Myongji University

Abstract Conventional signal decomposition of raw flow visualizations with a limited field of view fails to accurately identify cavitation instabilities in turbopump inducers. Therefore, a new deep learning-based framework is proposed to directly analyze visualization data and identify such instabilities. The framework employs a hybrid convolutional-recurrent neural network to learn the spatio-temporal features of instability modes. The trained model successfully captures alternate blade cavitation in a two-bladed inducer and super-synchronous rotating cavitation in a three-bladed inducer. Notably, the approach requires neither pre-processing of the visualization data nor extensive measurement durations, as only a few shaft revolutions are sufficient for accurate identification. By eliminating the dependence on unsteady pressure transducers, the proposed framework provides a robust tool for identifying turbopump inducer cavitation instabilities and can be extended to other systems where low-order modal patterns are dominant.

Accurate and reliable short-term passenger flow prediction is crucial for optimizing operational organization and enhancing intelligent management in urban rail transit. However, passenger flow remains relatively stable during off-peak periods, while intense fluctuations during peak hours increase the complexity and uncertainty of prediction, posing challenges for comprehensive and accurate short-term forecasting. To accurately capture the complex dynamic changes in peak-hour passenger flow and reduce the emphasis on the stable characteristics of off-peak periods, this study proposes a composite model. Initially, we construct a passenger flow dataset by processing passenger card swipe information from the Automatic Fare Collection (AFC) system. Temporal features are extracted from the data using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. Furthermore, we introduce a Causal Temporal Pattern Attention (CTPA) mechanism enhanced with dilated causal convolution to optimize temporal feature weights specifically for peak and off-peak passenger flow periods. The optimized features from the regression BiLSTM and CTPA modules are integrated, and multi-step passenger flow predictions are obtained through a fully connected layer. During the training process of the BiLSTM-CTPA model, we utilize the Bayesian Optimization (BO) algorithm to optimize key hyperparameters such as the number of hidden layers, neurons, batch size, learning rate, regularization, and iterations, thereby enhancing training efficiency. Experimental results demonstrate that compared to the baseline model, our approach achieves lower Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). In conclusion, the model presented in this paper provides robust data support for the daily operation and management of urban rail transit.

Accurate prediction of the state of charge (SoC) of batteries is essential for ensuring the safe, reliable, and uninterrupted operation of electric vehicles (EVs). The prediction fundamentally depends on the ability to accurately predict power consumption. This study investigates the use of GPS-derived information to support SoC prediction, with a particular focus on repeated loop routes such as campus shuttles and closed-circuit EV operations. Real-world driving data are collected using a self-built electric vehicle equipped with a custom battery management system (BMS). These data are used to train three deep learning models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and Transformer, to predict the future SoC of the EV. Experimental results show that the GPS-assisted model consistently outperforms the non-GPS baseline, achieving up to a 23% improvement in prediction accuracy for one-minute-ahead predictions and up to a 76% improvement for ten-minute-ahead predictions. These results demonstrate that GPS-assisted SoC prediction can be effective for forward-looking energy management in practical electric mobility applications.

Semiconductor manufacturing relies heavily on automatic test equipment (ATE), and yet thermal aging poses a critical risk to equipment reliability. This study proposes a novel anomaly detection framework for ATE driver boards by integrating cumulative degradation time (CDT)—derived from accelerated life testing (ALT)—with artificial intelligence models. Specifically, the approach quantifies the cumulative effects of thermal stress as CDT and utilizes it as a key input feature to enable the early detection of degradation under prolonged high-temperature conditions. The proposed framework successfully demonstrates the capability to diagnose real-time anomalies before critical CDT thresholds are reached. Consequently, this approach allows for efficient management, significantly contributing to reduced maintenance costs, minimized downtime, and enhanced equipment reliability, serving as a foundational strategy for condition-based maintenance (CBM) strategies in semiconductor manufacturing.

All-solid-state sodium-ion batteries (ASSSIBs) based on halide solid electrolytes (HSEs) are emerging as promising systems for high energy density and stable energy storage. Although HSEs are generally regarded as compatible with high-voltage oxide cathodes, their interfacial stability remains insufficiently understood. Here, we evaluate the interfacial chemical compatibility between representative HSEs and high-voltage sodium cathode materials through mutual decomposition reaction energy calculations. The analysis reveals interfacial instability of HSEs against high voltage cathodes, challenging the prevailing assumption of their intrinsic stability and highlighting the need for targeted interface design. To address this issue, a high-throughput computational screening of 12800 sodium-containing compounds was performed, identifying several coating materials that effectively suppress interfacial reaction driving forces. These coatings promote stable SE-cathode interfaces, ensuring chemical compatibility under high voltage operation. This study establishes a strategic framework for interfacial design that deepens the understanding of HSE stability and advances the development of durable, high-energy ASSSIBs.

This study investigated how people respond to driving errors committed by human drivers versus algorithm-controlled autonomous driving systems, focusing on how the driving agent and error severity shape error tolerance and trust. Drawing on Social Identity Theory and the Perfect Automation Schema, we proposed that autonomous driving systems operated by algorithms are perceived as out-group entities held to extremely high performance standards; consequently, even minor errors caused by autonomous vehicle algorithms may elicit disproportionately negative reactions. We conducted an online experiment employing a 2 (driving agent: algorithm vs. human) × 2 (error severity: fatal vs. minor) between-subjects design. Participants (N = 800) were randomly assigned to read one of four driving error scenarios, and then evaluated the error as well as the driving agent's trustworthiness. Results revealed a significant interaction between driving agent and error severity. When the error was fatal, both driving agents received highly negative evaluations on both error tolerance and trust. By contrast, when the error was minor, participants were more tolerant of the human error and expressed greater trust in the human driver than in the algorithm. These findings suggest that even minor, noncritical malfunctions in autonomous driving systems can undermine users' confidence and exacerbate negative evaluations. Overall, this study highlights social and cognitive biases in how people perceive and judge autonomous driving systems, deepening our understanding of the algorithm aversion phenomenon.