Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen

CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE

Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended.Plymouth Marine Laborator

Constructivist neural network models of cognitive development

In this thesis I investigate the modelling of cognitive development with constructivist neural networks. I argue that the constructivist nature of development, that is, the building of a cognitive system through active interactions with its environment, is an essential property of human development and should be considered in models of cognitive development. I evaluate this claim on the basis of evidence from cortical development, cognitive development, and learning theory. In an empirical evaluation of this claim, I then present a constructivist neural network model of the acquisition of the English past tense and of impaired inflectional processing in German agrammatic aphasics. The model displays a realistic course of acquisition, closely modelling the U-shaped learning curve and more detailed effects such as frequency and family effects. Further, the model develops double dissociations between regular and irregular verbs. I argue that the ability of the model to account for the hu..

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Multimodal machine learning for intelligent mobility

Scientific problems are solved by finding the optimal solution for a specific task. Some problems can be solved analytically while other problems are solved using data driven methods. The use of digital technologies to improve the transportation of people and goods, which is referred to as intelligent mobility, is one of the principal beneficiaries of data driven solutions. Autonomous vehicles are at the heart of the developments that propel Intelligent Mobility. Due to the high dimensionality and complexities involved in real-world environments, it needs to become commonplace for intelligent mobility to use data-driven solutions. As it is near impossible to program decision making logic for every eventuality manually. While recent developments of data-driven solutions such as deep learning facilitate machines to learn effectively from large datasets, the application of techniques within safety-critical systems such as driverless cars remain scarce.Autonomous vehicles need to be able to make context-driven decisions autonomously in different environments in which they operate. The recent literature on driverless vehicle research is heavily focused only on road or highway environments but have discounted pedestrianized areas and indoor environments. These unstructured environments tend to have more clutter and change rapidly over time. Therefore, for intelligent mobility to make a significant impact on human life, it is vital to extend the application beyond the structured environments. To further advance intelligent mobility, researchers need to take cues from multiple sensor streams, and multiple machine learning algorithms so that decisions can be robust and reliable. Only then will machines indeed be able to operate in unstructured and dynamic environments safely. Towards addressing these limitations, this thesis investigates data driven solutions towards crucial building blocks in intelligent mobility. Specifically, the thesis investigates multimodal sensor data fusion, machine learning, multimodal deep representation learning and its application of intelligent mobility. This work demonstrates that mobile robots can use multimodal machine learning to derive driver policy and therefore make autonomous decisions.To facilitate autonomous decisions necessary to derive safe driving algorithms, we present an algorithm for free space detection and human activity recognition. Driving these decision-making algorithms are specific datasets collected throughout this study. They include the Loughborough London Autonomous Vehicle dataset, and the Loughborough London Human Activity Recognition dataset. The datasets were collected using an autonomous platform design and developed in house as part of this research activity. The proposed framework for Free-Space Detection is based on an active learning paradigm that leverages the relative uncertainty of multimodal sensor data streams (ultrasound and camera). It utilizes an online learning methodology to continuously update the learnt model whenever the vehicle experiences new environments. The proposed Free Space Detection algorithm enables an autonomous vehicle to self-learn, evolve and adapt to new environments never encountered before. The results illustrate that online learning mechanism is superior to one-off training of deep neural networks that require large datasets to generalize to unfamiliar surroundings. The thesis takes the view that human should be at the centre of any technological development related to artificial intelligence. It is imperative within the spectrum of intelligent mobility where an autonomous vehicle should be aware of what humans are doing in its vicinity. Towards improving the robustness of human activity recognition, this thesis proposes a novel algorithm that classifies point-cloud data originated from Light Detection and Ranging sensors. The proposed algorithm leverages multimodality by using the camera data to identify humans and segment the region of interest in point cloud data. The corresponding 3-dimensional data was converted to a Fisher Vector Representation before being classified by a deep Convolutional Neural Network. The proposed algorithm classifies the indoor activities performed by a human subject with an average precision of 90.3%. When compared to an alternative point cloud classifier, PointNet[1], [2], the proposed framework out preformed on all classes. The developed autonomous testbed for data collection and algorithm validation, as well as the multimodal data-driven solutions for driverless cars, is the major contributions of this thesis. It is anticipated that these results and the testbed will have significant implications on the future of intelligent mobility by amplifying the developments of intelligent driverless vehicles.</div

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Análise de desempenho de métricas de grafos para reconhecimento de tarefas de imaginação motora das mãos a partir de dados de eletroencefalografia

