scholarly journals Lower complexity error location detection block of adjacent error correcting decoder for SRAMs

2020 ◽  
Vol 14 (5) ◽  
pp. 210-216 ◽  
Author(s):  
Raj Kumar Maity ◽  
Sayan Tripathi ◽  
Jagannath Samanta ◽  
Jaydeb Bhaumik
2021 ◽  
Author(s):  
Yannick Duensing ◽  
Oliver Richert ◽  
Katharina Schmitz

Abstract To meet future goals of more electric airplanes conventional hydraulic airplane control systems, consisting of redundant centralized pumps within the airplane’s fuselage, need to be substituted for compact electro-hydraulic actuators (EHA). The capsulated architecture of EHAs results in higher safety due to separate hydraulic circuits, simple practicability of redundancy, decreased maintenance because of simplified error location detection as well as an overall reduction in weight and complexity of the airplane control system. Currently, EHAs are only used as backup devices as the reliability does not achieve normative requirements for a frontline application. Thus, recent studies aim to increase the reliability. The axial piston pump of current EHA is the source of most failures. High dynamic requirements and challenging operation points and environments result in wear of contact pairs such as swash plate/piston shoes, pistons/cylinder block and cylinder block/valve plate. In the scope of the project MODULAR at ifas one goal is to increase the robustness of the contact surfaces. A second goal addresses the topic of developing a condition monitoring approach to constantly track the pumps’ health status. Next to signals such as pressures and temperatures, acceleration and oil status signals describing the actual particle contamination are needed. In this contribution different methods of oil status detection are explained and the method of electric conductivity analysis for condition monitoring is further investigated. Filtered HLP46 is used and impurities in form of metallic powders are added. Furthermore, degraded oil of a disc-on-disc Tribometer test bench is measured and compared.


2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.


2020 ◽  
pp. 1-10
Author(s):  
Carl J. Wenning ◽  
Rebecca E. Vieyra
Keyword(s):  

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