Marine propeller optimisation tools for scenario-based design

The marine propulsion system is one of the most important components of a ship in order to cover the demanding operating needs of propulsion nowadays and to increase performance in a wide range of operating conditions. Marine propellers are designed with the purpose of matching the hull and machinery system, create the required thrust for the entire operational profile, and fulfil the techno-economical requirements that depend on the decision-making of several stakeholders. The final product must represent a unique propeller, designed for a specific vessel, and is a trade-off between all requirements. In an industrial framework, the marine propeller design process should therefore be straightforward and well-developed. The limited time under which the design process must be performed, plays a decisive role in the methods utilised to carry it out, as for example in the selection of the analysis tools, which must be fast and they usually involve semi-empirical evaluations. Since blade design is a multi-objective and multidisciplinary problem, automated optimisation has been used with the aim to search good solutions in the design space efficiently. However, automated optimisation has failed to be used in industrial applications due to obtaining solutions with high performance but with infeasible geometries, and as a method it proved to be inferior to the manual design process, something that shows the importance of the designer\u27s expertise. The main research question of this thesis is therefore related to incorporating optimisation in a systematic way in order to improve the propeller design process and assist the blade designers to obtain feasible and high-performing propellers in strict time constraints. A methodology is proposed that combines interactive optimisation with machine learning and in parallel new objectives are implemented for more complex scenarios. The designer is enabled to manually evaluate cavitation nuisance during the optimisation and guide the algorithm towards areas of the design space with satisfactory cavitation characteristics. Several scenario-based situations have been investigated by using the proposed methodology, that involve different propeller types, design and off-design conditions, several objectives and constraints, cavitation nuisance on the suction and the pressure side of the blade, and applications within conventional and wind propulsion. The results have shown that by involving the blade designer\u27s expertise in the design and optimisation process systematically, competitive propeller designs with feasible geometries can be obtained efficiently

Bee Hive Monitoring System - Solutions for the automated health monitoring

Cerca de um terço da produção global de alimentos depende da polinização das abelhas, tornando-as vitais para a economia mundial. No entanto, existem diversas ameaças à sobrevivência das espécies de abelhas, tais como incêndios florestais, stress humano induzido, subnutrição, poluição, perda de biodiversidade, agricultura intensiva e predadores como as vespas asiáticas. Destes problemas, pode-se observar um aumento da necessidade de soluções automatizadas que possam auxiliar na monitorização remota de colmeias de abelhas. O objetivo desta tese é desenvolver soluções baseadas em Aprendizagem Automática para problemas que podem ser identificados na apicultura, usando técnicas e conceitos de Deep Learning, Visão Computacional e Processamento de Sinal. Este documento descreve o trabalho da tese de mestrado, motivado pelo problema acima exposto, incluindo a revisão de literatura, análise de valor, design, planeamento de testes e validação e o desenvolvimento e estudo computacional das soluções. Concretamente, o trabalho desta tese de mestrado consistiu no desenvolvimento de soluções para três problemas – classificação da saúde de abelhas a partir de imagens e a partir de áudio, e deteção de abelhas e vespas asiáticas. Os resultados obtidos para a classificação da saúde das abelhas a partir de imagens foram significativamente satisfatórios, excedendo os que foram obtidos pela metodologia definida no trabalho base utilizado para a tarefa, que foi encontrado durante a revisão da literatura. No caso da classificação da saúde das abelhas a partir de áudio e da deteção de abelhas e vespas asiáticas, os resultados obtidos foram modestos e demonstram potencial aplicabilidade das respetivas metodologias desenvolvidas nos problemas-alvo. Pretende-se que as partes interessadas desta tese consigam obter informações, metodologias e perceções adequadas sobre o desenvolvimento de soluções de IA que possam ser integradas num sistema de monitorização da saúde de abelhas, incluindo custos e desafios inerentes à implementação das soluções. O trabalho futuro desta dissertação de mestrado consiste em melhorar os resultados dos modelos de classificação da saúde das abelhas a partir de áudio e de deteção de objetos, incluindo a publicação de artigos para obter validação pela comunidade científica.Up to one third of the global food production depends on the pollination of honey bees, making them vital for the world economy. However, between forest fires, human-induced stress, poor nutrition, pollution, biodiversity loss, intensive agriculture, and predators such as Asian Hornets, there are plenty of threats to the honey bee species’ survival. From these problems, a rise of the need for automated solutions that can aid with remote monitoring of bee hives can be observed. The goal of this thesis is to develop Machine Learning based solutions to problems that can be identified in beekeeping and apiculture, using Deep Learning, Computer Vision and Signal Processing techniques and concepts. The current document describes master thesis’ work, motivated from the above problem statement, including the literature review, value analysis, design, testing and validation planning and the development and computational study of the solutions. Specifically, this master thesis’ work consisted in developing solutions to three problems – bee health classification through images and audio, and bee and Asian wasp detection. Results obtained for the bee health classification through images were significantly satisfactory, exceeding those reported by the baseline work found during literature review. On the case of bee health classification through audio and bee and Asian wasp detection, these obtained results were modest and showcase potential applicability of the respective developed methodologies in the target problems. It is expected that stakeholders of this thesis obtain adequate information, methodologies and insights into the development of AI solutions that can be integrated in a bee health monitoring system, including inherent costs and challenges that arise with the implementation of the solutions. Future work of this master thesis consists in improving the results of the bee health classification through audio and the object detection models, including publishing of papers to seek validation by the scientific community

