Desarrollo de un sistema de simulación interactivo de un paciente neonato para entrenamiento médico

Se presenta el desarrollo de un monitor neonatal orientado al entrenamiento médico. Para esto se modelaron y simularon los principales signos vitales como son: señal ECG, señal de pulso, la presión arterial, el nivel de CO2, entre otras. Las señales fueron integradas en una interfaz gráfica, la cual permite generar diferentes escenarios de pacientes, no solo normales sino también con patologías. Las señales simuladas fueron validadas contra señales reales y en general el error es inferior al 5%. El monitor neonatal fue evaluado por 16 expertos médicos quienes manifestaron que las señales simuladas son “de excelente calidad”, “fidedignas” y que la interfaz es “amigable al usuario”. / Abstract. The design of a neonatal monitor for medical training purposes is hereby presented. In order to do that, the following main vital signs were modeled and simulated: ECG, pulse, blood pressure, CO2 level, among others. The signals were integrated to a graphic interface that generates different scenarios showing signals of patients with or without pathologies. Simulated signals were validated against real ones and, in general, the error is less than 5%; in addition, the neonatal monitor was assessed by 16 experts; those doctors stated that simulated signals are of “excellent quality”, “realistic” and that the interface is “user friendly”.Maestrí

Short-term forecasting of wind energy: A comparison of deep learning frameworks

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented

Wind power long-term scenario generation considering spatial-temporal dependencies in coupled electricity markets

This article belongs to the Section A3: Wind, Wave and Tidal EnergyWind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined

Développement d'une nouvelle technique pour l'évaluation objective des gestes en chirurgie mini-invasive

One of the most difficult tasks in surgical education is to teach students what is the optimal magnitude of forces and torques to guide the instrument during operation. This problem becomes even more relevant in the field of Mini Invasive Surgery (MIS), where the depth perception is lost and visual field is reduced. In this way, the evaluation of surgical skills involved in this field becomes in a critical point in the learning process. Nowadays, this assessment is performed by expert surgeons observation in different operating rooms, making evident subjectivity issues in the results depending on the trainer in charge of the task. Research works around the world have focused on the development of the automated evaluation techniques, that provide an objective feedback during the learning process. Therefore, first part of this thesis describe a new method of classification of 3D medical gestures based on biomechanical models (kinematics). This new approach analyses medical gestures based on the smoothness and quality of movements related to the tasks performed during the medical training. Thus, gesture classification is accomplished using an arc length parametrization to compute the curvature for each trajectory. The advantages of this approach are mainly oriented towards time and location independence and problem simplification. The study included several gestures that were performed repeatedly by different subjects; these data sets were acquired, also, with three different devices. Second part of this work is focused in a classification technique based on kinematic and dynamic data. In first place, an empirical expression between movement geometry and kinematic data is used to compute a different variable called the affine velocity. Experiments carried out in this work show the constant nature of this feature in basic medical gestures. In the same way, results proved an adequate classification based on this computation. Parameters found in previous experiments were taken into account to study movements more complex. Likewise, affine velocity was used to perform a segmentation of pick and release tasks, and the classification stage was completed using an energy computation, based on dynamic data, for each segment. Final experiments were performed using six video cameras and an instrumented laparoscope. The 3-D position of the end effector was recorded, for each participant, using the OptiTrack Motive Software and reflective markers mounted on the laparoscope. Force and torque measurements, on the other hand, were acquired using force and torque sensors attached to the instrument and located between the tool tip and the handle of the tool in order to capture the interaction between participant and the manipulated material. Results associated to these experiments present a correlation between the energy values and the surgical skills of the participants involved in these experiments.L'une des tâches les plus difficiles de l'enseignement en chirurgie, consiste à expliquer aux étudiants quelles sont les amplitudes des forces et des couples à appliquer pour guider les instruments au cours d'une opération. Ce problème devient plus important dans le domaine de la chirurgie mini-invasive (MIS) où la perception de profondeur est perdue et le champ visuel est réduit. Pour cette raison, l'évaluation de l'habileté chirurgicale associée est devenue un point capital dans le processus d'apprentissage en médecine. Des problèmes évidents de subjectivité apparaissent dans la formation des médecins, selon l'instructeur. De nombreuses études et rapports de recherches concernent le développement de techniques automatisées d'évaluation du geste. La première partie du travail présenté dans cette thèse introduit une nouvelle méthode de classification de gestes médicaux 3D reposant sur des modèles cinématiques et biomécaniques. Celle-ci analyse de manière qualitative mais aussi quantitative les mouvements associés aux tâches effectuées. La classification du geste est réalisée en utilisant un paramétrage reposant sur la longueur d'arc pour calculer la courbure pour chaque trajectoire. Les avantages de cette approche sont l'indépendance du temps, un système de repérage absolu et la réduction du nombre de données. L'étude inclue l'analyse expérimentale de plusieurs gestes, obtenus avec plusieurs types de capteurs et réalisés par différents sujets. La deuxième partie de ce travail se concentre sur la classification reposant sur les données cinématiques et dynamiques. En premier lieu, une expression empirique, entre la géométrie du mouvement et les données cinématiques, sert à calculer une nouvelle variable appelée vitesse affine. Les expériences conduites dans ce travail de thèse montrent la nature constante de cette grandeur lorsque les gestes médicaux sont simples et identiques. Une dernière technique de classification a été implémentée en utilisant un calcul de l'énergie utilisée au cours de chaque segment du geste. Cette méthode a été validée expérimentalement en utilisant six caméras et un laparoscope instrumenté. La position 3-D de l'extrémité de l'effecteur a été enregistrée, pour plusieurs participants, en utilisant le logiciel OptiTrack Motive et des marqueurs réfléchissants montés sur le laparoscope. Les mesures de force et de couple, d'autre part, ont été acquises à l'aide des capteurs fixés sur l'outil et situés entre la pointe et la poignée de l'outil afin de capturer l'interaction entre le participant et le matériau manipulé. Les résultats expérimentaux présentent une bonne corrélation entre les valeurs de l'énergie et les compétences chirurgicales des participants impliqués dans ces expériences

