Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, Jun Xie. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NO_x emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NO_x emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NO_x emissions by combining different weight coefficients as needed.

In recent years, multispectral pedestrian detection as a multimodal solution, using visible light and thermal image multimodal information to solve pedestrian detection challenges in low light environments, plays a crucial role. However, most existing methods directly mix the results of the two modalities or by simply integrating the two modality features. This fusion strategy ignores some features that have specific meaning and are important to the detection problem, and lacks reliable comprehensibility. Drawing inspiration from this insight, in this letter, we propose a pedestrian detection method based on the fusion of multimodal edge information extraction and global semantic consistency, aiming to achieve more full use of edge information-assisted guidance multimodal for accurate fusion detection. We first build an edge attention operation module (EAO) to extract pedestrian edge features, and then introduce a global semantic consistency module (GSC) to make full use of edge features to stimulate guidance modality fusion, and improve the modality consistency between visible light and thermal information. By using challenging KAIST and CVC-14 benchmarks, we observed significant improvements compared to previous state-of-the-art methods. Our proposed method not only outperformed existing techniques but also achieved remarkable accuracy and robustness in the challenging domain of pedestrian detection.