Showing papers by "Ricardo A. Ramirez-Mendoza published in 2022"

Digital Twin Technology Challenges and Applications: A Comprehensive Review

Abstract: A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

Abstract: Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.

Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles

Abstract: This work presents the development of a classification method that can contribute to precise and increased awareness of the situational context of vehicles, for it to be used in autonomous driving applications. This work aims to obtain a method for machine-learning-based driving environment classification that does not involve computer vision but instead makes use of dynamics variables from Inertial-Measurement-Unit (IMU) sensors and instantaneous energy consumption measurements. This article includes details about the data acquisition, the electric vehicle used for the experiments, and the pre-processing methods employed. This explores the viability of a method for classifying a vehicle’s driving environment. The results of such a system can potentially be used to provide precise information for path planning, energy optimization, or safety purposes. Information about the driving context could be also used to decide if the conditions are safe for autonomous driving or if human intervention is recommended or required. In this work, the feature selection process and statistical data pre-processing methods are evaluated. The pre-processed data are used to compare 13 different classification algorithms and then the best three are selected for further testing and data dimensionality reduction. Two approaches for feature selection based on feature importance and final classification scores are tested, achieving a classification mean accuracy of 93 percent with a real testing dataset that included three driving scenarios and eight different drivers. The obtained results and high classification accuracy represent a first approach for the further development of such classification systems and the potential for direct implementation into autonomous driving technology.

Lab-Tec@Home: A Cost-Effective Kit for Online Control Engineering Education

Abstract: It is widely recognized that a hands-on laboratory experience is useful in control engineering education. Herein, the students overcome the main gaps between theoretical knowledge and experimental setups. Nowadays, in times of crisis due to the COVID-19 pandemic, virtual and remote laboratories are emerging as primary educational resources. However, in virtual labs, the students are not exposed to real life issues (i.e., equipment problems, noise, etc.) while in remote labs, communication and connectivity problems arise (i.e., network security, synchronization management, internet speed, etc.). Henceforth, this work presents an unpublished educational project named Lab-Tec@Home, and the aim of this research is to expand the access of hands-on control education at the undergraduate level. Here, students easily assemble a cost-effective laboratory kit at home and use it on their own computing devices connected with the external MATLAB/SimulinkTM application. Thus, students can test and validate theoretical concepts of control engineering such as: system model identification, and PID control design and test. The assessment results show that the proposed educational project enhances the learning experience and has outstanding positive feedback of more than 290 students who undertook massive flexible digital courses at Tecnologico de Monterrey. This makes the proposed educational project mainly suitable for control engineering courses.

Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

Abstract: This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis.

Smart Automotive E-Mobility—A Proposal for a New Curricula for Engineering Education

Abstract: Automotive engineering is an area of great value and development. Lately, it has evolved rapidly because of autonomous vehicles. The development of smart mobility will be crucial in the coming years. Related research and companies related to intelligent transportation require trained and capable engineers. It is essential to generate an updated and specialized academic program that provides state-of-the-art technologies and related areas with smart mobility. This paper presents a novel two-year graduate academic program focused on smart electromobility. Programs around the globe were analyzed to find opportunity areas related to autonomous and electric vehicles, and smart mobility. Multi- and transdisciplinary courses were designed, according to the findings, on areas related to computer science, mechanical and electric engineering, law, marketing, and public policy. The proposed program fulfills the needs of a graduate student who will later work in a smart electromobility environment. The program offers a balanced curriculum that includes technical, business and social courses. Virtual and physical labs are proposed to develop a high-quality educational experience. This proposal can be used as a model for upcoming and related programs in other universities.

Multi-Output Sequential Deep Learning Model for Athlete Force Prediction on a Treadmill Using 3D Markers

Abstract: Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (Fx, Fy, Fz), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding Fz it reached 0.92.

Digital Twin for Urban Spaces: an Application

Abstract: A Digital Twin is a virtual representation of a real dynamic system that can simulate its current conditions, predict its future behavior, and log valuable information about its internal operation and interactions with other systems. A key feature is the capability of automatic bidirectional information flow between virtual and physical worlds. The objective of this work is to create a Living Lab for the demonstration of interacting urban Digital Twins under the United Nations’ Sustainable Development Goals of sustainable cities and communities, health and wellbeing, and industry, innovation, and infrastructure. By using a network of sensing devices mounted on a vehicle, the proposed system is capable of processing real-life data through edge computing, modeling software and Machine Learning algorithms. With the processed information, a 3D virtual representation of urban spaces and the vehicle itself, the interactions between both subjects and the evolution of each is enabled. This approach of Digital Twin technology for urban spaces has significant value when it comes to analyzing a community’s evolution, mobility, a vehicle’s dynamic behavior, and its interaction with urban infrastructure. This work presents the proposed methodology for developing Digital Twin concepts for urban spaces and vehicles as well as their respective characteristic components.

