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Hellena Hempe, Alexander Bigalke and Mattias Paul Heinrich
Background: Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities enables proactive measures to mitigate the risk of severe back pain and disabilit...
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Shenghan Zhou, Tianhuai Wang, Linchao Yang, Zhao He and Siting Cao
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as...
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David Massegur, Declan Clifford, Andrea Da Ronch, Riccardo Lombardi and Marco Panzeri
Determining the aero-icing characteristics is key for safety assurance in aviation, but it may be a computationally expensive task. This work presents a framework for the development of low-dimensional models for application to aerofoil icing. The framew...
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Dawei Luo, Heng Zhou, Joonsoo Bae and Bom Yun
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distrib...
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Junwen Lu, Jinhui Wang, Xiaojun Wei, Keshou Wu and Guanfeng Liu
There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representatio...
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Jiamu Li, Ji Zhang, Mohamed Jaward Bah, Jian Wang, Youwen Zhu, Gaoming Yang, Lingling Li and Kexin Zhang
When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicate...
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Theiab Alzahrani, Baidaa Al-Bander and Waleed Al-Nuaimy
Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thu...
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Yuexuan Zhao and Jing Huang
Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction. However, several pieces of research based on GVAE employ a plain prior ...
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Durkhyun Cho, Jin Han Lee and Il Hong Suh
We live in an era of privacy concerns. As smart devices such as smartphones, service robots and surveillance cameras spread, preservation of our privacy becomes one of the major concerns in our daily life. Traditionally, the problem was resolved by simpl...
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Mou Wang, Xiao-Lei Zhang and Susanto Rahardja
Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but...
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Abdelkader Dairi, Fouzi Harrou, Ying Sun and Sofiane Khadraoui
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar p...
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Milad Memarzadeh, Bryan Matthews and Ilya Avrekh
The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continuall...
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Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for shor...
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Xiaoling Tao, Deyan Kong, Yi Wei and Yong Wang
Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditi...
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Xiaoling Tao, Deyan Kong, Yi Wei and Yong Wang
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Ganapathy Ramesh, Jaganathan Logeshwaran, Thangavel Kiruthiga and Jaime Lloret
In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovol...
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Chinyang Henry Tseng, Woei-Jiunn Tsaur and Yueh-Mao Shen
In detecting large-scale attacks, deep neural networks (DNNs) are an effective approach based on high-quality training data samples. Feature selection and feature extraction are the primary approaches for data quality enhancement for high-accuracy intrus...
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Mengjie Zeng, Shunming Li, Ranran Li, Jiantao Lu, Kun Xu, Xianglian Li, Yanfeng Wang and Jun Du
Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, ...
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Jie Lian, Pingping Dong, Yuping Zhang and Jianguo Pan
Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficul...
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Bingyu Li, Lei Wang, Qiaoyong Jiang, Wei Li and Rong Huang
In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this...
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