Doctorado
Permanent URI for this communityhttps://hdl.handle.net/11285/551013
Colección de Tesis presentadas por alumnos para obtener un Doctorado del Tecnológico de Monterrey.
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- Synthesis and Characterization of FAPbI3 Perovskite and its Incorporation into a Photovoltaic Heterostructure(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Miró Zárate, Jorge Luis; Elias Espinosa, Miilton Carlos; emimmayorquin; Rosas Meléndez, Samuel Antonio; Melo Máximo, Dulce Viridiana; Flores Ruíz, Francisco Javier; School of Engineering and Sciences; Campus Ciudad de México; Diliegros Godines, Carolina JananiConsidering the importance of having the α-FAPbI3 as it is the photoactive and functional phase for the use of this perovskite in a solar cell and understanding the growth process by incorporating an additive. In this work, it is presented a methodology that combine a method for deposition called sequential deposition with the incorporation of a pseudo halogen additive NH4SCN at various concentration of moles into the PbI2 solution, in order to have α-FAPbI3 perovskite deposited at open atmosphere. This research focuses on the mechanisms of growth of the FAPbI3 perovskite films over glass with the NH4SCN additive. Subsequently, the incorporation of the FAPbI3 perovskite into a heterostructure is presented. The architecture FAPbI3/ETL/ITO/Glass is presented, where the ETLs used are TiO2 and SnO2. The incorporation of FAPbI3 into a heterostructure allows us to evaluate the perovskite's properties for its photovoltaic application. Based on the outstanding electrical properties, WS2 was incorporated into the heterostructure through interface engineering, forming the heterostructure FAPbI3/WS2/ETL/ITO/Glass. Both architectures are compared in terms of their optoelectronic and morphological properties to determine the best FAPbI3-based heterostructure for improved photovoltaic application.
- Influence of human error and situational awareness in decision-making in complex tasks. Case of study: forklifts operators(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-19) Arias Portela, Claudia Yohana; Mora Vargas, Jaime; emipsanchez; Castillo Martínez, Juan Alberto; González Mendoza, Miguel; Thierry Aguilera, Ricardo; School of Engineering and Sciences; Campus Ciudad de México; Caro Gutiérrez, Martha PatriciaThis dissertation investigates situational awareness (SA) and human errors in logistics operations, using a multiphase and multifactorial approach as an innovative approach. The research responds the question of how SA errors can be assessed, along with their influence on decision-making in complex tasks, by considering a comprehensive HFE approach to various triggering factors. Characterization of the process with ethnography and process mapping, analysis of visual attention with Eye-tracking and retrospective think-aloud (RTA), an Error taxonomy and the bases of a data science approach were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. Analyzing 566 events across 18 tasks, the research highlights eye-tracking's potential by offering real-time insights into operator behavior, and RTA as a method for cross-checking the causal factors underlying errors. Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high cognitive demand tasks such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable and are the most affected by operator risk generators (ORG), representing 42% of events with a risk of incident. The study identifies driving, hoisting and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADAS). This study provides valuable insights for improving safety in logistics operations by proposing a multiphase and multifactorial approach to uncover patterns of attention, perception and cognitive errors, and their impact on decision-making in the logistic field
