Performance Evaluation of Piezoelectric Weigh-in-Motion Sensors Under Controlled Field-Loading Conditions

2001 ◽  
Vol 1769 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Sirous H. Alavi ◽  
Joseph A. Mactutis ◽  
Scott D. Gibson ◽  
A. Thomas Papagiannakis ◽  
David Reynaud
Keyword(s):  
Performance Evaluation ◽  
Motion Sensors ◽  
Loading Conditions ◽  
Weigh In Motion

scholarly journals Performance Evaluation of an Indoor Positioning Scheme Using Infrared Motion Sensors

Information ◽  
2014 ◽  
Vol 5 (4) ◽  
pp. 548-557 ◽  
Author(s):  
Changqiang Jing ◽  
Biao Zhou ◽  
Nammoon Kim ◽  
Youngok Kim
Keyword(s):  
Performance Evaluation ◽  
Indoor Positioning ◽  
Motion Sensors

Laboratory and Field Evaluation of Piezoelectric Weigh-in-Motion Sensors

2001 ◽  
Vol 29 (6) ◽  
pp. 535 ◽  
Author(s):  
RE Petersen ◽  
RE Link ◽  
AT Papagiannakis ◽  
EC Johnston ◽  
S Alavi ◽  
...  
Keyword(s):  
Field Evaluation ◽  
Motion Sensors ◽  
Weigh In Motion

Piezoelectric ceramic-polymer composites for weigh-in-motion sensors

1998 ◽  
Author(s):  
Rajesh K. Panda ◽  
Patrick J. Szary ◽  
Ali Maher ◽  
Ahmad Safari
Keyword(s):  
Polymer Composites ◽  
Piezoelectric Ceramic ◽  
Motion Sensors ◽  
Weigh In Motion

Locating Traffic Sensors on a Highway Network

2013 ◽  
Vol 2339 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Fatemeh Sayyady ◽  
Yahya Fathi ◽  
George F. List ◽  
John R. Stone
Keyword(s):  
Heuristic Algorithm ◽  
Large Scale ◽  
Budget Constraint ◽  
Motion Sensors ◽  
Traffic Sensors ◽  
Weigh In Motion ◽  
Highway Network ◽  
Lagrangian Heuristic ◽  
Candidate Location ◽  
Large Scale Networks

This paper considers the problem of finding optimal sensor locations on a traffic network with the goal of characterizing system use overall. The problem is studied for two practical scenarios. In the first scenario, it is assumed that there is a given number of sensors (p) to be located on the highway network. In this context, the problem is to find a collection of p locations among a given collection of candidate locations. In the second scenario, it is assumed that there is a cost (ci) associated with installing a sensor at each candidate location i and a total budget b. In this context, the problem is to find a collection of locations that provide the best possible characterization given the budget constraint. A metric is proposed for evaluating a potential solution, and then appropriate mathematical models are proposed for solving the problem for each scenario. It is shown that the budget-constrained problem is an extension of the well-known p-median problem. A new Lagrangian heuristic algorithm is presented for solving large instances of this problem when a budget constraint is imposed. A comprehensive computational experiment is used to demonstrate that the Lagrangian heuristic algorithm provides solutions for large-scale networks within reasonable execution times. Examples are based on locating weigh-in-motion sensors on a large-scale highway network.

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