Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks

© 2018 IEEE. Railway power supply infrastructure is one of the most important components of railway transportation. As the key step of railway maintenance system, power supply infrastructure defects recognition plays a vital role in the whole defects inspection sub-system. Traditional defects recognition task is performed manually, which is time-consuming and high-labor costing. Inspired by the great success of deep neural networks in dealing with different vision tasks, this paper presents an end-to-end deep network to solve the railway infrastructure defects detection problem. More importantly, this paper is the first work that adopts the idea of deep fine-grained classification to do railway defects detection. We propose a new bilinear deep network named Spatial Transformer And Bilinear Low-Rank (STABLR) model and apply it to railway infrastructure defects detection. The experimental results demonstrate that the proposed method outperforms both hand-craft features based machine learning methods and classic deep neural network methods

Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels

In recent years, convolutional neural networks (CNNs) have risen to prominence in vision tasks, demonstrating unmatched capabilities in pattern recognition and image classification. Despite their strengths, a persistent challenge is their vulnerability to label noise. When trained on datasets marred by mislabeling, CNNs often succumb to overfitting, which diminishes their performance on new, unseen data. A prevalent remedy for this issue is the early stopping strategy, which halts training before overfitting sets in, thereby preventing the model from assimilating the noise. The efficacy of early stopping can be further amplified when paired with insights from the biological vision system. Research in this domain has highlighted the unique roles of the amplitude spectrum (AS) and the phase spectrum (PS) in visual processing. Intriguingly, the phase spectrum, which encapsulates richer semantic information in images, has proven more potent in enhancing the resilience of CNNs to label noise than its amplitude counterpart. Inspired by these findings, we present the Phase-AmplituDe DisentangLed Early Stopping (PADDLES) method. This novel technique utilizes the discrete Fourier transform (DFT) to partition features into their respective amplitude and phase spectrum components. By judiciously applying early stopping at varied stages of training for each component, PADDLES capitalizes on the robust attributes of the phase spectrum while curbing the potential drawbacks of the amplitude spectrum. Through rigorous experimentation, PADDLES has showcased its effectiveness. Whether tested on synthetic datasets infused with artificial noise or real-world datasets with inherent mislabeling, PADDLES consistently surpasses conventional early stopping methods. Furthermore, it establishes new state-of-the-art benchmarks, redefining standards for training CNNs amidst label noise

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies