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The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human–computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This study proposes a method combining ResNet18 with differential entropy to identify five types of human emotions (happiness, sadness, fear, disgust, and neutral) from EEG signals. Our approach first calculates the differential entropy of EEG signals to capture the complexity and variability of the emotional states. Then, the ResNet18 network is employed to learn feature representations from the differential entropy measures, which effectively captures the intricate spatiotemporal dynamics inherent in emotional EEG patterns using residual connections. To validate the efficacy of our method, we conducted experiments on the SEED-V dataset, achieving an average accuracy of 95.61%. Our findings demonstrate that the combination of ResNet18 with differential entropy is highly effective in classifying multiple distinct human emotions from EEG signals. This method shows robust generalization and broad applicability, indicating its potential for extension to various pattern recognition tasks across different domains. [ABSTRACT FROM AUTHOR]

The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method. [ABSTRACT FROM AUTHOR]

According to the main construction procedures of underground caverns, an analysis model for the excavation of a certain pumping station underground cavern group was established, and its excavation and support process are dynamically simulated. The calculation schemes of unsupport and support are compared with indicators such as plastic zone, bolt axial force and displacement field distribution. The analysis results show that after the support, the plastic zone distribution and deformation characteristics of the rock around the cave are greatly improved. The bolt axial force also meets the requirements, indicating that the support parameters and support methods are reasonable. The supporting effect has been well simulated and realized.

Artificial intelligence has revolutionized image and speech recognition, but the neural network fitting method has limitations. Neuromorphic chips that mimic biological neurons can better simulate the brain's information processing mechanism. As the basic computing component of the new neuromorphic network, the new neural computing unit's design and implementation have important significance; however, complex dynamical features come with a high computational cost: approximate computing has unique advantages, in terms of optimizing the computational cost of neural networks, which can solve this problem. This paper proposes a hardware implementation of an approximate spiking neuron structure, based on a simplified piecewise linear model (SPWL), to optimize power consumption and area. The proposed structure can achieve five major neuron spiking generation patterns. The proposed design was synthesized and compared to similar designs, to evaluate its potential advantages and limitations. The results showed that the approximate spiking neuron had the lowest computational cost and the fastest computation speed. A typical spiking neural network was constructed, to test the usability of the SPWL model. The results showed that the proposed approximate spiking neuron could work normally in the spiking neural network, and achieved an accuracy of 94% on the MNIST dataset. [ABSTRACT FROM AUTHOR]