Optimización de la producción de un invernadero mediante control predictivo no lineal

Consultable des del TDXTítol obtingut de la portada digitalitzadaEn la presente tesis se presenta una aplicación práctica del control predictivo sobre un invernadero real. Por esta razón se desarrolla un algoritmo de control predictivo MELPC, no lineal, multivariable, basado en la linealización en línea y que no requiere un gran esfuerzo computacional. La investigación se ha centrado en el comportamiento de este controlador tanto en rendimiento como en aspectos computacionales. Se ha incluido, además, el análisis de un amplio rango de problemas relacionados con el control de sistemas con restricciones, tales como: estabilidad, factibilidad, optimización, implementación y cálculo, e influencia de las perturbaciones. Este estudio se ha complementado con una comparación entre los diferentes algoritmos que usan la misma técnica de linealización on-line ELPC, EPSAC y también con los algoritmos de control predictivo lineal y no lineal. También se han expuesto una serie de experimentos en simulaciones para probar el MELPC, controlador elaborado en esta tesis. Los experimentos consisten en aplicar este controlador sobre una amplia gama de sistemas con dinámicas diferentes. Finalmente se ha aplicado este controlador sobre un invernadero real construido en el Institute for Horticultural and Agricultural Engineering ITG de la Universidad de Hannover (Alemania) para el control de las variables clim ticas en tiempo real. Se ha desarrollado un modelo de invernadero real similar a los sistemas de producción en la industria agrícola, considerando las variables de estado: temperatura interna, humedad interna y concentración de CO2. También se ha presentado el modelo de crecimiento del cultivo que es la lechuga porque es un modelo sencillo con pocas variables de estado y que son: el peso seco no-estructural y el peso seco estructural.This work present a practical application of the predictive control on a real greenhouse. For this reason a predictive control algorithm MELPC is developed, This algorithm has the characteristics that is nonlinear, multivariable, based on the linearization and that does not require a great computational burden. He investigation has been centred in the behaviour of this controller as much in performance as in computational aspects. In addition, an analysis of an ample rank of problems related to the control of systems with restrictions is included, such as: stability, feasibility, optimisation, implementation and calculation, and influence of the disturbances. This study has been complemented with a comparison between the different algorithms that use the same technique of linealization online ELPC, EPSAC and also with the algorithms of linear and nonlinear predictive control. Also a series of experiments in simulations has been exposed to test the MELPC, controller elaborated in this thesis. The experiments consist of applying this controller on an ample range of systems with different dynamics. Finally this controller has been applied on a real greenhouse constructed in the Institute of Horticultural and Agricultural Engineering ITG of the University of Hannover (Germany) for the control of the climatic variables. A model of real greenhouse similar to the production systems in the agricultural industry has been developed, considering the state variables: internal temperature, internal humidity and CO2 concentration. Also a growth model of the culture is presented that is the lettuce

Architectural design of trust based recommendation system in customer relationship management

Most Companies are more customer centric than they were before. By adopting this strategy, it made the electronic commerce growing and enhance buyers experience. from the other side, Companies started to explore this customer experience -data generated- to extract knowledge about their customer to be well managed – eCRM - instead of classic customer relationship Management - CRM. Large quantity of Data motivated the companies to look for changes, and ask for more functionality, and this are what influenced software editors to adapt their solutions and implement the power of data. Nowadays, data available – Big Data – put the existing systems and architectures under question and push us to rethink the logical layer to explore this data. Following the data vague, puts a need to reconsider and study the strength of eCRM/CRM existing solutions and architectures. The main contribution of this paper is to propose architecture built on Trust-Based recommendation able to provide to companies better accuracy, coverage, novelty and diversity during the sales process

A Brief Survey on Weakly Supervised Semantic Segmentation

Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field

A Brief Survey on Weakly Supervised Semantic Segmentation

Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field

An Integrated Ensemble Learning Framework for Predicting Liver Disease

The liver disease has become a pressing global issue, with a sharp increase in cases reported worldwide. Detecting liver disease can be difficult as it often has few noticeable symptoms, which means that by the time it is detected, it may have already progressed to an advanced stage, resulting in many people dying without even realizing they had it. Early detection is crucial as it enables patients to begin treatment earlier, which can potentially save their lives. This study aimed to assess the efficacy of five ensemble machine learning (ML) models, namely RF, XGBoost, Extra Trees, bagging, and stacking methods, in predicting liver disease. It uses the ILPD dataset. To prevent overfitting and biases in the dataset, several pre-processing statistical techniques were employed to handle missing data, outliers, and data balancing. The study’s results underline the importance of using the RFE feature selection method, which allowed the use of only the most relevant features for the model, which may have improved the accuracy and efficiency of the model. The study found that the highest testing accuracy of 93% was achieved by the proposed model, which utilized an improved preprocessing approach and a stacking ensemble classifier with RFE feature selection. The use of ensemble ML has given promising results. Indeed, medical professionals can develop models better equipped to handle the complexity and variability of medical data, resulting in more accurate diagnoses, more effective treatment plans, and better patient outcomes

Liver Segmentation: A Weakly End-to-End Supervised Model

Liver segmentation in CT images has multiple clinical applications and is expanding in scope. Clinicians can employ segmentation for pathological diagnosis of liver disease, surgical planning, visualization and volumetric assessment to select the appropriate treatment. However, segmentation of the liver is still a challenging task due to the low contrast in medical images, tissue similarity with neighbor abdominal organs and high scale and shape variability. Recently, deep learning models are the state of art in many natural images processing tasks such as detection, classification, and segmentation due to the availability of annotated data. In the medical field, labeled data is limited due to privacy, expert need, and a time-consuming labeling process. In this paper, we present an efficient model combining a selective pre-processing, augmentation, post-processing and an improved SegCaps network. Our proposed model is an end-to-end learning, fully automatic with a good generalization score on such limited amount of training data. The model has been validated on two 3D liver segmentation datasets and have obtained competitive segmentation results