scholarly journals Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children

2019 ◽  
Vol 16 (7) ◽  
pp. 1230 ◽  
Author(s):  
Gabriella Tognola ◽  
Marta Bonato ◽  
Emma Chiaramello ◽  
Serena Fiocchi ◽  
Isabelle Magne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Real Life ◽  
Low Frequency ◽  
Personal Exposure ◽  
Epidemiological Studies ◽  
Electric Networks ◽  
High Exposure ◽  
Overhead Lines ◽  
The Impact

Characterization of children exposure to extremely low frequency (ELF) magnetic fields is an important issue because of the possible correlation of leukemia onset with ELF exposure. Cluster analysis—a Machine Learning approach—was applied on personal exposure measurements from 977 children in France to characterize real-life ELF exposure scenarios. Electric networks near the child’s home or school were considered as environmental factors characterizing the exposure scenarios. The following clusters were identified: children with the highest exposure living 120–200 m from 225 kV/400 kV overhead lines; children with mid-to-high exposure living 70–100 m from 63 kV/150 kV overhead lines; children with mid-to-low exposure living 40 m from 400 V/20 kV substations and underground networks; children with the lowest exposure and the lowest number of electric networks in the vicinity. 63–225 kV underground networks within 20 m and 400 V/20 kV overhead lines within 40 m played a marginal role in differentiating exposure clusters. Cluster analysis is a viable approach to discovering variables best characterizing the exposure scenarios and thus it might be potentially useful to better tailor epidemiological studies. The present study did not assess the impact of indoor sources of exposure, which should be addressed in a further study.

scholarly journals Cluster Analysis of Residential Personal Exposure to ELF Magnetic Field in Children: Effect of Environmental Variables

2019 ◽  
Vol 16 (22) ◽  
pp. 4363 ◽  
Author(s):  
Gabriella Tognola ◽  
Emma Chiaramello ◽  
Marta Bonato ◽  
Isabelle Magne ◽  
Martine Souques ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Cluster Analysis ◽  
Harmonic Component ◽  
Low Frequency ◽  
Electric Heating ◽  
Personal Exposure ◽  
Electric Networks ◽  
Field Exposure ◽  
Indoor Exposure ◽  
Exposure Patterns

Personal exposure to Extremely Low Frequency Magnetic Fields (ELF MF) in children is a very timely topic. We applied cluster analysis to 24 h indoor personal exposures of 884 children in France to identify possible common patterns of exposures. We investigated how electric networks near child home and other variables potentially affecting residential exposure, such as indoor sources of ELF MF, the age and type of the residence and family size, characterized the magnetic field exposure patterns. We identified three indoor personal exposure patterns: children living near overhead lines of high (63–150 kV), extra-high (225 kV) and ultra-high voltage (400 kV) were characterized by the highest exposures; children living near underground networks of low (400 V) and mid voltage (20 kV) and substations (20 kV/400 V) were characterized by mid exposures; children living far from electric networks had the lowest level of exposure. The harmonic component was not relevant in discriminating the exposure patterns, unlike the 50 Hz or broadband (40–800 Hz) component. Children using electric heating appliances, or living in big buildings or in larger families had generally a higher level of personal indoor exposure. Instead, the age of the residence was not relevant in differentiating the exposure patterns.

Ambient seismic noise suppression in COST action G2Net

2020 ◽  
Author(s):  
Velimir Ilić ◽  
Alessandro Bertolini ◽  
Fabio Bonsignorio ◽  
Dario Jozinović ◽  
Tomasz Bulik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Waves ◽  
Seismic Noise ◽  
Noise Suppression ◽  
Real Life ◽  
Low Frequency ◽  
Ambient Seismic Noise ◽  
Cost Action ◽  
The Cost ◽  
Life Challenge

<p>The analysis of low-frequency gravitational waves (GW) data is a crucial mission of GW science and the performance of Earth-based GW detectors is largely influenced by ability of combating the low-frequency ambient seismic noise and other seismic influences. This tasks require multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling.<br><br>In practice, this kind of research is conducted by large teams of researchers with different expertise, so that project management emerges as an important real life challenge in the projects for acquisition, processing and interpretation of seismic data from GW detector site. A prominent example that successfully deals with this aspect could be observed in the COST Action G2Net (CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning) and its seismic research group, which counts more than 30 members. </p><div>In this talk we will review the structure of the group, present the goals and recent activities of the group, and present new methods for combating the seismic influences at GW detector site that will be developed and applied within this collaboration.</div><div> <p> </p> <p>This publication is based upon work from CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning, supported by COST (European Cooperation in Science and Technology).</p> </div>

scholarly journals 625 Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events

2021 ◽  
Vol 23 (Supplement_G) ◽  
Author(s):  
Alberto Testa ◽  
Sabrina Anticoli ◽  
Francesca Romana Pezzella ◽  
Marilena Mangiardi ◽  
Alessandro Di Giosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Hierarchical Clustering ◽  
Metropolitan Area ◽  
Unsupervised Machine Learning ◽  
Large Metropolitan Area ◽  
Cerebrovascular Events ◽  
Increased Risk ◽  
The Impact ◽  
Cluster 2

