The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the scheduling agent. The DRL method is adaptive to the dynamic changes of the production environment in a flexible job shop, which contributes to the establishment of a flexible scheduling system with self-learning, real-time optimization and intelligent decision-making.

/pdf/deep-reinforcement-learning-for-dynamic-flexible-job-shop-1tbqc5xa.pdf

Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival

GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosis

Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction.

/pdf/application-of-quantum-genetic-optimization-of-lvq-neural-bngt6xkq17.pdf

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.

https://www.mdpi.com/1424-8220/22/17/6563/pdf?version=1661943106

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

Intelligent fault diagnosis of bearings is an essential issue in the field of health management and the prediction of rotating machinery systems The traditional bearing intelligent diagnosis algorithms based on the combination of feature extraction and classification for signal processing require high expert experience, which are time-consuming and lack universality Compared with traditional methods, the convolutional neural network(CNN) can extract features automatically from the original vibration time-domain signal without any preprocessing The accuracy of intelligent fault diagnosis can be improved by utilizing the multi-layer nonlinear mapping capability of deep convolutional neural networks In order to realize the intelligent diagnosis and improve the recognition rate, this paper adopts the strategy of widening convolution kernels to obtain a larger receptive field and proposes a network design process pattern based on this idea, in addition, obtains the convolutional neural network with wide convolution kernels (WKCNN) model through experiments Based on the time-domain vibration signal, this paper generates more input data through expansion and adopts the wide kernels of the first two convolutional layers to quickly extract features to improve efficiency The smaller convolution kernels are used for multi-layer nonlinear mapping to deepen the network and improve detection accuracy The results show that WKCNN performs well in accuracy, anti-noise, and timeliness compared with other diagnostic methods

A bearing fault diagnosis model based on CNN with wide convolution kernels

For the purpose of solving the dynamic flexible job-shop scheduling problem, this paper establishes the mathematical model to minimize the makespan and stability value, an improved double chains quantum genetic algorithm was proposed. Firstly, it is proposed that the method of double chains structure coding including machine allocation chain and process chain. Secondly, it is proposed that non- dominated sorting based on the crowding distance selection strategy. Thirdly, the most satisfying solution is obtained through the multi-attribute grey target decision model. Finally, the novel method is applied to the Brandimarte example and a dynamic simulation, the result of comparing with other classical algorithms verifies its effectiveness.

An improved quantum genetic algorithm based on MAGTD for dynamic FJSP

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A bearing fault diagnosis model based on CNN with wide convolution kernels

An improved quantum genetic algorithm based on MAGTD for dynamic FJSP