Application of Interval Predictor Model Into Model Predictive Control

Abstract: In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.

An Active Learning Didactic Proposal with Human-Computer Interaction in Engineering Education: A Direct Current Motor Case Study

Abstract: Engineering education requires learning strategies to engage students and improve the development of disciplinary and transversal competencies. Additionally, as economic resources are generally limited, it is sought to avoid investing large sums of money in software and hardware, as well as in fitting out laboratories. This work presents a didactic proposal within the framework of active and collaborative learning that includes the flipped classroom technique to be applied in the curriculum of undergraduate engineering programs and inside a massive flexible digital master class. The activity is the mathematical modeling, simulation, and control system of a direct current motor where simulation work is carried out in open license computational packages. Students understand the physical phenomena involved in the motor’s modeling and the input–output variables’ relations. Moreover, an analogy between an electromechanical and a pure electrical model is carried out, where the relevant variables respond in an agile and reliable manner. To validate the modeling, the differential equations are solved by applying numerical methods, and tested for control purposes. The activity has been validated with a rule-based system applied to a Likert scale survey data. This type of human–computer interaction, in the context of active learning, could engage students and motivate them to develop competencies that are highly appreciated by industry practitioners.

Direct Data Driven Model Reference Control for Flight Simulation Table

Abstract: We present a direct data driven model reference control framework for flight simulation table from both the theoretical analysis and engineering application. Sate of the art direct driven model reference control designs the unknown controller based on the input-output data and guarantees the actual model converge to the reference model, under a case of no any priori knowledge for the unknown plant. We improve this direct data driven model reference control strategy by considering its stability validation and synthesis analysis for nonlinear controller. The resulting direct data driven model reference control scheme can be implemented to ensure flight simulation table rotate accuracy, resulting in improved performance index. As the main technical contribution, we show that the proposed direct data driven model reference control framework also ensures closed loop stability and suitable extension for nonlinear controller.

The Importance of Robust Datasets to Assess Urban Accessibility: A Comparable Study in the Distrito Tec, Monterrey, Mexico, and the Stanford District, San Francisco Bay Area, USA

Abstract: Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community’s urban region based on their accessibility performance.

Synthesis analysis for data driven model predictive control*

Abstract: This paper shows our new contributions on data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor and stability. After reviewing the definition of persistent excitation and its important property, the idea of data driven is introduced in model predictive control to construct our considered data driven model predictive control, whose state information and output variable are generated by measured data online. Variation tool is applied to obtain the optimal controller or predictive controller through our own derivation. Furthermore, for the cost function in data driven model predictive control, its preliminary stability is analysed by using the linear matrix inequality and one single optimal state feedback controller is given. To bridge the gap between our derived results and other control strategies, output predictor is constructed from the point of data driven idea, i.e. using some collected input–output data from one experiment to establish the output predictor at any later time instant. Finally, one simulation example is given to prove the efficiency of our derived results.

Synthesis cascade estimation for aircraft system identification

Abstract: Purpose The purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results. Design/methodology/approach Firstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission. Findings This cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work. Originality/value To the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

On Online Adaptive Direct Data Driven Control

Abstract: Based on our recent contributions on direct data driven control scheme, this paper continues to do some new research on direct data driven control, paving another way for latter future work on advanced control theory. Firstly, adaptive idea is combined with direct data driven control, one parameter adjustment mechanism is constructed to design the parameterized controller online. Secondly, to show the input-output property for the considered closed loop system, passive analysis is studied to be similar with stability. Thirdly, to validate whether the designed controller is better or not, another safety controller modular is added to achieve the designed or expected control input with the essence of model predictive control. Finally, one simulation example confirms our proposed theories. More generally, this paper studies not only the controller design and passive analysis, but also some online algorithm, such as recursive parameter identification and online subgradient descent algorithm. Furthermore, safety controller modular is firstly introduced in direct data driven control scheme.

