Comments on "Optimal sensor selection strategy for discrete-time estimators" [with reply]

1995 ◽  
Vol 31 (2) ◽  
pp. 831-834
Author(s):  
T.H. Kerr ◽  
Y. Oshman
Keyword(s):  
Discrete Time ◽  
Sensor Selection ◽  
Selection Strategy

scholarly journals Sensor selection scheme in activity recognition based on hierarchical feature reduction

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879380 ◽  
Author(s):  
Yu Wei ◽  
Libin Jiao ◽  
Jie Sha ◽  
Jixin Ma ◽  
Anton Umek ◽  
...  
Keyword(s):  
Mutual Information ◽  
Activity Recognition ◽  
Complete Solution ◽  
Reduction Process ◽  
Feature Reduction ◽  
The Body ◽  
Sensor Selection ◽  
Selection Strategy ◽  
Selection Scheme ◽  
Low Dimensional

To better understand the activity state of human, we might need multiple sensors on different parts of the body. According to different types of activities, the number and slot of required sensors would also be different. Therefore, how to determine the number and slot of necessary sensors regarding to wearers’ experience and processing efficiency is a meaningful study in actual practice. In this work, we propose a novel sensor selection scheme that is based on the improvement of the feature reduction process of the recognition. This scheme applies a hierarchical feature reduction method based on mutual information with max relevance and low-dimensional embedding strategy. It divides the process of feature reduction into two stages: first, redundant sensors are removed with one-order sequential forward selection based on mutual information; second, feature selection strategy that maximizing class-relevance is integrated with low-dimensional mapping so that the set of features will be further compressed. To verify the feasibility and superiority of the scheme, we design a complete solution for real practice of human activity recognition. According to the results of the experiments, we are able to recognize human activities accurately and efficiently with as few sensors as possible.

Expanded Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

2009 ◽  
Author(s):  
T. Shane Sowers ◽  
James E. Fittje ◽  
George Kopasakis ◽  
Donald L. Simon
Keyword(s):  
Diagnostic Performance ◽  
Ad Hoc ◽  
Health Management ◽  
Operating Conditions ◽  
Sensor Selection ◽  
System Level ◽  
Selection Strategy ◽  
Turbofan Engine ◽  
Cost Weight ◽  
Fault Diagnostic

Data acquired from system sensors form the foundation upon which any health management system is based, and the available sensor suite directly impacts the absolute diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects sensor suites from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was originally applied to a large turbofan engine simulation at a single operating point, considering fault conditions represented by single component health parameter shifts. In the current study, sensor selection is expanded to consider multiple operating conditions such as takeoff and cruise, and more representative fault conditions.

scholarly journals Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

2008 ◽  
Author(s):  
T. Shane Sowers ◽  
George Kopasakis ◽  
Donald L. Simon
Keyword(s):  
Diagnostic Performance ◽  
Sensor Selection ◽  
System Level ◽  
Selection Strategy ◽  
Future Research ◽  
Turbofan Engine ◽  
Cost Weight ◽  
Fault Detection And Identification ◽  
Fault Diagnostic ◽  
Sensor Suite

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

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