- The impact of loading-unloading zones for freight vehicles on the last-mile logistics for nanostores in emerging markets(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Mora Quiñones, Camilo Andrés; Cárdenas Barrón, Leopoldo Eduardo; emimmayorquin; Fransoo, Jan C.; Smith Cornejo, Neale Ricardo; Loera Hernández, Imelda de Jesús; School of Engineering and Sciences; Campus Monterrey; Veláaquez Martínez, Josue CuauhtémocEvery year, more than 26 billion deliveries are made globally to serve nanostores, the largest grocery retail channel in the world. At each stop, company representatives face a persistent challenge: finding a place to park. While the problem seems simple, it is remarkably complex and far from easy to solve. In emerging markets, where cities have grown rapidly and often without proper planning, fragmented markets and inadequate infrastructure exacerbate the issue. Multiple stakeholders compete for limited curb space, and the lack of dedicated parking disrupts last-mile efficiency, forcing drivers to either cruise for parking or resort to illegal parking. These behaviors lead to increased vehicle emissions, noise pollution, and additional costs. This dissertation provides key insights into last-mile logistics for nanostores in emerging markets, contributing to academic literature and offering practical implications to address the parking problem. The first study addresses the parking challenges faced by freight vehicles serving nanostores, identifying key factors affecting dwell time efficiency and suggesting operational improvements. In the next study, the focus shifts to the implementation of Loading-Unloading Zones (LUZs) as a targeted intervention, analyzing their impact on reducing air and noise pollution in urban areas. The last study extends this analysis by exploring the effects of LUZs on traffic flow, evidencing how their introduction can improve vehicle speed and reduce congestion in densely populated city streets. Together, these studies provide a detailed exploration of the operational, environmental, and infrastructural challenges of last-mile logistics, while offering concrete strategies to improve urban logistics in emerging markets. This dissertation contributes by expanding the body of knowledge and offering actionable managerial insights with the potential to drive meaningful impact. These include enhancing air quality, reducing noise pollution, lowering carbon emissions, improving traffic flow, and achieving substantial cost savings for companies distributing goods to nanostores in emerging markets.
- A data-driven modeling approach for energy storage systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Silva Vera, Edgar Daniel; Valdez Resendíz, Jesús Elías; Rosas Caro, Julio César; emipsanchez; Escobar Valderrama, Gerardo; Guillén Aparicio, Daniel; Soriano Rangel, Carlos Abraham; School of Engineering and Sciences; Campus MonterreyThis disertation presents a versatile data-driven modeling methodology designed for various energy systems, including battery-based power systems, DC-DC power electronic converters, Lithium-Ion batteries, and Proton-Exchange Membrane Fuel Cells (PEMFC). The proposed approach captures the non linear dynamics of each system by leveraging fundamental measurements and operational data, thus eliminating the need for explicit theoretical models and significantly simplifying the modeling process. Specifically, the methodology allows for the identification of essential parameters by constructing state-space representations that describe both fast and slow system dynamics, which are crucial for accurately modeling transient behaviors and implementing adaptive control strategies. The models were validated across different applications, showing their ability to replicate real system behaviors with high precision. For instance, in the case of DC-DC converters, the models demonstrated an average error deviation of approximately 2% for current signals and 4% for voltage signals, confirming their capacity to track the actual converter dynamics. Similarly, the Lithium-Ion battery models enabled accurate estimation of state of charge (SoC) and opencircuit voltage using a modified recursive least-squares algorithm, achieving close alignment with real discharge curves. In the PEMFC stack modeling, the methodology utilized real-physic model operational data to refine model accuracy, yielding improved predictive capabilities over traditional approaches. These results underscore the efficacy and robustness of the data-driven approach in enhancing the design, control, and optimization of diverse energy systems. By providing a framework that can be readily adapted to different components and configurations, this methodology supports advancements in sustainable energy technologies, enabling the interconnection of multiple energy storage and conversion systems with minimal computational cost and measurement requirements.
