In this paper, a novel infrared maritime small target detection method, called local dissimilarity measure with anti interference based on global graph clustering (LDMGGC), is proposed. The Wasserstein distance is introduced to calculate the dissimilarity of gray level distribution between a central region and its neighborhoods. These dissimilarities construct the feature of a region. With this feature, detection for recalling all suspected targets is achieved. As the maritime interferences among suspected targets are able to be clustered, relaxing mutual k nearest neighbor graph is introduced in global graph clustering for filtering interferences. With this method, real targets are detected and maritime interferences are filtered out. Experiments are conducted on three maritime datasets and a non-maritime dataset for comparison. On three datasets, the proposed method achieves the best Receiver Operating Characteristic curves and Area Under Curve (0.99529, 0.99945, 0.99573, and 0.9906) values, meaning that the proposed method has high detection probability and low false-alarm ratio. Target Hit Rate (98.04%, 97.96%, 100%, and 99.24%) and Intersection of Union (0.8170, 0.7542, 0.5824, 0.7707) on four datasets of the proposed method show it has a strong ability to suppress the interferences

Welding defect detection based on radiographic images plays a vital role in industrial nondestructive testing. It provides an effective guarantee with respect to welding quality in shipbuilding, chemical industry, and aerospace applications. A variety of related computer-based image processing technologies have been designed for the detection of weld defects. However, this is a challenging task because weld defects can exhibit different shapes, sizes, positions, and contrasts in radiographic images. This paper proposes an end-to-end weld defect recognition method that mainly includes three steps. In the first step, we propose an improved algorithm based on deep belief network, which classifies weld feature curves extracted by applying infinite norm for detecting defective weld base on radiographic images. In the second step, a new cylindrical projection method is proposed to increase the proportion of defect parts in the images and solve the problem of loss of defects with small size. And in the third step, we propose an improved deep learning network that is based on SegNet to identify weld defects. Experimental verification shows that this method can realize end-to-end weld defect recognition and strong robustness. Compared with existing methods, this method exhibits obvious advantages and can effectively assist inspectors in Welding defect detection and significantly improve the detection efficiency of industrial nondestructive testing.

A novel network for enhancement of underwater optical images is proposed in this paper. It contains a Reinforcement Fusion Module for Haar wavelet (RFM-Haar) images. Fusion is performed to enrich available underlying information for better enhancement. As this network transform Haar Images into Fusion Images, it is called HIFI-Net. The experimental results based on Mean Square Error (0.1952, 0.1512 and 0.4683), Peak Signal to Noise Ratio (25.2241, 26.3342 and 21.4253) and Structure Similarity Index Measure (0.8265, 0.8819 and 0.8012) on three public datasets (EUVP, UFO-110 and UIEB) demonstrate that the proposed HIFI-Net performs better than some existing state-of-the-art methods.

Recently, natural material-based triboelectric nanogenerators (TENGs) have increasingly attracted attention in academic circles. In this work, we have developed an innovative triboelectric nanogenerator (BL-TENG) utilizing bamboo leaves to capture biomechanical energy. Bamboo leaf, as a natural plant material, possesses a diverse array of applications due to its remarkable durability, which surpasses that of many other types of trees. Furthermore, bamboo leaf has the advantages of low cost, widely distributed, non-toxic and environmentally protected. The output power of the BL-TENG (size: 5 cm × 5 cm) is able to generate approximately 409.6 µW and the internal resistance of the BL-TENG is 40 MΩ. Furthermore, the BL-TENG can realize an open-circuit voltage (Voc) of 191 V and a short-circuit current (Isc) of 5 µA, respectively. The biomechanical energy harvesting effect of the BL-TENG device means that it can drive 18 commercial light-emitting diodes (LEDs) through the full-wave bridge rectifier. Furthermore, the BL-TENG can also serve as a self-powered touch sensor to reflect hand touch states. This study proposed a novel plant-based TENG device that can enhance the development of green TENG devices and self-powered sensing systems.

Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a neural network is used to optimize the fitting of the preload data. The limitations of conventional preload testing methods are identified, and the integration of complementary information from multiple sensors is used to achieve accurate predictions, offering valuable insights into the optimal preload force. Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this paper proposes a super-resolution-based dimensional detection method for small modulus injection molding gears. The method introduces an improved super-resolution algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear’s precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756um and 1.531um respectively, and the repeat errors of the repeated measurements are less than 2um. Therefore, the research results of this paper provide a new detection method for related industries, which is of positive significance for improving the production efficiency and quality.