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5G en transporte y logística
(Universidad Andrés Bello, 2022) Cornejo Quezada, Tomás Ignacio; Villalobos, Julio; Facultad de Ingeniería
En los últimos años, el uso de tecnologías móviles y su evolución han contribuido a la digitalización del mundo y su interconexión. La tecnología de quinta generación (5G) va a impulsar tecnologías como lo son el Internet de las Cosas (IoT: Internet of Things), información en bases de datos (Big Data), inteligencia artificial interconectadas, etc. Todas estas tecnologías mencionadas, son aplicables en el transporte y la logística. En el presente artículo mediante la revisión de literatura se analizaron artículos científicos y web con el objetivo de comprender y analizar los usos y potencialidades que tendrá la tecnología de quinta generación (5G), en el transporte y la logística, en búsqueda de la forma en que estas puedan aplicarse conjuntamente logrando una completa integración y mejora de desempeño en la cadena de suministros.
A bibliometric review of the use of 3D printing in maritime and port education: trends and perspectives
(Elsevier, 2024) Mojica, J.; Piñeres, A.; Huyke, A.; Gatica, G.; Olmedo, A.
Three-dimensional (3D) printing, a technology in constant evolution, stands as a resource of great significance in the transformation of maritime and port education. In this article, we conduct a comprehensive bibliometric review with the purpose of identifying emerging trends and perspectives in this field. In the methods section, we develop on the search equation and the rigorous selection and eligibility criteria that guided our research. The results, presented in the corresponding section, reveal a steady growth in the use of 3D printing in maritime and port education in recent years, as well as a diversity of applications that enrich the teaching of concepts and skills related to these disciplines. Furthermore, we highlight the most relevant thematic areas and analyze the key trends and challenges that will require attention in the future. In conclusion, this study underscores the significant potential of 3D printing to enhance both learning and teaching in the realm of maritime and port education and provides crucial recommendations for future research in this promising field.
A bidirectional versatile buck–boost converter driver for electric vehicle applications
(MDPI, 2021-08) González-Castaño, Catalina; Restrepo, Carlos; Kouro, Samir; Vidal-Idiarte, Enric; Calvente, Javier
This work presents a novel dc-dc bidirectional buck–boost converter between a battery pack and the inverter to regulate the dc-bus in an electric vehicle (EV) powertrain. The converter is based on the versatile buck–boost converter, which has shown an excellent performance in different fuel cell systems operating in low-voltage and hard-switching applications. Therefore, extending this converter to higher voltage applications such as the EV is a challenging task reported in this work. A high-efficiency step-up/step-down versatile converter can improve the EV powertrain efficiency for an extended range of electric motor (EM) speeds, comprising urban and highway driving cycles while allowing the operation under motoring and regeneration (regenerative brake) conditions. DC-bus voltage regulation is implemented using a digital two-loop control strategy. The inner feedback loop is based on the discrete-time sliding-mode current control (DSMCC) strategy, and for the outer feedback loop, a proportional-integral (PI) control is employed. Both digital control loops and the necessary transition mode strategy are implemented using a digital signal controller TMS320F28377S. The theoretical analysis has been validated on a 400 V 1.6 kW prototype and tested through simulation and an EV powertrain system testing. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A CHASIDE Test-based Analysis for Identifying Adolescents Characteristics Impacting their Vocational Orientation: Case of private schools in the city Barranquilla, Colombia
(Elsevier, 2024) Acosta, C.; Acuña-Rodríguez, M.; Peñaranda-Osorio, E.; Gatica, G.; Córdova, A.
This research is oriented to the identification of characteristics in the choice of professional careers in adolescents in eleventh grade. A total of 270 young people between the ages of 15 and 20 years old, all students at private schools in the city of Barranquilla, Colombia, were surveyed. A test called CHASIDE was used, which is focused on the occupational environment. Among the results, we found career options chosen by the students, the different inclinations in the workplaces where they plan to work, competencies in adolescents regarding the choice of career, and the identification of learning channels according to areas of knowledge. In the future, we seek to analyze whether there is a relationship between learning channels, personality, and career choice, and thus develop technological tools and algorithms for career choice, in order to address career orientation as the main axis of success in higher education.
