Utforsking av overgangen fra tradisjonell dataanalyse til metoder med maskin- og dyp læring

Data analysis methods based on machine- and deep learning approaches are continuously replacing traditional methods. Models based on deep learning (DL) are applicable to many problems and often have better prediction performance compared to traditional methods. One major difference between the traditional methods and machine learning (ML) approaches is the black box aspect often associated with ML and DL models. The use of ML and DL models offers many opportunities but also challenges. This thesis explores some of these opportunities and challenges of DL modelling with a focus on applications in spectroscopy. DL models are based on artificial neural networks (ANNs) and are known to automatically find complex relations in the data. In Paper I, this property is exploited by designing DL models to learn spectroscopic preprocessing based on classical preprocessing techniques. It is shown that the DL-based preprocessing has some merits with regard to prediction performance, but there is considerable extra effort required when training and tuning these DL models. The flexibility of ANN architecture designs is further studied in Paper II when a DL model for multiblock data analysis is proposed which can also quantify the importance of each data block. A drawback of the DL models is the lack of interpretability. To address this, a different modelling approach is taken in Paper III where the focus is to use DL models in such a way as to retain as much interpretability as possible. The paper presents the concept of non-linear error modelling, where the DL model is used to model the residuals of the linear model instead of the raw input data. The concept is essentially a shrinking of the black box aspect since the majority of the data modelling is done by a linear interpretable model. The final topic explored in this thesis is a more traditional modelling approach inspired by DL techniques. Data sometimes contain intrinsic subgroups which might be more accurately modelled separately than with a global model. Paper IV presents a modelling framework based on locally weighted models and fuzzy partitioning that automatically finds relevant clusters and combines the predictions of each local model. Compared to a DL model, the locally weighted modelling framework is more transparent. It is also shown how the framework can utilise DL techniques to be scaled to problems with huge amounts of data.Metoder basert på maskin- og dyp læring erstatter i stadig økende grad tradisjonell datamodellering. Modeller basert på dyp læring (DL) kan brukes på mange problemer og har ofte bedre prediksjonsevne sammenlignet med tradisjonelle metoder. En stor forskjell mellom tradisjonelle metoder og metoder basert på maskinlæring (ML) er den "svarte boksen" som ofte forbindes med ML- og DL-modeller. Bruken av ML- og DL-modeller åpner opp for mange muligheter, men også utfordringer. Denne avhandlingen utforsker noen av disse mulighetene og utfordringene med DL modeller, fokusert på anvendelser innen spektroskopi. DL-modeller er basert på kunstige nevrale nettverk (KNN) og er kjent for å kunne finne komplekse relasjoner i data. I Artikkel I blir denne egenskapen utnyttet ved å designe DL-modeller som kan lære spektroskopisk preprosessering basert på klassiske preprosesseringsteknikker. Det er vist at DL-basert preprosessering kan være gunstig med tanke på prediksjonsevne, men det kreves større innsats for å trene og justere disse DL-modellene. Fleksibiliteten til design av KNN-arkitekturer er studert videre i Artikkel II hvor en DL-modell for analyse av multiblokkdata er foreslått, som også kan kvantifisere viktigheten til hver datablokk. En ulempe med DL-modeller er manglende muligheter for tolkning. For å adressere dette, er en annen modelleringsframgangsmåte brukt i Artikkel III, hvor fokuset er på å bruke DL-modeller på en måte som bevarer mest mulig tolkbarhet. Artikkelen presenterer konseptet ikke-lineær feilmodellering, hvor en DL-modell blir bruk til å modellere residualer fra en lineær modell i stedet for rå inputdata. Konseptet kan ses på som en krymping av den svarte boksen, siden mesteparten av datamodelleringen er gjort av en lineær, tolkbar modell. Det siste temaet som er utforsket i denne avhandlingen er nærmere en tradisjonell modelleringsvariant, men som er inspirert av DL-teknikker. Data har av og til iboende undergrupper som kan bli mer nøyaktig modellert hver for seg enn med en global modell. Artikkel IV presenterer et modelleringsrammeverk basert på lokalt vektede modeller og "fuzzy" oppdeling, som automatisk finner relevante grupperinger ("clusters") og kombinerer prediksjonene fra hver lokale modell. Sammenlignet med en DL-modell, er det lokalt vektede modelleringsrammeverket mer transparent. Det er også vist hvordan rammeverket kan utnytte teknikker fra DL for å skalere opp til problemer med store mengder data

A Decision Support System for Integrated Risk Management

This report provides a detailed description of the Risk Assessment Support System (RASS) for use in municipal water supply. The report explores the utility of the developed support system for evaluating the performance of a complex water supply system. A regional water supply system for the city of London is used as the case study. The theoretical foundations and computational requirements for the implementation of the RASS are provided in the report.https://ir.lib.uwo.ca/wrrr/1013/thumbnail.jp

Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach

Accurately annotated ultrasonic images are vital components of a high-quality medical report. Hospitals often have strict guidelines on the types of annotations that should appear on imaging results. However, manually inspecting these images can be a cumbersome task. While a neural network could potentially automate the process, training such a model typically requires a dataset of paired input and target images, which in turn involves significant human labour. This study introduces an automated approach for detecting annotations in images. This is achieved by treating the annotations as noise, creating a self-supervised pretext task and using a model trained under the Noise2Noise scheme to restore the image to a clean state. We tested a variety of model structures on the denoising task against different types of annotation, including body marker annotation, radial line annotation, etc. Our results demonstrate that most models trained under the Noise2Noise scheme outperformed their counterparts trained with noisy-clean data pairs. The costumed U-Net yielded the most optimal outcome on the body marker annotation dataset, with high scores on segmentation precision and reconstruction similarity. We released our code at https://github.com/GrandArth/UltrasonicImage-N2N-Approach.Comment: 10 pages, 7 figure

