ABSTRACT. This presentation will look at the consequences of the roadmaps for aircraft propulsion electrification and road vehicles as well as drawing on comparisons with other transportation electrification platforms, including motorbikes, and the technological developments which are going to be needed to make these visions possible. These developments and technology challenges will include the electrical drivetrain design and the applications of motor design and power converter topology choices as well as the impact of emerging technology advances including cooling techniques, integration, system optimisation and wide-bandgap semiconductors. The future ongoing challenges for Power Electronics and Electrical Machines experts will also be considered.

ABSTRACT. This paper proposes a new single-phase consensus-based distributed secondary control strategy for frequency control, optimal dispatch (OD) and phase shift regulation in isolated three-phase four-wire microgrids (MGs). Moreover, the proposed scheme maintains all the converter-based generators (CBG) within operation limits. The dispatch of the single-phase CBGs complies with the Karush-Kuhn-Tucker (KKT) conditions of a linear optimal power flow (OPF) formulation. Furthermore, since the power dispatch is considered in single-phase CBGs, the voltage phase difference is not maintained at 120\textdegree. Therefore, the proposed algorithm also corrects this condition and restores the voltage phase difference to the balanced condition. Only local measurements and information from the neighbours are needed to achieve the aforementioned characteristics, which allows for considering a distributed architecture. The good performance of the controllers proposed in this article is validated through extensive simulation tests,driving the system to an optimal economic operation.

ABSTRACT. Distributed energy resources (DER), such as photovoltaic systems and storage have noticeably increased during the last decade in many countries. These DER could be aggregated and dispatched to offer different system-level services, such as frequency/voltage control. However, as DER penetration increases, distribution network constraints cannot be ignored as their aggregated dispatch can result in voltage violations and/or congestions. Therefore, in the future, the distribution system operator (DSO) will need to check DER aggregator proposals when aiming to participate in a system-level service provision. If issues are anticipated by the DSO, smaller volumes of services will have to be implemented by aggregators, communicating, and acting over DER setpoints. This work, describes a realistic lab implementation of an aggregator acting over remote hybrid inverters (PV+storage), using internet and the modbus protocol. This will be later integrated with a real-time network simulator (hardware in the loop) representing the network run by the DSO.

ABSTRACT. In response to the water scarcity in rural and isolated areas with difficult access to water resources, this study presents a mathematical model for a water production system that utilizes a solar-wind hybrid generation source. The system is based on Cooling-based Atmospheric Water Generator, capable of producing up to 5,000 liters of water daily from the air, with its own purification and storage system. The main objective is to develop an optimization model that minimizes the levelized cost of water and obtains the optimal sizing of the hybrid generation plant and the storage system. This model is implemented in the software AIMMS to determine the optimal condition and analyze the behavior of the variables. The results of the optimization model simulation are compared with a previously proposed power supply design, validating the utility of the model.

ABSTRACT. This ipaper delves into the important of demand-side management (DSM) in optimizing energy consumption in smart distribution systems. By allowing customers and vendors to make informed decisions about energy use, DSM can modify load profiles and reduce demand for peak loads. The article provides a icomprehensive ianalysis iof iDSM itactics, idemand iresponse iprograms, iand ienergy iefficiency iregulations, with a specific focus on enhancing DSM for residential energy consumption. The paper also highlights the limitations of existing energy indicators and explores the unrealized potential and constraints to improving energy efficiency. To enable future-oriented smart grid applications, ithe iinfrastructure iof iinternet-based ionline ienergy imanagement isystems imust ibe iestablished. iThe ipaper offers an overview of various DSM procedures and techniques, including their goals, limitations, and potential areas for future research.

ABSTRACT. To technically inform policy makers about the potential of Chilean distribution networks to accommodate distributed generation, this work presents a computational platform developed for the first Industrial Reseach Chair between CENTRA at Universidad Adolfo Ibáñez and ACESOL. The platform enables an automated modeling of integrated real MV-LV distribution networks, ensuring electrical connectivity and data completeness, particularly developed and tested for the distribution network database of Chile's Superintendency of Electricity and Fuels. The platform also proposes a clustering tool for allocating real load profiles, minimizing differences against aggregated power records at the head of the modeled MV feeder.

