Aiming to Realize a Smart Society and Seamless Mobility with ICT: JR-EAST’s Challenge for Business Innovation

Suzuki, Masahiko

doi:10.1007/978-981-15-7658-4_10

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work came to announce an JR-EAST’s working plan on technical development to deploy the device to more sections, including the sections not yet electrified. Suzuki, 22 in his chapter entitled “Aiming to Realize a Smart Society and Seamless Mobility with ICT: JR-EAST’s Challenge for Business Innovation,” presented the research made by the JR-EAST. The aim is to detect abnormalities in a signal bond, which is a part of train detection facilities called track circuits.…”

Section: Previous Workmentioning

confidence: 99%

Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

2023

View full text Add to dashboard Cite

The railway system of Japan plays a vital role in the national transportation network.A key issue in public transport safety is anomaly detection in railways. Lately, developing robust algorithms and methods for anomaly detection has become the premier task in this field. Recently introduced approaches based on convolutional neural networks, generative adversarial networks, and vision transformers (ViTs) have remarkably improved the research in anomaly detection. Our work proposes a high-performance module for the anomaly detection of signal bonds. First, we present an overview of the proposed module; then, the object detection model and the proposed hybrid classification model based on texture-biased convolutional neural networks and edge-biased ViTs are introduced. Finally, the proposed anomaly detection module is evaluated for accuracy using the dataset from the East Japan Railway Company and the receiver operating characteristic curve. The results show that our proposed module achieves a better performance on real-life data than the two methods that were combined to generate it, raising hope toward possible usage in other areas as well.

show abstract

Section: Previous Workmentioning

confidence: 99%