<p>QC 20130530</p>

No description available.

The measurement of water-levels is critical within hydropower production and with already existing camera surveillance in abundance for the purpose of manual supervision. The allure of automatic visual reading to replace the need for manual oversight is significant in the pursuit of fully data driven solutions within hydropower systems. Could images of water level scales along with machine learning functionality produce a reliable and feasible solution? There are many aspects of visually reading any water-level in practice, such as lighting conditions, environmental interference. Great water level fluctuation needs to be overcome by providing an expansive and diverse dataset based on high resolution image capture. The provided solution is based on machine learning algorithms such as two- dimensional convolution, computationally performed and trained by a high power desktop computer. This algorithm is deployed in the field on a low power System-on-a-Chip (SoC) computer with dedicated in system high resolution camera. Basic image manipulation is performed in algorithm to eliminate image noise and to focus on level scale region of interest. The provided solution overcomes the issues at hand and results in a tested proof of concept system capable of ±5mm level reading accuracy with reliability of up to ≥ 99%, within a predefined data range. The results prove that the solution is feasible and a system implementing it or a derivative solution is practically implementable for real life use cases at edge locations.

Real-time systems such as industrial robots and autonomous navigation vehicles integrate a wide range of algorithms to achieve their functional behavior. In certain systems, these algorithms are deployed on dedicated single-core hardware platforms that exchange information over a real-time network. With the availability of current multi-core platforms, there is growing interest in an integrated architecture where these algorithms can run on a shared hardware platform. In addition, the benefits of virtualization-based cloud and fog architectures for non-real-time applications have prompted discussions about the possibility of achieving similar benefits for real-time systems. Although many useful solutions such as resource reservations and hierarchical scheduling have been proposed to facilitate hardware virtualization for real-time applications, the current state of the art is mainly concerned with applications whose timing requirements can be modelled according to the periodic or the sporadic task model. Since the computational demand of many real-time algorithms can be flexibly adjusted at runtime, e.g., by changing the periods, they can be better abstracted with the elastic task model in the context of virtualized hardware platforms. Therefore, in this thesis, we first propose a scheduling framework with reservations based on periodic resource supply for real-time elastic applications with single-core workloads, and then extend this solution for applications with multi-core workloads  where reservations are based on the minimum-parallelism model. Since many existing applications run on dedicated single-core platforms, we simultaneously provide a systematic methodology for migrating an existing real-time software application from a single-core to a multi-core platform. In doing so, we focus on recovering the architecture of the existing software and transforming it for implementation on a multi-core platform. Next, we explore the advantages of a fog-based architecture over an existing robot control architecture and identify the key research challenges that must be addressed for the adoption of the fog computing architecture.

Multi-core computers are infamous for being hard to use in time-critical systems due to execution-time variations as an effect of shared resource contention. In this thesis we study the problem of shared resource contention which occurs when multiple applications executing on different cores do not have exclusive ownership of a shared resource. We investigate performance variations of parallel tasks in multi-core systems and present a method to pinpoint the source of the resource contention using existing hardware performance counters. Furthermore, we investigate methods to mitigate performance variations using resource isolation techniques. We present a methodology for verifying isolation and tested the achieved isolation using the Jailhouse hypervisor. We further investigate shared cache memory isolation techniques using a page coloring tool called PALLOC. Page-coloring is used for partitioning the cache, assigning specific cache lines to specific processes. Page coloring can however cause system performance degradation since it decreases the total amount of cache memory available for each process. Finally, we propose a dynamic partitioning assignment policy which assigns cache partitions to a process according to an adaptive model based on the process performance. The general conclusion from our investigations is that a large body of applications can suffer from shared resource contention and that techniques for mitigating resource contention are in dire need. Our methods measure and characterise applications, identifies resource contention and finally study isolation techniques.

A significant part of our daily lives is dependent on the continuous operation of Industrial Automation and Control Systems (IACS). They are used to control the processes of delivering electricity and clean water to our households, to run and supervise manufacturing industries that produce things we use every day. Therefore, undisturbed, safe and secure operation of IACS are highly important for us all. A malfunctioning IACS may cause damage to the environment, stop production of goods or disrupt essential infrastructure.  The ongoing transformations related to the Industry 4.0 paradigm is having a great impact on IACS, forcing a shift from a rigid, hard-wired system architecture towards a service-oriented structure, where different modules can collaborate dynamically to adapt to volatile production requirements. This shift entails a substantial increase in connectivity and is hence potentially increasing exposure of these systems to cybersecurity threats. Understanding potential risks, and protection against such threats are of great importance.  Access Control is one of the main security mechanisms in a software system, aiming at limiting access to resources to privileged entities. Within IACS, this mechanism is mainly used as means to limit human users’ privileges on system assets. In the dynamic manufacturing systems of Industry 4.0, there is a need to include fine-grained Access Control also between devices, raising a number of issues with regards to policy formulation and management.  This licentiate thesis contributes towards the overall goal of improving the security of IACS in the evolving systems of Industry 4.0 by (1) discussing high-level security challenges of large industrial IoT systems, (2) assess one of the main standards for IACS cybersecurity from an Industry 4.0 perspective, (3) derive requirements on Access Control models within a smart manufacturing system, and (4) presenting an algorithm for automatic Access Control policy generation within the context of modular automation, based on formal process descriptions.

