Carrasco, M., Alvarez, F., Velazquez, R., Concha, J., & Perez-Cotapos, F. (2019). Brush-Holder Integrated Load Sensor Prototype for SAG Grinding Mill Motor. Electronics, 8(11), 14 pp.
Abstract: One of the most widely used electro-mechanical systems in large-scale mining is the electric motor. This device is employed in practically every phase of production. For this reason, it needs to be inspected regularly to maintain maximum operability, thus avoiding unplanned stoppages. In order to identify potential faults, regular check-ups are performed to measure the internal parameters of the components, especially the brushes and brush-holders. Both components must be properly aligned and calibrated to avoid electric arcs to the internal insulation of the motor. Although there is an increasing effort to improve inspection tasks, most inspection procedures are manual, leading to unnecessary costs in inspection time, errors in data entry, and, in extreme cases, measurement errors. This research presents the design, development, and assessment of an integrated measurement prototype for measuring spring tension and other key parameters in brush-holders used in electric motors. It aims to provide the mining industry with a new, fully automatic inspection system that will facilitate maintenance and checking. Our development research was carried out specifically on the brush system of a SAG grinding mill motor. These machines commonly use SIEMENS motors; however, the instrument can be easily adapted to any motor by simply changing the physical dimensions of the prototype.
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Carrasco, M., Alvarez, F., Velazquez, R., Concha, J., & Perez-Cotapos, I. (2018). Inline Force Sensor Development for Electrical Motors for Mining Operations in Chile: A New Inspection Protocol. IEEE Latin Am. Trans., 16(1), 66–74.
Abstract: Electric motors are among the most widely used electro-mechanical systems in the mining industry in Chile. As they are physically present in virtually all phases of the production process, they must be regularly inspected. In order to maintain a maximum level of operability and to avoid the occurrence of catastrophic failures and unscheduled stoppages, regular, planned reviews to measure the internal parameters of their components are performed manually. This part of the process presents serious shortcomings, such as significant inspection times, information input errors and, in some cases, measurement errors resulting from the technical difficulty of physically taking the measurement. This research presents the design, development and evaluation of a new sensor to support the inspection of brush-holder spring tensions as well as the integration of a digital gauge to control other critical dimensions of the brush-holder mounting. Its purpose is to provide the mining industry with a new system and a fully automated inspection protocol to ease maintenance and control tasks, particularly focusing on reducing inspection times. These products are expected to increase the equipment operating life, as well as delivering a maintenance report of the motor's key parameters which directly benefits the Key Performance Indicators (KPI) of mining management
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Carrasco, M., Mery, D., Concha, A., Velazquez, R., De Fazio, R., & Visconti, P. (2021). An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses. Electronics, 10(3), 246.
Abstract: Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.
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Celis, P., de la Cruz, R., Fuentes, C., & Gomez, H. W. (2021). Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications. Symmetry, 13(5), 908.
Abstract: We introduce a new class of distributions called the epsilon-positive family, which can be viewed as generalization of the distributions with positive support. The construction of the epsilon-positive family is motivated by the ideas behind the generation of skew distributions using symmetric kernels. This new class of distributions has as special cases the exponential, Weibull, log-normal, log-logistic and gamma distributions, and it provides an alternative for analyzing reliability and survival data. An interesting feature of the epsilon-positive family is that it can viewed as a finite scale mixture of positive distributions, facilitating the derivation and implementation of EM-type algorithms to obtain maximum likelihood estimates (MLE) with (un)censored data. We illustrate the flexibility of this family to analyze censored and uncensored data using two real examples. One of them was previously discussed in the literature; the second one consists of a new application to model recidivism data of a group of inmates released from the Chilean prisons during 2007. The results show that this new family of distributions has a better performance fitting the data than some common alternatives such as the exponential distribution.
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de Fazio, R., Giannoccaro, N. I., Carrasco, M., Velazquez, R., & Visconti, P. (2021). Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey. Front. Inf. Technol. Electron. Eng., 22(11), 1413–1442.
Abstract: Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics.
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Dewitte, B., Concha, E., Saavedra, D., Pizarro, O., Martinez-Villalobos, C., Gushchina, D., et al. (2023). The ENSO-induced South Pacific Meridional Mode. Front. Clim., 4, 18 pp.
