1,721,071 research outputs found
A new laser vibrometry-based 2D selective intensity method for source identification in reverberant fields: part II. Application to an aircraft cabin
No full textThe selective intensity technique is a powerful tool for the localization of acoustic sources and
for the identification of the structural contribution to the acoustic emission. In practice, the
selective intensity method is based on simultaneous measurements of acoustic intensity, by
means of a couple of matched microphones, and structural vibration of the emitting object. In
this paper high spatial density multi-point vibration data, acquired by using a scanning laser
Doppler vibrometer, have been used for the first time. Therefore, by applying the selective
intensity algorithm, the contribution of a large number of structural sources to the acoustic
field radiated by the vibrating object can be estimated. The selective intensity represents the
distribution of the acoustic monopole sources on the emitting surface, as if each monopole
acted separately from the others. This innovative selective intensity approach can be very
helpful when the measurement is performed on large panels in highly reverberating
environments, such as aircraft cabins. In this case the separation of the direct acoustic field
(radiated by the vibrating panels of the fuselage) and the reverberant one is difficult by
traditional techniques. The work shown in this paper is the application of part of the results of
the European project CREDO (Cabin Noise Reduction by Experimental and Numerical
Design Optimization) carried out within the framework of the EU. Therefore the aim of this
paper is to illustrate a real application of the method to the interior acoustic characterization of
an Alenia Aeronautica ATR42 ground test facility, Alenia Aeronautica being a partner of the
CREDO project
A tool for the optimal sensor placement to optimize temperature monitoring in large sports spaces
No full textThe Sensor Optimization Unit (SOU) is a tool, meant to be used by HVAC engineer, for the optimization of temperature sensors placement in large sports spaces, where the HVAC system usually maintains climate conditions actuating control rules based on one temperature sensor, installed in the return air duct or in a single point of the space, without taking into account the indoor air temperature distribution. The SOU characterizes the indoor horizontal air temperature distribution which can be retrieved with a simulation model or field measurement. A dedicated measurement performance index is calculated to determine the optimal sensors location that provides the maximum accuracy with the minimum number of sensors to be deployed. The application and validation of the tool in a real indoor swimming pool outlined that the measurement uncertainty due to the incorrect location of the existing thermostat was higher than ± 0.5 °C (thermostat uncertainty datasheet) for the 42% of the period considered. The optimized placement determined with the SOU decreased that period to 1.5% of the overall time
A new laser vibrometry-based 2D selective intensity method for source identification in reverberant fields: part I. Development of the technique and preliminary validation
No full textThe selective intensity technique is a powerful tool for the localization of acoustic sources and
for the identification of the structural contribution to the acoustic emission. In practice, the
selective intensity method is based on simultaneous measurements of acoustic intensity, by
means of a couple of matched microphones, and structural vibration of the emitting object. In
this paper high spatial density multi-point vibration data, acquired by using a scanning laser
Doppler vibrometer, have been used for the first time. Therefore, by applying the selective
intensity algorithm, the contribution of a large number of structural sources to the acoustic
field radiated by the vibrating object can be estimated. The selective intensity represents the
distribution of the acoustic monopole sources on the emitting surface, as if each monopole
acted separately from the others. This innovative selective intensity approach can be very
helpful when the measurement is performed on large panels in highly reverberating
environments, such as aircraft cabins. In this case the separation of the direct acoustic field
(radiated by the vibrating panels of the fuselage) and the reverberant one is difficult by
traditional techniques. The first aim of this work is to develop and validate the technique in
reverberating environments where the location and the quantification of each source are
difficult by traditional techniques. The reverberant field is clearly challenging also for the
proposed technique, affecting the achievable accuracy, mainly due to the fact that coherence
between radiated and reverberated fields is often unknown and may be relevant. Secondly, the
applicability of the method to real cases is demonstrated. A laboratory test case has been
developed using a large wooden panel. The measurement is performed both in anechoic
environment and under simulated reverberating conditions, for testing the ability of the
selective intensity method to remove the reverberation
Non-contact modal analysis by laser excitation: Estimation of the "equivalent" input force
No full text
AI-based sensor network for ADLs monitoring on ageing people during COVID-19 epidemic
No full textThis paper presents an application of an AI-based sensors network characterized by Passive Infrared (PIR) and door contact sensors for remote home monitoring of activities of daily living (ADLs) on ageing people during the epidemic event caused by the SARS-CoV-2 virus. The scope of the work is to demonstrate the relevance in the combined use of artificial intelligence (AI) and sensor networks on the measurement of living behavior of ageing people. To this scope, an AI-based sensor network has been installed in the living environment of three Italian ageing users diagnosed with early dementia and collected data have been used to assess the effect of the confinement during the lockdown period on the ADLs variation. The main ADLs (toileting, eating, going outside, sleep and location change) measured through the AI-based sensor network have been analysed on a time period of 2 months before the lockdown (January and February 2020) and 2 months during the lockdown (March and April 2020). Analysis has been performed considering the mean and standard deviation of the measured occurrences on a daily basis, before and during the lockdown. A Student t-test has been computed to evaluate the significance of the reported changes on the ADLs, demonstrating that there are statistically significant differences between the two observed periods for most of the considered ADLs
Introductory notes for the Acta IMEKO Special Issue on the 40th Measurement Day jointly organised by the Italian associations GMEE and GMTT
No full textThe Problem of Monitoring Activities of Older People in Multi-Resident Scenarios: An Innovative and Non-Invasive Measurement System Based on Wearables and PIR Sensors
Full text linkThis paper presents an innovative multi-resident activity detection sensor network that uses the Bluetooth Low Energy (BLE) signal emitted by tags worn by residents and passive infrared (PIR) motion sensors deployed in the house to locate residents and monitor their activities. This measurement system solves the problem of monitoring older people and measuring their activities in multi-resident scenarios. Metrics are defined to analyze and interpret the collected data to understand daily habits and measure the activity level (AL) of older people. The accuracy of the system in detecting movements and discriminating residents is measured. As the sensor-to-person distance increases, the system decreases its ability to detect small movements, while still being able to detect large ones. The accuracy in discriminating the identity of residents can be improved by up to 96% using the Decision Tree (DT) classifier. The effectiveness of the measurement system is demonstrated in a real multi-resident scenario where two older people are monitored during their daily life. The collected data are processed, obtaining the AL and habits of the older people to assess their behavior
Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV
No full textA fine-grained regulation of the HVAC emitters, capable of providing heat and cool only where effectively needed, can lead to a significant energy saving. This paper presents the results from an experimental test of an energy-efficient sub-zonal heating management system, based on an innovative comfort sensor. The objective is to demonstrate how the real-time PMV (Predicted Mean Vote) measurement of different positions in a room can be used to apply optimal rules for the climate control. The case study is an office, located in Central Italy, equipped with two separately controllable electrical heaters. The heating system has been coupled with a low-cost, IR-based comfort sensor, named Comfort Eye, to regulate the heating output of each heater in function of the local comfort conditions. A PID (Proportional–integral–derivative) controller, tuned by fuzzy logic, uses the PMV measured in the respective sub-zone as controlled variable, regulates the power of each heater. The system ran for one winter day and results have been compared with a reference condition, representative of the typical ON/OFF control of the room. The reference condition has been created with the same heating system, but without the sub-zonal division. The comparison, considering the specific application presented, turned out that the sub-zonal control system could achieve an energy saving up to 17% with respect to the typical ON/OFF control with a slight improvement of thermal comfort, reduced deviation from the neutral condition (PMV = 0). This shows that the possibility of measuring comfort distributions is crucial to achieve optimal environmental control
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