Experimental Air Impingement Crossflow Comparison and Theoretical Application to Photovoltaic Efficiency Improvement

The photovoltaic cell temperature is a key factor in solar energy harvesting. Solar radiation raises temperature on the cell, lowering its peak efficiency. Air jet impingement is a high heat transfer rate system and has been previously used to cool the back surface of photovoltaic modules and cells. In this work, an experimental comparison of the cooling performance of two different air jet impingement crossflow schemes was performed. Crossflow is defined as the air mass interacting with a certain jet modifying its movement. This leads to a change in its heat exchange capabilities and is related with the inlet-outlet arrangement of the fluid. In this work, zero and minimum crossflow schemes were compared. The main contribution of this work considered the consumption of the flow supplying devices to determine the most suitable system. The best configuration increased the net power output of the cell by 6.60%. These results show that air impingement cooling can play a role in increasing photovoltaic profitability. In terms of uniformity, on small impingement plates with a low number of nozzles, the advantages expected from the zero crossflow configuration did not stand out.This work was funded by the Regional Development Agency of the Basque Country (SPRI) [grant number KK-2018/00109]

Stability Analysis for Autonomous Vehicle Navigation Trained over Deep Deterministic Policy Gradient

The Deep Deterministic Policy Gradient (DDPG) algorithm is a reinforcement learning algorithm that combines Q-learning with a policy. Nevertheless, this algorithm generates failures that are not well understood. Rather than looking for those errors, this study presents a way to evaluate the suitability of the results obtained. Using the purpose of autonomous vehicle navigation, the DDPG algorithm is applied, obtaining an agent capable of generating trajectories. This agent is evaluated in terms of stability through the Lyapunov function, verifying if the proposed navigation objectives are achieved. The reward function of the DDPG is used because it is unknown if the neural networks of the actor and the critic are correctly trained. Two agents are obtained, and a comparison is performed between them in terms of stability, demonstrating that the Lyapunov function can be used as an evaluation method for agents obtained by the DDPG algorithm. Verifying the stability at a fixed future horizon, it is possible to determine whether the obtained agent is valid and can be used as a vehicle controller, so a task-satisfaction assessment can be performed. Furthermore, the proposed analysis is an indication of which parts of the navigation area are insufficient in training terms.The current study has been sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 research program “Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industrials”

Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.This research was financed by the plant of Mercedes-Benz Vitoria through the PIF program to develop an intelligent production. Moreover, The Regional Development Agency of the Basque Country (SPRI) is gratefully acknowledged for their economic support through the research project “Motor de Accionamiento para Robot Guiado Automáticamente”, KK-2019/00099, Programa ELKARTEK

Dynamical Analysis of a Navigation Algorithm

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.The current study has been sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 (“Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales”) research program

Desarrollo de Redes Neuronales Convolucionales para algoritmos de navegación

Un AGV (Automated Guided Vehicle) es un robot móvil inteligente ampliamente utilizado para mover objetos o realizar tareas en diversos ámbitos como el industrial, los puertos, los almacenes o las zonas de trabajo peligrosas en las que el ser humano tendría importantes dificultades para trabajar. Dentro de las tecnologías que envuelve un AGV la navegación es clave. Entre las diferentes alternativas existentes, para solucionar el problema de la navegación, los sistemas basados en visión están tomando especial relevancia en los últimos años, gracias en gran medida al aumento de la capacidad de computación que proporciona el uso de las GPUs (Graphics processing unit). Los sistemas de navegación basados en visión emplean cámaras como sensor de entrada. Las cámaras son más fiables, más baratas y capaces de proporcionar una gran cantidad de información espacial. Además, la información extraída de la cámara puede utilizarse para la servo-orientación visual, la estimación del estado, la evitación de obstáculos y la planificación de la trayectoria. Las Redes Neuronales Convolucionales, (CNN) por sus siglas en inglés, se han empleado ampliamente en el dominio de la imagen, mejorando significativamente el rendimiento de la clasificación de imágenes, la detección de objetos, la clasificación de escenas, etc. Por esto representan una herramienta de gran capacidad para tratar las imágenes y dar solución al problema de navegación. En este trabajo se ha propuesto un modelo basado en redes neuronales convolucionales para solucionar el problema de navegación de un prototipo de AGV. Aplicando una clasificación basada en la segmentación semántica, se detecta una línea marcada en el suelo que representa la trayectoria a seguir por el robot. Emulando los sistemas filoguiados magnéticos existentes en la actualidad. La respuesta obtenida tras el procesamiento de la imagen se traduce en consignas al robot. Adicionalmente se ha desarrollado un sistema de comunicación entre el PC que realiza el procesado de la imagen y el microcontrolador que maneja los motores del robot. También se ha realizado el cableado y programación del microcontrolador

Multi-Objective Particle Swarm Based Optimization of an Air Jet Impingement System

Air jet impingement systems have proven to be a very efficient way of heat transfer in single phase flows, which has allowed them to be applied in several industries. However, the complexity of the physical phenomena that take place in the cooling or heating processes makes the task of designing and sizing a system of this type very difficult. The objective of this work is to develop a methodology for the optimization of the impingement plate for electronic components cooling systems. The component chosen to exemplify this work is an insulated gate bipolar transistor (IGBT) such as those employed in photovoltaic inverters. The proposed methodology is divided into the thermo-hydraulic calculation process and the optimization of the system. This optimization is carried out using a multi-objective particle swarm optimization (PSO) algorithm that seeks the best compromise between two variables: Component temperature and manufacturing time of the impingement plate. The result is a calculation tool that can quickly find the solution that meets the requirements of the designer without the need to evaluate all possible solutions

ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design

Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a comparison between the results of a regular optimization and the results obtained in the present study is presented

A New Loss Function for Simultaneous Object Localization and Classification

Robots play a pivotal role in the manufacturing industry. This has led to the development of computer vision. Since AlexNet won ILSVRC, convolutional neural networks (CNNs) have achieved state-of-the-art status in this area. In this work, a novel method is proposed to simultaneously detect and predict the localization of objects using a custom loop method and a CNN, performing two of the most important tasks in computer vision with a single method. Two different loss functions are proposed to evaluate the method and compare the results. The obtained results show that the network is able to perform both tasks accurately, classifying images correctly and locating objects precisely. Regarding the loss functions, when the target classification values are computed, the network performs better in the localization task. Following this work, improvements are expected to be made in the localization task of networks by refining the training processes of the networks and loss functions.The current study was sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 (“Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales”) research program