In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual feature extraction. Then the temporal relationship learning between multidimensional feature vectors is performed by introducing a long short-term memory (LSTM) network to make up for the lack of long-short distance dependence of the captured sequence of the CNN network. Finally, an attention mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then performs as a tool wear predictor. The model is compared with several state-of-the-art models on the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction accuracy, but with similar computational complexity.

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

The research on virus propagation process and control method in wireless sensor networks (WSNs) is one of the essential challenges of network security. This paper proposes a virus control mechanism of degree, betweenness centrality, and k-core-based analytical hierarchy process (DBC-AHP) for WSNs. According to the topology of WSNs, the virus control mechanism uses the DBC-AHP to identify the crucial nodes of the network. It uses the way of crucial nodes’ self-disconnection to suppress the spread of the virus, to improve the network security. In this paper, the effectiveness of the virus control mechanism based on the DBC-AHP is verified by comparing and analyzing the effect of four different crucial node recognition algorithms. With the research of virus control mechanisms in various network environments, it is found that the average degree of nodes, the communication radius of nodes, and the probability of virus infection can affect the inhibition effect of the virus control mechanism. Furthermore, the inhibition effect of virus control mechanisms is studied under the condition with/without MAC mechanism.