A Cyber-physical System (CPS) can be described as a network of interlinked, concurrent computational components that interact with the physical world. Such a system is usually of reactive nature and must satisfy strict timing requirements to guarantee a correct behaviour. The components can be of mixed-criticality which implies different progress models and communication models, depending whether the focus of a component lies on predictability or resource efficiency. In this dissertation I present a novel approach that bridges the gap between stream processing models and Labelled Transition Systems (LTSs). The former offer powerful tools to describe concurrent systems of, usually simple, components while the latter allow to describe complex, reactive, components and their mutual interaction. In order to achieve the bridge between the two domains I introduce the novel LTS Synchronous Interface Automaton (SIA) that allows to model the interaction protocol of a process via its interface and to incrementally compose simple processes into more complex ones while preserving the system properties. Exploiting these properties I introduce an analysis to identify permanent blocking situations in a network of composed processes. SIAs are wrapped by the novel component-based coordination model Process Network with Synchronous Communication (PNSC) that allows to describe a network of concurrent processes where multiple communication models and the co-existence and interaction of heterogeneous processes is supported due to well defined interfaces. The work presented in this dissertation follows a holistic approach which spans from the theory of the underlying model to an instantiation of the model as a novel coordination language, called Streamix. The language uses network operators to compose networks of concurrent processes in a structured and hierarchical way. The work is validated by a prototype implementation of a compiler and a Run-time System (RTS) that allows to compile a Streamix program and execute it on a platform with support for ISO C, POSIX threads, and a Linux operating system.

De nos jours, la validation des unités de contrôle électronique ECU se fonde généralement sur la simulationHardware-In-the-Loop où les systèmes physiques qui manquent sont modélisés à l’aide deséquations différentielles hybrides. La complexité croissante de ce type de modèles rend le compromisentre le temps de calcul et la précision de la simulation difficile à satisfaire. Cette thèse étudie et proposedes méthodes d’analyse et d’expérimentation destinées à la co-simulation temps-réel ferme de modèlesdynamiques hybrides. Elle vise notamment à définir des solutions afin d’exploiter plus efficacement leparallélisme fourni par les architectures multi-coeurs en utilisant de nouvelles méthodes et paradigmesde l’allocation des ressources. La première phase de la thèse a étudié la possibilité d’utiliser des méthodesd’intégration numérique permettant d’adapter l’ordre comme la taille du pas de temps ainsi quede détecter les événements et ceci dans le contexte de la co-simulation modulaire avec des contraintestemps-réel faiblement dures. De plus, l’ordre d’exécution des différents modèles a été étudié afin dedémontrer l’influence du respect des dépendances de données entre les modèles couplés sur les résultatsde la simulation. Nous avons proposé pour cet objectif, une nouvelle méthode de co-simulationqui permet le parallélisme complet entre les modèles impliquant une accélération supra-linéaire sanspour autant ajouter des erreurs liées à l’ordre d’exécution. Enfin, les erreurs de retard causées par lataille de pas de communication entre les modèles ont été améliorées grâce à une nouvelle méthoded’extrapolation par contexte des signaux d’entrée. Toutes les approches proposées visent de manièreconstructive à améliorer la vitesse de simulation afin de respecter les contraintes temps-réel, tout engardant la qualité et la précision des résultats de simulation sous contrôle. Ces méthodes ont été validéespar plusieurs essais et expériences sur un modèle de moteur à combustion interne et intégrées àun prototype du logiciel xMOD. / Nowadays the validation of Electronic Control Units ECUs generally relies on Hardware-in-The-Loopsimulation where the lacking physical systems are modeled using hybrid differential equations. Theincreasing complexity of this kind of models makes the trade-off between time efficiency and the simulationaccuracy hard to satisfy. This thesis investigates and proposes some analytical and experimentalmethods towards weakly-hard real-time co-simulation of hybrid dynamical models. It seeks in particularto define solutions in order to exploit more efficiently the parallelism provided by multi-core architecturesusing new methods and paradigms of resource allocation. The first phase of the thesis studied the possibilityof using step-size and order control numerical integration methods with events detection in thecontext of real-time modular co-simulation when the time constraints are considered weakly-hard. Moreover,the execution order of the different models was studied to show the influence of keeping or not thedata dependencies between coupled models on the simulation results. We proposed for this aim a newmethod of co-simulation that allows the full parallelism between models implying supra-linear speed-upswithout adding errors related to their execution order. Finally, the delay errors due to the communicationstep-size between the models were improved thanks to a proposed context-based inputs extrapolation.All proposed approaches target constructively to enhance the simulation speed for the compliance toreal-time constraints while keeping the quality and accuracy of simulation results under control and theyare validated through several test and experiments on an internal combustion engine model and integratedto a prototype version of the xMOD software.