The platform is used to assess the hosting capacity for residential PV systems in real MV-LV feeders from Valdivia and Osorno in Chile, demostrating its capabilities and functionalities, but also quantifying the potential for integrating large amounts of distributed generation in the south of Chile, and the technical barriers that limit them.

ABSTRACT. In this paper, we address the identification problem of a continuous-time stochastic system. We propose approximating the system using orthonormal continuous basis functions, particularly the Kautz basis functions. We assume that only discrete-time measurements are available. The estimation problem is approached through a frequency domain identification algorithm based on the Whittle likelihood function. We illustrate the benefits of our proposal with numerical simulations.

ABSTRACT. In this paper, a distributed secondary MPC control for microgrids based on a multi-agents technique is proposed. This secondary control regulates voltage and frequency, and at the same time imposes restrictions on the output current of each DER. This ensures an adequate current level for electronic converters. To achieve these objectives, the predictive model incorporates the inner primary control loop and utilizes partial information about the microgrid acquired from neighboring DERs, following a multiagent consensus approach. The MPC algorithm is formulated as a Quadratic Programming problem and is solved using a Primal-Dual Interior-Point optimization method. To validate this approach, simulation results are presented, conducted with MATLAB/Simulink and the Simscape toolbox. The results confirm the efficacy of the proposed secondary control method.

ABSTRACT. A novel and improved approach is provided for designing a filter in networked control systems (NCS) with Markovian jump processes, incorporating temporal delays and packet losses. In this approach, packet losses are modeled as a Bernoulli distribution, aiming to achieve the condition of stochastic stability for the filtering error system. The projection lemma is used to structure new analysis conditions and to involve some free-weighting matrices to reduce conservatism and guarantee a level of H∞ performance. The proposed result is tested and confirmed by an example from the literature.

ABSTRACT. In this study, we investigate factors that may have contributed to recent findings on the advantages of fractional proportional-integral-derivative (FOPID) controllers over conventional PID and the potential for their use in process industry applications. Our objective was to demonstrate that FOPID controllers offer better adjustability and efficiency in the feedback system. Consequently, it can be used to guarantee rigorous specifications on stability, robustness, performance for set-point tracking, and load disturbance rejection, as well as improve characteristics of noise reduction present in the transmitter output compared to those achievable with the classical PID controller using a performance indices (IAE, ISE, TVu)

ABSTRACT. This paper focuses on the state estimation of an Anaerobic Digestion (AD) model using two filtering algorithms: the Extended Kalman Filter (EKF) and the Particle Filter (PF). AD is a biological process converting organic matter into biogas, offering both a valuable renewable energy source and a sustainable solution for waste treatment. We investigate the applicability of these methods to the state space model called AM2, a simplified AD model involving only the key acetogenesis and methanogenesis reactions and excluding other specific biochemical steps. Subsequently, we evaluate their performance through simulation experiments.

ABSTRACT. In the present study, we analyzed the spectral emission of the sodium and potassium elements in the flame emitted by wood from a burning Eucalyptus Globulus tree. The main analysis is performed on a single tree, taking different samples at different heights of the trunk, in the visible spectral width between 400 and 850 [nm], where the spectral emission of sodium can be observed around 589 [nm] and potassium around the 766 and 770 [nm]. All of the above looking for similar patterns in the different samples.

The results show that the proportion of the optical power emitted by sodium and potassium in each of the samples tends to be constant over time or at least with a similar slope, keeping a certain relation. As you go up the tree, the constants become more similar in values and shapes, with the optical power of K always being higher than Na. To support what is observed, the correlation metrics and mean square error (MSE) are used, where mainly the first one will show us how similar the trend relation is in the graphs studied.

