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In recent years, the surge of artificial intelligence has propelled autonomous driving technology to the forefront, capturing growing interest and enthusiasm. As a sub-module within autonomous driving, research in traffic sign detection and recognition has significantly advanced with a growing focus on utilizing deep learning methods. Nevertheless, in complex real-world road scenarios, traffic signs often exhibit a long-tailed distribution, with the majority of instances concentrated in a few frequent categories and a scarcity in the remaining ones. Considering conventional Traffic Sign Detection and Recognition methods are crafted using manually curated datasets, the class imbalance may detrimentally impact the efficacy of the detection model. In this paper, we first propose a gradient-guided loss reweighting model that dynamically reweights the loss for positive and negative samples based on the cumulative gradients across each category. Additionally, a classification bias-based refinement module is proposed to fine-tune these weights during training, based on the false positive and false negative rate. This serves to suppress a drop in precision for tail categories resulting from the gradient-guided loss reweighting module, thus further balancing the entire training process for improved results. Extensive experiments are performed on the TT100K and GTSDB datasets, yielding significant advancements surpassing state-of-the-art methods.

A timely fault diagnosis is imperative to significantly improve the operational stability and safety of rotating machinery. Until now, several parameterized time-frequency (TF) analysis (TFA) methods that incorporate the proportional relationship of instantaneous frequencies (IFs) have been developed specifically for analyzing vibration signals of varying speed rotating machinery. However, they are sensitive to noise and exhibit limited TF resolution due to the truncation strategy, particularly in the case of narrow frequency intervals between neighboring components. Therefore, this article proposes a low-rank constrained multikernel general parameterized TF transform (MKGPTFT) to generate an ideal TF plane under challenging conditions characterized by substantial noise and closely spaced IFs. First, the low-rank property of proportional IFs is revealed, and this physical prior information serves as the fundament of the proposed method. Second, a multikernel demodulated operator is constructed to replace the single-kernel demodulated operator and the corresponding MKGPTFT is developed to address multicomponent signals. Third, the low-rank constrained MKGPTFT is proposed by incorporating the low-rank constraint into the update process of IFs of MKGPTFT. Thanks to the incorporation of physical knowledge, the proposed low-rank constrained MKGPTFT demonstrates its capability in accurately characterizing multicomponent signals with closely spaced and proportional IFs and generating a high-quality TF plane. The results of simulation and experiment illustrate the proposed method can discern time-varying IFs with greater precision when compared with six typical comparison methods in analyzing strong noise signals and successfully detect the fault of varying speed rotating machine.

Aluminum electrolytic capacitors (AECs) are utilized as the key components in power electronic transformers (PETs). The AEC degradation monitoring is crucial for the maintenance of PET. Degradation of AEC performance is often reflected by changes of capacitance (C) and equivalent series resistance (ESR). Since C and ESR dominate the low- and mid-frequency impedance characteristics of the AEC, respectively, the features of the corresponding frequency bands of the respective signals need to be simultaneously extracted. However, the current studies on parameter identification of AECs have not focused on this issue. In this article, the double-scale convolutional autoencoder and extreme learning machine (DCAE-ELM) framework is proposed to identify C and ESR based on AEC voltage. Specifically, DCAE extracts the low- and mid-frequency features of AEC voltages with large- and small-scale convolutional kernels, respectively. Then, ELM is employed to identify C and ESR based on the features extracted by DCAE. Moreover, the mathematical mechanisms between the gradients and reconstructed data of DCAE with data concatenated in columns (cDCAE) and rows (rDCAE) are analyzed. Validation results of both simulation and experimental data have verified the data reconfiguration performance of rDCAE and the parameter identification capability of the proposed DCAE-ELM framework.

ABSTRACTCurrently, abnormally elevated rail potential exists in the DC traction power system, which greatly threatens the safe operation of the line. In order to realize the reflux safety, timetable optimization of multitrain based on improved Seeker Optimization Algorithm (SOA) is proposed to suppress the rail potential of the system. Firstly, the simulation model of the system is built and the calculation process of rail potential is proposed. Secondly, an optimization model based on maximum rail potential is formulated, where the dwell time is the decision variable and is adjusted to change the regenerative power distribution of the system. Finally, the improved SOA is used to handle the optimization model. Compared with the maximum rail potential before optimization, the simulation results demonstrate that the maximum rail potential is reduced by 55.5% with the proposed method, which greatly improves the reflux safety of the multi-train subway system.