Machine learning-based algorithms to knowledge extraction from time series data: A review

To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data

Numerical simulation for the sound absorption properties of ceramic resonators

This work reports the results of experimental measurements of the sound absorption coefficient of ceramic materials using the principle of acoustic resonators. Subsequently, the values obtained from the measurements were used to train a simulation model of the acoustic behavior of the analyzed material based on artificial neural networks. The possible applications of sound-absorbing materials made with ceramic can derive from aesthetic or architectural needs or from functional needs, as ceramic is a fireproof material resistant to high temperatures. The results returned by the simulation model based on the artificial neural networks algorithm are particularly significant. This result suggests the adoption of this technology to find the finest possible configuration that allows the best sound absorption performance of the material

Modelling sound absorption properties of broom fibers using artificial neural networks

The use of broom to produce fibers has ancient roots. The Greeks appreciated its resistance to water and for this reason they used it to manufacture sailing ropes. But broom fiber was also appreciated for its sound absorption qualities. In this study, a new methodology was developed for the numerical modeling of the acoustic behavior of broom fibers. First, the characteristics of the different varieties of broom were examined and the procedures for processing the samples to be analyzed were described. Subsequently, the results of the measurements of the following acoustic properties of the material were analyzed: air flow resistance, porosity, and sound absorption coefficient. Finally, the results of the numerical modeling of the acoustic coefficient were reported using an algorithm based on artificial neural networks. The results obtained are compared with a model based on linear regression. The model based on neural networks showed high values of the Pearson correlation coefficient (0.989), indicating a high number of correct predictions

Wind turbine noise prediction using random forest regression

Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coeffcient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

Research for the presence of unmanned aerial vehicle inside closed environments with acoustic measurements

Small UAVs (unmanned aerial vehicle) can be used in many sectors such as the acquisition of images or the transport of objects. Small UAVs have also been used for terrorist activities or to disturb the flight of airplanes. Due to the small size and the presence of only rotating parts, drones escape traditional controls and therefore represent a danger. This paper reports a methodology for identifying the presence of small UAVs inside a closed environment by measuring the noise emitted during the flight. Acoustic measurements of the noise emitted by a drone inside a large environment (12.0 x 30.0 x 12.0 m) were performed. The noise was measured with a sound level meter placed at different distances (5, 10, and 15 m), to characterize the noise in the absence of anthropic noise. In this configuration, a typical tonal component of drone noise is highlighted at the frequency of one-third of an octave at 5000 Hz due to the rotation of the blades. This component is also present 15 m away from the source point. Subsequent measurements were performed by introducing into the environment, through a loudspeaker, the anthropogenic noise produced by the buzz of people and background music. It is possible to distinguish the typical tonal component of UAV noise at the frequency of 5000 Hz even when the level of recording of anthropogenic noise emitted by the loudspeaker is at the maximum power tested. It is therefore possible to search for the presence of small UAVs inside a specific closed environment with only acoustic measurements, paying attention to the typical frequency of noise emission equal to 5000 Hz

Sound attenuation of an acoustic barrier made with metamaterials

Although the first studies of them date back to a half century ago to Viselago, metamaterials represent a new solution in applied acoustics and noise control fields. In this paper, after a brief introduction to the state of art of metamaterials for acoustic applications, the sound attenuation of an acoustic barrier made following metamaterial rules is investigated. A 1:10 scale model was built using cylindrical bars, 30 cm high and 1.5 cm in diameter. The length of the barrier was 100 cm. The barrier was investigated for four combinations of the rows of the bars, spacing bars to create different regular geometries. The insertion losses of each configuration are reported

Machine learning-based tools for wind turbine acoustic monitoring

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines

A comparison between numerical simulation models for the prediction of acoustic behavior of giant reeds shredded

Giant reeds represent a natural fiber widely available in some areas of the world. Its use can be particularly useful as the uncontrolled growth of giant reeds can be a problem because large areas are invaded by them and the crops are damaged. In this study, two models of numerical simulation of the acoustic behavior of giant reeds were put in comparison: the Hamet model and a model based on artificial neural networks. First, the characteristics of the reeds were examined and the procedures for the preparation of the samples to be analyzed were described. Then air flow resistance, porosity and sound absorption coefficient were measured and analyzed in detail. Finally, the results of the numerical modeling of the acoustic coefficient were compared. The neural network-based model showed high Pearson correlation coefficient value, indicating a large number of correct predictions

An artificial neural network approach to modelling absorbent asphalts acoustic properties

Sound-absorbing asphalts are particularly useful for reducing noise emissions from vehicular traffic. This solution is perfectly suited for urban areas, in fact the use of sound-absorbing asphalt represents a noise control measure with a negligible environmental impact. In the present work, the results of an experimental investigation on sound-absorbing asphalts were reported. First, the characteristics of the sound-absorbing asphalts used were experimentally found. Then, the measurements of the sound absorption coefficient of the asphalt specimens were investigated. In the final part, numerical simulation model with artificial neural networks of the acoustic coefficient were compared with the data obtained from the measurements. The neural network model showed good Pearson correlation coefficient values (0.894) which can be used with good accuracy to predict the sound absorption coefficient

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better