Noise attenuation is crucial in ground-penetrating radar (GPR) data processing. In recent years, deep learning (DL) methods have shown excellent performance in GPR denoising tasks, but they typically focus only on recovering the target signal, which can lead to over-denoising. To enhance the generalizability and the practicality of denoising networks, we propose a strategy to generate random dielectric models from natural image datasets, which can quickly construct model datasets with low redundancy and reasonable distribution. To enhance the fidelity of GPR denoising, we leverage the powerful nonlinear fitting capabilities of convolutional neural networks (CNNs) and introduce a closed-loop denoising network framework for GPR. The framework consists of a denoising sub-network and a noise extraction sub-network, effectively achieving signal-noise separation in noised GPR data. Specifically, the denoising sub-network is used to recover weak reflection signals and initially remove noise, while the noise extraction sub-network is used to restore the true noise, mitigating the problem of over-denoising. A key innovation of our approach is the integration of bandpass filtering, which enhances the robustness of network training and supports effective weak signal recovery. This network framework forms a closed loop through the residual loss between the signal-noise separation results and the noised GPR data, the closed-loop structure is capable of further refining the signal and noise prediction results of the two sub-networks, thereby enhancing the numerical accuracy of the signal-to-noise separation results. Finally, the effectiveness of the GPR closed-loop denoising network is verified from multiple perspectives using both synthetic and field measured data. The results indicate that our proposed method is more competitive in GPR denoising tasks.

For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find that the estimated source wavelet is not accurate and needs to be manually corrected repeatedly in the iteration. This process is highly operator-intensive, and the update process is time-consuming and increases the potential for errors. We propose a source-independent waveform inversion (SIEWI) scheme for cross-hole GPR data, and use the envelope objective function combined with this method to effectively reduce the nonlinearity of inversion. The residual field used by SIEWI to construct the gradient inherits the characteristics of the envelope wavefield. Compared with full waveform inversion (FWI), SIEWI is more robust and less sensitive to frequency components and inaccurate source wavelet. To avoid cycle jumping, the multi-scale strategy effectively utilizes the properties of convolutional wavefields. In one iteration, the wavefield is decomposed into multiple frequency bands through multiple convolutions in the time domain to construct a multi-scale inversion strategy that preferentially inverts low-frequency information.

This paper presents a region boundaries determination method for critical commutation failures (CF) in multi-infeed high-voltage direct-current (HVDC) systems under unbalanced short circuit faults. By using the nodal impedance matrix, a calculation approach for the converter extinction angles is deduced. First, the extinction angles under unbalanced short circuit faults are calculated. If the extinction angle of a bus is less than the minimum extinction angle, the bus is a failed bus set. By this means, region boundaries of critical commutation failures are demarcated by examining each bus in the AC system. Finally, the effectiveness of the proposed approach was verified by using a modified IEEE 39-bus system.

In electrical resistivity tomography (ERT), the anomaly effects of different electrode arrays vary depending on the geological model. The appropriate combination of different electrode arrays can optimize detection performance and enhance the reliability of interpretation results. However, traditional inversion methods, constrained by single-array data, sparse observations, and ill-posed problem-solving, often yield low-resolution or inaccurate results. To address the resolution challenges in ERT inversion, inspired by the outstanding fusion and nonlinear mapping capabilities of multi-modal deep learning (DL) image methods, we propose the super-resolution ERT fusion network (SRERTF-Net), which utilizes traditional inversion results of multi-array as the initial models, efficiently leveraging and integrating prior physical information to achieve multi-array data joint super-resolution inversion. In SRERTF-Net, different down-sampling paths are employed to process the inversion results of various electrode arrays, while Inception modules are introduced to enhance feature extraction. Additionally, dense connections are implemented both within and across paths to effectively integrate complementary information from different arrays, ensuring robust multi-modal feature fusion. Finally, we designed training samples that include randomly generated typical structural models and comprehensive complex models, in order to enhance the practicality and adaptability of the network. Experiments on synthetic and field measured data indicate that SRERTF-Net outperforms other methods in terms of resistivity accuracy, resolution, and background performance.

Deep learning has been widely applied to denoising ground-penetrating radar (GPR) signals. However, most existing methods lack physical constraints consistent with GPR data characteristics, especially in the frequency domain, leading to the loss of weak reflections and blurred reconstruction. Conventional networks also treat GPR denoising as a generic image restoration task without explicit weak-signal enhancement. To address these issues, this paper proposes a frequency-domain multi-scale loss function to introduce physical constraints into network training. Combined with traditional loss functions, the proposed method effectively improves the fidelity of weak reflection recovery. A multi-domain constrained TransUNet is further developed for GPR noise reduction. Experiments on synthetic data and field GPR data demonstrate that the proposed method achieves stronger robustness and competitive denoising performance.

Three-phase four-wire cascaded multilevel inverter can be utilized in lithium battery cascade utilization and other fields. However, the conditions of power balance control may conflict with the voltage support conditions, which may cause the failure of the control strategy. To solve the problems existed in the three-phase three-wire system, a split-capacitor three-phase four-wire system is adopted in this paper with the dc side midpoint connected to the neutral line which can provide access to zero-sequence components. Thus the new constraint equations are formed from the power generated by zero sequence components to achieve power balance and voltage support simultaneously. A new control strategy with peak current limitation for three-phase four-wire cascaded multilevel inverter under asymmetrical grid fault is proposed in this paper. The control strategy proposed in this paper ensures that the inverter operates safely during voltage sags, limiting the injected current to the maximum allowed by the inverter. Simulation and experiment results verified the effectiveness of the strategy.