Lab

Leonardo Trujillo's Lab

About the lab

Tree-LAB: Technology and Research for Engineering inspired by Evolution

Featured projects (2)

Project
CONACYT Fronteras de la Ciencia Project, taking evolution based machine learning to big data problems of the modern era.
Project
MOEAs for High-Level Synthesis consists of applying multi-objective optimization for scheduling, allocation and mapping. The problem of finding an ideal scheduling, allocation and mapping is Non-deterministic Polynomial time hard (NP-hard), hence, metaheuristic algorithms are needed to solve it.

Featured research (5)

We propose a marker-based geometric framework for the high-frequency absolute 3D pose estimation of a binocular camera system by using the data captured during the exposure of a single rolling shutter scanline. In contrast to existing approaches enforcing temporal or motion models among scanlines (e.g. linear motion, constant velocity or small motion assumptions), we strive to determine the pose from instantaneous binocular capture (i.e. without using data from previous scanlines) and achieve drift-free pose estimation. We leverage the projective invariants of a novel rigid planar pattern, to both define a geometric reference as well as to determine 2D-3D correspondences from raw edge detection measurements from individual scanlines. Moreover, to tackle the ensuing multi-view estimation problem, achieve real-time operation, and minimize latency, we develop a pair of custom solvers leveraging our geometric setup. To mitigate sensitivity to noise, we propose a geometrically consistent measurement refinement mechanism. We verify the quality of our solvers by comparing with state of the art general solvers for absolute pose estimation of generalized cameras. Finally, we demonstrate the effectiveness of our proposed approach with an FPGA-based implementation which achieves a localization throughput of 129.6 KHz with a 1.5 μs latency.
Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.
This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.
The coding of observational data is commonly used to analyse and evaluate human behaviours. The technique can help researchers inform the design and impact of, for example, an Ubicomp system by studying specific behaviours of interest. There are some tools that can alleviate the burden of observational coding, like those that help to collect and organise data, but can still be error‐prone and time‐consuming. Moreover, most of these tools lack automation, requiring intense human interaction. In order to mitigate these issues, computer vision (CV) and machine learning (ML) techniques could be used to automate observational coding, but little work has focused on analysing the feasibility of such an approach, with the goal of reducing the total coding time while maintaining accuracy. In this work, we address this question by proposing an automated approach for a real‐world case study and compare it to manual coding. The study is composed of 10 videos with an average duration of 17 min each, where the goal is to determine the attention of children with autism that participate in a neurofeedback therapy session. Each video was hand‐coded by three human observers to define the ground truth and to measure the manual coding time. Results show that it is feasible to automate the coding of observational behaviours and obtain a noticeable reduction in coding time, but with a slight loss in accuracy. Moreover, we illustrate that the best solution would be a hybrid approach, using a semi‐automated system that combines human expertise and ML predictions Keywords Observational studies, Coding behaviours, Automatic coding, Computer‐Vision, Machine Learning.
This work deals with modelling the vibration response of a gas turbine obtained during the start‐up process until reaching the nominal speed for power generation. Analysing the vibrations of a complex systems like a gas turbine is useful for the diagnostic of faults or damages in the internal mechanical components of the different stages that integrate a turbine. This work focuses on the study of the shaft vibrations of the bearing radial type mounted between the shaft and the bearing compressor associated with the speed of the turbine. This relationship is studied using experimental data collected from a particular gas turbine model. In particular, we propose a methodology to synthesize a computational model following a supervised learning approach implemented through different machine learning techniques, including a multi‐layers perceptron network, support vector machine (SVM), random forest (RF) and genetic programming (GP) with local search. Results show that SVM, RF and GP perform very well in this task, producing accurate predictive models. Moreover, there are some interesting trade‐offs between the methods, regarding generalization error, overfitting and model interpretability that are relevant for future applications and research.

Lab head

Leonardo Trujillo
Department
  • Doctorado en Ciencias de la Ingeniería
About Leonardo Trujillo
  • I am working at the intersection of Evolutionary Computation, Machine Learning and Engineering, focusing on Genetic Programming and solving a variety of real-world problems, from the domains of predictive modeling, machine vision and artificial intelligence.

Members (5)

Luis Muñoz
  • Tijuana Institute of Technology
Victor R. Lopez-Lopez
  • Tijuana Institute of Technology
Perla Juarez
  • Tijuana Institute of Technology
Francisco Fernando Zuñiga Alvarez
  • Tijuana Institute of Technology

Alumni (1)

Emigdio Z-Flores
  • Tijuana Institute of Technology