<p><em>Imbulgoda Liyangahawatte, Gihan Janith Mendis Ph.D., Purdue University, May</em></p> <p><em>2023. Deep learning for securing critical infrastructure with the emphasis on power</em></p> <p><em>systems and wireless communication. Major Professor: Dr. Jin Kocsis.</em></p> <p><br></p> <p><em>Critical infrastructures, such as power systems and communication</em></p> <p><em>infrastructures, are of paramount importance to the welfare and prosperity of</em></p> <p><em>modern societies. Therefore, critical infrastructures have a high vulnerability to</em></p> <p><em>attacks from adverse parties. Subsequent to the advancement of cyber technologies,</em></p> <p><em>such as information technology, embedded systems, high-speed connectivity, and</em></p> <p><em>real-time data processing, the physical processes of critical infrastructures are often</em></p> <p><em>monitored and controlled through cyber systems. Therefore, modern critical</em></p> <p><em>infrastructures are often viewed as cyber-physical systems (CPSs). Incorporating</em></p> <p><em>cyber elements into physical processes increases efficiency and control. However, it</em></p> <p><em>also increases the vulnerability of the systems to potential cybersecurity threats. In</em></p> <p><em>addition to cyber-level attacks, attacks on the cyber-physical interface, such as the</em></p> <p><em>corruption of sensing data to manipulate physical operations, can exploit</em></p> <p><em>vulnerabilities in CPSs. Research on data-driven security methods for such attacks,</em></p> <p><em>focusing on applications related to electrical power and wireless communication</em></p> <p><em>critical infrastructure CPSs, are presented in this dissertation. As security methods</em></p> <p><em>for electrical power systems, deep learning approaches were proposed to detect</em></p> <p><em>adversarial sensor signals targeting smart grids and more electric aircraft.</em></p> <p><em>Considering the security of wireless communication systems, deep learning solutions</em></p> <p><em>were proposed as an intelligent spectrum sensing approach and as a primary user</em></p> <p><em>emulation (PUE) attacks detection method on the wideband spectrum. The recent</em></p> <p><em>abundance of micro-UASs can enable the use of weaponized micro-UASs to conduct</em></p> <p><em>physical attacks on critical infrastructures. As a solution for this, the radio</em></p> <p><em>frequency (RF) signal-analyzing deep learning method developed for spectrum</em></p> <p><em>sensing was adopted to realize an intelligent radar system for micro-UAS detection.</em></p> <p><em>This intelligent radar can be used to provide protection against micro-UAS-based</em></p> <p><em>physical attacks on critical infrastructures.</em></p>

Stop-and-Go Adaptive Cruise Control (ACC+) is an extension of Adaptive Cruise Control (ACC) that works at low speed as well as normal highway speeds to regulate the speed of the vehicle relative to the vehicle it is following. In this thesis, we design an ACC+ controller for a scale model electric vehicle that ensures the robust performance of the system under various models of uncertainty. We capture the operation of the hybrid system via a state-chart model that performs mode switching between different digital controllers with additional decision logic to guarantee the collision freedom of the system under normal operation. We apply different controller design methods such as Linear Quadratic Regulator (LQR) and H-infinity and perform multiple simulation runs in MATLAB/Simulink to validate the performance of the proposed designs. We compare the practicality of our design with existing formally verified ACC designs from the literature. The comparisons show that the other formally verified designs exhibit unacceptable behaviour in the form of mode thrashing that produces excessive acceleration and deceleration of the vehicle. While simulations provide some assurance of safe operation of the system design, they do not guarantee system safety under all possible cases. To increase confidence in the system, we use Differential Dynamic Logic (dL) to formally state environmental assumptions and prove safety goals, including collision freedom. The verification is done in two stages. First, we identify the invariant required to ensure the safe operation of the system and we formally verify that the invariant preserves the safety property of any system with similar dynamics. This procedure provides a high level abstraction of a class of safe solutions for ACC+ system designs. Second, we show that our ACC+ system design is a refinement of the abstract model. The safety of the closed loop ACC+ system is proven by verifying bounds on the system variables using the KeYmaera verification tool for hybrid systems. The thesis demonstrates how practical ACC+ controller designs optimized for fuel economy, passenger comfort, etc., can be verified by showing that they are a refinement of the abstract high level design. / Thesis / Master of Applied Science (MASc)

