Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Learning and reasoning with physical, structural, and symbolic priors

General purpose neural network models such as transformers have achieved remark- able success in solving complicated tasks across multiple domains and modalities. However, in many applications, the need for domain-specific models remains evident. Moreover, large-scale general-purpose models often demand an excessive amount of data to achieve satisfactory generalization. This PhD research aims to address these challenges by focusing on several application domain settings and improving model generalization through the integration of domain-specific priors into the learning and reasoning process. To accomplish this goal, we explore four distinct computational domains: traffic light control, graph cold start recommendation, program synthesis, and optimizer learning. For traffic light control, we tackle the task by incorporating the delayed propagation physical prior into the model architecture. We introduce the Delayed Propagation Transformer (DePT), a transformer-based model that leverages a cone-shaped spatial-temporal attention prior. DePT enables global modeling of CPS by considering the immutable constraints from the physical world, resulting in improved generalization performance compared to state-of-the-art expert methods. In the context of graph cold start recommendation, we address the limitations of Graph Neural Networks (GNNs) under cold start scenarios. Specifically, we introduce Cold Brew, a teacher-student distillation approach that incorporates neighborhood message passing, and quantified the behavior of inductive GNNs through the feature contribution ratio. For program synthesis, we focus on the challenge of generating high-quality code solutions by integrating structured thought processes. We propose ChainCoder, a program synthesis language model that progressively generates Python code in multiple passes, reflecting the “outline-then-detail" paradigm. By decomposing source code into layout frame components and accessory components, ChainCoder incorporates hierarchical generation, syntactic structure priors, and a tailored transformer architecture. In the domain of optimizer learning, we leverage the symbolic regression tool to overcome scalability and interpretability challenges in Learning to Optimize (L2O) models. By introducing a holistic symbolic representation and analysis framework for L2O, we gain insights into learnable optimizers and explicitly develop optimizers in symbolic form. This approach eliminates the scalability limitations associated with numerical rule representation in L2O models and provides interpretability and comparability among different L2O models. This PhD research has explored and developed methods to integrate domain-specific priors into the learning process, both by incorporating them into neural network architecture and by explicitly leveraging symbolic representations. By incorporating these physical, structural, and symbolic priors, we have improved generalization with less data requirements, and demonstrated the potential for more efficient and generalizable learning and reasoning systems.Electrical and Computer Engineerin

A multi-dimensional trust-model for dynamic, scalable and resources-efficient trust-management in social internet of things