Development of a new technique for objective assessment of gestures in mini-invasive surgery

Una de las tareas más complicadas durante la enseñanza en cirugía consiste en explicarles a los estudiantes cuáles son las magnitudes óptimas de las fuerzas y los torques, al momento de guiar el instrumento durante la operación. Este problema obtiene mayor trascendencia en el campo de la cirugía mini-invasiva (MIS), donde se pierde la percepción de profundidad y se reduce el campo visual. Debido a esto, la evaluación de habilidades quirúrgicas, asociadas a este campo, se convierte en un punto crítico en el proceso de aprendizaje. Hoy en dia, esta evaluación se realiza mediante la observación de cirujanos expertos en diferentes salas de operación, haciendo evidente los problemas de subjetividad en los resultados, dependiendo del entrenador a cargo de la tarea. Investigaciones alrededor del mundo se han enfocado en el desarrollo de nuevas técnicas de evaluación automáticas, con el fin de brindar una realimentación objetiva durante el proceso de aprendizaje. De esta manera, la primera parte de este trabajo de tesis describe un nuevo método de clasificación de gestos médicos 3D basado en modelos biomecánicos (cinemáticos). Este nuevo enfoque permite analizar los gestos médicos, en relación con la suavidad y calidad de los movimientos, asociados a las tareas realizadas durante el entrenamiento médico. La clasificación de gestos, es entonces, lograda usando una parametrización de longitud de arco con el fin de calcular la curvatura para cada trayectoria. Las ventajas del método están orientadas principalmente a la independencia del tiempo y de la localización espacial y a la simplificación del problema estudiado. Este estudio involucra diversos gestos realizados repetidamente por diferentes participantes, cuyos datos fueron adquiridos por 3 dispositivos diferentes. La segunda parte de este trabajo se enfoca en una técnica de clasificación basada tanto en datos cinemáticos como dinámicos. En primer lugar, se implementó una expresión empírica entre la geometría del movimiento y los datos cinemáticos con el fin de calcular una variable diferente llamada Velocidad Afín. Los experimentos desarrollados en este trabajo muestran la naturaleza constante de esta característica en gestos médicos básicos. De la misma forma, los resultados muestran que una adecuada clasificación es lograda con base en esta implementación. Finalmente, los parámetros encontrados en los experimentos previos fueron tomados en cuenta para estudiar movimientos más complejos. Así, la velocidad afín fue usada para realizar la segmentación de tareas de tomar y soltar y la etapa de clasificación se implementó usando el cálculo de la energía para cada segmento. Los últimos experimentos de este trabajo fueron desarrollados usando seis cámaras de video y un instrumento laparoscópico. La posición 3D del efector final fue registrada, para cada participante, usando el software Motive OptiTrack y se utilizaron marcadores reflectivos instalados sobre el laparóscopo. Por otra parte, las medidas de fuerza y torque fueron adquiridas usando sensores de fuerza y torque atados al instrumento y localizados entre la punta del instrumento y la manija de la herramienta con el fin de capturar la interacción entre el participante y el material manipulado. Los resultados asociados a estos experimentos muestran una correlación entre los valores de energía y las habilidades quirúrgicas de los participantes involucrados en los experimentos.Abstract One of the most di!cult tasks in surgical education is to teach students what is the optimal magnitude of forces and torques to guide the instrument during operation. This problem becomes even more relevant in the field of Mini Invasive Surgery (MIS), where the depth perception is lost and visual field is reduced. In this way, the evaluation of surgical skills involved in this field becomes in a critical point in the learning process. Nowadays, this assessment is performed by expert surgeons observation in di↵erent operating rooms, making evident subjectivity issues in the results depending on the trainer in charge of the task. Research works around the world have focused on the development of the automated evaluation techniques, that provide an objective feedback during the learning process. Therefore, first part of this thesis describe a new method of classification of 3D medical gestures based on biomechanical models (kinematics). This new approach analyses medical gestures based on the smoothness and quality of movements related to the tasks performed during the medical training. Thus, gesture classification is accomplished using an arc length parametrization to compute the curvature for each trajectory. The advantages of this approach are mainly oriented towards time and location independence and problem simplification. The study included several gestures that were performed repeatedly by di↵erent subjects; these data sets were acquired, also, with three di↵erent devices. Second part of this work is focused in a classification technique based on kinematic and dynamic data. In first place, an empirical expression between movement geometry and kinematic data is used to compute a di↵erent variable called the a!ne velocity. Experiments carried out in this work show the constant nature of this feature in basic medical gestures. In the same way, results proved an adequate classification based on this computation. Parameters found in previous experiments were taken into account to study movements more complex. Likewise, a!ne velocity was used to perform a segmentation of pick and release tasks, and the classification stage was completed using an energy computation, based on dynamic data, for each segment. Final experiments were performed using six video cameras and an instrumented laparoscope. The 3-D position of the end e↵ector was recorded, for each participant, using the OptiTrack Motive Software and reflective markers mounted on the laparoscope. Force and torque measurements, on the other hand, were acquired using force and torque sensors attached to the instrument and located between the tool tip and the handle of the tool in order to capture the interaction between participant and the manipulated material. Results associated to these experiments present a correlation between the energy values and the surgical skills of the participants involved in these experiments.Doctorad

