Emotion is a complex psychophysiological response to external stimuli, essential for human survival, social interaction, and human-computer interaction. Emotion recognition plays a critical role in both biological systems and artificial agents. However, existing research often treats these systems independently, limiting opportunities for interaction and hindering the development of more advanced models. This study employs representational similarity analysis (RSA) to bridge this gap by comparing emotional representations between the human brain and neural networks, aiming to improve understanding of emotion recognition in deep learning models. By correlating the emotion recognition model EmoNet with EEG signals from the human brain during emotional image processing and introducing AlexNet for comparison, we reveal EmoNet’s human-like representation for emotional images and its hierarchical structure for emotion recognition. The results show that RSA effectively aligns human emotional processing with deep neural networks, offering new avenues for improving the interpretability and performance of emotional AI models. Moreover, they underscore EmoNet’s potential to simulate human emotional responses, paving the way for future research to enhance emotion recognition models by incorporating human emotional evaluations into their training processes, thereby improving efficiency and specificity.

This study presents the design and implementation of a real-time monitoring system for traction elevators, leveraging piezoelectric sensors for vibration measurement and speed sensors for velocity data acquisition. The system is powered by a LattePanda dashboard equipped with an integrated Real-Time Clock (RTC), ensuring precise data collection and timestamping. Vibration data is captured through piezoelectric sensors, while velocity data from speed sensors is used to calculate acceleration. The collected data is stored locally and can also be transmitted remotely. Aimed at improving elevator safety and efficiency, the system detects potential issues such as misalignments and mechanical wear. Given the increasing number of elevator accidents, this study focuses on enhancing monitoring capabilities using advanced technologies. Data from an electric elevator was analyzed with three anomaly detection algorithms: Isolation Forest, Support Vector Machine (SVM), and Z-score. The results revealed that Isolation Forest identified 15 anomalies (1.06% of the data), SVM detected 25 anomalies (1.77% of the data), and Z-score identified 86 anomalies (6.08% of the data). This research not only enhances elevator condition monitoring but also lays the groundwork for future digital twin systems in passenger elevator applications.