With the rapid expansion of Industrial Internet of Things, cloud computing and artificial intelligence, many intelligent information services have been developed in smart factories. One of the most important applications is helping factory managers predict the quality of assembled products. Traditional prediction methods of assembly quality mainly focus on building classification or regression models with high accuracy. However, less attention is paid to high-dimensional and imbalanced data, which is a special but common scenario at real-life assembly quality prediction. In this paper, we first use random forest to reduce dimension and analyze critical-to-quality characteristics. Then, a SMOTE-Adaboost method with jointly optimized hyperparameters is proposed for imbalanced data classification in assembly quality prediction. In addition, edge computing is introduced to improve the efficiency and flexibility of quality prediction. Finally, the practicality and effectiveness of the proposed method are verified by a case study of wheel bearing assembly line, and the experimental results show that the proposed method is superior to other classification methods in assembly quality prediction.INDEX TERMS Assembly quality prediction, high-dimensional, imbalanced data classification, SMOTE-Adaboost, edge computing.
With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely manner. Firstly, it introduces the background of deep learning and the knowledge of convolutional neural networks. The purpose is to understand the basic model structure of deep learning applications in the image field, and then outlines the mainstream framework; secondly, it briefly introduces the application of deep learning in CG and NI forensics, and finally points out the problems of deep learning in this field and the prospects for the future.
With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.
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