The manufacturing industry is competitive and is constantly striving to improve OEE. In the transition to smart production, digital technologies such as IIoT are highlighted as important. IIoT platforms enable real-time monitoring. In this sense, digital technologies such as IIoT are expected to improve OEE by enabling the analysis of real-time data and production availability.  A qualitative study with an abductive approach has been conducted. The empirical material has been collected through a case study of a heavy-duty vehicle industry and the theoretical framework is based on a literature study. Lastly, a thematic analysis has been used for the derivation of appropriate themes for analysis. The study concluded that challenges and enablers related to the implementation of IIoT to improve OEE can be divided into technical and cultural factors. Technical challenges and enablers mainly consider the achievement of interoperability, compatibility, and cyber security, while cultural factors revolve around digital acceptance, competence, encouragement of digital curiosity, and creating knowledge and understanding towards OEE. Lastly, conclusions can be drawn that implementation of IIoT has a positive effect on OEE since it ensures consistent and accurate data, which lies a solid foundation for production decisions. Also, digitalization of production enhances lean practices which are considered a key element for improving OEE.

<p dir="ltr">In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.</p>

Lite-Agro studies applications of TinyML in pear (Pyrus communis) tree disease identification and explores hardware implementations with an ESP32 microcontroller. The study works with the DiaMOS Pear Dataset to learn through image analysis whether the leaf is healthy or not, and classifies it according to curl, healthy, spot or slug categories. The system is designed as a low cost and light-duty computing detection edge solution that compares models such as InceptionV3, XceptionV3, EfficientNetB0, and MobileNetV2. This work also researches integration with federated learning frameworks and provides an introduction to federated averaging algorithms.

No description available.

Cyber-Physical System (CPS) or Digital-Twin approach are becoming popular in industry 4.0 revolution. CPS not only allow to view the online status of equipment, but also allow to predict the health of tool. Based on the real time sensor data, it aims to detect anomalies in the industrial operation and prefigure future failure, which lead it towards smart maintenance. CPS can contribute to sustainable environment as well as sustainable production, due to its real-time analysis on production. In this thesis, we analyzed the behavior of a tool of Ringvalsverk 4, at Ovako with its twin model (known as Digital-Twin) over a series of data. Initially, the data contained unwanted signals which is then cleaned in the data processing phase, and only before production signal is used to identify the tool’s model. Matlab’s system identification toolbox is used for identifying the system model, the identified model is also validated and analyzed in term of stability, which is then used in CPS. The Digital-Twin model is then used and its output being analyzed together with tool’s output to detect when its start deviate from normal behavior.

