Insulator defects in transmission lines can threaten the safe and stable operation of the power system. Therefore, insulator defect detection plays a crucial role in the electricity sector. Currently, You Only Look Once (YOLO) algorithm is widely used in insulator fault detection. However, insulator defect detection using YOLO still faces the double challenges of detection precision and real-time performance. Therefore, we construct a lightweight model for insulator defect detection named coordinate attention mechanism (CAM) and feature channel shuffle operation (CSO) YOLO (CACS-YOLO). By introducing the CAM and CSO in the original YOLOv8m, we improve the detection precision, and reduce the parameters of model, compared with existing insulator defect detection models. Meanwhile, in order to solve the problem of insufficient data and cope with different weather conditions, we use synthetic weather algorithms for data enhancement, and construct a Synthetic Foggy and Rainy Insulator Dataset (SFRID). The experimental results show that the mean average precision of CACS-YOLO is 96.9% and the parameters of proposed model is 27.44M. These outcomes show the high-precision and lightweight of our proposed model in insulator defect detection. The source code and dataset are available on Github.

A parameterized controller design approach is proposed to solve the problem of multi-objective control for vehicle active suspension systems by using symbolic computation. The considered model is a quarter-vehicle model of the active suspension system. The multi-objective robust control performances include the sprung mass acceleration, suspension deflection, and tire deflection. Based on dissipative Hamiltonian systems and Lyapunov function, a multi-objective. H∞ controller design approach is developed, which can avoid solving Hamilton-Jacobi-Issacs equations. Then, an algorithm of solving semi-positive definite polynomial with tuning parameters is proposed by using symbolic computation. Furthermore, a method of parameter optimization is proposed. Simulations and comparation show that the control performance is significantly improved comparing with passive controlled systems and existing other control systems for active suspension systems.

Double-line spectroscopic binaries (SB2s) are a vital class of spectroscopic binaries for studying star formation and evolution. Searching for SB2s has been a hot topic in astronomy. Although considerable efforts have been made with fruitful outcomes, limitations in automation and accuracy still persist. In this study, we developed a convolutional neural network model to search for SB2 candidates in LAMOST medium-resolution survey (MRS) data release (DR) 9 v1.0 by detecting double peaks in the cross-correlation function (CCF). We first generated a large number of spectra of single stars and binaries using the iSpec spectral synthesis software. The CCFs of these synthesized spectra were then calculated to form our training set. To efficiently detect the peaks of the CCFs, we applied a Softmax function-based noise reduction method. After testing and validation, the model achieved an accuracy of 97.76% in the testing set and was validated for more than 90% of the sample in several published SB2 catalogs. Finally, by applying the model to examine approximately 1.59 million LAMOST-MRS DR9 spectra, we identified 728 candidate SB2s, including 281 newly discovered ones.

In the field of industrial control, edge computing puts forward higher requirements for the local processing ability of data. The traditional industrial programmable logic controller (PLC) cannot complete this task. The edge intelligent controller (EIC) is developed according to the demand for edge computing. As the core component of edge computing, the security and reliable operation of EIC has great significance and influence on the promotion and development of edge computing. For the access authentication of EIC, an efficient revocable anonymous authentication scheme for EICs by utilizing a group signature technique is proposed in this article, which can trace the identity information of the EIC and protect the privacy of the EIC, and apply an efficient revocation mechanism to revoke any illegal or abnormal EICs. Through security analysis, we prove that the authentication scheme is anonymity, integrity, traceability, forward secrecy, resistance to the replay attack, and efficient revocability. Furthermore, the performance analysis and comparisons show that the authentication scheme is efficient and feasible for EICs.

The Internet of Things (IoT) network intrusion detection (NID) has emerged as a powerful technique for effectively detecting attacks and protecting internet safety across a wide range of applications. However, existing NID models often struggle to achieve good detection performance when deployed in resource-restricted environments. To achieve an optimal balance between detection performance and computational cost, we propose a lightweight knowledge distillation bidirectional encoder representations from Transformers (KD-BERT) framework for efficient IoT intrusion detection. Specifically, we first develop a header-payload pairs tokenization method to preprocess the raw data. Then, we distill rich knowledge from a large pre-trained $\text{BERT}_{\text{teacher}}$ model into a smaller $\text{BERT}_{\text{student}}$ model with fewer training parameters. By leveraging KD-BERT, the $\text{BERT}_{\text{teacher}}$ can be compressed into the $\text{BERT}_{\text{student}}$, reducing the number of parameters from 135 to 9 M, while maintaining high detection accuracy. To effectively obtain the essential features, we design a data transformation strategy and fine-tuned the $\text{BERT}_{\text{student}}$ model on several labeled IoT NID tasks. Comprehensive experimental results on three public datasets (telemetry datasets, operating systems datasets and network traffic dataset of IoT (TON-IoT), Edge-IIoTset, and IoMT2024) have demonstrated superior performance compared to state-of-the-art methods.