A Community Disaster Resilience Index for Chile
(MDPI, 2023-04) Bronfman, Nicolás C.; Castañeda, Javiera V.; Guerrero, Nikole F.; Cisternas, Pamela; Repetto, Paula B.; Martínez, Carolina; Chamorro, Alondra
Although Chile is one of the countries most exposed to natural hazards, to date there is no national index that shows the differences in resilience levels within the country. This study develops a community resilience index on a national scale based on the Baseline Resilience Community (BRIC) index. The BRIC index for Chile was built with 49 indicators, from different sources at the district level. Our results determined that resilience is not distributed homogeneously throughout the country. The highest levels of resilience are concentrated in the central macro-zone. In comparison, the extreme zones of Chile focus close to 90% of their population in the lowest levels, accounting for an uneven distribution of resources and services that impact resilience levels. These differences were mainly explained by indicators such as the percentage of the population without a health insurance system, the percentage of the population without internet access, and the percentage of electoral participation, among others. The results demonstrate that the BRIC model can be successfully implemented to assess community resilience in Chile and suggests the possibility of targeting resources and strategies to increase resilience in areas with the lowest levels of community resilience. © 2023 by the authors.
A comparison of trajectory granular based algorithms for the location-routing problem with heterogeneous fleet (LRPH)
(Universidad Nacional de Colombia, 2017) Bernal-Moyano, J.A.; Escobar, J.W.; Marín-Moreno, C.; Linfati, R.; Gatica, G.
We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.
A competitive constraint programming approach for the group shop scheduling problem
(Elsevier B.V., 2023-03) Yuraszeck, Francisco; Mejia, Gonzalo; Canut-De-Bon, Dario
In this paper, we propose a competitive Constraint Programming (CP) approach to solve the Group Shop Scheduling Problem (GSSP) under the makespan minimization criteria. Our contribution is two-fold: we provide a flexible mathematical formulation to solve the GSSP that can be used without change to solve other closed-related scheduling problems such as the Open Shop Scheduling Problem (OSSP), Job Shop Scheduling Problem (JSSP), and Mixed Shop Scheduling Problem (MSSP); and we improve several lower bounds and upper bounds from 130 classical GSSP instances from the literature. We evaluate our approach by comparing the performance with competitive methods mainly based on metaheuristics, where we were able to prove optimality in more than 85% of the instances in competitive running time, with a relative percentage deviation lower than 3% on average. In contrast to metaheuristics approaches, our CP method does not require calibrations of multiple parameters, several replicates for each instance, and complex computational coding to be competitive in both, solution quality and computational running times. © 2023 Elsevier B.V.. All rights reserved.
A Composite DC–DC Converter Based on the Versatile Buck–Boost Topology for Electric Vehicle Applications
(MDPI, 2022-07) González-Castaño, Catalina; Restrepo, Carlos; Flores-Bahamonde, Freddy; Rodriguez, Jose
The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture. © 2022 by the authors.
A conceptual study of a self-centering semi-rigid connection with energy dissipation for wooden frames
(Brazilian Association of Computational Mechanics, 2020) Padilla-Reyes, J.; Maureira-Carsalade, N.; San-Martin-canales, J.; Roco-Videla, A.
A proposal of a semi-rigid self-centering connection for the seismic protection of wooden structures is studied. An analytical model for the characterization of the response of the connection is developed. Quasi-static tests of a wooden column with the proposed connection allowed to validate the model and calibrate its parameters, obtaining errors smaller than 6% between analytical prediction and experimental results. The self-centering capacity and the effectiveness of the connection to limit the loads transmitted to the connected elements were evidenced. Damped free vibration tests evidenced the capacity of the connection to reduce structural vibrations. It was verified that the resistance and dissipation capacity of the connection can be modified by two design parameters that can be adjusted at the assembly. The proposed connection has desirable characteristics for its use in the protection of structural wooden frames, is versatile, and its design is simple, with explicit physical parameters.
A Constraint Programming Formulation of the Multi-Mode Resource-Constrained Project Scheduling Problem for the Flexible Job Shop Scheduling Problem
(Institute of Electrical and Electronics Engineers Inc., 2023) Yuraszeck, Francisco; Montero, Elizabeth; Canut-De-Bon, Dario; Cuneo, Nicolas; Rojel, Maximiliano
In this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area. © 2013 IEEE.
