To realize the stable tracking control of the optoelectronic stabilized platform system under nonlinear friction and external disturbance, an active disturbance rejection controller (ADRC) with friction compensation is proposed to improve the target tracking ability and anti-disturbance performance. First, a nonlinear LuGre observer is designed to estimate friction behavior and preliminarily suppress the interference of friction torque on the system. Then, an ADRC is introduced to further suppress the residual disturbance after friction compensation, and the stability of the ADRC system is also proved. The effectiveness of this scheme is proved by simulation experiments, and this scheme is compared with conventional ADRC and LuGre friction feedforward compensation. The simulation results show that an ADRC with LuGre friction compensation is better with trajectory tracking performance, which suppresses the influence of disturbance and improves the stability of the optoelectronic stabilized platform system.

Ground–airborne frequency-domain electromagnetic method (GAFEM) is a geophysical exploration technique capable of detecting subsurface electrical structures over large areas and at great depths in complex terrains. Tipper divergence imaging, one of the principal GAFEM interpretation approaches, offers high lateral resolution and rapid imaging speed. However, conventional tipper divergence imaging relies on magnetic field data generated by orthogonal dual-source excitation, which is difficult—or in extreme cases impossible—to deploy in complex terrains, and it also fails to delineate the vertical boundaries of anomalies effectively. In this paper, we propose a tipper apparent resistivity-united-gradient imaging method with single-source excitation. The proposed approach combines lateral and vertical spatial gradients of tipper apparent resistivity derived from a single-source excitation to achieve high-resolution detection of subsurface electrical anomalies. We first present the derivation of the theoretical formulation underpinning the proposed imaging method. We then apply this approach to GAFEM datasets generated from three‐dimensional simulation models—incorporating anomalies at varying depths, anomalies whose resistivities closely match the background, and multiple coexisting anomalies—to further analyze the method’s three‐dimensional imaging characteristics. We further assess the effect of background resistivity estimation errors and noise interference on the imaging method. Finally, field experiments validate the method’s practical applicability. Compared with conventional tipper-based techniques, our method requires only a single-source excitation, overcoming the challenge of orthogonal dual-source deployment and enhancing feasibility in field surveys; moreover, the tipper apparent resistivity-united-gradien t strategy resolves the inability of conventional divergence imaging to identify vertical anomaly boundaries, thus offering a novel pathway for precise geophysical exploration.

Aiming at the problem that the degree of icing of transmission lines in cold regions is difficult to predict, a method for predicting icing galloping of overhead transmission lines based on multi-information fusion is proposed. Through the mechanical structure model of the transmission tower established using Design Modeler, and the stress analysis performed using ANSYS Workbench, calculate the displacement of the characteristic point and the change of the inclination angle when the transmission line tower is covered with ice and gallops, and then classify and predict according to the information above. When the line is running, the real-time meteorological data during icing is collected through the sensor information of the tower body, and a nonlinear four-class SVM is constructed to predict and classify the icing situation. The verification results show that the prediction accuracy of this method is high, which is convenient for promotion.

In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats.

Natural-source signals in airborne geomagnetic sounding (AGS) are characterized by weak amplitudes and broad frequency bandwidths. During flight, they are highly susceptible to low-frequency motion noise induced by platform attitude fluctuations, as well as various types of strong interference. These disturbances degrade data quality and subsequent imaging and interpretation. To address these issues, this paper proposes an adaptive AGS data denoising method based on ROCKET noise classification and an improved U-Net architecture. First, a Mamba module is incorporated to enhance the long-range temporal dependency modeling capability of the U-Net, enabling effective preliminary suppression of low-frequency motion noise. Subsequently, high-dimensional discriminative representations are constructed using ROCKET random convolutional features to efficiently identify and accurately classify strong interference noise types in the remaining data segments, including power-line interference, triangular-wave interference, and impulsive interference. Finally, noise-specific improved U-Net models with tailored network structures and optimized parameters are invoked according to the identified noise categories, achieving customized and refined noise suppression for different noise types. Experimental results are based on a large-scale synthetic dataset constructed from high-signal-to-noise-ratio field measurements, together with airborne survey data acquired in Baishan, Jilin Province. The results demonstrate that the proposed method significantly outperforms typical deep learning methods, as well as the non-adaptive conventional U-Net and non-adaptive improved U-Net in both time-domain and frequency-domain metrics. The time-domain metrics include signal-to-noise ratio, normalized cross-correlation, and reconstruction error, while the frequency-domain metrics include spectral mean squared error and logarithmic spectral distortion. Multi-line tipper magnitude imaging validation using field data indicates that the proposed approach can suppress artifacts, recover continuous electrical boundary features, and improve the reliability of imaging results. These results suggest that the proposed adaptive noise suppression method offers high accuracy, robustness, and good scalability, providing reliable technical support for high-quality processing and geological interpretation of AGS data.