, thereby reducing the spectral entropy of the EMI source. This information optimization is achieved using a CPLD-based sequential gate drive circuit, which eliminates the need for complex active gate profiling algorithms. Experimental results obtained using a 1140 V explosion-proof motor drive platform demonstrate harmonic attenuation of 4–16 dB μV within a 2 MHz band. Importantly, this targeted entropy reduction occurs alongside a 68.7% reduction in active-region switching losses, suggesting a concurrent decrease in local thermodynamic entropy production during switching transients. Increasing spectral determinism and relaxing requirements for subsequent physical filters effectively lower the conditional entropy of the overall electromagnetic environment. Leveraging the structural flexibility of series IGBTs, this method provides a practical, low-complexity solution and establishes a novel framework between power electronics and information theory for electromagnetic compatibility.