Orientadores: Gabriela Castellano, Romis Ribeiro de Faissol AttuxDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Interfaces cérebro-computador (BCIs, brain-computer interfaces) são sistemas cuja finalidade é fornecer um canal de comunicação direto entre o cérebro e um dispositivo externo, como um computador, uma prótese ou uma cadeira de rodas. Por não utilizarem as vias fisiológicas convencionais, BCIs podem constituir importantes tecnologias assistivas para pessoas que sofreram algum tipo de lesão e, por isso, tiveram sua interação com o ambiente externo comprometida. Os sinais cerebrais a serem extraídos para utilização nestes sistemas devem ser gerados mediante estratégias específicas. Nesta dissertação, trabalhamos com a estratégia de imaginação motora (MI, motor imagery), e extraímos a resposta cerebral correspondente a partir de dados de eletroencefalografia (EEG). Os objetivos do trabalho foram caracterizar as redes cerebrais funcionais oriundas das tarefas de MI das mãos e explorar a viabilidade de utilizar métricas da teoria de grafos para a classificação dos padrões mentais, gerados por esta estratégia, de usuários de um sistema BCI. Para isto, fez-se a hipótese de que as alterações no espectro de frequências dos sinais de eletroencefalografia devidas à MI das mãos deveria, de alguma forma, se refletir nos grafos construídos para representar as interações cerebrais corticais durante estas tarefas. Em termos de classificação, diferentes conjuntos de pares de eletrodos foram testados, assim como diferentes classificadores (análise de discriminantes lineares ¿ LDA, máquina de vetores de suporte ¿ SVM ¿ linear e polinomial). Os três classificadores testados tiveram desempenho similar na maioria dos casos. A taxa média de classificação para todos os voluntários considerando a melhor combinação de eletrodos e classificador foi de 78%, sendo que alguns voluntários tiveram taxas de acerto individuais de até 92%. Ainda assim, a metodologia empregada até o momento possui várias limitações, sendo a principal como encontrar os pares ótimos de eletrodos, que variam entre voluntários e aquisições; além do problema da realização online da análiseAbstract: Brain-computer interfaces (BCIs) are systems that aim to provide a direct communication channel between the brain and an external device, such as a computer, a prosthesis or a wheelchair. Since BCIs do not use the conventional physiological pathways, they can constitute important assistive technologies for people with lesions that compromised their interaction with the external environment. Brain signals to be extracted for these systems must be generated according to specific strategies. In this dissertation, we worked with the motor imagery (MI) strategy, and we extracted the corresponding cerebral response from electroencephalography (EEG) data. Our goals were to characterize the functional brain networks originating from hands¿ MI and investigate the feasibility of using metrics from graph theory for the classification of mental patterns, generated by this strategy, of BCI users. We hypothesized that frequency alterations in the EEG spectra due to MI should reflect themselves, in some manner, in the graphs representing cortical interactions during these tasks. For data classification, different sets of electrode pairs were tested, as well as different classifiers (linear discriminant analysis ¿ LDA, and both linear and polynomial support vector machines ¿ SVMs). All three classifiers tested performed similarly in most cases. The mean classification rate over subjects, considering the best electrode set and classifier, was 78%, while some subjects achieved individual hit rates of up to 92%. Still, the employed methodology has yet some limitations, being the main one how to find the optimum electrode pairs¿ sets, which vary among subjects and among acquisitions; in addition to the problem of performing an online analysisMestradoFísicaMestre em Física165742/2014-31423625/2014CNPQCAPE

Automatic Multilevel Feature Abstraction in Adaptable Machine Vision Systems

Vision is a complex task which can be accomplished with apparent ease by biological systems, but for which the design of artificial systems is difficult. Although machine vision systems can be successfully designed for a specific task, under certain conditions, they are likely to fail if circumstances change. This was the motivation for the research into ways in which systems can be self-designing and adaptable to new visual tasks. The research was conducted in three vital areas of concern for machine vision systems. The first area is finding a suitable architecture for forming an appropriate representation for the current task. The research investigated the application of Hypernetworks theory to building a multilevel, generally-applicable representation, through repeated application of a fundamental 'self-similarity' principle, that parts of objects assembled under a particular relation at one level, form whole objects at the next. Results show that this is potentially a powerful approach for autonomously generating an adaptable system-architecture suitable for multiple visual tasks. The second area is the autonomous extraction of suitable low-level features, which the research investigated through random generation of minimally-constrained pixel-configurations and algorithmic generation of homogeneous and heterogeneous polygons. The results suggest that, despite the simplicity of the features making them vulnerable to image transformations, these are promising approaches worth developing further. The third area is automatic feature selection. The research explored management of 'dimensionality' and of 'combinatorial explosion', as well as how to locate relevant features at multiple representation levels, in the context of 'emergence' of structure. Results indicate that this approach can find useful 'intermediate-level' constructs through analysis of the connectivity of the simplices representing objects at higher levels. The research concludes that the proposed novel approaches to tackling the above issues, in particular the application of hypernetworks to the formation of multilevel representations and the resulting emergence of higher-level structure, is fruitful