Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines

Lee, Hojin; Jeong, Haedo; Koo, Gyogwon; Ban, Jaepil; Kim, Sang Woo

doi:10.1109/tie.2020.2978690

Cited by 85 publications

(29 citation statements)

References 22 publications

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“…There are many papers using attention to improve their networks. Lee et al (2021) proposed an attention recurrent neural network to estimate severity. Song et al (2021) presented a coarse-to-fine dual-view attention network for click-through rate prediction.…”

Section: Human Visual System and Attention Mechanismmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

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Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.

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Section: Human Visual System and Attention Mechanismmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

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“…A comprehensive review of machine learning methods for wind turbine condition monitoring is given in [26]. Applications of machine learning for detection of electrical faults in PM machines are scarce, some examples are found in [27][28][29].…”

Section: Wind Turbine Condition Monitoring Methodsmentioning

confidence: 99%

Fault Detection and Condition Monitoring of PMSGs in Offshore Wind Turbines

Freire

Cardoso

2021

Machines

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Research on fault detection (FD) and condition monitoring (CM) of rotating electrical generators for modern wind turbines has addressed a wide variety of technologies. Among these, permanent magnet synchronous generators (PMSGs) and the analysis of their electromagnetic signatures in the presence of faults deserve emphasis in this paper. PMSGs are prominent in the offshore wind industry, and methods for FD and CM of PMSGs based on electromagnetic measurements are extensively discussed in academia. This paper is a concise review of FD and CM in wind turbines and PMSGs. Terminology and fundamentals of PMSG’s operation are introduced first, aiming to offer an easy read and good reference to a broad audience of engineers and data scientists. Experience and research challenges with stator winding failures are also discussed.

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“…For reliable fault detection and diagnosis in manufacturing, numerous methods have exploited DL with RNN and CNN architectures to achieve high accuracy while keeping a real-time monitoring. For instance, a RNN [150] was developed with an encoderdecoder structure coupled with attention mechanism to predict and diagnose interturn short-circuit faults in permanent magnet synchronous systems. In [151], a data-driven LSTMbased fault diagnosis approach was introduced to early detect multiple open-circuit faults in wind turbine systems.…”

Section: B Manufacturingmentioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

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Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

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Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines

Cited by 85 publications

References 22 publications

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Fault Detection and Condition Monitoring of PMSGs in Offshore Wind Turbines

Artificial Intelligence for the Metaverse: A Survey

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