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Author |
M. Diaz; Dennys Paillacho; C. Angulo; O. Torres; J. Gonzálalez; J. Albo Canals |
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Title |
A Week-long Study on Robot-Visitors Spatial Relationships during Guidance in a Sciences Museum |
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Conference Article |
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2014 |
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ACM/IEEE International Conference on Human-Robot Interaction |
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152-153 |
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social human-robot interaction, spatial relationships, proxemics behavior |
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In order to observe spatial relationships in social human- robot interactions, a field trial was carried out within the CosmoCaixa Science Museum in Barcelona. The follow me episodes studied showed that the space configurations formed by guide and visitors walking together did not always fit the robot social affordances and navigation requirements to perform the guidance successfully, thus additional commu- nication prompts are considered to regulate effectively the walking together and follow me behaviors. |
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cidis @ cidis @ |
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29 |
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Author |
Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa |
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Title |
Detection of utility poles from noisy Point Cloud Data in Urban environments. |
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Conference Article |
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2018 |
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Artificial Intelligence and Cloud Computing Conference (AICCC 2018) |
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53-57 |
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In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners. |
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gtsi @ user @ |
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94 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
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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cidis @ cidis @ |
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42 |
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Author |
Wilton Agila; Ricardo Cajo; Douglas Plaza |
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Title |
Experts Agents in PEM Fuel Cell Control |
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Conference Article |
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Year |
2015 |
Publication |
4ta International Conference on Renewable Energy Research and Applications |
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896 - 900 |
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Keywords |
s- PEM Fuel Cell; Expert Agent; Perceptive Agents; Acting Agent; Fuzzy Controller |
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Abstract |
In the control of the PEM (Proton Exchange Membrane) fuel cell, the existence of both deliberative and reactive processes that facilitate the tasks of control resulting from a wide range of operating scenarios and range of conditions it is required. The latter is essential to adjust its parameters to the multiplicity of circumstances that may occur in the operation of the PEM stack. In this context, the design and development of an expert-agents based architecture for autonomous control of the PEM stack in top working conditions is presented. The architecture integrates perception and control algorithms using sensory and context information. It is structured in a hierarchy of levels with different time window and level of abstraction. The monitoring model and autonomic control of PEM stack has been validated with different types of PEM stacks and operating conditions demonstrating high reliability in achieving the objective of the proposed energy efficiency. Dynamic control of the wetting of the membrane is a clear example. |
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IEEE |
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Palermo, Italy |
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English |
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English |
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2015 International Conference on Renewable Energy Research and Applications (ICRERA) |
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no |
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Call Number |
cidis @ cidis @ |
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46 |
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Author |
Ricardo Cajo; Wilton Agila |
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Title |
Evaluation of algorithms for linear and nonlinear PID control for Twin Rotor MIMO System |
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Conference Article |
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2015 |
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Computer Aided System Engineering (APCASE), 2015 Asia-Pacific Conference on, Quito, 2015 |
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214-219 |
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Keywords |
Twin Rotor MIMO System (TRMS); Proportional-Integral-Derivative (PID); Linear PID Controller; Nonlinear PID Controller; Nonlinear Observer |
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In this paper the linear and nonlinear PID control algorithms are analyzed and for a twin rotor MIMO system (TRMS), whose characteristic is not linear with two degrees of freedom and cross-links. The aim of this work is to stabilize the TRMS, to achieve a particular position and follow a trajectory in the shortest time. Mathematical modeling of helicopter model is simulated using MATLAB / Simulink, the two degrees of freedom are controlled both horizontally and vertically through the proposed controllers. Also nonlinear segmented observers for each degree of freedom are designed in order to measure statements required by the nonlinear controller. Followed, a comparative analysis of both algorithms is presented to evaluate their performance in the real TRMS. |
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IEEE |
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English |
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2015 Asia-Pacific Conference on Computer Aided System Engineering |
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Call Number |
cidis @ cidis @ |
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36 |
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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
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Conference Article |
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Year |
2015 |
Publication |
Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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417 |
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503-515 |
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Scene reconstruction, Point cloud, Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Springer International Publishing Switzerland 2016 |
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English |
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English |
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Second Iberian Robotics Conference |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
45 |
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Author |
Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. |
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Title |
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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Author |
Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon |
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Title |
Open Source system for identification of corn leaf chlorophyll contents based on multispectral images |
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Conference Article |
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2019 |
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International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador |
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572-581 |
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It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.
Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear
regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.
It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.
However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1]. |
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gtsi @ user @ |
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116 |
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Author |
Jacome-Galarza L.-R |
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Title |
Crop yield prediction utilizing multimodal deep learning |
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Conference Article |
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2021 |
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16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
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Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
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Abstract |
La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación. |
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cidis @ cidis @ |
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150 |
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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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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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