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Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Time series in sensor data using state of the art deep learning approaches: A systematic literature review. |
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Conference Article |
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Year |
2022 |
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VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. |
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252 |
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503-514 |
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time series, deep learning, recurrent networks, sensor data, IoT. |
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IoT (Internet of Things) and AI (Artificial Intelligence) are becoming
support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity
recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results. |
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cidis @ cidis @ |
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152 |
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Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla |
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Title |
Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language |
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Conference Article |
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2020 |
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ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 |
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77 |
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This paper explains how Natural Language (NL) processing by computers, through smart
programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written
statements. The study recognized the need to improve the implemented web platform using a
dashboard in which we collected a set of extensive data to measure assessment factors of using
children´s educational games. In this case, applying NL is a strategy to give assessments, build, and
display more precise written statements to enhance the understanding of children´s gaming behavior.
We propose the development of a new tool to assess the use of written explanations rather than a
statistical representation of feedback information for the comprehension of parents and teachers with
a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim
explanations of children´s behavior playing educational videogames as NL interpretation instead of
statistical representations. An educational series of digital game applications for mobile devices,
identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a
dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary
schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing
the degrees of playability and usability factors obtained from the data produced when children play a
MIDI game. The results obtained are presented in a comprehensive guiding evaluation report
applying NL for parents and teachers. These guiding evaluations are useful to enhance children's
learning understanding related to the school curricula applied to ludic digital games. |
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cidis @ cidis @ |
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131 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Sensor Fault Detection and Diagnosis for autonomous vehicles |
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Conference Article |
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2015 |
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2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 |
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30 |
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MATEC Web of Conferences |
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1-6 |
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In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor. |
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EDP Sciences |
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English |
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English |
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cidis @ cidis @ |
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42 |
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Author |
Wilton Agila; Victor M. Huilcapi |
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Title |
Lógica borrosa para la estimación de estados críticos de una pila de combustible PEM |
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Conference Article |
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2014 |
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Reconocimientos de Patrones, Control Inteligente y Comunicaciones (MACH 2014) |
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5 |
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Caracterización de pilas de combustible PEM, estado de inundación y deshidratación de la membrana polimérica, árbol de decisión borroso, control, lógica difusa |
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La determinación en tiempo real de los estados críticos de operación de la pila de combustible de membrana intercambio protónico (siglas en ingles, PEM) es uno de los principales retos para los sistemas de control de pilas de combustible PEM. En este trabajo, se presenta el desarrollo e implementación de un método no invasivo de bajo coste basado en técnicas de decisión borrosa que permite estimar los estados críticos de operación de la pila de combustible PEM. La estimación se realiza mediante perturbaciones al estado de operación de la pila y el análisis posterior de la evolución temporal del voltaje generado por la pila. La implementación de esta técnica de estimulación-percepción de estado de la pila de combustible para la detección de estados críticos constituye una novedad y un paso hacia el control autónomo en óptimas condiciones de la operación de las pilas de combustible PEM. |
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Universidad de Cuenca |
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Español |
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Español |
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cidis @ cidis @ |
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31 |
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