Target Recognition Based on Infrared and Visible Image Fusion and Improved YOLOv8 Algorithm.

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives.

Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion.

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.

Infrared Target Detection Based on Joint Spatio-Temporal Filtering and L1 Norm Regularization.

Infrared target detection is often disrupted by a complex background, resulting in a high false alarm and low target recognition. This paper proposes a robust principal component decomposition model with joint spatial and temporal filtering and L1 norm regularization to effectively suppress the complex backgrounds. The model establishes a new anisotropic Gaussian kernel diffusion function, which exploits the difference between the target and the background in the spatial domain to suppress the edge contours. Furthermore, in order to suppress the dynamically changing background, we construct an inversion model that combines temporal domain information and L1 norm regularization to globally constrain the low rank characteristics of the background, and characterize the target sparse component with L1 norm. Finally, the overlapping multiplier method is used for decomposition and reconstruction to complete the target detection.Through relevant experiments, the proposed background modeling method in this paper has a better background suppression effect in different scenes. The average values of the three evaluation indexes, SSIM, BSF and IC, are 0.986, 88.357 and 18.967, respectively. Meanwhile, the proposed detection method obtains a higher detection rate compared with other algorithms under the same false alarm rate.

Interface stress transfer model and modulus parameter equivalence method for composite materials embedded with tensile pre-strain shape memory alloy fibers.

The constitutive model and modulus parameter equivalence of shape memory alloy composites (SMAC) serve as the foundation for the structural dynamic modeling of composite materials, which has a direct impact on the dynamic characteristics and modeling accuracy of SMAC. This article proposes a homogenization method for SMA composites considering interfacial phases, models the interface stress transfer of three-phase cylinders physically, and derives the axial and shear stresses of SMA fiber phase, interfacial phase, and matrix phase mathematically. The homogenization method and stress expression were then used to determine the macroscopic effective modulus of SMAC as well as the stress characteristics of the fiber phase and interface phase of SMA. The findings demonstrate the significance of volume fraction and tensile pre-strain in stress transfer between the fiber phase and interface phase at high temperatures. The maximum axial stress in the fiber phase is 705.05 MPa when the SMA is fully austenitic and the pre-strain increases to 5%. At 10% volume fraction of SMA, the fiber phase's maximum axial stress can reach 1000 MPa. Ultimately, an experimental verification of the theoretical calculation method's accuracy for the effective modulus of SMAC lays the groundwork for the dynamic modeling of SMAC structures.

Sound Quality Performance of Orthogonal Antisymmetric Composite Laminates Embedded with SMA Wires.

Orthogonal antisymmetric composite laminates embedded with shape memory alloys (SMAs) wires have the potential to improve the sound quality of vibro-acoustics by taking advantage of the special superelasticity, temperature phase transition, and pre-strain characteristics of SMAs. In this research, space discretion and mode decoupling were employed to establish a vibro-acoustic sound quality model of SMA composite laminates. The association between the structural material parameters of SMA composite laminates and the sound quality index is then approached through methodologies. Numerical analysis was implemented to discuss the effects of SMA tensile pre-strain, SMA volume fraction, and the ratio of resin-to-graphite in the matrix on the vibro-acoustic sound quality of SMA composite laminates within a temperature environment. Subsequently, the sound quality test for SMA composite laminates is thus completed. The theoretically predicted value appears to agree well with the experimental outcomes, which validates the accuracy and applicability of the dynamic modeling theory and method for the sound quality of SMA composite laminates. The results indicate that attempting to alter the SMA tensile pre-strain, SMA volume fraction, and matrix material ratio can be used to modify loudness, sharpness, and roughness, which provides new ideas and a theoretical foundation for the design of composite laminates with decent sound quality.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this paper. SSMN consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function. Based on attention mechanism, the encoder is able to extract distinct features to generate prototypes and enhance the identification accuracy. With semi-supervised few-shot learning, SSMN utilizes unlabeled data to refine original prototypes for better fault recognition. A combinatorial learning optimizer is designed to optimize SSMN efficiently. The effectiveness of the proposed method is demonstrated through three bearing vibration datasets and the results indicate the outstanding adaptability in different situations. Comparison with other approaches is also made under the same setup and the experimental results prove the superiority of the proposed method for few-shot fault diagnosis.