A crowdsourcing-based system for monitoring em radiation exposure in Chile
(CEUR-WS, 2018-10) Ulloa, J.J.; Encina, M.; Gutiérrez-Gaitán, M.; Ruete, D.; Gómez-Pantoja, C.
Public concern about the possible effects on health due to electromagnetic (EM) radiation exposure has led to the definition of regulations worldwide. In Chile, the so-called "Ley de Antenas" provides a regulatory framework that establishes limits and special conditions in this regard. Although the official record of measurements is available in a public information portal, this approach is not scalable and intuitive enough for all interested users. This paper propose a participatory measurement system to extend the monitored areas based on the concern of citizens themselves. © 2018 CEUR-WS. All rights reserved.
A Deep Q-Network based hand gesture recognition system for control of robotic platforms
(Nature Research, 2023-12) Cruz, Patricio J.; Vásconez, Juan Pablo; Romero, Ricardo; Chico, Alex; Benalcázar, Marco E.; Álvarez, Robin; Barona López, Lorena Isabel; Valdivieso Caraguay, Ángel Leonardo
Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The information obtained from the HGR systems has the potential to be helpful to control machines such as video games, vehicles, and even robots. Therefore, the key idea of the HGR system is to identify the moment in which a hand gesture was performed and it’s class. Several human-machine state-of-the-art approaches use supervised machine learning (ML) techniques for the HGR system. However, the use of reinforcement learning (RL) approaches to build HGR systems for human-machine interfaces is still an open problem. This work presents a reinforcement learning (RL) approach to classify EMG-IMU signals obtained using a Myo Armband sensor. For this, we create an agent based on the Deep Q-learning algorithm (DQN) to learn a policy from online experiences to classify EMG-IMU signals. The HGR proposed system accuracy reaches up to 97.45 ± 1.02 % and 88.05 ± 3.10 % for classification and recognition respectively, with an average inference time per window observation of 20 ms. and we also demonstrate that our method outperforms other approaches in the literature. Then, we test the HGR system to control two different robotic platforms. The first is a three-degrees-of-freedom (DOF) tandem helicopter test bench, and the second is a virtual six-degree-of-freedom (DOF) UR5 robot. We employ the designed hand gesture recognition (HGR) system and the inertial measurement unit (IMU) integrated into the Myo sensor to command and control the motion of both platforms. The movement of the helicopter test bench and the UR5 robot is controlled under a PID controller scheme. Experimental results show the effectiveness of using the proposed HGR system based on DQN for controlling both platforms with a fast and accurate response. © 2023, The Author(s).
A dynamic linguistic decision making approach for a cryptocurrency investment scenario
(Institute of Electrical and Electronics Engineers Inc., 2020-12) TORRES, ROMINA; SOLIS, MIGUEL A.; SALAS, RODRIGO; BARIVIERA, AURELIO F.
Cryptocurrencies have been receiving the sustained attention of investors since 2009. These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms. Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by developing algorithm-based trading strategies. Such strategies aim to beat the market. It has been previously reported that daily returns do not exhibit long range dependence. However, daily volatility in major cryptocurrencies is highly persistent. Therefore, buy/hold/sell decision support systems could be able to capture such market inefficiency. This is especially important for investors interested in periodically trading a set of cryptocurrencies, in order to maximize their wealth. This paper presents a dynamic linguistic decision making approach for building decision models to support cryptocurrency investors in buy/hold/sell decisions. This approach exhibits a good computational performance for obtaining recommendations based on quantitative data. Moreover, this procedure is able to identify some inefficient cryptocurrency behaviors which are not captured by traditional econometric techniques. Our results uncover arbitrage opportunities that outperform buy-and-hold or random strategies. CCBY
A fast converging hybrid mppt algorithm based on abc and p&o techniques for a partially shaded pv system
(MDPI, 2021-09) Restrepo, Carlos; Yanẽz-Monsalvez, Nicolas; González-Castaño, Catalina; Kouro, Samir; Rodriguez, Jose
Among all the conventional maximum power point tracking (MPPT) techniques for a photovoltaic (PV) system that have been proposed, incremental conductance (INC) and perturb and observe (P&O) are the most popular because of their simplicity and ease of implementation. However, under partial shading conditions (PSCs), these MPPT algorithms fail to track the global maximum power point (GMPP) and instead converge into local maximum power points (LMPPs), resulting in considerable PV power loss. This paper presents a new hybrid MPPT technique combining the artificial bee colony (ABC) and P&O algorithms named ABC-P&O. The P&O technique is used to track the MPP under uniform irradiance, and only during irradiance variations is the ABC algorithm employed. The effectiveness of the proposed hybrid algorithm at tracking the GMPP, under both uniform and nonuniform irradiance conditions, was assessed by hardware-in-the-loop (HIL) tests employed by a dc–dc boost converter. Then, the ABC-P&O strategy was applied to obtain the voltage reference for the outer PI control loop, which provided the current reference to the discrete-time sliding-mode current control. The ABC-P&O algorithm has a reasonable computational cost, allowing the use of a commercial, low-priced digital signal controller (DSC) with outer voltage and inner current control loops. Many challenging tests validated that the proposed ABC-P&O technique converges fast to the GMPP with high efficiency and superior performance under different PSCs.