Abstract: Previous studies have investigated the role of the Pacific meridional mode (PMM), a climate mode of the mid-latitudes in the Northern and Southern Hemisphere, in favoring the development of the El Niño Southern Oscillation (ENSO). However little is known on how ENSO can influence the development of the PMM. Here we investigate the relationship between ENSO and the South Pacific Meridional Mode (SPMM) focusing on strong SPMM events that follows strong El Niño events. This type of events represents more than 60% of such events in the observational record and the historical simulations of the CESM Large ensemble (CESM-LE). It is first shown that such a relationship is rather stationary in both observations and the CESM-LE. Our analyses further reveal that strong SPMM events are associated with a coastal warming o northern central Chile peaking in Austral winter resulting from the propagation of waves forced at the equator during the development of El Niño events. The time delay between the ENSO peak (Boreal winter) and this coastal warming (Austral winter) can be understood in terms of the diferential contribution of the equatorially-forced propagating baroclinic waves to the warming along
the coast. In particular, the diference in phase speeds of the waves (the
high-order mode the wave the slower) implies that they do not overlap along their propagation south of 20◦S. This contributes to the persistence of warm coastal SST anomalies o Central Chile until the Austral summer following the concurrent El Niño event. This coastal warming is favorable to the development of strong SPMM events as the South Pacific Oscillation become active during that season. The analysis of the simulations of the Coupled Intercomparison Project phases 5 and 6 (CMIP5/6) indicates that very few models realistically simulate this ENSO/SPMM relationship and associated oceanic teleconnection.
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Fernandez, T., Gretton, A., Rindt, D., & Sejdinovic, D. (2022). A Kernel Log-Rank Test of Independence for Right-Censored Data. J. Am. Stat. Assoc., Early Access.
Abstract: We introduce a general nonparametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a reproducing kernel Hilbert space (RKHS) of functions; and second, as the norm of the difference of embeddings of certain finite measures into the RKHS, similar to the Hilbert-Schmidt Independence Criterion (HSIC) test-statistic. We study the asymptotic properties of the test, finding sufficient conditions to ensure our test correctly rejects the null hypothesis under any alternative. The test statistic can be computed straightforwardly, and the rejection threshold is obtained via an asymptotically consistent Wild Bootstrap procedure. Extensive investigations on both simulated and real data suggest that our testing procedure generally performs better than competing approaches in detecting complex nonlinear dependence.
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Fuenzalida, C., Jerez-Hanckes, C., & McClarren, R. G. (2019). Uncertainty Quantification For Multigroup Diffusion Equations Using Sparse Tensor Approximations. SIAM J. Sci. Comput., 41(3), B545–B575.
Abstract: We develop a novel method to compute first and second order statistical moments of the neutron kinetic density inside a nuclear system by solving the energy-dependent neutron diffusion equation. Randomness comes from the lack of precise knowledge of external sources as well as of the interaction parameters, known as cross sections. Thus, the density is itself a random variable. As Monte Carlo simulations entail intense computational work, we are interested in deterministic approaches to quantify uncertainties. By assuming as given the first and second statistical moments of the excitation terms, a sparse tensor finite element approximation of the first two statistical moments of the dependent variables for each energy group can be efficiently computed in one run. Numerical experiments provided validate our derived convergence rates and point to further research avenues.
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Gutierrez-Portela, F., Arteaga-Arteaga, B. H., Almenares-Mendoza, F., Calderon-Benavente, L., Acosta-Mesa, H. G., & Tabares-Soto. R. (2023). Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset (IDSAI). IEEE Access, 11, 70542–70559.
Abstract: One of the fields where Artificial Intelligence (AI) must continue to innovate is computer security. The integration of Wireless Sensor Networks (WSN) with the Internet of Things (IoT) creates ecosystems of attractive surfaces for security intrusions, being vulnerable to multiple and simultaneous attacks. This research evaluates the performance of supervised ML techniques for detecting intrusions based on network traffic captures. This work presents a new balanced dataset (IDSAI) with intrusions generated in attack environments in a real scenario. This new dataset has been provided in order to contrast model generalization from different datasets. The results show that for the detection of intruders, the best supervised algorithms are XGBoost, Gradient Boosting, Decision Tree, Random Forest, and Extra Trees, which can generate predictions when trained and predicted with ten specific intrusions (such as ARP spoofing, ICMP echo request Flood, TCP Null, and others), both of binary form (intrusion and non-intrusion) with up to 94% of accuracy, as multiclass form (ten different intrusions and non-intrusion) with up to 92% of accuracy. In contrast, up to 90% of accuracy is achieved for prediction on the Bot-IoT dataset using models trained with the IDSAI dataset.
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Leao, J., Leiva, V., Saulo, H., & Tomazella, V. (2017). Birnbaum-Saunders frailty regression models: Diagnostics and application to medical data. Biom. J., 59(2), 291–314.
Abstract: In survival models, some covariates affecting the lifetime could not be observed or measured. These covariates may correspond to environmental or genetic factors and be considered as a random effect related to a frailty of the individuals explaining their survival times. We propose a methodology based on a Birnbaum-Saunders frailty regression model, which can be applied to censored or uncensored data. Maximum-likelihood methods are used to estimate the model parameters and to derive local influence techniques. Diagnostic tools are important in regression to detect anomalies, as departures from error assumptions and presence of outliers and influential cases. Normal curvatures for local influence under different perturbations are computed and two types of residuals are introduced. Two examples with uncensored and censored real-world data illustrate the proposed methodology. Comparison with classical frailty models is carried out in these examples, which shows the superiority of the proposed model.