- Design and Development of Conducting Polymer and Carbon Nanostructure based Efficient Thermoelectric Materials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Ebrahimibagha, Dariush; Mallar, Ray; emimmayorquin; Aguirre Soto, Héctor Alán; Niladri, Banerjee; Gallo Villanueva, Roberto Carlos; School of Engineering and Sciences; Campus Monterrey; Datta, ShubhabrataThermoelectric materials present a promising renewable energy technology for directly converting thermal energy into electricity and vice versa. However, their practical application is hindered by low conversion efficiencies, quantified by the dimensionless figure of merit, 𝑍𝑇 = 𝑆 2 𝜎 𝑘 𝑇 , where 𝑆,𝜎, and 𝑘 are the Seebeck coefficient, electrical onductivity, and thermal conductivity, respectively. Achieving a high 𝑍𝑇 is challenging because enhancing one parameter often degrades the others. Various nanoscale strategies have been explored, yet a comprehensive framework for improving 𝑍𝑇 remains elusive. Recently, polymer-based nanocomposites, particularly carbon nanotubes (CNTs) dispersed in polyaniline (PANI), have gained attention due to their flexibility, non-toxicity, and processability, key traits for next-generation flexible electronic devices. Despite this potential, optimizing thermoelectric performance in PANI-CNT systems is complex, as it depends on numerous factors, including CNT dimensions, functionality, and PANI's doping and morphology. This research employs machine learning (ML) and genetic algorithms (GA) to model and optimize the thermoelectric properties of PANI-CNT nanocomposites. By analyzing structural and compositional variables—such as CNT length, diameter, type, and PANI morphology—we identified strategies that enhance electrical conductivity and the Seebeck coefficient while minimizing thermal conductivity. Our ML models revealed that selecting appropriate dopants for PANI and using single-walled CNT (SWCNT) improves overall thermoelectric performance. Multi-objective GA optimization further refined these findings, demonstrating that SWCNTs help reduce thermal conductivity and that CNT length plays a dual role: shorter CNTs decrease 𝑘, while longer ones enhance both 𝑆 and 𝜎. Experimental validation was performed by fabricating PANI-CNT nanocomposite pellets, but achieving high 𝑍𝑇 remained elusive due to limitations in dataset quality and the variability introduced by diverse synthesis techniques. The synthesis method influences PANI dimensionality (e.g., 0D, 1D, 2D) and the morphology of PANI-CNT composites (core-shell vs. dispersed), complicating performance consistency. While the experiments confirmed the general trend of model predictions, they highlighted the necessity of cleaner, more comprehensive datasets for future research. Ultimately, this study lays the groundwork for designing high-efficiency thermoelectric nanocomposites and outlines the next steps in developing more accurate predictive models and synthesis methods for improved thermoelectric performance.
- Advanced modeling techniques in electric vehicles for battery sizing and Vertical Dynamic Control with CARSIM® and ADAMS(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Drivet González, Aline Raquel Lily; Cespi, Riccardo; emipsanchez; Vargas Martínez, Adriana; Lozoya Santos, Jorge de Jesús; School of Engineering and Sciences; Campus Monterrey; Tudón Martínez, Juan CarlosThis thesis addresses the rapidly accelerating shift from internal combustion engine vehicles to electric vehicles (EVs), a transition driven not only by market demands but also by the urgent need to mitigate climate change. As electrification reshapes the automotive landscape, the importance of advanced modeling techniques are essential to accelerate the adoption of EV technologies, ensuring competitiveness, and addressing environmental urgency. This research begins with a review of vehicle dynamics changes, highlighting the challenges and opportunities introduced by this swift transition to EV technology. The first contribution of this thesis is the application of modeling and simulation techniques using CARSIM®where real-world telemetry is used to optimize EV battery performance and battery sizing. This optimization focuses on maximizing efficiency while maintaining safety and reliability. The second contribution is the development of a model for EV suspension systems using ADAMS®which can be a platform to test critical dynamic behavior of EVs under various conditions. Together, these contributions advance the design and performance of electric vehicles, introducing advanced modeling tools to accelerate development processes, speeding design processes, and addressing the urgent challenges of vehicle electrification in the context of climate change. As a result of the research presented in this thesis, which includes methodologies for battery pack design and the modeling and control of active suspension systems for electric vehicles, two journal articles have been published, and four additional articles have been presented in conference proceedings, contributing significantly to the academic discourse in these areas.
