In this paper, considering the delay information of each area in the multi-area load frequency control (LFC) system with multiple time-varying delays, an augmented reciprocally convex approach and the Lyapunov theory is proposed for analyzing their delays-dependent stability. For the delay information about all N areas, the delay interval [−h, 0] is divided into 2 N subintervals by using a region partitioning approach, and then the previous LFC system model will be reconstructed. For this reconstructed model, the cross-convex method and the Wirtingerbased integral inequality are applied to the terms generated by the region partitioning approach, such that the stability criterion of multi-area LFC system with multiple time-varying delays can be attained. Meanwhile, comparing with the existing methods, the proposed method is less computation and less conservative. Three cases of the traditional two-area, deregulated two-area and the traditional three-area LFC systems are considered to verify the validity of the proposed stability criterion.

The fault diagnosis for the intermediate frequency inverter in the aviation industry has always been a difficult problem. However, most of the existing fault diagnosis methods are not directly applicable to it due to changes in system characteristics. In order to solve this problem, a new fault diagnosis model is proposed. Firstly, preliminary parameters are extracted from the measured current sequence. Secondly, two types of features values are obtained directly using the proposed current amplitude-deviation feature complement method, which can effectively distinguish usual open-circuit fault modes of the inverter. Thirdly, the dataset consisting of the above feature values is acquired under different load condition and fault modes. Finally, based on low-dimensional feature values data, the extreme learning machine is used to generate a fault diagnosis model rapidly through matrix operations. By experiments, the method proposed can accurately realize the fault diagnosis of aviation intermediate frequency inverter. Compared with most existing methods, proposed feature extraction method has advantages in fault discrimination ability and implementation difficulty. And extreme learning machine speeds up the training process and facilitates debugging.

In this paper, an ungrounded double-loop active differential magnetic field probe is proposed. The proposed probe includes two low noise amplifiers and an ungrounded double-loop detection structure, which can ensure a high magnetic field response. The detection structure also includes a shielded ground plate for suppressing electric field coupling and improving the common-mode rejection ratio (CMRR). Meanwhile, a rotate asymmetric calibration method (RACM) is used to correct the probing system’s asymmetry, improve the CMRR, and broaden the measurement frequency range. Therefore, the probing system combines several advanced design ideas, including adding two amplifiers, designing an ungrounded double-loop detection structure with shielded ground plate, using the asymmetric calibration method, and reserving differential output. The calibration data show that the proposed probe has a high magnetic field response factor of -19.8 dB at 2 GHz and a suppressed electric field response factor below -90 dB at 2 GHz. After using the RACM, a CMRR of more than 31.8 dB can be obtained when the frequency is less than 9 GHz. When the frequency is up to 14 GHz, the CMRR is better than 16 dB. The validation results of measuring three stand-wave points prove that the proposed probe and probing system can realize radio frequency magnetic field detection along a trace of up to 14 GHz.

In symmetric two-port probing system, the measured electromagnetic field signals can be decoupled. However, in practical application, asymmetric problem is difficult to avoid. So, correcting asymmetric probing system (APS) is significant to achieve ultra-wide-band near-field measurement. In this article, a virtual network matching method (VNMM) is proposed based on a circuit model, which is different from the previous asymmetric correction (or calibration) method. By setting up a virtual matching network, the APS is transformed into a virtual symmetric probing system (SPS), then the measured electromagnetic field signals can be decoupled. As a new forward correction method, the proposed VNMM is simpler and easier to use than the previous forward correction method. The measurement results of a two-port probing system in three asymmetric cases have verified the effectiveness of the VNMM, and a near-field scanning measurement proves the practical applicability of the VNMM. Furthermore, the VNMM can also be easily expanded to correct asymmetries of multiport probing systems.

This study addresses the limitations of conventional filtering methods in handling irregular outliers and missing observations, which can compromise filter robustness and accuracy. We propose the Transformer-based Outlier-Robust Kalman Filter (TORKF), a hybrid data and knowledge hybrid-driven framework for stochastic discrete-time systems. Initially, this study derives the filtering formulas applicable when outliers exist in observation vectors and, based on these formulations, proposes a novel method capable of accurately identifying observation vectors containing outliers. In addition, a transformer-based prediction compensation approach is employed to compute the prediction vector compensation value in scenarios involving outliers. This method utilizes a specially designed data structure to ensure the transformer encoder fully extracts the input features. Furthermore, to address outlier-induced inaccuracy in prediction error covariance, a compensation method aggregating all prediction outcomes is proposed, leading to enhanced filtering accuracy. Aircraft tracking presents challenges from complex motion models and outlier-prone observations, making it an ideal testbed for robust filtering algorithms. TORKF demonstrates superior performance, with a 12.7% lower RMSE than state-of-the-art methods across both propeller and jet datasets, while maintaining sub-90 ms single-frame processing to meet real-time requirements. Ablation studies confirm that all three proposed methods enhance accuracy and demonstrate synergistic improvements.