The method allowed us to observe that K/Na proportion curves in the samples from the lower part of the tree trunk are not very similar, with a correlation of 29.91\%, in the middle part a correlation of 45.80\% and the highest part, of 95.84\%, the latter being the best result.

ABSTRACT. In this paper, we analyze the performance of a wireless caching system with heterogeneous traffic and relaying capabilities satisfying secrecy constraints for one of two receiving users. In this setup, the second user has no secrecy requirements and receives cacheable content either from the relay helper or the core network through a wireless base station. The wireless relay helper can assist both users since it is equipped with finite storage that is split into cacheable and non-cacheable storage. Concurrently, a passive eavesdropper tries to overhear transmissions to the user with secrecy requirements. Consequently, we examine how this relay's storage split and the eavesdropper affect the performance of the average throughput and delay of the system as the transmission powers, the relay's transmission probability, and the relay's cache size vary.

ABSTRACT. Fifth-generation New-Radio (5G NR) networks have revolutionized how humanity interacts with technology, promising faster, more reliable, and more efficient connectivity. In this regard, simulation tools were born from the necessity of developers and researchers to have robust, versatile, and realistically yielding software to evaluate new developments in these networks before real-world implementations. This paper evaluates the performance of link adaptation algorithms using the discrete-event network simulator ns-3 and the 5G-LENA module, which implements the 3GPP standards. Some near real-life scenarios were defined for testing these algorithms and checking how they affect the performance of the link. Furthermore, a new algorithm was proposed based on existing ones. The results showed that none of the evaluated algorithms outperformed the rest, considering the assessed metrics and simulated scenarios, but the proposed algorithm had a better performance in packet delay than the standard while still having opportunities for enhancements.

ABSTRACT. Flame spectroscopy is a technique widely used to analyze combustion processes. In a flame, energy is emitted across a wide spectral range, and its corresponding spectra can be classified in both, continuous and discontinuous behaviors. Optimizing combustion, has the potential to maximize efficiency, thus reducing fuel consumption and emissions of residual gases. This report shows the spectral emission of a flame emitted by combustion of pellets at different humidity contents, from a specific brand (here referred to as Alfa), typical in the Chilean market. To perform the spectral analysis, the HR-4000 spectrophotometer (Ocean Optics), previously calibrated in absolute radiance (in uW/nm cm^2) was used. From the collected spectra, parameters such as flame temperature (in °C), total continuous radiation (in uW/cm^2) and total continuous energy (in uJ) were estimated. The spectral behavior, at different humidity contents, shown different spectral patterns. Thus, in this report we introduce the optical calorific power (in J/kg), quantifying the energy provided by a pellet sample, and calculated from spectral results. For the Alfa brand, with a humidity content between a 6-8% we estimated an optical calorific power of 6.12 (J/kg) and with a humidity content of 13.6%, an optical calorific power of 4.96 (J/kg). The different parameters proposed in this work hold promises for understanding biomass combustion phenomena, such as the dynamics of energy emission in flame distribution.

ABSTRACT. This article presents an initial comparative performance study between a Deep Reinforcement Learning (DRL) agent and baseline heuristic within the context of different inter-core spacings in multicore fiber (MCF) networks. The analysis focuses on a case study involving a three-core fiber configuration within the Eurocore topology.

The study's findings indicate that the DRL agents outperformed the reference heuristic in terms of blocking probability, particularly under specific inter-core spacing conditions. This superior performance can be attributed to the adaptability of DRL agents in adjusting their learned policies during training.

These findings suggest that DRL algorithms have significant potential in efficiently addressing resource allocation challenges within MCF-EON networks, even in scenarios characterized by stringent constraints.

