Browsing by Author "Gualan Saavedra, Ronald Marcelo"

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Author-topic classification based on semantic knowledge
(Springer Verlag, 2019) Segarra Flores, José Luis; Sumba Toral, Francisco Xavier; Ortiz Vivar, Jose Enrique; Gualan Saavedra, Ronald Marcelo; Espinoza Mejía, Jorge Mauricio; Saquicela Galarza, Víctor Hugo
We propose a novel unsupervised two-phased classification model leveraging from semantic web technologies for discovering common research fields between researchers based on information available from a bibliographic repository and external resources. The first phase performs coarse-grained classification by knowledge disciplines using as reference the disciplines defined in the UNESCO thesaurus. The second phase provides a fine-grained classification by means of a clustering approach combined with external resources. The methodology was applied to the REDI (Semantic Repository of Ecuadorian researchers) project, with remarkable results and thus proving a valuable tool to one of the main REDI’s goals discover Ecuadorian authors sharing research interests to foster collaborative research efforts.
Discovering research trends in the computer science area of Ecuador: an approach using semantic knowledge bases
(Institute of Electrical and Electronics Engineers Inc., 2019) Segarra Flores, Jose Luis; Ortiz Vivar, José Enrique; Gualan Saavedra, Ronald Marcelo; Saquicela Galarza, Víctor Hugo
We present a study of research trends for the area of Computer Sciences in Ecuador in recent years. This analysis was performed through a new method that leverages on semantic web technologies and external knowledge bases (i.e. DBpedia and UNESCO nomenclature) for identifying research topics within articles' metadata. This information takes into account the documents' publication date in order to construct time series which are analyzed and interpreted looking for trends. Concretely, we focused our study on the REDI (Semantic Repository of Ecuadorian Researchers) knowledge base which compiles most of the scholarly assets produced in Ecuador and more specifically on the Computer Science subset of publications. This study found that most of the research topics have shown an steady growth in the volume of publications over time, whereas the Semantic Web and E-Government research topics had a great impact initially and now have been slightly reducing its share in favor of new topics such as Information Integration, Machine Learning and Data Mining.
EDA and a tailored data imputation algorithm for daily ozone concentrations
(TICEC 2018, 2019) Gualan Saavedra, Ronald Marcelo; Saquicela Galarza, Víctor Hugo; Tran Thanh, Long
Air pollution is a critical environmental problem with detrimental effects on human health that is affecting all regions in the world, especially to low-income cities, where critical levels have been reached. Air pollution has a direct role in public health, climate change, and worldwide economy. Effective actions to mitigate air pollution, e.g. research and decision making, require of the availability of high resolution observations. This has motivated the emergence of new low-cost sensor technologies, which have the potential to provide high resolution data thanks to their accessible prices. However, since low-cost sensors are built with relatively low-cost materials, they tend to be unreliable. That is, measurements from low-cost sensors are prone to errors, gaps, bias and noise. All these problems need to be solved before the data can be used to support research or decision making. In this paper, we address the problem of data imputation on a daily air pollution data set with relatively small gaps. Our main contributions are: (1) an air pollution data set composed by several air pollution concentrations including criteria gases and thirteen meteorological covariates; and (2) a custom algorithm for data imputation of daily ozone concentrations based on a trend surface and a Gaussian Process. Data Visualization techniques were extensively used along this work, as they are useful tools for understanding the multi-dimensionality of point-referenced sensor data.
Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study
(2016) Ochoa Sánchez, Ana Elizabeth; Campozano Parra, Lenin Vladimir; Sánchez Cordero, Esteban Remigio; Gualan Saavedra, Ronald Marcelo; Samaniego Alvarado, Esteban Patricio
The downscaling of global climate models (GCMs) aims at incorporating finer scale information to their horizontal resolution in order to represent regional and local processes better. There are two main approaches to downscaling: statistical (based on data relationships between synoptic atmospheric variables and observations of local variables) and dynamical (based on the modelling of regional atmospheric processes and land-surface interactions). In this study, some predictive capabilities regarding the generation of station-scale mean monthly temperature and rainfall of both a statistical artificial neural network (ANN-based) and a dynamical weather research and forecasting (WRF-based) downscaling approach are assessed. We have devised two versions of the statistical downscaling approach. One of them includes regional orographic variables as predictors to allow for spatial extrapolation; the other is purely local. Historical observational data, from the period 1990 to 1999, of two major watersheds in the Ecuadorian Southern Andes, the Jubones and Paute river basins, were used. Since, to a certain extent, the value added by downscaling techniques can be attributed to terrain information, it is worth noting that some characteristics of the selected catchments (as notorious altitude differences and the presence of qualitatively different precipitation regimes) provide a scientifically interesting location for exploring how finer scale effects are captured. For this reason, we concentrate on the ability of downscaling techniques to reproduce seasonality. A decade of evaluation proved that both approaches were able to qualitatively describe precipitation and temperature seasonal variations for different regimes at representative weather stations. Furthermore, the seasonality of precipitation represented by both downscaling approaches surpassed the seasonality representation of reanalysis data. However, shortcomings on the estimates were found. Specifically, dynamical downscaled precipitation estimates were prone to overestimation. Despite the fact that the considered downscaling approaches are different in nature, their ability to represent the high spatio-temporal variability in this region highlights the importance of evaluating their strengths and limitations. © 2016 Royal Meteorological Society.
GPU Acceleration of the Horizontal Diffusion Method in the Weather Research and Forecasting (WRF) Model
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2015-07-14) Gualan Saavedra, Ronald Marcelo; Solano Quinde, Lizandro Damián
The Weather Research and Forecasting (WRF) is a next-generation mesoscale numerical weather prediction system. It is designed with a dual purpose, forecasting and research. The WRF software infrastructure consists of a number of components such as dynamic solvers and physical simulation modules. Dynamic solvers are intensive computational components of the WRF model. In this paper, the Horizontal Diffusion method, which is part of the ARW (Advanced Research WRF) dynamic solver, is accelerated using GPUs. The performance of the GPU-based method was compared to that one of a CPU-based single-threaded counterpart on a computational domain of 433x308 horizontal grid points with 35 vertical levels. Thus, the achieved speedup is 19x on a NVIDIA Tesla M2090, without considering data I/O.
Grid platform for medical federated queries supporting semantic and visual annotations
(SPIE, 2015-11-17) Gualan Saavedra, Ronald Marcelo; Guillermo Anguisaca, Juan Carlos; La Cruz Puente Alexandra; Pérez Rocano, Wilson Rodrigo; Solano Quinde, Lizandro Damián
Grid computing has been successfully applied on teleradiology, leading to the creation of important platforms such as MEDICUS, VirtualPACS and mantisGRID, among others. These platforms are studied on the basis of their available documentation in order to compare and discuss differences and similarities, advantages and disadvantages between them. Then, a grid platform architecture is proposed, based on the best features of the surveyed platforms with an additional emphasis on general federated queries involving CBIR (Content-Based Image Retrieval) and Semantic Annotations.
Multi-GPU implementation of the horizontal diffusion method of the weather research and forecast model
(ASSOCIATION FOR COMPUTING MACHINERY INC, 2016-03-12) Solano Quinde, Lizandro Damián; Gualan Saavedra, Ronald Marcelo; Zúñiga Prieto, Miguel Ángel
The Weather Research and Forecasting (WRF), a next generation mesoscale numerical weather prediction system, has a considerable amount of work regarding GPU acceleration. However, the amount of works exploiting multi-GPU sys- tems is limited. This work constitutes an effort on using GPU computing over the WRF model and is focused on a computationally intensive portion of the WRF: the Horizontal Diffusion method. Particularly, this work presents the enhancements that enable a single-GPU based implementation to exploit the parallelism of multi-GPU systems. The performance of the multi-GPU and single-GPU based implementations are compared on a computational domain of 433x308 horizontal grid points with 35 vertical levels, and the resulting speedup of the kernel is 3.5x relative to one GPU. The experiments were carried out on a multi-core computer with two NVIDIA Tesla K40m GPUs.