Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

Semiconductor wafer is widely used in welding robot, spray robot, unmanned material delivery vehicle and detection station sensor. The defects of semiconductor wafer, such as stains, burrs, scratches and holes generated in the manufacturing process, severely affect the quality of downstream products. Therefore, the inspection of wafer defect could not be neglected. Traditional semiconductor wafer defect inspection methods based on hand-craft features heavily rely on the expertise and are limited in some application scenario. In this paper, a novel method based on deep convolutional neural networks for semiconductor wafer surface defect inspection is proposed. Firstly, a new structure of feature pyramid networks with atrous convolution (FPNAC) is developed to extract the features and to generate feature maps. Secondly, the feature maps are fed into region proposal network (RPN) to generate region proposals. Finally, the region proposals are aligned to corresponding size as the inputs of deep multi-branches neural network (DMBNN) consisting of three branches, to classify and segment the defects precisely. Experimental results demonstrate that the proposed method yields good comprehensive performance with Mean Pixel Accuracy (MPA) 93.97% and Mean Intersection over Union (MIoU) 83.58%.

Electric motors find widespread application across various industrial fields. The pursuit of enhanced comprehensive electric motors performance has consistently drawn significant attention, prompting extensive research in this domain. With the rapid and profound integration of artificial intelligence (AI) technology into diverse sectors, an increasing array of AI-based methodologies is being employed in electric motors research. This review aims to comprehensively delineate the applications of AI within the realm of electric motors, specifically focusing into three pivotal stages: electric motors design, drive control, and health maintenance, covering a full-cycle research and development of electric motors. The review commences by outlining the elucidation of the fundamental principles and components of AI. Subsequently, the encompassed input signals, characteristic methodologies, and AI techniques are reviewed and analyzed as demonstrated by pertinent research cases. Furthermore, a synthesis of the distinctive attributes characterizing various techniques is presented. Conclusively, this article offers an unprecedented review of AI-based technology throughout the entirety of the electric motors research cycle. Considering the existing body of knowledge, this article stands as the pioneering endeavor to encapsulate the expansive domain of AI's influence in electric motors research.

Gas drainage is carried out based on output from each coal bed throughout commingling production of coalbed methane (CBM). A reasonable drainage process should therefore initially guarantee main coal bed production and then enhance gas output from other beds. Permanent damage can result if this is not the case, especially with regard to fracture development in the main gas-producing coal bed and can greatly reduce single well output. Current theoretical models and measuring devices are inapplicable to commingled CBM drainage, however, and so large errors in predictive models cannot always be avoided. The most effective currently available method involves directly measuring gas output from each coal bed as well as determining the dominant gas-producing unit. A dynamic evaluation technique for gas output from each coal bed during commingling CBM production is therefore proposed in this study. This technique comprises a downhole measurement system combined with a theoretical calculation model. Gas output parameters (i.e., gas-phase flow rate, temperature, pressure) are measured in this approach via a downhole measurement system; substituting these parameters into a deduced theoretical calculation model then means that gas output from each seam can be calculated to determine the main gas-producing unit. Trends in gas output from a single well or each seam can therefore be predicted. The laboratory and field test results presented here demonstrate that calculation errors in CBM outputs can be controlled within a margin of 15% and therefore conform with field use requirements.

A defect passivation strategy using [BMMIm]Cl ion liquid is demonstrated for the fabrication of high-performance, charge-transport-layer-free CsPbCl₃ UV photodetectors, achieveing an ultrahigh on/off ratio of 2.70 × 10⁵ at 0 V bias.

The rational fabrication of composite structures made of mixed components has shown great potential for boosting the energy density of supercapacitors. Herein, an elaborate hierarchical MOF-derived NiCo₂S₄@Mo-doped Co-LDH arrays hybrid electrode was fabricated through a step-wise method. By leveraging the synergistic effects of a uniform array of NiCo₂S₄ nanowires as the core and an MOF-derived porous shell, the NiCo₂S₄@Mo-doped Co-LDH hybrid electrode demonstrates an exceptional specific capacitance of 3049.3 F g⁻¹ at 1 A g⁻¹. Even at a higher current density of 20 A g⁻¹, the capacitance remains high at 2458.8 F g⁻¹. Moreover, the electrode exhibits remarkable cycling stability, with 91% of the initial capacitance maintained after 10,000 cycles at 10 A g⁻¹. Additionally, the as-fabricated asymmetric supercapacitor (ASC) based on the NiCo₂S₄@Mo-doped Co-LDH electrode achieves an impressive energy density of 97.5 Wh kg⁻¹ at a power density of 835.6 W kg⁻¹. These findings provide a promising approach for the development of hybrid-structured electrodes, enabling the realization of high-energy-density asymmetric supercapacitors.