ABSTRACT. The growing interest in grid-forming mode (GFM) and the increasing participation of photovoltaic (PV) technology in the grid have highlighted the necessity to investigate the frequency stability of power systems with high levels of PV power plants operating in grid-forming mode (PV-GFM). However, frequency stability studies with GFM generators mainly consider the converter connected to the grid and do not model the PV source and the DC-DC converter. In this work, we model and study the frequency stability in a power system with high participation of two-stage PV-GFM, where the DC-DC converter is represented. The study cases compare the dynamic frequency performance of PV-GFM plants and CIG-GFM generators without PV representation. The objective is to investigate the impacts of modeling PV source and DC-DC converter from a frequency stability perspective. The results reveal that oscillatory instabilities are observed for operating points in networks with the participation of two or more PV-GFM plants while their CIG-GFM counterparts are stable. Accordingly, the modeling of PV–GFM, especially the DC-DC converter, should be adequately represented in the frequency stability study to avoid misleading conclusions.

ABSTRACT. The high penetration of DG in distribution systems creates, among other problems, a violation of the voltage limits on the system’s buses. As a result, several methods have been proposed in the literature to promote voltage regulation in conditions of high penetration, including methods based on the reactive energy absorption by the smart inverters of the DG. Many standards currently regulate the application of this kind of methods, including the IEEE 1547:2018 and NBR 16149:2013 standards. In this context, this article presents the Watt-VAr and Volt-VAr voltage regulation methods as proposed by the aforementioned standards. A low-voltage network with high DG penetration was modeled in the OpenDSS software, then each of the methods was applied. Finally, a comparison was made between the results of each method, considering not only the ability to mitigate voltage violations, but also losses and reactive absorption levels. The simulations showed that although all the methods are capable of regulating the network voltage, they differ significantly when other performance parameters are taken into account.

ABSTRACT. Reduced order frequency response models have been used as an alternative to simulate the behavior of the electrical system during limited time intervals corresponding to contingencies of the system. This paper presents four model alternatives of this kind and a proposed methodology for their parameter identification. Specifically, the differential evolution algorithm is considered and tested experimentally over data from the Chilean electrical network. Emphasis of the study is placed on the proposed methodology's implementation aspects, advantages, and potential problems.

ABSTRACT. Within the context of transitioning towards a sustainable energy matrix, the incorporation of renewable energy sources into Power Systems (PS) has been rapidly increasing. However, this shift leads to a reduction in system inertia, which could potentially cause stability issues. To enhance operational stability, PS can implement Battery Energy Storage Systems (BESS). Nonetheless, it is crucial to conduct dynamic studies prior to their deployment to ensure optimal system performance. In this context, this paper presents a dynamic study of the General Carrera medium-scale power system in Chile, considering the integration of a BESS. The study, successfully conducted using the Power Factory tool by Digsilent, included the simulation of five unexpected generation disconnection scenarios and five short circuits for each of the seven generation buses, totaling 280 case studies. The objective of these simulations was to verify both angle and frequency stability and to define the optimal location for the BESS. The results obtained conclude that placing the BESS at the Kosten bus offers the highest number of scenarios with favorable dynamic performances, thus contributing to improving the stability of the General Carrera medium-scale system amidst the high inclusion of renewable energies. Therefore, this study provides a valuable framework for the future planning and operation of this system within the context of the energy transition.

ABSTRACT. In recent years, there has been a significant increase in wind power generation. One of the most used technologies in wind power plants is the double-fed induction generator (DFIG). Subsynchronous resonance (SSR) events have been reported when this technology is used, particularly when the DFIG wind farm is connected through weak grids or the transmission system has series compensation. The SSR phenomenon has been related to interactions of the DFIG control system implemented on the rotor side converter (RSC) and the grid side converter (GSC). This work aims to investigate the impact of operating conditions and tuning of critical control parameters of DFIG on SSR. The study is based on modal sensitivity analysis. Three case studies are performed, including variations in transmission line lengths, Phase-locked loop (PLL) bandwidths, transmission system compensation level, and changes in the RSC control parameters. The results show that DFIG power plants make the system more unstable when the line is long or has a high compensation level. However, the previous results are highly dependent on the tunning of control of RSC. It is possible to increase the level of compensation and the injection of active power from the DFIG by appropriately adjusting the RSC current controller.