- A generalist reinforcement learning agent for compressing multiple convolutional neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) González Sahagún, Gabriel; Conant ablos, Santiago Enrique; emipsanchez; Ortíz Bayliss, José Carlos; Cruz Duarte, Jorge Mario; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus MonterreyDeep Learning has achieved state-of-the-art accuracy in multiple fields. A common practice in computer vision is to reuse a pre-trained model for a completely different dataset of the same type of task, a process known as transfer learning, which reduces training time by reusing the filters of the convolutional layers. However, while transfer learning can reduce training time, the model might overestimate the number of parameters needed for the new dataset. As models now achieve near-human performance or better, there is a growing need to reduce their size to facilitate deployment on devices with limited computational resources. Various compression techniques have been proposed to address this issue, but their effectiveness varies depending on hyperparameters. To navigate these options, researchers have worked on automating model compression. Some have proposed using reinforcement learning to teach a deep learning model how to compress another deep learning model. This study compares multiple approaches for automating the compression of convolutional neural networks and proposes a method for training a reinforcement learning agent that works across multiple datasets without the need for transfer learning. The agents were tested using leaveone- out cross-validation, learning to compress a set of LeNet-5 models and testing on another LeNet-5 model with different parameters. The metrics used to evaluate these solutions were accuracy loss and the number of parameters of the compressed model. The agents suggested compression schemes that were on or near the Pareto front for these metrics. Furthermore, the models were compressed by more than 80% with minimal accuracy loss in most cases. The significance of these results is that by escalating this methodology for larger models and datasets, an AI assistant for model compression similar to ChatGPT can be developed, potentially revolutionizing model compression practices and enabling advanced deployments in resource-constrained environments.
- Evaluation of the impact on energy and thermal comfort of PCM-enhanced on roof buildings in semi-arid climates(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-08) Godoy Rangel, Caribay; Gijón Rivera, Miguel Ángel; emipsanchez; López Salinas, José Luis; Chávez Chena, Yvonne; Bates Prieto, Rafael; School of Engineering and Sciences; Campus Monterrey; Rivera Solorio, Carlos IvánIn recent decades, buildings have emerged as a significant contributor to urban energy consumption. In order to mitigate the environmental impact of this phenomenon, the utilization of thermal storage through phase change materials (PCM) in the building envelope has become an innovative strategy. Nevertheless, despite the assumption that the incorporation of PCM will improve the thermal efficiency of buildings, this is not a guaranteed outcome in all instances. In climates that exhibit pronounced seasonal extremes, the capacity of PCM for thermal storage is constrained. This experimental and numerical study, focused on semi-arid climates, aimed to enhance the performance of PCM in combination with natural ventilation thus the thermal performance of building roofs. In addition, the study extends the evaluation by combining PCM with insulating materials and reflective paint. It is anticipated that the integration of PCM with another passive strategy will enhance its thermal performance, particularly in extreme climates where the full potential of this material is often constrained. The results of the study focus on the analysis of the thermal behavior of the PCM, thermal comfort, and annual energy demand, with the environmental and cost implications that this represents. The experimental study, conducted in real climatic conditions, focused on the combination of PCM with natural ventilation. The numerical study generated 108 new scenarios, which were evaluated during a typical meteorological year. The results demonstrated that the incorporation of natural ventilation optimizes the thermal behavior of the PCM, enabling an increase in the time spent in a solid state by up to 41%. This enhances the efficiency of the complete cycle, ensuring its completion within a single day. Moreover, the utilization of PCM resulted in a reduction of the maximum peak indoor air temperature during the summer months by between 1.9% and 7.2%, while an increase in the minimum valley during the winter months was observed, ranging from 4.4% to 10.4%. The duration of time spent within the comfort zone was increased, up to a maximum of one hour during the summer months. Similarly, the introduction of natural ventilation through ducts into an air chamber with PCM has been demonstrated to reduce annual energy gains by up to 92.9%. Ultimately, the combination of PCM with reflective paint and XPS insulation has the potential to result in annual savings of up to 12.95 tCO₂e, with an estimated investment return period of 2.8 years.
