The dynamic job-shop scheduling problem is a complex and uncertain task that involves optimizing production planning and resource allocation in a dynamic production environment. Traditional methods are limited in effectively handling dynamic events and quickly generating scheduling solutions; in order to solve this problem, this paper proposes a solution by transforming the dynamic job-shop scheduling problem into a Markov decision process and leveraging deep reinforcement learning techniques. The proposed framework introduces several innovative components, which make full use of human domain knowledge and machine computing power, to realize the goal of man–machine collaborative decision-making. Firstly, we utilize disjunctive graphs as the state representation, capturing the complex relationships between various elements of the scheduling problem. Secondly, we select a set of dispatching rules through data envelopment analysis to form the action space, allowing for flexible and efficient scheduling decisions. Thirdly, the transformer model is employed as the feature extraction module, enabling effective capturing of state relationships and improving the representation power. Moreover, the framework incorporates the dueling double deep Q-network with prioritized experience replay, mapping each state to the most appropriate dispatching rule. Additionally, a dynamic target strategy with an elite mechanism is proposed. Through extensive experiments conducted on multiple examples, our proposed framework consistently outperformed traditional dispatching rules, genetic algorithms, and other reinforcement learning methods, achieving improvements of 15.98%, 17.98%, and 13.84%, respectively. These results validate the effectiveness and superiority of our approach in addressing dynamic job-shop scheduling problems.

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.

With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.