Abstract Anomaly detection in the Controller Area Network (CAN) bus is critical for ensuring the security and reliability of intelligent connected vehicles, which are increasingly prevalent. While existing anomaly detection strategies offer some benefits, they often face challenges such as limited feature extraction and data imbalance, which reduce their effectiveness. To address these issues, in this paper, we propose an unsupervised intrusion detection method based on CAN message graph named CGTS. Specifically, we first construct a message graph based on CAN message sequences. A Graph Transformer is then employed to extract complex structural information, accurately capturing the intrinsic connections between messages. Furthermore, to address the data imbalance problem, we integrate the Support Vector Data Description algorithm after the Graph Transformer model. This algorithm identifies anomalous behaviors efficiently without relying on a priori labels. Experiments conducted on public datasets, including Car-Hacking and CAN-Train-and-Test, demonstrate the efficacy of CGTS. The model achieves an average accuracy exceeding 0.990, precision above 0.995, and an F1-score nearing 0.993. These results highlight CGTS can effectively detect multiple injection attacks and significantly improve the CAN bus intrusion detection performance.

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of controller area network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system’s front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks.

Abstract Recently, existing FCNs‐based methods have shown their advantages in processing object boundaries. However, these methods still suffer from false object interference, which appears in saliency predictions. To solve this problem, an edge‐interior feature fusion (EIFF) framework is proposed, which consists of an internal‐boundary decoupled generation structure with receptive field enlargement and attention mechanism enhancement, and a salient feature refinement module. Specifically, the framework first learns edge features and interior features through an internal‐boundary decoupling generation network, which is supervised by labels obtained by decoupling ground‐truth through an image erosion algorithm. Then, feature refinement module (FRM) is designed to purify the coarse prediction by focusing on the ambiguous regions through a mining strategy to generate the final saliency map. To compensate for shortcomings of the BCE and IU loss, we also introduce a weighted loss to guide our model to focus more on the error‐prone parts. Experimental results on five benchmark datasets demonstrate that the proposed method performs favorably against 19 state‐of‐the‐art approaches under four standard metrics.

Seismic data denoising has gained much attention from scholars as a crucial part of seismic data processing. With the development of deep learning technology, numerous algorithms well employed in natural image denoising have been used for seismic data denoising. However, seismic data are crucially an array of geophone vibration signals, which have numerous unique structural properties compared with natural images. Thus, employing the traditional image algorithms rather than those developed specifically for seismic data is inadequate to extract all seismic features. Additionally, compared with natural images, it is difficult to acquire noise-free ground truth, which restricts the use of supervised deep learning approaches in seismic data denoising. To this end, a self-supervised inter-trace seismic data denoising network (STSNet) that requires only one seismic section for random noise attenuation is proposed. Furthermore, we adopt a single trace vibration signal as the basic unit and fully consider the characteristics of seismic signals. This is the first time to introduce a self-learning spatial attention mechanism among seismic traces to focus on the noise components, which prompts the network’s fitting performance. Different comparative experiments demonstrate that our approach can achieve exceptional denoising performance even if only observing a single seismic section. Additionally, the ablation experiments also confirm the efficiency of spatial attention.

This paper presents an improved method based on Speed up Robust Features (SURF) algorithm to achieve fast image stitching. As the variability of scenes lead to instability of features, expecting to obtain accurate number of features is pretty difficult and time-consuming. support vector machine (SVM) applied in this paper to predict primary threshold of determinant of Hessian matrix can conspicuously reduce detected feature points and simplify the process of features matching. This paper also combines an optimized method of image preprocessing-cylindrical projection and image interpolation to weigh the final quality of stitching image and stitching time. Several experiments are conducted to verify the performance of improved SURF.

Substation is the hub of the power grid. Regular inspection is very crucial in order to confirm the electrical equipment in substation to operate normally. Recently, manual inspection is gradually replaced by inspection robot. Local environment modelling and path planning are mainly key problems when patrol robot carry out the inspection work. For dealing with this challenging but imperative issue, there are numerous researchers have strove for this scientific field and have proposed some valuable approaches. The LIDAR is one of excellent sensors for environment perception and collision avoidance for robot. For enhancing the suitability of path planning, a novel local environment modelling method is proposed in which the safety, accessibility, stability and reachability are taken into account when the patrol robot moves in unknown environment, one robot control algorithm which meet the kinematics and dynamics motion principle is investigate as well. Some simulation experiments have also been conducted for validating modelling and planning performance of the proposed approach.