This paper addresses the challenge of small object detection in remote sensing image recognition by proposing an improved YOLOv8-based lightweight attention cross-scale feature fusion model named LACF-YOLO. Prior to the backbone network outputting feature maps, this model introduces a lightweight attention module, Triplet Attention, and replaces the Concatenation with Fusion (C2f) with a more convenient and higher-performing dilated inverted convolution layer to acquire richer contextual information during the feature extraction phase. Additionally, it employs convolutional blocks composed of partial convolution and pointwise convolution as the main body of the cross-scale feature fusion network to integrate feature information from different levels. The model also utilizes the faster-converging Focal EIOU loss function to enhance accuracy and efficiency. Experimental results on the DOTA and VisDrone2019 datasets demonstrate the effectiveness of the improved model. Compared to the original YOLOv8 model, LACF-YOLO achieves a 2.9% increase in mAP and a 4.6% increase in mAP_S on the DOTA dataset and a 3.5% increase in mAP and a 3.8% increase in mAP_S on the VisDrone2019 dataset, with a 34.9% reduction in the number of parameters and a 26.2% decrease in floating-point operations. The model exhibits superior performance in aerial object detection.

Traditional fault diagnosis methods often require extracting features from raw vibration signals based on prior knowledge, which are then input into intelligent classifiers for pattern recognition. This process is prone to information loss and can be inaccurate when relying on human experience for fault identification. To address this issue, this paper proposes an intelligent fault classification and diagnosis model for rolling bearings based on Fast Fourier Transform (FFT) combined with a time convolutional network (SE-TCN) incorporating an attention mechanism, with a Support Vector Machine (SVM) used as the classifier. First, the FFT is applied to transform the collected raw time-domain data of bearing faults into the frequency domain, obtaining the sequence information in the frequency domain. Second, the frequency–domain sequence data are fed into the SE-TCN model, which uses multiple convolutional layers and a channel attention mechanism to extract deep fault features. Finally, the extracted feature vectors are input into the SVM classifier, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the SVM parameters. The optimal separating hyperplane is obtained through training to classify the fault types of the rolling bearings. To verify the effectiveness and diagnostic performance of the proposed method, experiments are conducted using bearing fault datasets from Case Western Reserve University (CWRU) and a laboratory self-built fault diagnosis experimental platform. The experimental results show that the classification accuracy of the proposed method exceeds 99% on the CWRU test dataset, and it also demonstrates advantages in handling small sample data, with an accuracy of over 90%. Additionally, it exhibits good diagnostic performance on the bearing fault data collected from the laboratory self-built platform. The results validate the effectiveness of the proposed classification model in bearing a fault diagnosis.

Due to the viscoelasticity of rubber materials, hysteresis loss due to deformation is the main reason for the rolling resistance of high-speed rubber tracks. Since the structure and material of high-speed rubber track assemblies are different from traditional tires and metal tracks, the rolling resistance theory of traditional wheeled and tracked vehicles is not applicable. Therefore, in order to determine the rolling resistance scientifically and accurately, the mechanism research of the rolling resistance of high-speed rubber track assembly is the key to the design of high-speed rubber crawler vehicles. In this paper, the stress–strain characteristics of rubber track under the action of compression, tension, bending, and driving were studied. The strain load spectrum of rubber tracks was established, and the strain cyclic load was extracted by the rainflow method. The temperature model of the rubber track was developed based on its dynamic characteristics. On the basis of energy conservation, the hysteresis loss of rubber is equivalent to the energy consumption of rolling resistance, and the theoretical model of rolling resistance of high-speed rubber track assembly is established. In accordance with the model above, the key influencing factors and changing trends of rolling resistance are analyzed, which provides a theoretical basis for the performance optimization of high-speed rubber track assembly.

Noise reduction for manufacturing enterprises is favorable for workers because it relieves occupational diseases and improves productivity. An acoustic metamaterial with parallel, unequal cavities is proposed and optimized, aiming to achieve an optimal broadband sound absorber in the low–frequency range with a limited total thickness. A theoretical model for the acoustic metamaterial of a hexagonal column with 6 triangular cavities and 12 right–angled trapezoidal cavities was established. The lengths of these embedded apertures were optimized using the particle swarm optimization algorithm, with initial parameters obtained from acoustic finite element simulation. Additionally, the impacts of manufacturing errors on different regions were analyzed. The experimental results prove that the proposed acoustic metamaterials can achieve an average absorption coefficient of 0.87 from 384 Hz to 667 Hz with a thickness of 50 mm, 0.83 from 265 Hz to 525 Hz with a thickness of 70 mm, and 0.82 from 156 Hz to 250 Hz with a thickness of 100 mm. The experimental validation demonstrates the accuracy of the finite element model and the effectiveness of the optimization algorithm. This extensible acoustic metamaterial, with excellent sound absorption performance in the low-frequency range, can be mass-produced and widely applied for noise control in industries.

Bearing fault is a process of gradual development and deepening. In the early stage of the fault, if it can be found out in time and taken reasonable prevention and elimination measures, we can avoid serious losses and safety accidents. Therefore, the feature extraction and analysis of early weak fault has important practical significance. In this paper, an improved multiscale permutation entropy (IMPE) method was proposed to overcome the shortcomings in the coarse-grained process. In order to solve the problem that only considering a single coarse-grained sequence under a certain scale may lead to the loss of feature information, this paper proposed to calculate the time series with equal overlapping segments, that was to consider all coarse-grained sequences under the same scale to reflect the feature information of fault signals more comprehensively. In order to solve the problem that feature extraction is not refined enough when using the first-order moment mean value calculation in traditional MPE calculation, a calculation method based on the skewness of the third-order moment was proposed. The calculation method is more sensitive to the complexity and fluctuation of signals and can better describe the feature details and extract the fault features effectively. IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.

Object detection in remote sensing imagery is a challenging task in the field of computer vision and has high research value. To improve the classification accuracy and positioning accuracy of object detection, we propose a new multi-scale oriented object detector suitable for small objects. Firstly, the feature fusion network based on information balance (IBFF) is proposed to reduce the reuse of different layers’ features from the backbone network and reduce the interference of redundant information based on the premise that the output features have sufficient information, and retain enough shallow detail information. Secondly, to efficiently utilize deep and shallow features, enhance important features, and reduce background noise interference, different attention-based context feature fusion modules (DACFF) are designed according to the characteristics of different feature fusion stages. Finally, an improved strategy of oriented bounding box regression is proposed to obtain the oriented bounding box with a simpler and more effective strategy. The proposed method was evaluated on two public remote sensing datasets, DOTA and HRSC2016, and their mAP values are 80.96% and 95.01%, respectively, which verified the effectiveness of the proposed algorithm.