No abstract

No abstract

No abstract

Passengers principally rely on signage to making wayfinding decisions in transportation buildings. Most existing research focuses on the analysis of the wayfinding trajectory, but there is less attention on the process of how passengers make the wayfinding decision. So, it is hard to accurately locate the causes of the wrong wayfinding decision. Taking the Satellite Terminal of Shanghai Pudong International Airport (PVG Airport) as an example, we adopted the eye-tracking technology and recorded the eye-tracking data of passengers observing the signage and making wayfinding decisions. Then, we compared and analyzed the data, presenting it by data visualization. This study found the causes of passengers making wrong wayfinding decisions and the visual behavior of wayfinding: the reconfirmation behavior, the priority of attention, and the clockwise observation. Finally, corresponding suggestions for signage design optimization are put forward regarding some wayfinding decision points. As a result, the optimized signage system in the satellite terminal is welcomed by the passengers two months later according to monthly questionnaires.

Electromagnetic environment situation anomaly detection is a prerequisite for electromagnetic threat level assessment, and its research is of great practical value. However, because of the complexity of the electromagnetic environment, electromagnetic environment situation anomaly detection is not efficient. Therefore, we propose a dual-branch prediction network-based electromagnetic environment situation anomaly detection method to predict the future and achieve anomaly detection by fusing different development characteristics of electromagnetic environment situations learned by other branches. We extract the electromagnetic environment situation state and trend features using the manual feature extraction module and mine the electromagnetic environment situation in-depth data distribution features using ConvLSTM, improve the dynamic time regularization model according to the physical characteristics of electromagnetic space, and then provide the anomaly detection method. We experimentally demonstrate the effectiveness of the proposed method in electromagnetic environment situation prediction and anomaly detection accuracy.