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

Development of numerical and data models for the support of digital twins in offshore wind engineering

Error on title page. Date of award is 2022.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets

A modelling-oriented scheme for control chart pattern recognition

Control charts are graphical tools that monitor and assess the performance of production processes, revealing abnormal (deterministic) disturbances when there is a fault. Simple patterns belonging to one of six types can be observed when a fault is occurring, and a Normal pattern when the process is performing under its intended conditions. Machine Learning algorithms have been implemented in this research to enable automatic identification of simple patterns. Two pattern generation schemes (PGS) for synthesising patterns are proposed in this work. These PGSs ensure generality, randomness, and comparability, as well as allowing the further categorisation of the studied patterns. One of these PGSs was developed for processes that fulfil the NIID (Normally, identically and independently distributed) condition, and the other for three first-order lagged time series models. This last PGS was used as base to generate patterns of feedback-controlled processes. Using the three aforementioned processes, control chart pattern recognition (CCPR) systems for these process types were proposed and studied. Furthermore, taking the recognition accuracy as a performance measure, the arrangement of input factors that achieved the highest accuracies for each of the CCPR systems was determined. Furthermore, a CCPR system for feedback-controlled processes was developed

Predictive and prescriptive modeling for the clinical management of dengue: a case study in Colombia

En esta investigación, abordamos el problema del manejo clínico del dengue, que se compone del diagnóstico y el tratamiento de la enfermedad. El dengue es una enfermedad tropical transmitida por vectores que está ampliamente distribuida en todo el mundo. El desarrollo de enfoques que ayuden a la toma de decisiones en enfermedades de interés para la salud pública –como el dengue– es necesario para reducir las tasas de morbilidad y mortalidad. A pesar de la existencia de guías para el manejo clínico, el diagnóstico y el tratamiento del dengue siguen siendo un reto. Para abordar este problema, nuestro objetivo fue desarrollar metodologías, modelos y enfoques para apoyar la toma de decisiones en relación con el manejo clínico de esta infección. Nosotros desarrollamos varios artículos de investigación para cumplir los objetivos propuestos de esta tesis. El primer articulo revisó las últimas tendencias del modelamiento de dengue usando técnicas de aprendizaje automático. El segundo artículo propuso un sistema de apoyo a la decisión para el diagnóstico del dengue utilizando mapas cognitivos difusos. El tercer artículo propuso un ciclo autónomo de tareas de análisis de datos para apoyar tanto el diagnóstico como el tratamiento de la enfermedad. El cuarto artículo presentó una metodología basada en mapas cognitivos difusos y algoritmos de optimización para generar modelos prescriptivos en entornos clínicos. El quinto artículo puso a prueba la metodología anteriormente mencionada en otros dominios de la ciencia como, por ejemplo, los negocios y la educación. Finalmente, el último artículo propuso tres enfoques de aprendizaje federado para garantizar la seguridad y privacidad de los datos relacionados con el manejo clínico del dengue. En cada artículo evaluamos dichas estrategias utilizando diversos conjuntos de datos con signos, síntomas, pruebas de laboratorio e información relacionada con el tratamiento de la enfermedad. Los resultados mostraron la capacidad de las metodologías y modelos desarrollados para predecir la enfermedad, clasificar a los pacientes según su severidad, evaluar el comportamiento de las variables relacionadas con la severidad y recomendar tratamientos basados en las directrices de la Organización Mundial de la Salud.In this research, we address the problem of clinical management of dengue, which is composed of diagnosis and treatment of the disease. Dengue is a vector-borne tropical disease that is widely distributed worldwide. The development of approaches to aid in decision-making for diseases of public health concern –such as dengue– are necessary to reduce morbidity and mortality rates. Despite the existence of clinical management guidelines, the diagnosis and treatment of dengue remains a challenge. To address this problem, our objective was to develop methodologies, models, and approaches to support decision-making regarding the clinical management of this infection. We developed several research articles to meet the proposed objectives of this thesis. The first article reviewed the latest trends in dengue modeling using machine learning (ML) techniques. The second article proposed a decision support system for the diagnosis of dengue using fuzzy cognitive maps (FCMs). The third article proposed an autonomous cycle of data analysis tasks to support both diagnosis and treatment of the disease. The fourth article presented a methodology based on FCMs and optimization algorithms to generate prescriptive models in clinical settings. The fifth article tested the previously mentioned methodology in other science domains such as, business and education. Finally, the last article proposed three federated learning approaches to guarantee the security and privacy of data related to the clinical management of dengue. In each article, we evaluated such strategies using diverse datasets with signs, symptoms, laboratory tests, and information related to the treatment of the disease. The results showed the ability of the developed methodologies and models to predict disease, classify patients according to severity, evaluate the behavior of severity-related variables, and recommend treatments based on World Health Organization (WHO) guidelines