Automotive Embedded Image Classification Systems

Abstract: Electric vehicles are becoming more autonomous, so they must classify images using embedded systems and advanced classification methodologies to achieve a fast response when navigating. Thus, studying and analyzing classification algorithms and embedded systems is a mandatory endeavor to improve the performance of electric vehicles during their operation. On the other hand, artificial intelligence is one of the leading technology topics in autonomous electric vehicles; however, the computational requirements to analyze a large amount of data in real-time would mean having costly and powerful computers on board. Also, this can mean using a significant physical space in the vehicle and energy resources. An embedded system can handle the necessary data to classify standard traffic signals on the road so the principal processor can be released from these tasks. This paper proposes a traffic signal object detector and classifier that is implemented using a Tiny YOLOv4 and compares Frames Per Second obtained in an embedded system using the trained model, a web camera, and a Hardware Accelerator called Movidius Neural Stick by Intel are integrated into the proposed solution. The results show that the proposal is a good alternative for implementing a specialized image classification system into an embedded digital system for electric vehicles. This proposal could be extended to classify more images that can show up on a conventional road.

Data Driven Strategy for Linear Parameter Varying Control Design

Abstract: In this paper, we propose a novel data driven strategy framework for linear parameter varying closed loop system which consists of linear parameter varying system and linear parameter varying controller simultaneously. Firstly, we consider the model based control framework whose controller design is dependent of the considered system. In particular, due to the unknown system and unknown controller, data driven strategy is applied to obtain the system and controller respectively in their nonparametric and parametric forms. Such two different forms are related with system identification, power spectral and numerical optimization,etc. with a choice of the optimal input signal design. Secondly, to avoid the identification process of that system, data driven strategy is modified to design the controller directly from the observed input-output data sequence without providing any priori information on the considered system. We show that various techniques for data driven strategy to identify system or design controller, such as system identification, numerical optimization, power spectral and optimal input design etc within the proposed general framework.

E-Axle Effect on the Vibratory Response of a Vehicle

Abstract: Vehicle suspension systems are responsible for absorbing and filtering road irregularities to provide passenger safety and ride comfort. However, different vehicle and suspension parameters can affect the vibratory response. Consequently, vibrational analysis with different types of transmission is necessary. Furthermore, the electrification of the automotive chassis leads to the necessity of understanding the effects of introducing electric machines. This work analyzes the vibratory effect of changing a differential axis for an e-axle. This research presents a study on a half car model and body roll mode with different e-axles compared to a standard differential axle. Thus, changes concerning comfort, e-axle oscillation, and handling are considered in the time and frequency domains. In addition, a parametric sensitivity study is performed. The obtained results could be applied as design guidelines when sizing an e-axle solution.

Soft Passing over Traffic-Calming Devices by Controlled Suspension in Low-Speed Robotic Vehicles for Vulnerable People

Abstract: The usefulness of golf carts for transporting patients in hospital facilities is well known. Nursing homes, medical campuses, and any type of related service require the low-speed transport of patients either in a seat, in a wheelchair, or on a stretcher. This type of transport is not limited to hospitals, but also includes other environments where there are people with special requirements. Think for instance of handicapped or elderly people that need a van because they have to go from their homes to any destination; therefore, the use of golf carts becomes relevant and attractive. Moreover, these carts could be automated for path following and deal with bumps, potholes, or sinkholes. In this context, the present research proposes a novel way to deal with this kind of road obstacle when the gentle transport of patients is a key element. In order to pass over these obstacles, a soft upwards displacement of the front and rear sections of the vehicle was achieved with magnetorheological dampers as part of the vehicle’s suspension system. In this way, people who need this gentle transport will not have any discomfort. Moreover, this work is aligned with the spirit of Automated Vehicles 3.0.

Overview of Mexico´s transport sector: current situation, emissions trend and electromobility

Abstract: Due to the global interest in facing climate change, the world strives to control greenhouse gas emissions associated with anthropogenic activities, such as road transport. Mexico collaborates with the international agenda and establishes its national objectives and strategies, considering electromobility to mitigate emissions. However, the country faces implementation and compliance obstacles, such as the electricity produced to power electric vehicles generates indirect emissions. Therefore, the main contribution of this study is to provide a novel and robust overview of the Mexican road transport sector from a systemic and comprehensive perspective. This is with the objective to develop an updated business-as-usual emissions projection and compare it against the reduction targets, identifying challenges, strengths, and drivers that condition the performance of electromobility and the sustainable energy transition of the sector. The study creates a foundation for future studies of decarbonization scenarios strategies, including electromobility, to achieve an actual reduction without burden shifting.

