DSpace Collection:
http://hdl.handle.net/2117/5554
Thu, 28 May 2015 12:28:52 GMT2015-05-28T12:28:52Zwebmaster.bupc@upc.eduUniversitat Politècnica de Catalunya. Servei de Biblioteques i DocumentaciónoMonitoring drivers' ventilation using an electrical bioimpedance systems: tests in a controlled enviornment
http://hdl.handle.net/2117/25076
Title: Monitoring drivers' ventilation using an electrical bioimpedance systems: tests in a controlled enviornment
Authors: Macías Macías, Raúl; García González, Miguel Ángel; Ramos Castro, Juan José; Bragós Bardia, Ramon; Fernández Chimeno, Mireya
Abstract: As improving road safety is one of the first aims in the automotive
world, several new techniques and methods are being researched
in recent years. Some of them consist of monitoring the driver behavior
to detect non-appropriate states for driving, e.g. drowsy driving or
drunk driving. Usually, the appearance of these non-appropriate states
is related to changes in several physiological parameters. This work is
divided into two main parts. The first one presents an electrical bioimpedance
system capable of monitoring the ventilation using textile electrodes.
Apart from describing the system, in this part some tests done
in a controlled environment are also shown. In the second part of this
paper, an enhancement of the system is described and checked using a
patient simulator.Wed, 17 Dec 2014 18:17:31 GMThttp://hdl.handle.net/2117/250762014-12-17T18:17:31ZMacías Macías, Raúl; García González, Miguel Ángel; Ramos Castro, Juan José; Bragós Bardia, Ramon; Fernández Chimeno, MireyanoAs improving road safety is one of the first aims in the automotive
world, several new techniques and methods are being researched
in recent years. Some of them consist of monitoring the driver behavior
to detect non-appropriate states for driving, e.g. drowsy driving or
drunk driving. Usually, the appearance of these non-appropriate states
is related to changes in several physiological parameters. This work is
divided into two main parts. The first one presents an electrical bioimpedance
system capable of monitoring the ventilation using textile electrodes.
Apart from describing the system, in this part some tests done
in a controlled environment are also shown. In the second part of this
paper, an enhancement of the system is described and checked using a
patient simulator.Numerical validation methods
http://hdl.handle.net/2117/13404
Title: Numerical validation methods
Authors: Jauregui Tellería, Ricardo; Silva Martínez, Fernando
Abstract: In the last years, numerical simulation has seen a great development thanks to costs reduction and speed increases of the computational systems. With these improvements, the mathematical algorithms are able to work properly with more realistic problems.
Nowadays, the solution of a problem using numerical simulation is not just finding a result, but also to ensure the quality. However, can we say that the model results are correct regarding the behaviour of the system? In other words, how could we quantify the similarity between reality and simulations? To answer these questions, it is necessary to establish a validation criterion that allows an objective quantification of the difference between the results and the reality. Another way to say this is, how “true” our results are.
In the case of numerical methods, the main objective is to replicate as closely as possible the behaviour of the "real" world through numbers. Normally, the results of the numerical methods are expressed in terms of graphics, pictures, etc. These results represent the view of reality that the chosen method provides. In order to affirm that the result of a numerical solution is fully consistent with the reality, it must be satisfied that:
a. The mathematical model must incorporate all aspects of the real world.
b. The numerical method has to solve exactly the equations of the mathematical modelling.
The problem starts with these two conditions that guarantee the "truth" of the results, since none of them are fully accomplished and it must be admitted that the numerical prediction never completely matches the "real" world behaviour. Then you can only be sure that the numerical solution is a good approximation of the reality. Now, new questions arise: How much does the result obtained by a numerical method resemble the reality? How can we objectively quantify this similarity? The answers to these questions are those that give rise to the validation methods.Mon, 03 Oct 2011 10:33:34 GMThttp://hdl.handle.net/2117/134042011-10-03T10:33:34ZJauregui Tellería, Ricardo; Silva Martínez, FernandonoIn the last years, numerical simulation has seen a great development thanks to costs reduction and speed increases of the computational systems. With these improvements, the mathematical algorithms are able to work properly with more realistic problems.
Nowadays, the solution of a problem using numerical simulation is not just finding a result, but also to ensure the quality. However, can we say that the model results are correct regarding the behaviour of the system? In other words, how could we quantify the similarity between reality and simulations? To answer these questions, it is necessary to establish a validation criterion that allows an objective quantification of the difference between the results and the reality. Another way to say this is, how “true” our results are.
In the case of numerical methods, the main objective is to replicate as closely as possible the behaviour of the "real" world through numbers. Normally, the results of the numerical methods are expressed in terms of graphics, pictures, etc. These results represent the view of reality that the chosen method provides. In order to affirm that the result of a numerical solution is fully consistent with the reality, it must be satisfied that:
a. The mathematical model must incorporate all aspects of the real world.
b. The numerical method has to solve exactly the equations of the mathematical modelling.
The problem starts with these two conditions that guarantee the "truth" of the results, since none of them are fully accomplished and it must be admitted that the numerical prediction never completely matches the "real" world behaviour. Then you can only be sure that the numerical solution is a good approximation of the reality. Now, new questions arise: How much does the result obtained by a numerical method resemble the reality? How can we objectively quantify this similarity? The answers to these questions are those that give rise to the validation methods.