L'internet des Objets (IoT) est un paradigme qui a rendu les objets du quotidien, intelligents en leur offrant la possibilité de se connecter à Internet, de communiquer et d'interagir. L'intégration de la composante sociale dans l'IoT a donné naissance à l'Internet des Objets Social (SIoT), qui a permis de surmonter diverse problématiques telles que l'interopérabilité et la découverte de ressources. Dans ce type d'environnement, les participants rivalisent afin d'offrir une variété de services attrayants. Certains d'entre eux ont recours à des comportements malveillants afin de propager des services de mauvaise qualité. Ils lancent des attaques, dites de confiance, et brisent les fonctionnalités de base du système. Plusieurs travaux de la littérature ont abordé ce problème et ont proposé différents modèles de confiance. La majorité d'entre eux ont tenté de réappliquer des modèles de confiance conçus pour les réseaux sociaux ou les réseaux pair-à-pair. Malgré les similitudes entre ces types de réseaux, les réseaux SIoT présentent des particularités spécifiques. Dans les SIoT, nous avons différents types d'entités qui collaborent, à savoir des humains, des dispositifs et des services. Les dispositifs peuvent présenter des capacités de calcul et de stockage très limitées et leur nombre peut atteindre des millions. Le réseau qui en résulte est complexe et très dynamique et les répercussions des attaques de confiance peuvent être plus importantes. Nous proposons un nouveau modèle de confiance, multidimensionnel, dynamique et scalable, spécifiquement conçu pour les environnements SIoT. Nous proposons, en premier lieu, des facteurs permettant de décrire le comportement des trois types de nœuds impliqués dans les réseaux SIoT et de quantifier le degré de confiance selon les trois dimensions de confiance résultantes. Nous proposons, ensuite, une méthode d'agrégation basée sur l'apprentissage automatique et l'apprentissage profond qui permet d'une part d'agréger les facteurs proposés pour obtenir un score de confiance permettant de classer les nœuds, mais aussi de détecter les types d'attaques de confiance et de les contrer. Nous proposons, ensuite, une méthode de propagation hybride qui permet de diffuser les valeurs de confiance dans le réseau, tout en remédiant aux inconvénients des méthodes centralisée et distribuée. Cette méthode permet d'une part d'assurer la scalabilité et le dynamisme et d'autre part, de minimiser la consommation des ressources. Les expérimentations appliquées sur des de données synthétiques nous ont permis de valider le modèle proposé.The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by giving them the ability to connect to the Internet, communicate and interact. The integration of the social component in the IoT has given rise to the Social Internet of Things (SIoT), which has overcome various issues such as interoperability, navigability and resource/service discovery. In this type of environment, participants compete to offer a variety of attractive services. Some of them resort to malicious behavior to propagate poor quality services. They launch so-called Trust-Attacks (TA) and break the basic functionality of the system. Several works in the literature have addressed this problem and have proposed different trust-models. Most of them have attempted to adapt and reapply trust models designed for traditional social networks or peer-to-peer networks. Despite the similarities between these types of networks, SIoT ones have specific particularities. In SIoT, there are different types of entities that collaborate: humans, devices, and services. Devices can have very limited computing and storage capacities, and their number can be as high as a few million. The resulting network is complex and highly dynamic, and the impact of Trust-Attacks can be more compromising. In this work, we propose a Multidimensional, Dynamic, Resources-efficient and Scalable trust-model that is specifically designed for SIoT environments. We, first, propose features to describe the behavior of the three types of nodes involved in SIoT networks and to quantify the degree of trust according to the three resulting Trust-Dimensions. We propose, secondly, an aggregation method based on Supervised Machine-Learning and Deep Learning that allows, on the one hand, to aggregate the proposed features to obtain a trust score allowing to rank the nodes, but also to detect the different types of Trust-Attacks and to counter them. We then propose a hybrid propagation method that allows spreading trust values in the network, while overcoming the drawbacks of centralized and distributed methods. The proposed method ensures scalability and dynamism on the one hand, and minimizes resource consumption (computing and storage), on the other. Experiments applied to synthetic data have enabled us to validate the resilience and performance of the proposed model

