Dust detection and analysis in museum environment based on pattern recognition

Proietti, Andrea; Panella, Massimo; Leccese, Fabio; Svezia, Emiliano

doi:10.1016/j.measurement.2015.01.019

Cited by 56 publications

(22 citation statements)

References 33 publications

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“…In this regard, computational intelligence is considered as one of the most fruitful approaches for prediction [16]. Several forecasting methods, with different mathematical backgrounds, such as fuzzy predictors, artificial neural networks (ANN), evolutionary and genetic algorithms and support vector machines, have been considered [17]. Nevertheless, dealing with noisy and missing training examples and the lack of robustness against outliers are still open problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

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Abstract:The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

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Section: Introductionmentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

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“…In fact, common software suites for handling data (e.g., Microsoft Excel or LibreOffice) are unable to deal with very large data sets and this can be a special problem for conservators having limited ability to undertake computations with large amounts of collected data. In recent years, many remarkable efforts have been made in the field of monitoring design, exploiting machine learning to increase the efficiency in remote control, and dataloggers’ management [6,7]. Computational intelligence can also be extremely helpful in implementing low-cost tools for suitably improving sampling conditions [8,9], but preliminary surveys based on this approach are still rarely employed to configure museum monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Minimum Sampling Frequency to Avoid Measurement Redundancy in Microclimate Field Surveys in Museum Buildings

García-Diego

Verticchio

Beltrán

et al. 2016

Sensors

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Monitoring temperature and relative humidity of the environment to which artefacts are exposed is fundamental in preventive conservation studies. The common approach in setting measuring instruments is the choice of a high sampling rate to detect short fluctuations and increase the accuracy of statistical analysis. However, in recent cultural heritage standards the evaluation of variability is based on moving average and short fluctuations and therefore massive acquisition of data in slowly-changing indoor environments could end up being redundant. In this research, the sampling frequency to set a datalogger in a museum room and inside a microclimate frame is investigated by comparing the outcomes obtained from datasheets associated with different sampling conditions. Thermo-hygrometric data collected in the Sorolla room of the Pio V Museum of Valencia (Spain) were used and the widely consulted recommendations issued in UNI 10829:1999 and EN 15757:2010 standards and in the American Society of Heating, Air-Conditioning and Refrigerating Engineers (ASHRAE) guidelines were applied. Hourly sampling proved effective in obtaining highly reliable results. Furthermore, it was found that in some instances daily means of data sampled every hour can lead to the same conclusions as those of high frequency. This allows us to improve data logging design and manageability of the resulting datasheets.

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“…This configuration provides an inexpensive, platform-independent, and open-source platform to circadian analysis, similar to those utilized in other fields [12][13][14][15][16][17]. The data acquisition hardware is connected to the Raspberry-Pi computer by an electronic interface designed to operate under field conditions.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Analyzing of Circadian and Ultradian Locomotor Activity Based on Raspberry-Pi

et al. 2016

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Abstract:A new device based on the Raspberry-Pi to monitor the locomotion of Arctic marine invertebrates and to analyze chronobiologic data has been made, tested and deployed. The device uses infrared sensors to monitor and record the locomotor activity of the animals, which is later analyzed. The software package consists of two separate scripts: the first designed to manage the acquisition and the evolution of the experiment, the second designed to generate actograms and perform various analyses to detect periodicity in the data (e.g., Fourier power spectra, chi-squared periodograms, and Lomb-Scargle periodograms). The data acquisition hardware and the software has been previously tested during an Arctic mission with an arctic marine invertebrate.

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Dust detection and analysis in museum environment based on pattern recognition

Cited by 56 publications

References 33 publications

Prediction in Photovoltaic Power by Neural Networks

Prediction in Photovoltaic Power by Neural Networks

Assessment of the Minimum Sampling Frequency to Avoid Measurement Redundancy in Microclimate Field Surveys in Museum Buildings

Monitoring and Analyzing of Circadian and Ultradian Locomotor Activity Based on Raspberry-Pi

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