Abstract Aims The impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. Methods and results Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from three tertiary care centre from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for environmental protection, and from the Meteorologic Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rate of acute cardiac and cerebrovascular events with Poisson models. As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated four separate clusters: Cluster 1, including 60 (5.1%) days, Cluster 2 with 419 (35.8%) days, Cluster 3 with 673 (57.6%) days, and Cluster 4 with 17 (1.5%) days, with significant between-cluster differences in weather and pollution features. Notably, Cluster 1 was characterized by low temperatures and high ozone concentrations (P < 0.001). Overall cluster-wise comparisons showed significant overall differences in adverse cardiac and cerebrovascular events (P < 0.001), as well as in cerebrovascular events (P < 0.001) and strokes (P = 0.001). Between-cluster comparisons showed that Cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to Cluster 2, Cluster 3, and Cluster 4 (all P < 0.05), as well as AMI in comparison to Cluster 3 (P = 0.047). In addition, Cluster 2 was associated with a higher risk of strokes in comparison to Cluster 4 (P = 0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events, and strokes for Cluster 1 and Cluster 2. Conclusions Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increases risk of acute cardiovascular events, especially cerebrovascular events.

scholarly journals Pedestrian recognition using automotive radar sensors

2012 ◽  
Vol 10 ◽  
pp. 45-55 ◽  
Author(s):  
A. Bartsch ◽  
F. Fitzek ◽  
R. H. Rasshofer
Keyword(s):  
Machine Learning ◽  
Elevation Angle ◽  
Real Life ◽  
Doppler Frequency ◽  
Radar Data ◽  
Limiting Factors ◽  
Automotive Radar ◽  
Signal Processing Algorithm ◽  
Radar Sensors ◽  
The Impact

Abstract. The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

Learning-based error modeling in FDM 3D printing process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paschalis Charalampous ◽  
Ioannis Kostavelis ◽  
Theodora Kontodina ◽  
Dimitrios Tzovaras
Keyword(s):  
Machine Learning ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Real Life ◽  
Deep Understanding ◽  
Error Modeling ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Fused Deposition ◽  
The Impact

Purpose Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure. Design/methodology/approach The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process. Findings The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well. Originality/value The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

scholarly journals Dealing with Randomness and Concept Drift in Large Datasets

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Kassim S. Mwitondi ◽  
Raed A. Said
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Data Science ◽  
Concept Drift ◽  
Real Life ◽  
Quality Education ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Address Data ◽  
The Impact

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.

scholarly journals Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

2021 ◽  
Vol 2022 (1) ◽  
pp. 274-290
Author(s):  
Dmitrii Usynin ◽  
Daniel Rueckert ◽  
Jonathan Passerat-Palmbach ◽  
Georgios Kaissis
Keyword(s):  
Machine Learning ◽  
Collaborative Learning ◽  
Real Life ◽  
Model Adaptation ◽  
Theoretical Understanding ◽  
Model Inversion ◽  
Optimal Balance ◽  
Learning Scenarios ◽  
Utility Cost ◽  
The Impact

Abstract In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

Exposure of adults to extremely low frequency magnetic field in France: results of the EXPERS study

2019 ◽  
Vol 55 (1) ◽  
pp. 39-50 ◽  
Author(s):  
I. Magne ◽  
M. Souques ◽  
L. Courouve ◽  
A. Duburcq ◽  
E. Remy ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Low Frequency ◽  
Personal Exposure ◽  
Epidemiological Studies ◽  
Field Exposure ◽  
Extremely Low Frequency ◽  
Measurement Protocol ◽  
Main Variable ◽  
Frequency Magnetic Field ◽  
The Magnetic Field

Assessing the exposure of adults to magnetic field is a central point in the context of epidemiological studies. The EXPERS study is the first study at national scale in Europe with measurements of personal exposure to extremely low frequency magnetic fields, involving 1046 French adults with 24 h personal measurements. The proportion of adults with a 24 h AM of ≥ 1 µT was 2.1% for all adults and 0.3% for adults for which no alarm clock was identified, as this requirement of the measurement protocol was sometimes not respected. The alarm clocks were the main variable linked to the adults’ exposure measurements. The vicinity of the home to a high voltage power line increased the magnetic field exposure. However, only 1.7% of the adults were living close to a 63 to 400 kV overhead line, and only one of them had a personal exposure ≥ 1 μT with an AM of 1.1 μT. The exposure of adults was also correlated with some characteristics of the home and its environment, and some durations of activities, such as the duration of work and the duration in rail transport. The distribution of adults’ personal exposure was significantly different from the distribution of exposure during sleep, and from the distribution of exposure assessed from measurements during sleep and work. This highlights the complexity of the exposure assessment in epidemiological studies.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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