ABSTRACT. This work aims to explore the effects of the integral term in the discrete-time implementation of the LQI controller. To achieve this, a two-wheeled self-balancing robot system is used as a case study, and three types of numerical integration approximations are considered: Backward Euler, Forward Euler, and Tustin. Through simulations, the results obtained with each approximation are compared against the expected continuous-time design. The simulation results demonstrate that while there are no significant differences for small sampling times, the Tustin approximation exhibits notably better performance than the other alternatives as the sampling time increases.

ABSTRACT. Efective control of open channels poses a fundamental challenge in the agro-industry, where the consistent delivery of a minimum flow rate and the assurance of uninterrupted operation are paramount. To tackle this challenge, conventional approaches rely on models grounded in the Saint-Venant partial differential equations or approximations such as Zeigler-Nichols. These models often employ controllers based on PID strategies or their variants.

This article introduces a streamlined methodology for designing a robust controller for open channels, employing a coupledtank approach. We utilize a linear representation with timevarying parameters (LPV) to account for uncertainties stemming from the simplified model. The resulting robust controller is synthesized through H∞ norm optimization, ensuring D-stable performance. This approach convincingly demonstrates the controller’s capacity to uphold system stability, even when dealing with a model that offers a less precise representation of the underlying physical reality.

ABSTRACT. This work focuses on the control of an aeropendulum system an unattached pendulum with a fixed end, allowing for the application of a controlled moment to regulate its angular position. It encompasses both control theory and practical application using a physical aeropendulum setup. Initially, we propose a linear parameter varying (LPV) model for the system. Subsequently, we design an LPV H infinity controller and a PID controller to regulate the pendulum's angular position precisely. To assess the performance of these designed controllers, we conducted comparisons through simulations and implemented them in a physical plant within a laboratory setting.

ABSTRACT. This work addresses the finite-time stabilization problem via state-feedback control for discrete-time cyber-physical systems under the presence of DoS attacks and external disturbance. The cyber-physical system is described as a switched system and a design condition in the form of Linear Matrix inequality is proposed. The condition ensures the finite-time boundedness for the closed-loop cyber-physical system via state-feedback control. Numerical experiments illustrate the performance of the proposed method.

ABSTRACT. In this paper, we develop a Maximum likelihood estimation algorithm for a class of Wiener system written in a linear regression form. The output non-linearity is approximated by utilizing a piece-wise linear function. An iterative algorithm is proposed to estimate the linear regression model and the parameters of the piece-wise linear approximation. Closed-form expressions to estimate parameters are obtained. The benefits of our proposal are illustrated via numerical simulations.

ABSTRACT. Flame reconstruction, an indispensable instrument in the realm of combustion diagnostics, enhances the level of understanding with regards to flame radiative characteristics and can be inferred from the flame emission spectrum. This paper explores the feasibility of spectral reconstruction from low-resolution sensor data, specifically from digital color cameras capturing visible light (RGB). Two spectral reconstruction techniques are evaluated: the Maloney-Wandell method, requiring precise spectral sensitivity function (SSF) characterization, and a kernel-based regression method that avoids the need for explicit SSF characterization. The study focuses on flames generated in industrial settings, including hydrocarbon and copper concentrate combustion. Results indicate that both methods effectively reconstruct spectral emissions, with kernel-based regression slightly outperforming the Maloney-Wandell method in terms of accuracy. Moreover, kernel regression proves to be a cost-effective and practical solution for industrial settings, as it does not require SSF characterization, a challenging task in practice. This research contributes to advancing spectral reconstruction techniques for industrial combustion monitoring and process optimization.

