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Signal anomaly detection is commonly used to detect rogue or unexpected signals. It has many applications in interference mitigation, wireless security, optimized spectrum allocation, and radio coordination. Our work proposes a new method for anomaly detection on signal detection metadata using generative adversarial network output processed by a long short term memory recurrent neural network. We provide a performance analysis and comparison to baseline methods, and demonstrate that through the usage of metadata for analytics, we can provide robust detection, while also minimizing computation and bandwidth, and generalizing to numerous effects which differs from many prior works that focus on A.D. based signal processing on the raw RF sample data.
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If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection
In proportion to the immense construction of spatial structures is the emergence of catastrophes related to structural damages (e.g. loose connections), thus rendering personal injury and property loss. It is therefore essential to detect spatial bolt looseness. Current methods for detecting spatial bolt looseness mostly focus on contact-type measurement, which may not be practical in some cases. Thus, inspired by the sound-based human diagnostic approach, we develop a novel percussion method using the Mel-frequency cepstral coefficient and the memory-augmented neural network in this article. In comparison with current investigations, the main contribution of this article is the detection of multi-bolt looseness for the first time with higher accuracy than prior methods. In particular, in terms of new data obtained via similar joints, the memory-augmented neural network can help avoid inefficient relearn and assimilate new data to provide accurate prediction with only a few data samples, which effectively improves the robustness of detection. Furthermore, percussion was implemented with a robotic arm instead of manual operation, which preliminarily explores the potential of implementing automation applications in real industries. Finally, experimental results demonstrate the effectiveness of the proposed method, which can guide future development of cyber-physics systems for structural health detection.
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- Award ID(s):
- 1910973
- PAR ID:
- 10282860
- Date Published:
- Journal Name:
- Structural Health Monitoring
- Volume:
- 20
- Issue:
- 4
- ISSN:
- 1475-9217
- Page Range / eLocation ID:
- 1597 to 1608
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1475921720923147