Multi-Frequency Augmentation framework via information active capture for machinery intelligent fault diagnosis.

Data-driven methods, especially deep neural network, received increasing attention in machinery fault diagnosis field. Many works focus on how to design effective model while ignoring a fundamental problem, i.e., directly using raw machinery signal as the input of model. In this work, we analyze from two aspects: model mechanism and mechanical monitoring signal, it shows the limitation of learning raw data directly, which led to the research idea of improving the generalization ability of model by multi-frequency information augmentation. In order to make machinery intelligent model capture multi-frequency information more directly and actively, Multi-Frequency Augmentation framework is proposed in this paper. Firstly, we proposed a data augmentation method to split the raw sample into sample pair. And we could choose to further augment the dataset by Frequency Components Recombination, especially under few-shot scenes. Then, Multi-Frequency Capture Network is built to achieve feature augmentation by learning the sample pair. Finally, fault diagnosis is performed on testing set. The effectiveness and compatibility of Multi-Frequency Augmentation framework is verified with two experiments, which also verifies the feasibility of the proposed research idea. In addition, it could also achieve competitive performance with latest literature methods. Further discussion indicate that the proposed framework provides a new perspective to analyze the model and dataset, which has good application potential.

Multi-channel Calibrated Transformer with Shifted Windows for few-shot fault diagnosis under sharp speed variation.

In engineering practice, mechanical equipment is mainly operated under the working conditions of sharp speed variations, which results the data distribution domain shift. Furthermore, the domain shift and the lack of data in engineering practice render severe challenges for existing intelligent mechanical faults diagnosis technologies. To this end, this paper proposed a Multi-channel Calibrated Transformer with Shifted Windows (MCSwin-T) for computing self-attention in each non-overlapping window which models the relations between the sequences of split patches. Meanwhile, a new partitioning approach is designed by shifting the windows and alternately use the two different partitioning approach to establish the connections across windows. To extract low-level features of the signal and maintain the positional information, a plurality of convolution layers is applied before transformer block. A normalized method which is a multi-channel multiplication of the vector generated by each residual block is also developed to calibrate activation and increase the stability of the optimization. To evaluate the effectiveness, the proposed method is compared with multiple advanced transformer methods in two case studies under speed transient conditions. The experimental results indicate the superiority and higher accuracy of the proposed method under few-shot domain shift condition.

Green Preparation of Aqueous Graphene Dispersion and Study on Its Dispersion Stability.

The large-scale preparation of stable graphene aqueous dispersion has been a challenge in the theoretical research and industrial applications of graphene. This study determined the suitable exfoliation agent for overcoming the van der Waals force between the layers of expanded graphite sheets using the liquid-phase exfoliation method on the basis of surface energy theory to prepare a single layer of graphene. To evenly and stably disperse graphene in pure water, the dispersants were selected based on Hansen solubility parameters, namely, hydrophilicity, heterocyclic structure and easy combinative features. The graphene exfoliation grade and the dispersion stability, number of layers and defect density in the dispersion were analysed under Tyndall phenomenon using volume sedimentation method, zeta potential analysis, scanning electron microscopy, Raman spectroscopy and atomic force microscopy characterization. Subsequently, the long-chain quaternary ammonium salt cationic surfactant octadecyltrimethylammonium chloride (0.3 wt.%) was electrolyzed in pure water to form ammonium ions, which promoted hydrogen bonding in the remaining oxygen-containing groups on the surface of the stripped graphene. Forming the electrostatic steric hindrance effect to achieve the stable dispersion of graphene in water can exfoliate a minimum of eight layers of graphene nanosheets; the average number of layers was less than 14. The 0.1 wt.% (sodium dodecylbenzene sulfonate: melamine = 1:1) mixed system forms π-π interaction and hydrogen bonding with graphene in pure water, which allow the stable dispersion of graphene for 22 days without sedimentation. The findings can be beneficial for the large-scale preparation of waterborne graphene in industrial applications.