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

Smoke plume segmentation of wildfire images

Aquest treball s'emmarca dins del camp d'estudi de les xarxes neuronals en Aprenentatge profund. L'objectiu del projecte és analitzar i aplicar les xarxes neuronals que hi ha avui dia en el mercat per resoldre un problema en específic. Aquest és tracta de la segmentació de plomalls de fum en incendis forestals. S'ha desenvolupat un estudi de les xarxes neuronals utilitzades per resoldre problemes de segmentació d'imatges i també una reconstrucció posterior en 3D d'aquests plomalls de fum. L'algorisme finalment escollit és tracta del model UNet, una xarxa neuronal convolucional basada en l'estructura d'autoencoders amb connexions de pas, que desenvolupa tasques d'autoaprenentatge per finalment obtenir una predicció de la classe a segmentar entrenada, en aquest cas plomalls. de fum. Posteriorment, una comparativa entre algoritmes tradicionals i el model UNet aplicat fent servir aprenentatge profund s'ha realitzat, veient que tant quantitativament com qualitativament s'aconsegueix els millors resultats aplicant el model UNet, però a la vegada comporta més temps de computació. Tots aquests models s'han desenvolupat amb el llenguatge de programació Python utilitzant els llibres d'aprenentatge automàtic Tensorflow i Keras. Dins del model UNet s'han dut a terme múltiples experiments per obtenir els diferents valors dels hiperparàmetres més adequats per a l'aplicació del projecte, obtenint una precisió del 93.45 % en el model final per a la segmentació de fum en imatges d'incendis. forestals.Este trabajo se enmarca dentro del campo de estudio de las redes neuronales en aprendizaje profundo. El objetivo del proyecto es analizar y aplicar las redes neuronales que existen hoy en día en el mercado para resolver un problema en específico. Éste se trata de la segmentación de penachos de humo en incendios forestales. Se ha desarrollado un estudio de las redes neuronales utilizadas para resolver problemas de segmentación de imágenes y también una reconstrucción posterior en 3D de estos penachos de humo. El algoritmo finalmente escogido se trata del modelo UNet, una red neuronal convolucional basada en la estructura de autoencoders con conexiones de paso, que desarrolla tareas de autoaprendizaje para finalmente obtener una predicción de la clase a segmentar entrenada, en este caso penachos de humo. Posteriormente, una comparativa entre algoritmos tradicionales y el modelo UNet aplicado utilizando aprendizaje profundo se ha realizado, viendo que tanto cuantitativa como cualitativamente se consigue los mejores resultados aplicando el modelo UNet, pero a la vez conlleva más tiempo de computación. Todos estos modelos se han desarrollado con el lenguaje de programación Python utilizando libros de aprendizaje automático Tensorflow y Keras. Dentro del modelo UNet se han llevado a cabo múltiples experimentos para obtener los distintos valores de los hiperparámetros más adecuados para la aplicación del proyecto, obteniendo una precisión del 93.45 % en el modelo final para la segmentación de humo en imágenes de incendios forestales.This work is framed within the field of study of neural networks in Deep Learning. The aim of the project is to analyse and apply the neural networks that exist today in the market to solve a specific problem. This is about the segmentation of smoke plumes in forest fires. A study of the neural networks used to solve image segmentation problems and also a subsequent 3D reconstruction of these smoke plumes has been developed. The algorithm finally chosen is the UNet model, a convolutional neural network based on the structure of autoencoders with step connections, which develops self-learning tasks to finally obtain a prediction of the class to be trained, in this case smoke plumes. Also, a comparison between traditional algorithms and the UNet model applied using deep learning has been carried out, seeing that both quantitatively and qualitatively the best results are achieved by applying the UNet model, but at the same time it involves more computing time. All these models have been developed in the Python programming language using the Tensorflow and Keras machine learning books. Within the UNet model, multiple experiments have been carried out to obtain the different hyperparameter values most suitable for the project application, obtaining an accuracy of 93.45% in the final model for smoke segmentation in wildfire images

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Topology optimisation under uncertainties with neural networks

Topology optimisation is a mathematical approach relevant to different engineering problems where the distribution of material in a defined domain is distributed in some optimal way, subject to a predefined cost function representing desired (e.g., mechanical) properties and constraints. The computation of such an optimal distribution depends on the numerical solution of some physical model (in our case linear elasticity) and robustness is achieved by introducing uncertainties into the model data, namely the forces acting on the structure and variations of the material stiffness, rendering the task high-dimensional and computationally expensive. To alleviate this computational burden, we develop two neural network architectures (NN) that are capable of predicting the gradient step of the optimisation procedure. Since state-of-the-art methods use adaptive mesh refinement, the neural networks are designed to use a sufficiently fine reference mesh such that only one training phase of the neural network suffices. As a first architecture, a convolutional neural network is adapted to the task. To include sequential information of the optimisation process, a recurrent neural network is constructed as a second architecture. A common 2D bridge benchmark is used to illustrate the performance of the proposed architectures. It is observed that the NN prediction of the gradient step clearly outperforms the classical optimisation method, in particular since larger iteration steps become viable