- Botnet detection on twitter: a novel similarity-based clustering mechanism(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Samper Escalante, Luis Daniel; Monroy Borja, Raúl; emipsanchez; Castro Espinoza, Félix Agustín; González Mendoza, Miguel; School of Engineering and Sciences; Rectoría Tec de Monterrey; Loyola González, OctavioBotnet detection on Twitter represents a critical yet under-explored research problem,as botnets programmed with malicious intent threaten the platform’s security and credibility. Although Twitter has implemented mitigation strategies, such as imposing restrictions andbans, these measures remain insufficient due to botnets’ rapid creation and expansion. Existing solutions proposed by researchers for manual and automated botnet detection typically rely on individual metrics commonly used for detecting bots. However, these approaches lack the necessary group-oriented analysis and metrics critical for effectively identifying botnets of varying sizes and objectives. To address this issue, we have developed an innovative botnet detection mechanism based on similarity, which significantly enhances the detection rate of botnets on Twitter. Each bot, regardless of its complexity, leaves detectable traces of automation in its creation, behavior, or interactions with other accounts. By characterizing these traces, we can establish relationships between bots, enabling effective botnet detection. Our mechanism constructs a regression model to quantify the similarity between bots, leveraging features from user data, tweet patterns, and social interactions on the platform. Then, it uses this similarity measure to build a distance matrix, enabling the formation of groups with shared attributes, connections, and objectives through clustering methods. Our botnet detection mechanism achieved extraordinary success, evidenced by high scores on external Clustering Validation Indices (CVIs) and the Area under the ROC Curve (AUC) compared to existing solutions from the literature. Furthermore, the mechanism proved effective when confronted with unknown botnets with varied objectives. Our experimental findings suggest that this work is well-positioned to strengthen future botnet detection mechanisms, having shown the value of incorporating social interaction features. This integration offers a strategic advantage in the ongoing arms race against botmasters and their malicious objectives. Additionally, our mechanism consistently outperforms other approaches across various metrics, configurations, and algorithms, underscoring its effectiveness and adaptability in different detection scenarios.
- Environmental monitoring to estimate indoor occupancy levels based on Semi-supervised machine learning and data fusion for building management(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Mena Martínez, Alma Rosa; Ceballos Cancino, Hector Gibran; emipsanchez; Alvarado Uribe, Joanna; Cantu, Francisco J.; García, Juan Pablo; School of Engineering and Sciences; Campus Monterrey; Schmitt, JanOccupancy information is essential for space management, energy efficiency, and in times of the COVID-19 pandemic, for crowd control. Obtaining labeled data is challenging due to hardware limitations, privacy considerations, and the required underlying costs. Furthermore, venues over 200 m2 require data fusion techniques. Therefore, this thesis mainly focuses on exploring the potential of Semi-Supervised Learning (SSL), which only needs a few labeled data and a large amount of unlabeled data, to estimate the occupancy levels in enclosed spaces. This study presents an empirical comparison between Supervised ML and SSL models as well as data fusion techniques in real-life university classrooms and offices (uncontrolled conditions) at the University of the West of England, Bristol, UK, and Tecnologico de Monterrey, Mexico. The data was collected for three weeks at each scenario using an in-house developed Internet of Things (IoT) device that measures air temperature, relative humidity, and atmospheric pressure. The ground truth records were gathered through manual logging of occupancy levels. Datasets’ sizes averaged 2350 entries with only 280 labeled instances per dataset. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) were used to define a performance baseline for supervised ML. Self-Training (ST) and Label Propagation (LP) were tested for SSL. In addition, several feature fusion methods were explored, including Chi-squared, ANOVA F-test, Spearman and Kendall’s Tau correlation, Mutual Information, Averages, Recursive Feature Elimination, and Principal Component Analysis. The models were evaluated using Accuracy, Precision, Recall, F1-score, Confusion Matrix, and High - Quality Supervised Baseline. ST achieved superior performance compared to baseline models (SVM, RF, MLP) with a highest average accuracy of 90.96% compared to SVM (86.66%). Furthermore, the data fusion results indicated that the Chi-squared approach for feature fusion outperformed others with an F1-score average of 95% and an accuracy average of 99%. These results demonstrate the effectiveness of SSL for indirect occupancy estimation while reducing the need for extensive data collection and labeling.
