scholarly journals Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device”

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4991
Author(s):  
Mike Lakoju ◽  
Nemitari Ajienka ◽  
M. Ahmadieh Khanesar ◽  
Pete Burnap ◽  
David T. Branson
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Sampling Rate ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Sensor Data ◽  
Unsupervised Machine Learning ◽  
Fuzzy C Means ◽  
Product Use ◽  
Fuzzy C Means Algorithm

To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and manufacturing processes, aligned to the Industry 4.0 paradigm. In this paper, we propose a model that enables new forms of agile engineering product development via “chatty” products. Products relay their “experiences” from the consumer world back to designers and product engineers through the mediation provided by embedded sensors, IoT, and data-driven design tools. Our model aims to identify product “experiences” to support the insights into product use. To this end, we create an experiment to: (i) collect sensor data at 100 Hz sampling rate from a “Chatty device” (device with sensors) for six common everyday activities that drive produce experience: standing, walking, sitting, dropping and picking up of the device, placing the device stationary on a side table, and a vibrating surface; (ii) pre-process and manually label the product use activity data; (iii) compare a total of four Unsupervised Machine Learning models (three classic and the fuzzy C-means algorithm) for product use activity recognition for each unique sensor; and (iv) present and discuss our findings. The empirical results demonstrate the feasibility of applying unsupervised machine learning algorithms for clustering product use activity. The highest obtained F-measure is 0.87, and MCC of 0.84, when the Fuzzy C-means algorithm is applied for clustering, outperforming the other three algorithms applied.

scholarly journals Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1217
Author(s):  
Nicolò Bellin ◽  
Erica Racchetti ◽  
Catia Maurone ◽  
Marco Bartoli ◽  
Valeria Rossi
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Fuzzy C Means ◽  
Air Temperatures ◽  
Physico Chemical ◽  
Shallow Ponds

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5230
Author(s):  
Isabel Santiago ◽  
Jorge Luis Esquivel-Martin ◽  
David Trillo-Montero ◽  
Rafael Jesús Real-Calvo ◽  
Víctor Pallarés-López
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Automatic Classification ◽  
Sampling Frequency ◽  
Machine Learning Algorithms ◽  
Unsupervised Machine Learning ◽  
Average Efficiency ◽  
Clear Sky ◽  
Photovoltaic Plant

In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.

Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning

2021 ◽  
Vol 303 ◽  
pp. 117656
Author(s):  
Maitreyee Dey ◽  
Soumya Prakash Rana ◽  
Clarke V. Simmons ◽  
Sandra Dudley
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Anomaly Detection ◽  
Data Driven ◽  
Unsupervised Machine Learning
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