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Pina, S., Candia-Onfray, C., Hassan, N., Jara-Ulloa, P., Contreras, D., & Salazar, R. (2021). Glassy Carbon Electrode Modified with C/Au Nanostructured Materials for Simultaneous Determination of Hydroquinone and Catechol in Water Matrices. Chemosensors, 9(5), 88.
Abstract: The simultaneous determination of hydroquinone and catechol was conducted in aqueous and real samples by means of differential pulse voltammetry (DPV) using a glassy carbon electrode modified with Gold Nanoparticles (AuNP) and functionalized multiwalled carbon nanotubes by drop coating. A good response was obtained in the simultaneous determination of both isomers through standard addition to samples prepared with analytical grade water and multivariate calibration by partial least squares (PLS) in winery wastewater fortified with HQ and CT from 4.0 to 150.00 mu M. A sensitivity of 0.154 mu A mu M-1 and 0.107 mu A mu M-1, and detection limits of 4.3 and 3.9 mu M were found for hydroquinone and catechol, respectively. We verified the reliability of the developed method by simultaneously screening analytes in spiked tap water and industrial wastewater, achieving recoveries over 80%. In addition, this paper demonstrates the applicability of chemometric tools for the simultaneous quantification of both isomers in real matrices, obtaining prediction errors of lower than 10% in fortified wastewater.
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Pina, S., Sandoval, A. M., Jara-Ulloa, P., Contreras, D., Hassan, N., Coreno, O., et al. (2022). Nanostructured electrochemical sensor applied to the electrocoagulation of arsenite in WWTP effluent. Chemosphere, 306, 135530.
Abstract: A sensitive electroanalytical method for the determination of arsenite, based on a heterostructure of aminated multiwalled carbon nanotubes and gold nanoparticles, was applied in an electrocoagulation (EC) treatment for the elimination of arsenite. A sensitive quantitative response was obtained in the determination of As3+ in a secondary effluent from a wastewater treatment plant from Santiago (Chile). The preconcentration stage was optimized through a Central Composite Face design, and the most sensitive peak current was obtained at 200 s and -600 mV of time and accumulation potential, respectively, after a differential pulse voltammetry sweep. Electroanalytical determination was possible in an interval between 42.89 and 170.00 mu g L-1 with a detection limit of 0.39 mu g L-1, obtaining recoveries over 99.1%. The developed method was successfully applied in an electrocoagulation treatment to remove 250 mu g L-1 of arsenite from a polluted effluent in a batch system. Complete arsenite removal was achieved using a steel EC system with a current density of 6.0 mA cm(-2) in less than 3 min of treatment.
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Pinto, J., Aylwin, R., Silva-Oelker, G., & Jerez-Hanckes, C. (2021). Diffraction efficiency optimization for multilayered parametric holographic gratings. Opt. Lett., 46(16), 3929–3932.
Abstract: Multilayered diffraction gratings are an essential component in many optical devices due to their ability to engineer light. We propose a first-order optimization strategy to maximize diffraction efficiencies of such structures by a fast approximation of the underlying boundary integral equations for polarized electromagnetic fields. A parametric representation of the structure interfaces via trigonometric functions enables the problem to be set as a parametric optimization one while efficiently representing complex structures. Derivatives of the efficiencies with respect to geometrical parameters are computed using shape calculus, allowing a straightforward implementation of gradient descent methods. Examples of the proposed strategy in chirped pulse amplification show its efficacy in designing multilayered gratings to maximize their diffraction efficiency.
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Vinoth, V., Kaimal, R., Selvamani, M., Michael, R., Pugazhenthiran, N., Mangalaraja, R. V., et al. (2023). Synergistic impact of nanoarchitectured GQDs-AgNCs(APTS) modified glassy carbon electrode in the electrochemical detection of guanine and adenine. J. Electroanal. Chem., 934, 117302.
Abstract: In this work, a facile green approach for the synthesis of graphene quantum dots (GQDs) embedded on silicate network silver nanocrystals (GQDs-AgNCs(APTS)) is reported. Moreover, glassy carbon-GC electrodes were mod-ified with the prepared nanocomposite containing graphene quantum dots supported on silver nanocrystals (GQDs-AgNCs(APTS)) and applied for simultaneous detection of guanine (GA) and adenine (AD). The chemically modified electrode was assessed during the determination of purine bases by cyclic voltammetry-CV and dif-ferential pulse voltammetry-DPV. The incorporation of GQDs-AgNCs(APTS) nanocomposites over the surface of the GC electrode considerably enhances the anodic peak currents and decreases the adenine and guanine peak potentials. Compared to other electrodes, GQDs-AgNCs(APTS)/GC improved the electrochemical behavior towards the detection of adenine and guanine. At optimal conditions, calibration curves were obtained by DPV being linear in the range of 0.1-6.0 mu M and 0.1-5.0 mu M for guanine and adenine, respectively. The detec-tion limits of both guanine and adenine were estimated as 0.1 mu M. Additionally, interferences analyses were performed on the existence of other interferent compounds. Furthermore, the method developed for the iden-tification of GA and AD was proved using fish sperm DNA samples.
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