An integrated approach for traffic scene understanding from monocular cameras

This thesis investigates methods for traffic scene perception with monocular cameras as a foundation for a basic environment model in the context of automated vehicles. The developed approach is designed with special attention to the practical application in two experimental systems, which results in considerable computational limitations. For this purpose, three different scene representations are investigated. These consist of the prevalent road topology as the global scene context and the drivable road area, which are both associated with the static environment. In addition, the detection and spatial reconstruction of other road users is considered to account for the dynamic aspects of the environment. In order to cope with the computational constraints, an approach is developed that allows for the simultaneous perception of all environment representations based on multi-task convolutional neural networks. For this purpose methods for the respective tasks are first developed independently and adapted to the special conditions of traffic scenes. Here, the recognition of the road topology is realized as general image recognition. Furthermore, the perception of the drivable road area is implemented as image segmentation. To this end, a general image segmentation approach is adapted to improve the incorporation of the a-priori class distribution present in traffic scenes. This is achieved through the inclusion of element-wise weight factors through the Hadamard product, which resulted in increased segmentation performance in the conducted experiments. Also, a task decoder for the perception of vehicles is designed based on a compact 2D bounding box detection method, which is extended by auxiliary regressands. These are used for an appearance-based estimation of the orientation and dimension ratio of detected vehicles. Together with a subsequent method for the reconstruction of spatial object parameters based on constraints derived from the backprojection into the image plane, a scene description with all measurements for a basic environment model and subsequent automated driving functions can be generated. From the examination of alternative multi-task approaches and considering the computational restrictions of the experimental systems, an integrated convolutional neural network architecture is implemented, which combines all perceptual tasks in a single end-to-end trainable model. In addition to the definition of the architecture, a strategy is developed in which alternated training of the perception tasks, changing with each iteration, enables simultaneous learning from several single-task datasets in one optimization process. On this basis, a final experimental evaluation is performed in which a systematic analysis of different task combinations is conducted. The obtained results clearly show the importance of a combined approach to the perception tasks for automotive applications. Thus, the experiments demonstrate that the integrated multi-task architecture for all relevant representations of the scene is indispensable for practical models on realistic embedded processing hardware. Regarding this, especially the existence of common, shareable image features for the perception of the individual scene representations, which are clearly evident from the results, is to be mentioned.Die Arbeit untersucht Wahrnehmungsmethoden mit monokularen Kameras für die Erzeugung eines grundlegenden Umfeldmodells im Kontext automatisierter Fahrzeuge. Der entwickelte Ansatz wird dabei mit Fokus auf die praktische Anwendung in zwei Versuchssystemen ausgelegt, woraus strikte Beschränkungen der rechentechnischen Ressourcen resultieren. Zu diesem Zweck werden drei verschiedene Szenenrepräsentationen untersucht. Diese bestehen aus der Straßentopologie als globalem Szenenkontext und dem befahrbaren Straßenbereich,welche beide dem statischen Umfeld zugerechnet werden. Darüber hinaus wird die Detektion und Rekonstruktion von anderen Verkehrsteilnehmern zur Berücksichtigung der dynamischen Umfeldanteile einbezogen. Um die rechentechnischen Einschränkungen zu berücksichtigen, wird ein Ansatz basierend auf Multi-task Convolutional Neural Networks entwickelt, welcher die gleichzeitige Wahrnehmung aller Umfeldrepräsentationen erlaubt. Hierzu werden Ansätze für die Wahrnehmungsaufgaben unabhängig voneinander ausgearbeitet und an die Gegebenheiten von Verkehrsszenen angepasst. Die Erkennung der Straßentopologie wird dabei als allgemeine Bilderkennung realisiert. Darüber hinaus wird die Wahrnehmung des befahrbaren Straßenbereichs als Bildsegmentierung umgesetzt. Hierfür wird ein allgemeiner Ansatz zur Bildsegmentierung angepasst um eine stärkere Berücksichtigung der in Verkehrsszenen vorhandenen a-priori Klassenverteilung zu erzielen. Dies erfolgt durch elementweise Gewichtungsfaktoren mittels des Hadamard Produkts, was im Experiment zu einer gesteigerten Segmentierungsgüte führte. Ebenso wird zur Wahrnehmung anderer Fahrzeuge ein Verfahren zur Detektion von 2D Bounding Boxen um zusätzliche Hilfsregressanden erweitert. Diese dienen zur Erscheinungs-basierten Schätzung der Dimensionen sowie der Orientierung detektierter Objekte. Zusammen mit einer Rekonstruktion der räumlichen Parameter durch aus der Rückprojektion in die Bildebene abgeleitete Zwangsbedingungen kann eine für nachfolgende Fahrfunktionen geeignete Objektbeschreibung erzeugt werden. Weiterhin erfolgt, hergeleitet aus der Betrachtung alternativer Multi-task Ansätze und unter Berücksichtigung der rechentechnischen Beschränkungen, die Integration in ein Convolutional Neural Network welches alle Wahrnehmungsaufgaben kombiniert. Zudem wird eine alternierende Trainingsstrategie vorgestellt, welche durch mit jeder Iteration wechselnde Wahrnehmungsaufgaben das simultane Anlernen von mehreren Single-task Datensätzen ermöglicht. Auf dieser Grundlage erfolgt eine abschließende Evaluation, bei welcher eine systematische Untersuchung verschiedener Aufgabenkombinationen erfolgt. Die erzielten Ergebnisse zeigen klar die Bedeutung einer kombinierten Betrachtung der Wahrnehmungsaufgaben für eine Anwendung in der Fahrzeugtechnik auf. So ergibt sich in Hinsicht auf die betrachteten Versuchssysteme, dass eine integrierte Wahrnehmung aller Szenenrepräsentationen für praxistaugliche Modelle unabdingbar ist. In diesem Zusammenhang ist besonders das aus den Ergebnissen ersichtliche Vorhandensein gemeinsamer, mehrfach nutzbarer Bildmerkmale für die Wahrnehmung der einzelnen Szenenrepräsentationen zu nennen