Printed circuit boards (PCBs) are primarily used to connect electronic components to each other. It is one of the most important stages in the manufacturing of electronic products. A small defect in the PCB can make the final product inoperable. Therefore, careful and meticulous defect detection steps are necessary and indispensable in the PCB manufacturing process. The detection methods can generally be divided into manual inspection and automatic optical inspection (AOI). The main disadvantage of manual detection is that the detection speed is too slow, resulting in a waste of human resources and costs. Thus, in order to speed up the production speed, AOI techniques have been adopted by many PCB manufacturers. Most current AOI mechanisms use traditional optical algorithms. These algorithms can easily lead to misjudgments due to different light and shadow changes caused by slight differences in PCB placement or solder amount so that qualified PCBs are judged as defective products, which is also the main reason for the high misjudgment rate of AOI detection. In order to effectively solve the problem of AOI misjudgment, manual re-judgment is currently the reinspection method adopted by most PCB manufacturers for defective products judged by AOI. Undoubtedly, the need for inspectors is another kind of labor cost. To reduce the labor cost. of manual re-judgement, an accurate and efficient PCB defect reinspection mechanism based on deep learning algorithm is proposed. This mechanism mainly establishes two detection models, which can classify the defects of the product. When both models have basic recognition capabilities, the two models are then combined into a main model to improve the accuracy of defect detection. In the study, the data provided by Lite-On Technology Co., Ltd. were implemented. To achieve the practical application value in the industry, this research not only considers the problem of detection accuracy, but also considers the problem of detection execution speed. Therefore, fewer parameters are used in the construction of the model. The research results show that the accuracy rate of defect detection is about 95%, and the recall rate is 94%. Compared with other detection modules, the execution speed is greatly improved. The detection time of each image is only 0.027 s, which fully meets the purpose of industrial practical application.

Resources scarcity and environmental degradation have made sustainable resource utilization and environmental protection necessary worldwide. The development of the circular economy is considered an approach for more appropriate economic and environmental management. This work introduces a cooperative game network data envelopment analysis model for evaluating the implementation effect of recycling production systems from a closed loop and centralized control perspective. The factor efficiency analysis of the involved inputs and outputs is presented to provide guidance for the factor dominance of subsystem efficiencies. An application for assessing the circular economy of EU countries is provided to illustrate the validation of the proposed method. Our results show that the average performance of the production subsystem is superior to that of the recycling subsystem in EU countries. Furthermore, factor efficiency analysis reveals that the inefficient environmental treatment input is the culprit in worse performance of the recycling subsystem. A comparison of the proposed method with recent studies for circular economy performance evaluation is also included.

With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.

Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.