Room acoustics: Idealized field and real field considerations

How is an acoustically diffuse field defined? To what extent are the equations of diffuse field theory valid? These are the questions addressed in this presentation. The answers are explained through more general theories, in turn explained with figures. The starting point is the idealization of diffuse sound field, from where the basic calculation tools used in architectural acoustics are derived. Then, we go through the physical-mathematical models of wave theory and ray theory assuming diffuse field simplifications and analyze the scope of diffuse field models. Wave models and ray models are presented in a simple format with visual support and reference to the underlying mathematical models. The criteria used to define a diffuse field in frequency domain as well as in temporal domain are analyzed. Finally, we present a review of several state of the art tools used to address the real cases when diffuse field cannot be assumed.Fil: Accolti Mostazo, Ernesto Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

STATE ESTIMATION IN ALCOHOLIC CONTINUOUS FERMENTATION OF ZYMOMONAS MOBILIS USING RECURSIVE BAYESIAN FILTERING: A SIMULATION APPROACH

This work presents a state estimator for a continuous bioprocess. To this aim, the Non Linear Filtering theory based on the recursive application of Bayes rule and Monte Carlo techniques is used. Recursive Bayesian Filters Sampling Importance Resampling (SIR) is employed, including different kinds of resampling. Generally, bio-processes have strong non-linear and non-Gaussian characteristics, and this tool becomes attractive. The estimator behavior and performance are illustrated with the continuous process of alcoholic fermentation of Zymomonas mobilis. Not too many applications with this tool have been reported in the biotechnological area

Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pereira, Fernando M. Lobo. Universidad de Porto; PortugalFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

CONTROL BASED ON NUMERICAL METHODS AND RECURSIVE BAYESIAN ESTIMATION IN A CONTINUOUS ALCOHOLIC FERMENTATION PROCESS

Biotechnological processes represent a challenge in the control field, due to their high nonlinearity. In particular, continuous alcoholic fermentation from Zymomonas mobilis (Z.m) presents a significant challenge. This bioprocess has high ethanol performance, but it exhibits an oscillatory behavior in process variables due to the influence of inhibition dynamics (rate of ethanol concentration) over biomass, substrate, and product concentrations. In this work a new solution for control of biotechnological variables in the fermentation process is proposed, based on numerical methods and linear algebra. In addition, an improvement to a previously reported state estimator, based on particle filtering techniques, is used in the control loop. The feasibility estimator and its performance are demonstrated in the proposed control loop. This methodology makes it possible to develop a controller design through the use of dynamic analysis with a tested biomass estimator in Z.m and without the use of complex calculations

Control strategies with variable Setpoint applied to the C Crystallization process in the sugar industry

[EN] This work focuses on the C crystallization process in the sugar industry. Its objective is to improve the performance of a classical Proportional Integral Derivative (PID) controller and a Nonlinear Model Predictive Controller (NMPC) previously developed. In this order, a variable supersaturation Setpoint is added to the aforementioned control strategies. The variable Setpoint is obtained by applying a correction function to a constant reference value. The correction function depends on the boiling curve, which relates the level in the evaporator to the desired concentration. These improvements favorably influence the process, ensuring that supersaturation operates at adequate values and that the desired concentration is achieved with savings in energy consumption and process operation time.[ES] Este trabajo se enfoca en el proceso de Cristalización C en la industria azucarera. Su objetivo es mejorar el desempeño de un controlador clásico, con una ley de control Proporcional - Integral- Derivativa (PID) y un Controlador Predictivo Basado en Modelo No Lineal (NMPC) desarrollados previamente. Con este fin, se propone adicionar a dichas estrategias de control una referencia de sobresaturación variable, que se obtiene aplicando una función de corrección a un valor constante. La función de corrección depende de la curva de ebullición, que relaciona el nivel en el evaporador con la concentración deseada. Con estas mejoras se influye favorablemente en el proceso, garantizando que la sobresaturación opere en valores adecuados y que se alcance la concentración final con mayor eficiencia, en términos de ahorro de tiempo y consumo de energí­a en el proceso.Humberto Morales tiene un beca doctoral del Servicio de Intercambio Académico Alemán (DAAD), Estefanía Aguirre tiene una beca doctoral del Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET), y cofinanciada por el programa ENLAZAMUNDOS de la Agencia de Educación Postsecundaria (SAPIENCIA) de Medellín, Colombia.Morales, H.; Aguirre-Zapata, E.; Di Sciascio, F.; Amicarelli, AN. (2022). Estrategias de control con referencia variable aplicadas al proceso de Cristalización C en la industria azucarera. Revista Iberoamericana de Automática e Informática industrial. 20(1):81-92. https://doi.org/10.4995/riai.2022.17096819220

