Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenanceprogram. Partialdischarges(PDs)phenomenaaffecttheinsulationsystemofanelectrical machine and\u2014in the long term\u2014can lead to a breakdown, with a consequent, signi\ufb01cant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is speci\ufb01cally designed to run on low-cost embedded devices

Production and Sensory Evaluation of Novel Cheeses Made with Prebiotic Substances: Inulin and Oligofructose

In recent years, the processing and consumption of functional foods worldwide have greatly increased. These foods benefit the body functions which improve consumers’ health and also reduce the risk factors that cause the onset of disease. Furthermore, prebiotic substances favor the multiplication of beneficial intestinal bacteria rather than harmful ones. The purpose of this study was to conduct the sensory evaluation of two functional cheeses containing inulin and oligofructose as a distinctive ingredient, including testing a cheese made with conventional ingredients, called control cheese. Affective type tests, which measured the degree of liking or disliking, were conducted using a verbal 7-point hedonic scale. According to the inclusion and exclusion criteria, 57 untrained judges were selected. This study is a quantitative, analytic and experimental-cross design. Statistical analysis of the data was performed by ANOVA with repeated measures. The results show a similar average degree of liking for the three cheeses, above 5 on the scale or “like”. By analyzing the critical level and the result of the Mauchly’s sphericity test, it is concluded that there is no statistically significant difference in the degree of liking for the three cheeses. Therefore, the addition of prebiotics to artisanal cheeses achieves to satisfy consumers and provide them benefits superior to those provided by traditional foods.Fil: Machuca, Laura Marcela. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Rodriguez, Yamila E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Guastavino Meneguini, Daniela E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Bruzzo, Maria E.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Acuña Ojeda, María F.. Universidad de la Cuenca del Plata. Secretaria de Politicas del Conocimiento. Instituto de Investigaciones Cientificas (sede Goya); ArgentinaFil: Murguia, Marcelo Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

Prediction of severe thunderstorm events with ensemble deep learning and radar data

The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelligence algorithms. Within this latter framework, the most used techniques rely on video prediction deep learning methods which take in input time series of radar reflectivity images to predict the next future sequence of reflectivity images, from which the predicted rainfall quantities are extrapolated. Differently from the previous works, the present paper proposes a deep learning method, exploiting videos of radar reflectivity frames as input and lightning data to realize a warning machine able to sound timely alarms of possible severe thunderstorm events. The problem is recast in a classification one in which the extreme events to be predicted are characterized by a an high level of precipitation and lightning density. From a technical viewpoint, the computational core of this approach is an ensemble learning method based on the recently introduced value-weighted skill scores for both transforming the probabilistic outcomes of the neural network into binary predictions and assessing the forecasting performance. Such value-weighted skill scores are particularly suitable for binary predictions performed over time since they take into account the time evolution of events and predictions paying attention to the value of the prediction for the forecaster. The result of this study is a warning machine validated against weather radar data recorded in the Liguria region, in Italy

Active Learning for Auditory Hierarchy

Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether or not an object is a foreground sound, which should be reproduced at full quality if possible, or a background sound, which can be heavily compressed without causing a deterioration in the listening experience. Lack of suitably labelled data is an acknowledged problem in the domain. Active Learning is a method that can greatly reduce the manual effort required to label a large corpus by identifying the most effective instances to train a model to high accuracy levels. This paper compares a number of Active Learning methods to investigate which is most effective in the context of a hierarchical labelling task on an audio dataset. Results show that the number of manual labels required can be reduced to 1.7% of the total dataset while still retaining high prediction accuracy

Perception of urban park soundscape

A number of studies have been initiated to explore how to improve the soundscape quality in urban parks. However, good soundscape quality in parks cannot be provided without a thorough understanding of the complex relationships among sound, environment, and individuals. As acoustic comfort is considered to be an important outcome of soundscape quality, this study investigates the relative impacts of the factors influencing acoustic comfort evaluation by formulating a multivariate ordered logit model. This study also explores the inter-relationships among acoustic comfort evaluation, acceptability of the environment, and preference to stay in a park using a path model. A total of 595 valid responses were obtained from interview surveys administered in four parks in Hong Kong while objective sound measurements were carried out at the survey spots concurrently. The findings unveil that acoustic comfort evaluation, besides visual comfort evaluation of landscape, also plays an important role on users’ acceptability of the urban park environment. Compared with all the studied acoustic related factors, acoustic comfort evaluation serves as a better proxy for park users’ preference to stay in urban parks. Hearing the breeze will significantly increase the likelihood of individuals in giving high acoustic comfort evaluation. Conversely, hearing the sounds from heavy vehicles or sounds from bikes will significantly reduce the likelihood in giving a high acoustic evaluation.Department of Building Services EngineeringDepartment of Mechanical Engineerin

Mind-wandering and alterations to default mode network connectivity when listening to naturalistic versus artificial sounds

Naturalistic environments have been demonstrated to promote relaxation and wellbeing. We assess opposing theoretical accounts for these effects through investigation of autonomic arousal and alterations of activation and functional connectivity within the default mode network (DMN) of the brain while participants listened to sounds from artificial and natural environments. We found no evidence for increased DMN activity in the naturalistic compared to artificial or control condition, however, seed based functional connectivity showed a shift from anterior to posterior midline functional coupling in the naturalistic condition. These changes were accompanied by an increase in peak high frequency heart rate variability, indicating an increase in parasympathetic activity in the naturalistic condition in line with the Stress Recovery Theory of nature exposure. Changes in heart rate and the peak high frequency were correlated with baseline functional connectivity within the DMN and baseline parasympathetic tone respectively, highlighting the importance of individual neural and autonomic differences in the response to nature exposure. Our findings may help explain reported health benefits of exposure to natural environments, through identification of alterations to autonomic activity and functional coupling within the DMN when listening to naturalistic sounds

A computational study on outliers in world music

The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country

Combined Electrical and Thermal Stress on Twisted Pairs: Study of the Variation over Time of the Partial Discharges Inception Voltage

The electrical insulation of small power electrical machines is a critical element especially when they are powered by means of electronic power supplies using techniques such as PWM. Power supplies based upon pulsed voltage trains can introduce, under certain conditions, overvoltage and this overvoltage can produce the inception of partial discharge phenomena. Enameled wires, even in case of high insulating thermal classes, cannot resist for a long time to the erosive action of the partial discharges and therefore, inevitably, reaching the total discharge conditions. In addition, the erosion of the enamel causes a decrease of the inception voltage, aggravating the situation and quickly leading to the total breakdown. In this study enameled wires thermal class W-240 °C have been considered. These wires have been used to prepare twisted-pair specimen following the IEC 60851-5 standard. They have been subjected to thermo-electric ageing using a sinusoidal test voltage waveform, varying its amplitude and frequency. The obtained results show that, during the degradation, the PDIV values continuously decrease following an inverse power law relationship. Furthermore, the obtained data evidence that the PDIV derivatives do not depend on the test voltage amplitudes and frequencies but rather seems to depend on the temperatur