scholarly journals Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3512 ◽  
Author(s):  
Miguel Ángel López Medina ◽  
Macarena Espinilla ◽  
Cristiano Paggeti ◽  
Javier Medina Quero
Keyword(s):  
Fuzzy Logic ◽  
Activity Recognition ◽  
Heterogeneous Sensors ◽  
Temporal Features ◽  
Constant Change ◽  
Environmental Sensor ◽  
Spatio Temporal ◽  
Iot Devices ◽  
The University

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology’s potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers.

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.

Nonlinear diffusion-based interpretation of induced microseismicity: A Barnett Shale hydraulic fracturing case study

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. B211-B226 ◽  
Author(s):  
Nicolas Hummel ◽  
Serge A. Shapiro
Keyword(s):  
Pore Pressure ◽  
Pressure Dependence ◽  
Nonlinear Diffusion ◽  
Induced Seismicity ◽  
Pressure Field ◽  
Barnett Shale ◽  
Permeability Evolution ◽  
Temporal Features ◽  
Spatio Temporal

For the successful development and operation of hydrocarbon or geothermal reservoirs, knowledge of the hydraulic transport is of crucial importance. Because fundamental physical processes of borehole fluid injections are still insufficiently understood, gathering information about transport properties of rocks under field conditions is quite difficult. However, a substantial contribution in determining the permeability evolution can be obtained by understanding the distribution of induced seismicity in space and time. We have analyzed spatio-temporal characteristics of seismicity recorded during a hydraulic fracturing treatment in the Barnett Shale. In this study, we show that the fluid-rock interaction is nonlinear. To explain corresponding spatio-temporal features of induced seismicity, we considered pore pressure diffusion based on a power-law pressure dependence of permeability. A scaling approach was used to transform clouds of hypocenters of events obtained in a hydraulically anisotropic nonlinear medium into a cloud which would be obtained in an equivalent isotropic but still nonlinear medium. For this, we used a concept of a factorized anisotropic pressure dependence of permeability and found that it is in agreement with the microseismic data under consideration. We used a numerical modeling approach to generate synthetic seismicity by solving nonlinear diffusion equations. The pore-pressure field obtained from flow rates was calibrated with the pore-pressure field computed for injection pressures. This yielded an estimate of the uniaxial storage coefficient and permitted us to compute the permeability evolution inside the fracture stimulated reservoir. Following our modeling, we generated synthetic seismicity whose spatio-temporal features are similar to the ones observed in the case study. This indicates that a nonlinear diffusion with a pressure-dependent permeability seems to provide a reasonable model of the hydraulic-fracture stimulation under consideration. A power-law pressure dependence of stimulated permeability may be a more general characteristic for shales.

scholarly journals Determination of the Innovative Capacity of Ecuadorian Universities

2017 ◽  
Vol 6 (1) ◽  
pp. 57-63 ◽  
Author(s):  
María Isabel Loaiza ◽  
Paola Salomé Andrade Abarca ◽  
Ángela Del Cisne Salazar
Keyword(s):  
Higher Education ◽  
Higher Education Institutions ◽  
Academic Innovation ◽  
Constant Change ◽  
Innovation Model ◽  
The University ◽  
Innovative Capacity ◽  
Organizational Competence

Today the Higher Education Institutions are immersed in a world of constant change, and it is necessary to adapt to this environment with skill and speed. This has forced universities to adopt practices that create or strengthen their capacity to innovate, based on the determination of factors that influence it positively. This study is centered on the implementation of the Innovation Model of Higher Education (MIES) prepared by Villa, Escotet and Goni (2007), adapted to the university context. This model allows Higher Education Institutions to diagnose their innovative capacity. This model is developed in the field of academic innovation, which is defined as the organizational competence of institutions to respond to the environment quickly enough, to cover existing needs and to anticipate future ones. As a case study, MIES was applied to Ecuadorian universities where the perception of different university actors was obtained and the factors driving innovation in this context were determined as a reference for the application of strategies promoting innovation.

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