Chunsheng Yang

Rahatara Ferdousi, Fedwa Laamarti, Chunsheng Yang et al.

Accurate Defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches rely on single deep-learning models, like CNNs, which employ a large amount of data to capture underlying patterns. Training a new defect classifier with limited samples often leads to overfitting and poor performance on unseen images. To address this, researchers have advocated transfer learning and fine-tuning the pre-trained models. However, using a single backbone network in transfer learning still may cause bottleneck issues and inconsistent performance if it is not suitable for a specific problem domain. To overcome these challenges, we propose a reusable AI-enabled defect detection approach. By combining ensemble learning with transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the classification accuracy and achieved consistent performance at a certain phase of training. Our empirical analysis demonstrates better and more consistent performance compared to other state-of-the-art approaches. The consistency substantiates the reusability of the defect detection system for newly evolved defected rail parts. Therefore we anticipate these findings to benefit further research and development of reusable AI-enabled solutions for railway systems.

Rahatara Ferdousi, M. Anwar Hossain, Chunsheng Yang et al.

A Digital Twin (DT) replicates objects, processes, or systems for real-time monitoring, simulation, and predictive maintenance. Recent advancements like Large Language Models (LLMs) have revolutionized traditional AI systems and offer immense potential when combined with DT in industrial applications such as railway defect inspection. Traditionally, this inspection requires extensive defect samples to identify patterns, but limited samples can lead to overfitting and poor performance on unseen defects. Integrating pre-trained LLMs into DT addresses this challenge by reducing the need for vast sample data. We introduce DefectTwin, which employs a multimodal and multi-model (M^2) LLM-based AI pipeline to analyze both seen and unseen visual defects in railways. This application enables a railway agent to perform expert-level defect analysis using consumer electronics (e.g., tablets). A multimodal processor ensures responses are in a consumable format, while an instant user feedback mechanism (instaUF) enhances Quality-of-Experience (QoE). The proposed M^2 LLM outperforms existing models, achieving high precision (0.76-0.93) across multimodal inputs including text, images, and videos of pre-trained defects, and demonstrates superior zero-shot generalizability for unseen defects. We also evaluate the latency, token count, and usefulness of responses generated by DefectTwin on consumer devices. To our knowledge, DefectTwin is the first LLM-integrated DT designed for railway defect inspection.

Shouhua Zhang, Jiehan Zhou, Xue Ma et al.

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. TSViT incorporates a convolutional layer to extract local features from vibration signals, alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison on three diverse datasets demonstrates TSViT's effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model's performance, computational demand, and parameter count. Remarkably, TSViT achieves an unprecedented 100% average accuracy on two test sets and 99.99% on another, showcasing its exceptional diagnostic capabilities.

Fengchuang Xing, Xiaowen Shi, Yuan-Gen Wang et al.

Unveiling the real appearance of retouched faces to prevent malicious users from deceptive advertising and economic fraud has been an increasing concern in the era of digital economics. This article makes the first attempt to investigate the face retouching reversal (FRR) problem. We first collect an FRR dataset, named deepFRR, which contains 50,000 StyleGAN-generated high-resolution (1024*1024) facial images and their corresponding retouched ones by a commercial online API. To our best knowledge, deepFRR is the first FRR dataset tailored for training the deep FRR models. Then, we propose a novel diffusion-based FRR approach (FRRffusion) for the FRR task. Our FRRffusion consists of a coarse-to-fine two-stage network: A diffusion-based Facial Morpho-Architectonic Restorer (FMAR) is constructed to generate the basic contours of low-resolution faces in the first stage, while a Transformer-based Hyperrealistic Facial Detail Generator (HFDG) is designed to create high-resolution facial details in the second stage. Tested on deepFRR, our FRRffusion surpasses the GP-UNIT and Stable Diffusion methods by a large margin in four widespread quantitative metrics. Especially, the de-retouched images by our FRRffusion are visually much closer to the raw face images than both the retouched face images and those restored by the GP-UNIT and Stable Diffusion methods in terms of qualitative evaluation with 85 subjects. These results sufficiently validate the efficacy of our work, bridging the recently-standing gap between the FRR and generic image restoration tasks. The dataset and code are available at https://github.com/GZHU-DVL/FRRffusion.

Rahatara Ferdousi, Chunsheng Yang, M. Anwar Hossain et al.

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional Neural Networks (CNN) in visual defect classification, the scarcity of large datasets for rail defect detection remains a challenge due to infrequent accident events that would result in defective parts and images. Contemporary researchers have addressed this data scarcity challenge by exploring rule-based and generative data augmentation models. Among these, Variational Autoencoder (VAE) models can generate realistic data without extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for rail defects, incorporating weight decay regularization and image reconstruction loss to prevent overfitting. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real samples across five classes. Remarkably, 500 synthetic samples are generated with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy rates (98%-99%) in classifying the five defect classes. This research offers a promising solution to the data scarcity challenge in rail defect detection, showcasing the potential for robust ICS development in this domain.