Supervisor-Based Switching Strategy for Semi-Active Suspension Control

Abstract: Vehicle suspensions are the main responsible subsystems for vehicle ride quality. There is an intrinsic trade-off between comfort and road holding features on all types of suspensions. To tackle this shortcoming, semi-active suspensions provide controllable damping, i.e. they can change their dissipative behavior according to the required type of response and achieve acceptable levels of comfort and stability based on performance standards. In literature, multiple controllers are proposed for semi-active dampers; however, they fail to comply with passenger comfort and vehicle stability criteria simultaneously. This paper presents an online supervisor model that can switch over multiple controllers based on the dynamics of the vehicle and specific performance indexes. Multiple road roughness are constructed and the supervisor must switch the controllers in order to accomplish the desired performances. In particular, the proposed switching strategy can select among skyhook and groundhook controllers for a magnetorheological damper. Results show a favorable response of the switching strategy and an improvement in driving maneuverability of 33% when compared to a passive suspension system.

Conscious Mobility for Urban Spaces: Case Studies Review and Indicator Framework Design

Abstract: A lack of data collection on conscious mobility behaviors has been identified in current sustainable and smart mobility planning, development and implementation strategies. This leads to technocentric solutions that do not place people and their behavior at the center of new mobility solutions in urban centers around the globe. This paper introduces the concept of conscious mobility to link techno-economic analyses with user awareness on the impact of their travel decisions on other people, local urban infrastructure and the environment through systematic big data collection. A preliminary conscious mobility indicator framework is presented to leverage behavioral considerations to enhance urban-community mobility systems. Key factors for conscious mobility analysis have been derived from five case studies. The sample offers regional diversity (i.e., local, regional and the global urban contexts), as well as different goals in the transformation of conventional urban transport systems, from improving public transport efficiency and equipment electrification to mitigate pollution and climate risks, to focusing on equity, access and people safety. The case studies selected provide useful metrics on the adoption of cleaner, smarter, safer and more autonomous mobility technologies, along with novel people-centric program designs to build an initial set of conscious mobility indicators frameworks. The parameters were applied to the city of Monterrey, Nuevo Leon in Mexico focusing on the needs of the communities that work, study and live around the local urban campus of the Tecnologico de Monterrey’s Distrito Tec. This case study, served as an example of how conscious mobility indicators could be applied and customized to a community and region of interest. This paper introduces the first application of the conscious mobility framework for urban communities’ mobility system analysis. This more holistic assessment approach includes dimensions such as society and culture, infrastructure and urban spaces, technology, government, normativity, economy and politics, and the environment. The expectation is that the conscious mobility framework of analysis will become a useful tool for smarter and sustainable urban and mobility problem solving and decision making to enhance the quality of life all living in urban communities.

Synthesis of optimal controllers for model predictive control

Abstract: This paper studies the synthesis analysis for robust quadratic programming, whose data are not known, but in the uncertainty set, such as ellipsoids. Synthesis analysis means both the combination with the theory and practice. Given one quadratic programming problem, whose data are not always known exactly, but in typically known in a domain, i,e, one uncertainty set, this robust quadratic programming problem becomes one conic quadratic programming through our own derivations. After applying semidefinite relaxation and linear matrix inequality on this robust quadratic programming, one necessary and sufficient condition of the existence of the optimal robust feasible is formulated as one linear matrix inequality. And one special affinely adjustable robust counterpart of quadratic programming is shown to solve other two decision variables. To complete the synthesis analysis for robust quadratic programming from the points of theory and practice, all our derived theoretical results are applied in state estimation with uncertainty set through using the idea of direct data driven.

Interval state estimation for bounded noise and its applications

Abstract: Consider the problem of state estimation in identification and control theory, the traditional Kalman filter method and its modified forms can estimate the unknown state only on the condition of probabilistic distribution on external noise, such as white noise or colored noise. To relax this strict condition on external noise, interval state estimation is proposed to achieve the goal in case of the unknown but bounded noise, due to external noise with unknown but bounded property is more realistic then white noise. Given one state space form with bounded noises and bounded initial state, two intervals are constructed to include the state estimation and output prediction respectively through our own derivations. One easy way to determine the terminate state estimation is to choose the center of midpoint of the constructed interval. The equivalent property between interval state estimation and our previous zonotope state estimation is also described.