A fast-tracking hybrid mppt based on surface-based polynomial fitting and p&o methods for solar pv under partial shaded conditions
(MDPI, 2021-11-01) González-Castaño, Catalina; Restrepo, Carlos; Revelo-Fuelagán, Javier; Lorente-Leyva, Leandro L.; Peluffo-Ordóñez, Diego H.
The efficiency of photovoltaic (PV) systems depends directly on solar irradiation, so drastic variations in solar exposure will undoubtedly move its maximum power point (MPP). Furthermore, the presence of partial shading conditions (PSCs) generates local maximum power points (LMPPs) and one global maximum power point (GMPP) in the P-V characteristic curve. Therefore, a proper maximum power point tracking (MPPT) technique is crucial to increase PV system efficiency. There are classical, intelligent, optimal, and hybrid MPPT techniques; this paper presents a novel hybrid MPPT technique that combines Surface-Based Polynomial Fitting (SPF) and Perturbation and Ob-servation (P&O) for solar PV generation under PSCs. The development of the experimental PV system has two stages: (i) Modeling the PV array with the DC-DC boost converter using a real-time and high-speed simulator (PLECS RT Box), (ii) and implementing the proposed GMPPT algorithm with the double-loop controller of the DC-DC boost converter in a commercial low-priced digital signal controller (DSC). According to the simulation and the experimental results, the suggested hybrid algorithm is effective at tracking the GMPP under both uniform and nonuniform irradiance conditions in six scenarios: (i) system start-up, (ii) uniform irradiance variations, (iii) sharp change of the (PSCs), (iv) multiple peaks in the P-V characteristic, (v) dark cloud passing, and (vi) light cloud passing. Finally, the experimental results—through the standard errors and the mean power tracked and tracking factor scores—proved that the proposed hybrid SPF-P&O MPPT technique reaches the convergence to GMPP faster than benchmark approaches when dealing with PSCs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A faster optimal model slotting in rack positions with mono SKU pallet
(Elsevier, 2022) Rios, J.; Morillo-Torres, D.; Olmedo, A.; Coronado-Hernandez, J.; Gatica, G.
The warehouse, with its racking positions, oversees regulate the flow of goods in the distribution center (DC). An adequate distribution of the products in the warehouse (slotting), reduces the operating picking times once is done by a full pallet. This paper presents a mathematical model that optimizes slotting times in a selective shelving and random storage warehouse. The model places the products with the highest turnover rate and ABC inventory valuation close to the dispatch area. In addition, it places high-value products on the upper shelf levels and heavy pallets on the lower levels. The results applied to a company dedicated to the manufacture of cellulose pulp and derivatives, obtained a 19.24% reduction in operating time.
A flexible mathematical model for the planning and designing of a sporting fixture by considering the assignment of referees
(Growing Science, 2019-04) Linfati, R.; Gatica, G.; Escobar, J.W.
This paper deals with the problems faced with the designing and planning of a sporting fixture considering correct referee assignments. A non-linear binary program model is proposed to solve the problems, which aims to minimize the sums of the differences that exist between the requirements of each match and the quality of the referee assigned achieving the design of the most adequate referee for each match. The efficiency of the proposed model is proved using some real data obtained from various fixtures for sports such as soccer, volleyball, and basketball. The mathematical model is solved by using CPLEX 12.7.0., which allows the automatic linearization of the problems. The results obtained demonstrate the efficiency of the proposed methodology for tackling problems, as well as its extension to other sporting disciplines, which require a similar type of planning. Similarly, given the robust nature of the proposed model, it is possible to implement other objective functions in accordance with the requirements of each league. © 2019 by the authors; licensee Growing Science, Canada.