Den globala industrin har under det senaste decenniet genomgått en enorm digital transformation, där tillämpandet av digitala och smarta verktyg inom företag aldrig har varit mer påtagligt. Under november 2011 presenterades begreppet Industrial 4.0 i en artikel skriven av den Tyska regeringen som beskriver en teknikintensiv strategi för år 2020 och omfattar vad idag betraktas som den fjärde industriella revolutionen. Industri 4.0 utgörs till stor del av integrationsprocessen mellan teknik och övrig verksamhet inom ett tillverkningsföretag, vilket i sin tur ger upphov till teknik såsom; automation, förstärkt verklighet, simuleringar, intelligenta tillverkningsprocesser samt övriga processindustriella IT-verktyg och -system. Flertal forskningsstudier hävdar att Industri 4.0-teknologier har potential att revolutionera sättet företag idag tillverkar produkter, men i och med att begreppet är relativt nytt, abstrakt samt består av väldigt komplexa tekniker och komponenter, är införandet av dessa inom en tillverkningsmiljö för närvarande en stor utmaning för tillverkande företag. Denna studie syftar alltså till att belysa de utmaningar och hinder som större tillverkande företag möter vid implementering av digital och smart teknik, samt åtgärder för att motverka dessa. Målet med studien är att leverera ett användbart resultat både för aktiva företag inom tillverkningsindustrin i form av stöd vid analys och diskussion av eventuella implementeringsstrategier och -satsningar inom Industri 4.0, men också ge övriga intressenter en uppfattning kring ämnet med tanke på att det, som sagt, är ett abstrakt system. En litteraturstudie genomfördes både för att få en överblick kring ämnet Industri 4.0 och hur det har behandlats i tidigare examensarbeten, avhandlingar samt forskningsstudier, men även för att identifiera tidigare identifierade hinder. Därefter genomfördes fältstudier på två tillverkande företag, Scania och Atlas Copco, samt teknikkonsultföretaget Knightec. Syftet med detta var framförallt att få en mer påtaglig och verklighetsförankrad uppfattning av Industri 4.0 men även verifiera att informationen i den teoretiska delen är relevant i praktiken för en tillverkande verksamhet. Studien påvisade därtill att identifierade utmaningar och hinder återfinns bland flertal organisatoriska områden inom ett tillverkande företag, varav de mest framgående aspekterna omfattade strategi, ledarskap, kunder, kultur, anställda, juridik samt teknik. Resultatet avslöjade vidare att tillverkande företag präglas av bristfälliga planer och strategier för att identifiera samt implementera nya tekniska lösningar, konflikter bland de anställda, svårigheter att integrera kundsystem enhetligt inom produktionen, avsaknad av lämplig teknisk kompetens, juridiska problem vad gäller hantering av data samt svårigheter att integrera nya och gamla teknologier. / The global industry has during the last decade undergone a considerable digital transformation, whereas the application of digital and smart technology within companies has never been more of a relevant field. During November of 2011, the term Industrial 4.0 was presented in an article written by the German government to describe a technology intensive strategy for the year 2020 and signifies what today is defined as the fourth industrial revolution. Industry 4.0 largely consists of the integration process between technology and remaining operations within a manufacturing company, which enables the development of technologies such as; automation, augmented reality, simulations, intelligent manufacturing processes and other process industrial IT-tools and systems. Several research studies has suggested that Industry 4.0 technologies has the potential to revolutionize the way companies today manufacture products, however, since the concept is relatively new, abstract and consists of various complex technologies and components, the implementation process of these within a manufacturing environment is one largest challenges that manufacturing companies are facing. This study therefore aims to highlight the challenges and difficulties that large manufacturing companies are facing when implementing digital and smart technology, as well as provide solutions regarding how they can be overcome. The overall goal is to deliver useful results both for active companies within the manufacturing industry in regards to serving as support when analyzing and discussing possible implementation strategies as well investments related to Industry 4.0, but also to provide surrounding stakeholders with a perception of the subject. At the commencement of the project, a literature study was performed to develop an overview of how Industry 4.0 has been discussed in previous theses and research studies as well as to find previously identified difficulties regarding the implementation process. Finally, a field study was performed at Scania and Atlas Copco and at the technology consulting firm Knightec. The main purpose was to gain a more realistic perspective regarding how digitalization and Industry 4.0 systems are applied and to verify that the information from our theoretical study is relevant and applicable within an actual manufacturing company. The study furthermore revealed that the identified difficulties and challenges can be found within multiple organizational areas of a manufacturing company, whereas the most distinct aspects consisted of strategy, leadership, customers, culture, employees, legal governance as well as technology. The results showed that companies were characterized by an overall lack of strategy to implement new technologies, conflicts with employees during implementation, difficulties to integrate customer orders with production, lack of technical skills in staff, legal issues regarding data storage and difficulties integrating new and old technologies.