A pattern-driven corpus to predictive analytics in mitigating SQL injection attack

The back-end database provides accessible and structured storage for each web application’s big data internet web traffic exchanges stemming from cloud-hosted web applications to the Internet of Things (IoT) smart devices in emerging computing. Structured Query Language Injection Attack (SQLIA) remains an intruder’s exploit of choice to steal confidential information from the database of vulnerable front-end web applications with potentially damaging security ramifications.Existing solutions to SQLIA still follows the on-premise web applications server hosting concept which were primarily developed before the recent challenges of the big data mining and as such lack the functionality and ability to cope with new attack signatures concealed in a large volume of web requests. Also, most organisations’ databases and services infrastructure no longer reside on-premise as internet cloud-hosted applications and services are increasingly used which limit existing Structured Query Language Injection (SQLI) detection and prevention approaches that rely on source code scanning. A bio-inspired approach such as Machine Learning (ML) predictive analytics provides functional and scalable mining for big data in the detection and prevention of SQLI in intercepting large volumes of web requests. Unfortunately, lack of availability of robust ready-made data set with patterns and historical data items to train a classifier are issues well known in SQLIA research applying ML in the field of Artificial Intelligence (AI). The purpose-built competition-driven test case data sets are antiquated and not pattern-driven to train a classifier for real-world application. Also, the web application types are so diverse to have an all-purpose generic data set for ML SQLIA mitigation.This thesis addresses the lack of pattern-driven data set by deriving one to predict SQLIA of any size and proposing a technique to obtain a data set on the fly and break the circle of relying on few outdated competitions-driven data sets which exist are not meant to benchmark real-world SQLIA mitigation. The thesis in its contributions derived pattern-driven data set of related member strings that are used in training a supervised learning model with validation through Receiver Operating Characteristic (ROC) curve and Confusion Matrix (CM) with results of low false positives and negatives. We further the evaluations with cross-validation to have obtained a low variance in accuracy that indicates of a successful trained model using the derived pattern-driven data set capable of generalisation of unknown data in the real-world with reduced biases. Also, we demonstrated a proof of concept with a test application by implementing an ML Predictive Analytics to SQLIA detection and prevention using this pattern-driven data set in a test web application. We observed in the experiments carried out in the course of this thesis, a data set of related member strings can be generated from a web expected input data and SQL tokens, including known SQLI signatures. The data set extraction ontology proposed in this thesis for applied ML in SQLIA mitigation in the context of emerging computing of big data internet, and cloud-hosted services set our proposal apart from existing approaches that were mostly on-premise source code scanning and queries structure comparisons of some sort

The Camera in conservation: determining photography’s place in the preservation of wildlife

This MA by research study is a reflection of photography’s past, current and future role within wildlife conservation, or whether there is indeed a necessity for it moving forwards. The following investigation and analysis of photography seeks to materialise how in fact the photographic medium can be both beneficial and negatively impactful to the preservation of wildlife, and how best it can be used by photographers in future conservation projects to ensure the preservation of wildlife. Several significant aspects of photography and external influences are engaged with in this study, firstly investigating the importance of empathy within wildlife conservation and how it can be elicited through imagery and photographic methods. Furthermore, I investigate the other side of conservation photography’s success, analysing what negative or neutral impacts it can bring with it, before researching the role that social media does and has the potential to play in conservation, and how photography can adapt to it to maximise its success. Lastly, I explore alternative visual media such as moving image, and how photography can best applicate successful techniques learned from them to reinterpret how conservation photography is perceived. Finally, using information and research from across my thesis, I have produced a ‘guide’ as to how conservation photography can be shaped to achieve its full potential for success, drawing upon previous successes and failures of other conservation attempts and photographers