ABSTRACT. This research article presents the development of an innovative device designed to precisely calibrate Aviat antennas, with a specific focus on the ODU 600 model. The article's introduction emphasizes the necessity for accurate calibration in communication systems and outlines the project's goals. Through a comprehensive review of current practices, the article analyzes the existing challenges and identifies areas for enhancement. The detailed methodology encompasses the proposed solution approach and its procedural steps. Key features and overall functionality of the prototype are highlighted. Briefly addressing the assembly process underscores the importance of robust construction. The article outlines technical laboratory tests and validation using Aviat equipment, showcasing achieved results and discussing benefits such as enhanced calibration quality and efficiency. Concluding insights address the potential impact and suggest future directions, encompassing diverse antenna types, improved geolocation, and automated data transmission.

ABSTRACT. This paper presents a comprehensive study on signal interference techniques for neutralizing a DJI Mavic Air drone. The research involved both lab-controlled experiments and field tests conducted on campus. A Software Defined Radio (SDR) and a custom-designed RF circuit were employed to implement the interference system. The focus was on disrupting the drone's video transmission, telemetry communications channel, and Global Positioning System (GPS) signal. Mathematical models and signal parameters were utilized to evaluate the effectiveness of different jamming strategies. The results provide valuable insights into the interference methodology for the IEEE 802.11b/n/g Wi-Fi standard and GPS C/A L1 signal, which are commonly used in commercial drones.

ABSTRACT. En el presente trabajo se realiza una detección de una falla en cortocircuito en una máquina de inducción mediante la Transformada de Wavelet. Para su análisis, se realiza un análisis multi-resolución a la corriente de la máquina en estado sano y con falla. Seguidamente se utiliza la técnica de la desviación de la energía, la cual es aplicada a cada detalle del análisis multi-resolución, para así poder comparar ambos resultados. Se constata que es posible detectar la falla en cortocircuito mediante el análisis multi-resolución, comparando las curvas de desviación de la energía resultante con la curva de cortocircuito

ABSTRACT. The decarbonization of electric power systems, accompanied by the electrification of transportation, presents significant challenges in operating the power system, but at the same time, it can offer opportunities to provide flexibility to the system. This paper studies the potential of electric vehicle to enhance the flexibility of the Chilean power system. An operation model of electric vehicles considering intelligent charging and discharging modes is presented. The model is integrated to unit commitment model, with the objective of analyzing the impact of flexible EV operation modes on the system operation. The results show that a flexible operation of electric vehicles allows reducing operating costs, renewable curtailment, load shedding, and the system peak demand.

ABSTRACT. he widespread adoption of electric vehicles (EVs) worldwide has garnered significant attention due to their potential to contribute to the reduction of greenhouse gases and CO2 emissions, thereby supporting efforts to mitigate climate change. However, this surge in EV adoption also presents a formidable challenge for power system operators and planners. This challenge stems from the increased demand for system flexibility, which is vital to address the complexities associated with maintaining a secure and reliable power system operation. In this context, this paper presents the assessment of the impact of EV demand management (DM) on the transmission expansion planning (TEP) and operation of power systems. The EV demand comprises both public and private vehicles. The TEP model is formulated as a mixed-integer linear programming, and is applied to a case study representing the Chilean power system. The results have shown that the implementation of EV DM brings about significant reductions in operating costs, power spillage, unserved demand, and investment expenditures when compared to scenarios where DM is not considered. These results underscore the importance of incorporating EV demand management strategies into power TEP and operation, highlighting its potential benefits for system efficiency and sustainability.

ABSTRACT. The increasing use of electric vehicles entails significant challenges related to battery maintenance and life extension. Within this context, various effective methods have been proposed to evaluate battery health. However, current methods have not considered geographical variables in predicting battery health. In this paper, a fuzzy logic-based method for estimating battery degradation is proposed. This model takes into account the topography of the area where the vehicle is used. To validate its effectiveness, simulations are carried out in Santiago (Chile) and Valparaíso (Chile), revealing that both the maximum charge and the internal resistance of the battery, influenced by terrain inclination, are critical factors in their degradation. These simulations demonstrate that the proposed model is useful for estimating battery health.

