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Ricardo Cajo, & Wilton Agila. (2015). Evaluation of algorithms for linear and nonlinear PID control for Twin Rotor MIMO System. In Computer Aided System Engineering (APCASE), 2015 Asia-Pacific Conference on, Quito, 2015 (pp. 214–219). IEEE.
Abstract: 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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Dennis G. Romero, A. F. Neto, T. F. Bastos, & Boris X. Vintimilla. (2012). An approach to automatic assistance in physiotherapy based on on-line movement identification. In VI Andean Region International Conference – ANDESCON 2012. Andean Region International Conference (ANDESCON), 2012 VI: IEEE.
Abstract: This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Monica Villavicencio, & Alain Abran. (2011). Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs. In Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (pp. 239–244). IEEE.
Abstract: In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement.
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Cristina L. Abad, Yi Lu, & Roy H. Campbell. (2011). DARE: Adaptive Data Replication for Efficient Cluster Scheduling. In IEEE International Conference on Cluster Computing, 2011 (pp. 159–168).
Abstract: Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.
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Wilton Agila, & Victor M. Huilcapi. (2014). Lógica borrosa para la estimación de estados críticos de una pila de combustible PEM. In Reconocimientos de Patrones, Control Inteligente y Comunicaciones (MACH 2014) (Vol. 5). Universidad de Cuenca.
Abstract: 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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M. Diaz, Dennys Paillacho, C. Angulo, O. Torres, J. Gonzálalez, & J. Albo Canals. (2014). A Week-long Study on Robot-Visitors Spatial Relationships during Guidance in a Sciences Museum. In ACM/IEEE International Conference on Human-Robot Interaction (pp. 152–153).
Abstract: 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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Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
Abstract: Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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Cristhian A. Aguilera, Cristhian Aguilera, & Angel D. Sappa. (2018). Melamine faced panels defect classification beyond the visible spectrum. In Sensors 2018, Vol. 11(Issue 11).
Abstract: In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
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Juan A. Carvajal, Dennis G. Romero, & Angel D. Sappa. (2017). Fine-tuning deep convolutional networks for lepidopterous genus recognition. Lecture Notes in Computer Science, Vol. 10125 LNCS, pp. 467–475.
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Cristhian A. Aguilera, Angel D. Sappa, & Ricardo Toledo. (2017). Cross-Spectral Local Descriptors via Quadruplet Network. In Sensors Journal, Vol. 17, pp. 873.
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