scholarly journals Smartphone-Based Participatory Soundscape Mapping for a More Sustainable Acoustic Environment

2020 ◽  
Vol 12 (19) ◽  
pp. 7899 ◽  
Author(s):  
Giovanni Brambilla ◽  
Francesca Pedrielli
Keyword(s):  
Environmental Planning ◽  
Machine Learning Techniques ◽  
Acoustic Environment ◽  
Acoustic Data ◽  
Learning Techniques ◽  
Potential Benefits ◽  
Experimental Protocols ◽  
Internet Connections ◽  
Use Of Internet ◽  
Urban Environmental Planning

The urban environmental planning, a fundamental dynamic process for cities’ sustainability, could benefit from the soundscape approach, dealing with the perception of the acoustic environment in which sound is considered as a resource rather than a waste (noise). Noise and soundscape maps are useful tools for planning mitigation actions and for communication with citizens. Both mappings can benefit from crowdsourcing and participatory sound monitoring that has been made possible due to the large use of internet connections and mobile devices with dedicated apps. This paper is a “scoping review” to provide an overview of the potential, benefits, and drawbacks of participatory noise monitoring in noise and soundscape mapping applications, while also referring to metrological aspects. Gathering perceptual data on soundscapes by using digital questionnaires will likely be more commonly used than printed questionnaires; thus, the main differences between the experimental protocols concern the measurement of acoustic data. The authors propose to classify experimental protocols for in-field soundscape surveys into three types (GUIDE, MONITOR, and SMART) to be selected according to the survey’s objectives and the territorial extension. The main future developments are expected to be related to progress in smartphone hardware and software, to the growth of social networks data analysis, as well as to the implementation of machine learning techniques.

scholarly journals Crack detection using tap-testing and machine learning techniques to prevent potential rockfall incidents

2021 ◽  
Author(s):  
Roya Nasimi ◽  
Fernando Moreu ◽  
John Stormont
Keyword(s):  
Crack Detection ◽  
Confusion Matrix ◽  
Principal Component ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Rock Surface ◽  
Acoustic Data ◽  
Learning Techniques ◽  
Different Characteristics ◽  
2D Space

Abstract Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.

scholarly journals Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3113 ◽  
Author(s):  
Miguel Ángel Antón ◽  
Joaquín Ordieres-Meré ◽  
Unai Saralegui ◽  
Shengjing Sun
Keyword(s):  
Ambient Intelligence ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Transfer Of Knowledge ◽  
Noise Levels ◽  
Non Invasive ◽  
Learning Techniques ◽  
Potential Benefits ◽  
High Level

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.

scholarly journals Data Mining and Machine Learning Techniques for Aerodynamic Databases: Introduction, Methodology and Potential Benefits

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5807
Author(s):  
Esther Andrés-Pérez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Techniques ◽  
Aerodynamic Coefficients ◽  
Data Mining Techniques ◽  
Naca0012 Airfoil ◽  
Learning Techniques ◽  
The Future ◽  
Potential Benefits ◽  
Business Sectors

Machine learning and data mining techniques are nowadays being used in many business sectors to exploit the data in order to detect trends, discover certain features and patters, or even predict the future. However, in the field of aerodynamics, the application of these techniques is still in the initial stages. This paper focuses on exploring the benefits that machine learning and data mining techniques can offer to aerodynamicists in order to extract knowledge from the CFD data and to make quick predictions of aerodynamic coefficients. For this purpose, three aerodynamic databases (NACA0012 airfoil, RAE2822 airfoil and 3D DPW wing) have been used and results show that machine-learning and data-mining techniques have a huge potential also in this field.

scholarly journals Node Behavior Classification for Traffic Prediction in Optical Burst Switched Networks using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1964-1968
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Traffic Prediction ◽  
Support Vector ◽  
Huge Amount ◽  
Switched Networks ◽  
Switched Network ◽  
Learning Techniques ◽  
Use Of Internet ◽  
Optical Burst

Currently due to massive use of internet there is need of huge amount of bandwidth. The utilization of bandwidth can be managed up with optical burst switched networks. These networks cannot provide good QoS due to problems like wavelength contention and congestion problem. Also it is not necessary that contention in a network leads to congestion. It can be due to nodes behavior which affects the flow of traffic from source to destination. Hence there is a need to classify the traffic through the node at correct juncture to avoid congestion. This can be achieved using machine learning techniques. In this paper, support vector machine, AdaBoost classifier and Bagging classifier are evaluated .Experimental work is carried on Optical Burst Switched network dataset using 22 attributes which is available on UCI repository. The results show that bagging classifier performed better with accuracy of 95% in classifying the nodes behavior.

scholarly journals Artificial Intelligence and the Ethics of Self-Learning Robots

2017 ◽  
Author(s):  
Shannon Vallor ◽  
George A. Bekey
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning Techniques ◽  
Artificial Agents ◽  
Human Behaviors ◽  
Ethical Concerns ◽  
Learning Techniques ◽  
Wide Range ◽  
Complex Skills ◽  
Potential Benefits ◽  
Self Learning

The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent advances in machine learning techniques have produced artificial agents that can acquire highly complex skills formerly thought to be the exclusive province of human intelligence. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence—particularly those equipped with self-learning capacities. While the potential benefits of self-learning robots are immense, their potential dangers are equally serious. While some warn of a future where AI escapes the control of its human creators or even turns against us, this chapter focuses on other, far less cinematic risks of AI that are much nearer to hand, requiring immediate study and action by technologists, lawmakers, and other stakeholders.

scholarly journals Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources

2020 ◽  
Author(s):  
Brydon Eastman ◽  
Cameron Meaney ◽  
Michelle Przedborski ◽  
Mohammad Kohandel
Keyword(s):  
Transmission Rate ◽  
Epidemiological Data ◽  
Machine Learning Techniques ◽  
Model Parameters ◽  
Medical Resources ◽  
Policy Makers ◽  
Social Distancing ◽  
Effective Strategies ◽  
Learning Techniques ◽  
Potential Benefits

AbstractThe outbreak of SARS-CoV-2 in China has spread around the world, infecting millions and causing governments to implement strict policies to counteract the spread of the disease. One of the most effective strategies in reducing the severity of the pandemic is social distancing, where members of the population systematically reduce their interactions with others to limit the transmission rate of the virus. However, the implementation of social distancing can be difficult and costly, making it imperative that both policy makers and the citizenry understand the potential benefits if done correctly and the risks if not. In this work, a mathematical model is developed to study the effects of social distancing on the spread of the SARS-CoV-2 virus in Canada. The model is based upon a standard epidemiological SEIRD model that has been stratified to directly incorporate the proportion of individuals who are following social distancing protocols. The model parameters characterizing the disease are estimated from current epidemiological data on COVID-19 using machine learning techniques. The results of the model show that social distancing policies in Canada have already saved thousands of lives and that the prolonged adherence to social distancing guidelines could save thousands more. Importantly, our model indicates that social distancing can significantly delay the onset of infection peaks, allowing more time for the production of a vaccine or additional medical resources. Furthermore, our results stress the importance of easing social distancing restrictions gradually, rather than all at once, in order to prevent a second wave of infections. Model results are compared to the current capacity of the Canadian healthcare system by examining the current and future number of ventilators available for use, emphasizing the need for the increased production of additional medical resources.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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