ABSTRACT. Residential electric-vehicle (EV) chargers are expected to become increasingly prevalent in the near future as the motivation for removing internal-combustion engines is rising. At the same time, since EVs are storage devices when parked, the possibility of using them as a backup when the grid is absent (e.g., due to failure), frequently called Vehicle-to-Home (V2H), has been gathering more attention recently. In this paper, we propose to use residential chargers not only to allow the V2H operation but also to be able to provide ancillary services to the grid when charging. In that sense, we propose to have a low-power DC charger, i.e., directly connected to the internal DC bus bypassing the on-board-charger, that can be operated in grid-feeding and grid-forming mode, depending on the grid availability. We show the requirements of the converter for a typical Chilean house, the converter topology and control design, and simulation results for both operation modes.

ABSTRACT. The increased usage of Electric Vehicles (EV) due to the transition from fossil fuels to clean energy sources presents the challenge of integrating EV charging into the electrical grid that leads to the analysis of charging patterns for decision making. This document proposes an evolutionary biclustering algorithm which main objective is to find EV charging patterns in a time series. The algorithnm has been tested with real life data. Our numerical simulation results proved that our algorithm is able to identify concurrent peak usage in different regions in big time intervals. This new technique can be used to avoid creation of new voltage peak and energy loss while integrating EV charge infrastructure into the current electrical grid.

ABSTRACT. This paper presents a Dynamic Sliding Mode Control (DSMC) for a quadruple tank process (QTP) with a long time-delay. In this work, an ideal decoupler is designed to reduce the interaction between variables and divide the process into two monovariable systems, which are obtained by using reaction curve identification. In addition, a DSMC is designed for each system, and its parameters are tuned using three swarm intelligence optimization techniques (PSO, WOA, and GWO). Finally, the performance of the tuned controller is measured through a simulation test using MatLab. Several performance indices of each algorithm showed different impacts on the search mechanism, evaluated by simulation

ABSTRACT. Wind speed prediction in the mesosphere and lower thermosphere is crucial for atmospheric and telecommunication studies. This study addresses the challenge of short-term wind speed prediction at altitudes between 80 and 95 km over Peru's coastal north and central Peru. First, the maximum likelihood approach accurately imputes missing data, thus ensuring the integrity of the time series. Subsequently, the Variational Mode Decomposition (VMD) technique decomposes the entire time series into it's inherent modal components. Then, each component is processed with the Long short-term memory (LSTM) neural network and the Optuna hyperparameter optimizer. This model is capable of two-day forecasts. Finally, the metric used is the root mean square error (RMSE) that presented values between 9m/s and 25 m/s.

ABSTRACT. This research is focused on classifying security data in industrial control systems (ICS) using machine learning models. Currently, ICS mainly focus on the technical and industrial operation of technological infrastructures, neglecting their security. This practice is dangerous as it affects various critical sectors of society. Due to the scarce information and difficult access to security incident data in industrial systems, this study employed web scraping to create a data set called ”SI ICS UPS 2023” with 2914 records of non-null security incidents in text format. For the labeling phase, regular expressions were applied to standardize the data set and propose two main classes of interest in this study. Data cleaning and processing stages were implemented, followed by the training of four machine learning models from scratch. The best-performing model in terms of the area under the curve (AUC) was the Random Forest with a score of 0.76 and an accuracy of 71.20%. These results demonstrate the efficiency of automating processes for the collection and classification of cyber incident data in industrial environments using techniques like web scraping and the utilization of machine learning models

ABSTRACT. In this paper, we develop a Probabilistic Graphical Model using a Bayesian network that may enhance the decisionmaking process for operating a semi-autogenous grinding (SAG) mill. The Bayesian network is initially constructed based on prior knowledge. Conditional probabilities are calibrated using a subset for training and validated using a test subset. The obtained results demonstrate that the model achieves high accuracy in determining the fresh mineral feed rate and the power consumed by the mill. This showcases the model’s effectiveness in capturing the primary operational standards.

