Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

2019 ◽  
Vol 35 (7) ◽  
pp. 685-700 ◽  
Author(s):  
FuTao Ni ◽  
Jian Zhang ◽  
Mohammad N. Noori
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Data Compression ◽  
Suspension Bridge ◽  
Long Span

Control of Vibrations due to Moving Loads on Suspension Bridges

2006 ◽  
Vol 11 (3) ◽  
pp. 293-318 ◽  
Author(s):  
M. Zribi ◽  
N. B. Almutairi ◽  
M. Abdel-Rohman
Keyword(s):  
Bridge Deck ◽  
Suspension Bridge ◽  
Lyapunov Theory ◽  
Dynamic Responses ◽  
Suspension Bridges ◽  
Control Force ◽  
Moving Loads ◽  
Hydraulic Actuator ◽  
Long Span ◽  
Suspended Cables

The flexibility and low damping of the long span suspended cables in suspension bridges makes them prone to vibrations due to wind and moving loads which affect the dynamic responses of the suspended cables and the bridge deck. This paper investigates the control of vibrations of a suspension bridge due to a vertical load moving on the bridge deck with a constant speed. A vertical cable between the bridge deck and the suspended cables is used to install a hydraulic actuator able to generate an active control force on the bridge deck. Two control schemes are proposed to generate the control force needed to reduce the vertical vibrations in the suspended cables and in the bridge deck. The proposed controllers, whose design is based on Lyapunov theory, guarantee the asymptotic stability of the system. The MATLAB software is used to simulate the performance of the controlled system. The simulation results indicate that the proposed controllers work well. In addition, the performance of the system with the proposed controllers is compared to the performance of the system controlled with a velocity feedback controller.

Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 211-214
Author(s):  
Nidhi Thakkar ◽  
Miren Karamta ◽  
Seema Joshi ◽  
M. B. Potdar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Cloud Environment ◽  
Learning Techniques

scholarly journals Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 59406-59419
Author(s):  
Milos Savic ◽  
Milan Lukic ◽  
Dragan Danilovic ◽  
Zarko Bodroski ◽  
Dragana Bajovic ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Smart Logistics

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

The anomaly detection mechanism using deep learning in a limited amount of data for fog networking

2021 ◽  
Vol 170 ◽  
pp. 130-143
Author(s):  
Gwo-Jiun Horng ◽  
Min-Xiang Liu ◽  
Chien-Chin Hsu
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Detection Mechanism

Deep Learning for Anomaly Detection

2020 ◽  
Author(s):  
Ruoying Wang ◽  
Kexin Nie ◽  
Tie Wang ◽  
Yang Yang ◽  
Bo Long
Keyword(s):  
Deep Learning ◽  
Anomaly Detection
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