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Research on the rectangle packing problems has mainly focused on rectangular raw material sheets without defects, while natural slate has irregular and defective characteristics, and the existing packing method adopts manual packing, which wastes material and is inefficient. In this work, we propose an effective packing optimization method for nature slate, to the best of our knowledge, this is the first attempt to solve the guillotine packing problem of rectangular items in a single irregular and defective slate. This method is modeled by the permutation model, uses the horizontal level (HL) heuristic proposed in this paper to obtain feasible solutions, and then applies the genetic algorithm to optimize the quality of solutions further. The HL heuristic is constructed on the basis of computational geometry and level packing. This heuristic aims to divide the irregular plate into multiple subplates horizontally, calculates the movable positions of the rectangle in the subplates, determines whether or not the rectangle can be packed in the movable positions through computational geometry, and fills the scraps appropriately. Theoretical analysis confirms that the rectangles obtained through the HL heuristic are inside the plate and do not overlap with the defects. In addition, the packed rectangles do not overlap each other and satisfy the guillotine constraint. Accordingly, the packing problem can be solved. Experiments on irregular slates with defects show that the slate utilization through our method is between 89% and 95%. This result is better than manual packing and can satisfy actual production requirements.

Certificates of assets and capabilities contain personal privacy information. At present, certificate management still adopts the traditional centralized management method, and the registered information is stored in a clear text, thereby leading to a weak tamper resistance of information and is not conducive to the protection of privacy information. Ciphertext storage leads to the difficulty of information query and statistics. In addressing the two problems mentioned above, this study applies blockchain and homomorphic encryption technology in certificate management system, and proposes to use Paillier homomorphic encryption algorithm under Fabric blockchain platform to protect personal privacy, realize ciphertext storage of personal information, improve the ability to resist external network intrusion, and use the homomorphic additivity of Paillier algorithm to create statistics on data in ciphertext space thus improving the protection of users' privacy information. Finally, in accordance with the method proposed in this study, the chaincode is written, and the blockchain network performance under the chaincode is tested using the caliper test framework. The BatchTimeout and PreferredMaxBytes parameters are optimized to reduce the delay in time and improve the throughput rate of the system.

Regular inspection and repair of drainage pipes is an important part of urban construction. Currently, many classification methods have been used for defect diagnosis using images inside pipelines. However, most of these classification models train the classifier with the goal of maximizing accuracy without considering the unequal error classification cost in defect diagnosis. In this study, the authors analyze the characteristics of sewer pipeline defect detection and design an automated detection framework based on the cost-sensitive deep convolutional neural network (CNN). The method makes the CNN network cost sensitive by introducing learning theories at the structural and loss levels of the network. To minimize misclassification costs, the authors propose a new auxiliary loss function Cost-Mean Loss, which allows the model to obtain the original parameters of the network to maximize the accuracy and improve the performance of the model by minimizing total misclassification costs in the learning process. Theoretical analysis shows that the new auxiliary loss function can be applied to the classification task to optimize the expected value of misclassification costs. The inspection images collected from multiple drainage pipes were used to train and test the network. Results show that after the cost-sensitive strategy was added, the defect detection rate decreased from 2.1% to 0.45%. Moreover, the model with Cost-Mean Loss has better performance than the original model.

Generative adversarial networks (GAN) have made great progress in areas, such as computer vision, but evaluation of GAN models is still a daunting task. Numerous researchers have proposed many methods for GAN model evaluation. However, most methods have strong pertinence, which leads to large deviations in the results of different evaluation methods. To date, no consensus has been reached on which method best captures the advantages and limitations of GAN models. In this study, a multi-method fusion approach is proposed to evaluate GAN models from many aspects, such as accuracy, diversity of the images generated by the model, and similarity with the training images. This method provides a new idea for GAN model assessment. Meanwhile, a strategy similar to ensemble learning is used to analyze the generalization of multi-method fusion for GAN assessment. This method and the human eye cognitive assessment model are used on celebA benchmarks and self-acquired image data sets to generate results. Through human eye cognition and comparison with the latest GAN model assessment method, showing that the multi-method fusion GAN evaluation method proposed in this paper has strong robustness and effectiveness.