ABSTRACT. Multimodal emotion recognition involves identifying human emotions in specific situations using artificial intelligence across multiple modalities. MERDWild, a multimodal emotion recognition dataset, addresses the challenge of unifying, cleaning, and transforming three datasets collected in uncontrolled environments with the aim of integrating and standardizing a database that encompasses three modalities: facial images, audio, and text. A methodology is presented that combines information from these modalities, utilizing ”in-the-wild” datasets including AFEW, AffWild2, and MELD. MERDWild consists of 15 873 audio samples, 905 281 facial images, and 15 321 sentences, all of them considered usable quality data. The project outlines the entire process of data cleaning, transformation, normalization, and quality control, resulting in a unified structure for recognizing seven emotions.

ABSTRACT. Underground mining visible light communications (UM-VLC) is a prospective solution to improve the well-being of workers by having a constant flux of information with them. However, the conditions inside underground mines present critical divergences to indoor environments. These differences exist due to the irregularity of the walls, the scattering produced by heavy dust particles, and the user mobility relative to other objects inside the mine. These hazards reduce the reliability of the system. Multiple input and multiple output schemes can improve the reliability and throughput of the system. Nonetheless, the presence of mobility and shadowing impacts the error rate of the signal. Here, we use an optimization method based on particle swarm optimization (PSO) to obtain optimal spatial parameters for the communication system. Numerical simulations prove the improvement compared to the previously used setting, allowing better reliability for the spatial multiplexing techniques.

ABSTRACT. This article considers the strategic management of traffic within a Software-Defined Network (SDN) framework. We explore two different models. The first one is designed with a clear objective: to minimize the worst flow of data through individual connections within the studied SDN. This entails identifying and addressing potential bottlenecks to optimize network performance. Moreover, we aim to achieve this objective while strategically selecting an appropriate number of controllers. Conversely, the second model takes on a similar challenge but within the context of a predefined and fixed number of controllers. To facilitate our study, we use 13 real-world network scenarios. We subject these networks to rigorous testing using our models and harness the computational capabilities of the CPLEX software to derive the solutions. Our endeavor represents a pioneering effort in the mathematical modeling of optimization within this specific network paradigm. After a rigurous analysis of our test results, it becomes apparent that the efficacy of the first model is closely correlated with the scale of the network under consideration. Furthermore, we observe that solutions derived from the second model remain remarkably stable despite fluctuations in the number of controllers, ranging from 20% to 40% of the network's size. These empirical insights provide a deep understanding of the underlying functionalities and optimization dynamics of these models. Ultimately, these findings contribute to the refinement of network efficiency and performance within the realm of SDN.

ABSTRACT. This work presents the feasibility of developing a low-cost motorized fiber optic polarization controller. To validate the operation of the design, a Mach Zehender interferometer was built on single-mode optical fiber, whose visibility of optical interference will depend on the difference in polarization between its arms. Using this design, a visibility greater than 0.99 was obtained. These results demonstrate the proposed device's high precision, whose construction cost is lower by an order of magnitude compared to commercial devices. The low optical losses, the flexibility of the electronic design, and the feasibility of its miniaturization suggest that the results of our system allow progress in autonomous optical systems, which can be applied to quantum communications systems.

ABSTRACT. The areas in which the Infrared (IR) spectrum gives vital information are as different as they are unique. However, this versatility comes with limitations. One of these obstacles is how to deal with the noise that affects this spectrum, which heavily distorts the data acquired from a scene. This investigation centers itself around how to fix the Fixed Pattern Noise (FPN) in the Short Wave Infrared (SWIR) using a refocusing algorithm, as an alternative to the two point black body calibration. Plants are chosen because of the ease to identify hidric stress in them using IR imaging due to the water natural reflectance and absorbance properties. The elimination of noise using this method will be revised, comparing it to the well known two point calibration method. Furthermore, by using filters a multispectral cube will be created and a spectral range of interest within the SWIR will be defined.