Examinando por Autor "Goya-Esteban, Rebeca"

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A practical method for vibration frequency response characterization of handheld probes using Bootstrap in building acoustics
(Elsevier, 2019-02) San Millán-Castillo, Roberto; Goya-Esteban, Rebeca; Morgado, Eduardo
Vibration measurement in building acoustics can help understand and estimate different physical phenomena for both researchers and practitioners. Sound insulation and flanking sound transmission are just some of these phenomena and interesting information can be obtained from wall vibration. Different approaches are available in terms of instruments and techniques, ranging from laser interferometry to single axis accelerometers. The latter are simple and cost-effective solutions because they allow many practitioners to use them in an affordable way. In order to deal with the problem in a more efficient way, there is a need to employ a less intrusive mounting technique and we therefore performed a study of the handheld probe solution in detail. Calibration and theoretical data on probe tips attached to different sensors is extremely difficult to find in relation to frequency response, resonance or repeatability. A new and simple sensor characterization procedure is presented to study deviations in probes, depending on the mounting technique and its comparison to a more robust wax fixing method. Handheld probes modify accelerometer response, mainly due to the probe length and the material. Sensor size, weight and connector location were also observed as influencing variables, in addition to others, such as operator hand tremor and the way the sensor is held. Nevertheless, a study of all these variables would provide a very complex model and we therefore used a statistical approach to simplify the characterization tasks. In building acoustic vibration, a Gaussian probability distribution is usually assumed in the collected data, although not being true in all cases. An innovative Bootstrap approach was thus employed in this study without any assumptions on data probability distribution. Bootstrap is a non-parametric method that provides further information than typical average values on a particular experimental population, when the real population is unknown and difficult to estimate. Bootstrap statistical mean and its confidence interval are used as performance indexes. Ninety probe types and sensor set-ups were characterized according to their frequency response and repeatability in a real environment, as compared to regular Wax fixing. Probes show less repeatability than wax or simply handheld broadband techniques, but 95% Bootstrap statistical mean confidence intervals were less than 0.5 dB in a low frequency range, up to a maximum of 3.8 dB at higher frequency bands of interest. Higher deviations are found in system resonance. Nevertheless, uncertainty values on repeatability in building acoustics are not far from these values. A good similarity is found in a probe useful bandwidth ranging from 50 Hz to 800 Hz–1 kHz, depending on the probe’s features. Bootstrap statistical mean is useful to correct measurements of deviations in frequency response. This handheld vibration probe data approach can provide more efficient resource management in real test situations.
Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population
(Springer Science and Business Media LLC, 2017-12) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Barquero-Perez, Oscar; Goya-Esteban, Rebeca; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Ramos-Lopez, Javier
Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events
(Springer Science and Business Media LLC, 2019-07-25) Garcia-Carretero, Rafael; Barquero-Perez, Oscar; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Goya-Esteban, Rebeca; Ramos-Lopez, Javier
On the Use of Decision Tree Regression for Predicting Vibration Frequency Response of Handheld Probes
(IEEE, 2020-04-15) San Millán-Castillo, Roberto; Morgado, Eduardo; Goya-Esteban, Rebeca
This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a probe to a vibrating surface downshifts sensor resonancesi and underestimates levels. Therefore, the calibration response of the sensor included in a probe differs from the frequency response of that same probe. Simulation techniques of complex mechanical systems may describe this issue, but they include hardly obtainable parameters, ultimately restricting the model. Thus, this study discusses an alternativemethod, which comprises different parts. Firstly, the vibration frequency responses of 85 probes were measured and labelled according to six features. Then, Linear Regression, Decision Tree Regression and Artificial NeuralNetworks algorithmswere analysed. Itwas revealed that decision tree regression is themore appropriate technique for this data. The best decision tree models, in terms of scores and model structure, were fine-tuned. Eventually, the final suggested model employs only four out of the six original features. A trade-off solution that involved a simple structure, an interpretable model and accurate predictions was accomplished. It showed a maximum average deviation from test measurements ranging from 0.6 dB in low- frequency to 3 dB in high-frequency while remaining at a low computational load. This research developed an original and reliable prediction tool that provides the vibration frequency response of handheld probes.