An Immuno-engineering Approach for Anomaly Detection in Swarm Robotics

2009 ◽  
pp. 136-150 ◽  
Author(s):  
HuiKeng Lau ◽  
Iain Bate ◽  
Jon Timmis
Keyword(s):  
Anomaly Detection ◽  
Swarm Robotics ◽  
Engineering Approach

On the nominal transverse shear strain to characterize the severity of creasing

TAPPI Journal ◽  
2018 ◽  
Vol 17 (04) ◽  
pp. 231-240
Author(s):  
Douglas Coffin ◽  
Joel Panek
Keyword(s):  
Shear Strain ◽  
Transverse Shear ◽  
Elastic Limit ◽  
Experimental Results ◽  
Transverse Shear Strain ◽  
Maximum Strain ◽  
Machine Direction ◽  
Engineering Approach ◽  
Wide Range ◽  
Analytic Expression

A transverse shear strain was utilized to characterize the severity of creasing for a wide range of tooling configurations. An analytic expression of transverse shear strain, which accounts for tooling geometry, correlated well with relative crease strength and springback as determined from 90° fold tests. The experimental results show a minimum strain (elastic limit) that needs to be exceeded for the relative crease strength to be reduced. The theory predicts a maximum achievable transverse shear strain, which is further limited if the tooling clearance is negative. The elastic limit and maximum strain thus describe the range of interest for effective creasing. In this range, cross direction (CD)-creased samples were more sensitive to creasing than machine direction (MD)-creased samples, but the differences were reduced as the shear strain approached the maximum. The presented development provides the foundation for a quantitative engineering approach to creasing and folding operations.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms
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