High performance control of a three-phase PWM rectifier using odd harmonic high order repetitive control

The control goal for three-phase pulse width-modulated rectifiers focuses on generating sinusoidal input currents and regulating the DC output voltage. Despite the fact that control strategies such as resonant and repetitive control have been proposed in recent works, with many notable results on the area, they have significant performance decay when the frequency changes in the exogenous signal. In this paper, it is shown that the use of an Odd Harmonic High Order Repetitive Controller can be used to control the three-phase rectifier current loops with a performance that is considerably superior to traditional alternatives developed in this field. This compensator’s Odd Harmonic property keeps a computational complexity similar to that of the conventional repetitive controllers but it has the advantage of increasing the robustness when the signal frequency varies. Simulation and experimental results show the high performance that was obtained even in the case of deviation of network frequency from its nominal value

High performance control of a three-phase PWM rectifier using odd harmonic high order repetitive control

The control goal for three-phase pulse width-modulated rectifiers focuses on generating sinusoidal input currents and regulating the DC output voltage. Despite the fact that control strategies such as resonant and repetitive control have been proposed in recent works, with many notable results on the area, they have significant performance decay when the frequency changes in the exogenous signal. In this paper, it is shown that the use of an Odd Harmonic High Order Repetitive Controller can be used to control the three-phase rectifier current loops with a performance that is considerably superior to traditional alternatives developed in this field. This compensator’s Odd Harmonic property keeps a computational complexity similar to that of the conventional repetitive controllers but it has the advantage of increasing the robustness when the signal frequency varies. Simulation and experimental results show the high performance that was obtained even in the case of deviation of network frequency from its nominal value.El objetivo de control en rectificadores de potencia trifásicos se basa en generar corrientes de entrada sinusoidales y regular el voltaje de salida DC. Aunque el Control Repetitivo y Resonante son enfoques de control que han presentado excelentes resultados, su principal desventaja se basa en la pérdida considerable de desempeño cuando la frecuencia de la red se desvía de su valor nominal. En este artículo, se presenta el uso de un Controlador Repetitivo Impar de Alto Orden para controlar los lazos de corrientes de un rectificador trifásico con un desempeño considerablemente superior a otras alternativas tradicionalmente implementadas en este campo. Este controlador permite el rechazo de los armónicos impares introducidos en el sistema, lo que mantiene una complejidad computacional similar a la obtenida con los controladores repetitivos convencionales con la ventaja de incrementar la robustez cuando la frecuencia de la señal varíe. Las simulaciones y los resultados experimentales muestran un alto desempeño aún cuando se presenten desviaciones de la frecuencia de la red respecto a su valor nominal