Infrared small target segmentation technology plays an important role in fields such as missile warning, maritime rescue, and military reconnaissance. However, CNN methods based on convolution tend to lose information regarding infrared small targets, resulting in poor segmentation performance. On the other hand, methods based on transformers, lacking convolution-induced biases, also struggle to achieve good results. To address this issue, this article proposes a model called Multiscale Feature Fusion Spatial-channel Attention Network (MFFSANet) for the segmentation of infrared small targets. The MFFSANet model consists of three blocks: the Multi-scale Convolution Fusion Attention (MCFA) block, the Hierarchical Guided Channel Attention (HGCA) block, and the Atrous Residual U-Block (ARU). The MCFA block leverages multi-scale atrous convolutions and self-attention mechanisms to obtain both local and global information about the image, learning the difference between target features and background noise features, thus enabling the model to suppress background noise in infrared images. The HGCA block leverages coarser information to guide the learning of finer features, assigning weights to decisive channels, and reducing redundant information. This reduces background noise in infrared images, making small targets stand out more clearly against the background. The ARU facilitates interaction between feature maps of different layers and scales, enabling the model to recognize the characteristics of small infrared targets in a more detailed and comprehensive manner. Extensive experiments conducted on four publicly available datasets, namely SIRST, IRSTD-1k, NUDT-SIRST, and SIRST-Aug, demonstrate the effectiveness and superiority of the proposed MFFSANet method compared to several SOTA infrared small target segmentation methods. The source code is available athttps://github.com/change68/MFFSANet.

This paper proposes a finite-time sliding mode adaptive tracking control strategy with a neural network disturbance observer to address problems of inertia uncertainties, unknown external disturbances, singularity, and chattering. First, an accurate mathematical model of the Bernoulli-Euler beam system is constructed using an in-domain constraint strategy for surface-mounted sensors. Next, a novel finite-time disturbance observer based on the Radial Basis Function (RBF) neural network is designed to handle uncertainties, enabling fast response. In addition, a novel integral sliding surface is designed to resolve the singularity issue in terminal sliding mode. Finally, an adaptive terminal sliding mode controller is developed based on the finite-time disturbance observer and integral sliding surface to ensure finite-time convergence of tracking errors; its stability is rigorously analyzed. Simulation and experimental results demonstrate that the proposed sliding mode control strategy achieves superior tracking performance and finite-time stability compared to traditional methods in industrial beam systems.

The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pai n states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.

This paper focuses on the sensorless high-precision position correction strategy for a 100 kW@20 000 r/min brushless dc (BLdc) motor with low stator inductance (0.051 mH). Two key points related to the sensorless were studied. The one is the rotor position detection with high-frequency interference. The other one is the commutation compensation for the high-speed BLdc motor with low stator inductance in whole speed range. In order to filter out the high-frequency interference, the main factors, which that can influence the rotor position detection and the commutation accuracy were analyzed. In order to compensate the commutation error in high-precision and whole speed range, the relationship between the commutation error angle and the phase current deviation was derived. It is noted that the commutation time and the phase current error can exactly reflect the commutation error angle. Then, a novel sensorless high-precision position correction strategy based on the uncommutation current was proposed. Finally, the experiment platform based on a 100 kW@20 000 r/min BLdc motor was built, and the validity and efficiency of the proposed strategy were proved.