Railway system maintenance, crucial for ensuring safety and efficiency, faces challenges in effectively managing its vital components, including tracks, railroad ties, and fasteners. While various methodologies target these components, the limited availability of diverse railway datasets presents a significant hurdle. Addressing this, we introduce SeMA-UNet, a pioneering deep learning model designed to optimize performance in data-constrained scenarios. Seamlessly integrating semi-supervised learning with multimodal strategies, SeMA-UNet excels in preprocessing railway images, conducting comprehensive feature extraction to generate rich multimodal data. This process is further augmented by advanced techniques, notably the Monte Carlo simulation. Empirical results underscore SeMA-UNet’s robustness, with metrics such as an IoU of 0.9464, an AUC of 0.9796, and an mAP of 0.9468. Beyond its primary function of accurately identifying maintenance-critical regions, the model’s capabilities extend to advanced anomaly detection, heralding a new era in enhancing the reliability and safety of railway systems.

Railway tracks are facilities for trains that can efficiently transport large-scale objects and it is inevitable to occur track defects that threaten the safety of train operation. Therefore, an inspection of track defects is essential to prevent large-scale accidents caused by anomaly events such as abrasion, fatigue, and deformation. In recent years, Deep Learning-based strategies for robust and highly accurate defect detection have been proposed to replace methods depending on the subjectivity and expertise of inspectors. However, the diversity of abnormal types and the rarity of defective samples make accurate segmentation of defect regions challenging. Therefore, in this paper, we propose a one-class classification algorithm called Residual Attention Guided PaDiM (RAG-PaDiM) for the segmentation of defects in railway tracks. First, the Residual Attention Guided U-Net, which is used as a backbone for generating embedding vectors in RAG-PaDiM, minimizes loss of information using residual connections and gives large weights to the regions of interest through an attention gate. Finally, the distribution of the normal data calculated by learning the training data consisting of only normal samples is used together with the Mahalanobis distance in the test phase to output the anomaly score. The proposed model was rigorously evaluated using the publicly available benchmark dataset, Rail Surface Defect Datasets (RSDD). Experimental results show that the proposed model correctly segmented the pixel-level area under the curve score by about 98.5% for defects in RSDD, suggesting that the proposed method improved the performance in the defect segmentation task for the railway track.

Alzheimer’s Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other’s weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.