A geodynamic model linking Cretaceous orogeny, arc migration, foreland dynamic subsidence and marine ingression in southern South America
(Elsevier B.V., 2018-10) Gianni, Guido M.; Dávila, Federico M.; Echaurren, Andrés; Fennell, Lucas; Tobal, Jonathan; Navarrete, César; Quezada, Paulo; Folguera, Andrés; Giménez, Mario
This study synthesizes the tectonomagmatic evolution of the Andes between 35°30′S to 48°S with the aim to spotlight early contractional phases on Andean orogenic building and to analyze their potential driving processes. We examine early tectonic stages of the different fold-thrust belts that compose this Andean segment. Additionally, we analyzed the spatio-temporal magmatic arc evolution as a proxy of dynamic changes in Andean subduction during critical tectonic stages of orogenic construction. This revision proposes a hypothesis related the existence of a continuous large-scale flat subduction setting in Cretaceous times with a similar size to the present-largest flat-slab setting on earth. This potential process would have initiated diachronically in the late Early Cretaceous and achieved full development in Late Cretaceous to earliest Paleocene times, constructing a series of fold-thrust belts on the retro-arc zone from 35°30′S to 48°S. Moreover, we assess major paleogeographic changes that took place during flat-slab full development in Maastrichtian-Danian times. At this moment, an enigmatic Atlantic-derived marine flooding covered the Patagonian foreland reaching as far as the Andean foothills. Based on flexural and dynamic topography analyses, we suggest that focused dynamic subsidence at the edge of the flat-slab may explain sudden marine ingression previously linked to continental tilting and orogenic loading during a high sea level global stage. Finally, flat-subduction destabilization could have triggered massive outpouring of synextensional intraplate volcanic rocks in southern South America and the arc retraction in late Paleogene to early Neogene times. © 2018 Elsevier B.V.
A Heuristic Approach for Determining Efficient Vaccination Plans under a SARS-CoV-2 Epidemic Model
(MDPI, 2023-02) Hazard-Valdés, Claudia; Montero, Elizabeth
In this work, we propose a local search-based strategy to determine high-quality allocation of vaccines under restricted budgets and time periods. For this, disease spread is modeled as a SEAIR pandemic model. Subgroups are used to understand and evaluate movement restrictions and their effect on interactions between geographical divisions. A tabu search heuristic method is used to determine the number of vaccines and the groups to allocate them in each time period, minimizing the maximum number of infected people at the same time and the total infected population. Available data for COVID-19 daily cases was used to adjust the parameters of the SEAIR models in four study cases: Austria, Belgium, Denmark, and Chile. From these, we can analyze how different vaccination schemes are more beneficial for the population as a whole based on different reproduction numbers, interaction levels, and the availability of resources in each study case. Moreover, from these experiments, a strong relationship between the defined objectives is noticed.
A highly active Ca/Cu/YCeO2-TiO2catalyst for the transient reduction of NO with CO and naphthalene under oxidizing conditions
(Royal Society of Chemistry, 2021-12) Sánchez, Luis; Aguila, Gonzalo; Araya, Paulo; Quijada, Sergio; Guerrero, Sichem
The transient combustion of biomass leads to the evolution of a variety of pollutants (NO, CO, organic compounds, and many others) that can react with each other on a suitable catalyst to generate compounds of lower toxicity. Here, the transient reduction of NO with CO and naphthalene in the presence of oxygen was studied on a Ca/Cu/YCeO2-TiO2catalyst. Response surface methodology was used to identify the optimum amounts of calcium, copper, and cerium. The optimized Ca/Cu/YCeO2-TiO2catalyst was then extensively studied and characterized. The coupling of yttrium-stabilized ceria with TiO2provided an active support that effectively activated naphthalene. When calcium and copper were added to the support, the obtained Ca/Cu/YCeO2-TiO2catalyst achieved the full conversion of CO and naphthalene and 72% conversion of NO. The Ca/Cu/YCeO2-TiO2catalyst possessed labile oxygen species, which might be related to the high catalytic activity. © The Royal Society of Chemistry 2021.

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