Methodology for modeling and parameter estimation of the growth process of Lobesia botrana

[EN] Lobesia botrana (L. botrana), is a quarantine pest that causes damage to grapevines and generates economic losses for the region of Cuyo in Argentina. Different researchers have sought to safeguard the integrity of the vineyards, generating alert systems based on models that allow detecting the peaks of occurrence of the pest, and knowing the growth process of the moth, according to the environmental conditions of each region. In this work, a methodology for estimating unknown parameters in semi-physical models based on first principles (MSBPP) is proposed, with a particular application in the growth model of L. botrana under laboratory conditions. The main contribution consists of a methodology for parameter estimation of an MSBPP, which considers a mathematical model developed by the authors in previous work, the structural identifiability analysis of the model in question, and the estimation of the set of unknown parameters that meet the structural identifiability property. In this work, the non-linear least squares algorithm and an Extended Kalman Filter are considered the main estimation tools. An improvement in the adjustment of the mathematical model to the experimental data was evidenced, in relation to those previously obtained. In addition, the degree of affinity of each growth stage for its limiting factor was established, and new mortality profiles were presented.[ES] Lobesia botrana (L. botrana), es una plaga cuarentenaria que provoca danos a la vid, y genera perdidas económicas para la región de Cuyo en Argentina. Diferentes investigaciones han buscado salvaguardar la integridad de los viñedos, generando sistemas de alerta basados en modelos que permitan detectar los picos de ocurrencia de la plaga, y conocer el proceso de crecimiento de lapolilla, de acuerdo a las condiciones ambientales de cada región. En este trabajo, se propone una metodología para la estimación de parámetros desconocidos en los modelos semi físicos basados en primeros principios (MSBPP), con una aplicación particular en el modelo de crecimiento de L. botrana, en condiciones de laboratorio. La principal contribucion consiste en una metodología para la estimación de parámetros de un MSBPP, que considera un modelo matemático desarrollado por los autores en un trabajo previo, el análisis de identificabilidad estructural del modelo en cuestión y la estimación del conjunto de parámetros desconocidos que cumplen con la propiedad de identificabilidad estructural. En este trabajo se consideran, como herramientas principales para la estimación, el algoritmo de mínimos cuadrados no lineales, y un Filtro de Kalman Extendido. Se evidencio una mejoría en el ajuste del modelo matematico a los datos experimentales, con relación a los obtenidos previamente. Además, se estableció el grado de afinidad de cada estadio de crecimiento por el factor limitante del mismo, y se presentaron nuevos perfiles de mortalidad.Estefanía Aguirre-Zapata esta financiada por una beca doctoral latinoamericana del Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) de Argentina, y cofinanciada por el programa ENLAZAMUNDOS de la Agencia de Educación Postsecundaria (SAPIENCIA) de Medellín, Colombia. Humberto Morales tiene una beca doctoral del Servicio de Intercambio Académico Alemán (DAAD). Los datos experimentales utilizados para el proceso de ajuste y validación del modelo fueron proveídos por el Instituto Nacional de Tecnología Agropecuaria (INTA) - Mendoza, Argentina.Aguirre-Zapata, E.; Garcia-Tirado, J.; Morales, H.; Di Sciascio, F.; Amicarelli, AN. (2022). Metodología para el modelado y la estimación de parámetros del proceso de crecimiento de Lobesia botrana. Revista Iberoamericana de Automática e Informática industrial. 20(1):68-79. https://doi.org/10.4995/riai.2022.17746687920

SLAM algorithm applied to robotics assistance for navigation in unknown environments

<p>Abstract</p> <p>Background</p> <p>The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).</p> <p>Methods</p> <p>In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents.</p> <p>Results</p> <p>The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.</p> <p>Conclusions</p> <p>The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.</p