Once the cache memory was introduced in computer systems, the well-known gap in speeds between the memory and the CPU was reduced. However, various issues can occur within the cache, which has a significant impact on the performance and timing-predictability of an application. This thesis investigates one such issue, which is a cache contention. Most commonly, this problem can be detected inside of multicore architecture, but also can be present within all systems that use a scheduler with multiple threads. In this thesis, we show a scenario where the cache contention occurs locally in the L1 data cache on a single-core, multi-threaded system. In this way, we will be able to examine the impact of local cache contention on system performance and timing-predictability. We furthermore mitigate cache contention through a way-based partitioning technique, where we propose a way to avoid cache contention, while still maintaining reasonable overall performance. Our results show that way-partitioning offers inter-thread isolation whilst showing a slight performance drop

No description available.

Smart maintenance of historic buildings involves integration of digital technologies and data analysis methods to help maintain functionalities of these buildings and preserve their heritage values. However, the maintenance of historic buildings is a long-term process. During the process, the digital transformation requires overcoming various challenges, such as stable and scalable storage and computing resources, a consistent format for organizing and representing building data, and a flexible design to integrate data analytics to deliver applications. This licentiate thesis aims to address these challenges by proposing a digitalization framework that integrates Internet of Things (IoT), cloud computing, ontology, and machine learning. IoT devices enable data collection from historic buildings to reveal their latest status. Using a public cloud platform brings stable and scalable resources for storing data, performing analytics, and deploying applications. Ontologies provide a clear and concise way to organize and represent building data, which makes it easier to understand the relationships between different building components and systems. Combined with IoT devices and ontologies, parametric digital twins can be created to evolve with their physical counterparts. Furthermore, with machine learning, digital twins can identify patterns from data and provide decision-makers with insights to achieve smart maintenance. Papers I-III have shown that data can be reliably collected, transmitted, and stored in the cloud. Results of Paper IV indicate that a digital twin that depicts the latest status of a historic building can be created and fed with real-time sensor data. The insights discovered from the digital twin provide facts for improving the indoor climate to achieve both heritage conservation and human comfort. Papers V and VI have shown that deep learning methods exhibit strong capabilities in capturing tendency and uncertainty in building energy consumption. Incorporating future information that determines energy consumption is critical for making multi-horizon predictions. Moreover, changes in the operating mode of a building and activities held in a building bring more uncertainty in energy consumption and deteriorate the performance of point forecasts.  Overall, this thesis contributes to the field of preservation of historic buildings by proposing a comprehensive digitalization framework that integrates various advanced digital technologies to provide a holistic approach to achieve smart maintenance of historic buildings. / Smart underhåll av kulturhistoriska byggnader med digital teknologi och dataanalys underlättar bevarandet av det kulturhistoriska värdet såväl som anpassning för olika användning. Lokalt utplacerade uppkopplade sakernas internet enheter (Internet of Things, IoT) möjliggör realtidsövervakning av miljösensordata. Genom att analysera insamlade data så kan beslutsfattare identifiera och proaktivt hantera potentiella risker i byggnaden. Underhåll av kulturhistoriska byggnader är ett långsiktigt arbete där varje åtgärd kan få långtgående konsekvenser. Digitala verktyg kan därför bidra dels genom bättre historisk spårbarhet, dels genom bättre prediktion av vad som kommer att hända med byggnaden. En lyckad digital transformering kräver stabila och skalbara lagrings- och beräkningsresurser för att organisera och presentera byggnadsdata. Flexibla applikationer med väl integrerad dataanalys är viktigt för att teknologins fulla potential ska kunna nås. Denna licentiatavhandling presenterar ett digitaliseringsramverk som adresserar dessa utmaningar genom att integrera IoT, molnberäkning, ontologisk modellering och maskininlärning. IoT-enheterna möjliggör realtidsövervakning av byggnadens status. Användningar en publika molnplattform erbjuder stabila och skalbara resurser för att lagra och analysera data. Ontologi ger ett klart och koncist sätt att organisera och representera byggnadsdata, vilket gör det enklare att förstå hur olika ingående delar påverkar byggnaden. Från detta kan fysikaliskt motsvarande digitala tvillingar skapas. Genom att applicera maskininlärning på dessa tvillingar så kan mönster identifieras som ger beslutsfattaren all nödvändig information för ett smart, väl optimerat underhåll av byggnaden. Artikel I och II fokuserar på konceptformulering och validering av principen. Artikel I går igenom metoden som används för att skapa digitala tvillingar av historiska byggnader. Artikel II presenterar en referensimplementation av metoden. Den implementerade lösningen är ett komplett system för datainsamling, dataöverföring genom en edge-plattform och datalagring med Microsoft Azure Cloud. Artikel III presenterar fälttest med det egenutvecklade systemet i tre olika historiska byggnader, nämligen Stadsteatern, Stadsmuseet och Hörsalen i Norrköping, Sverige. Fälttestet verifierar stabiliteten hos systemet när det gäller långsiktig drift för datainsamling. Artikel IV introducerar ontologisk modellering till systemet för att tillhandahålla ett enhetligt format för att organisera och representera byggnadsdata. En fallstudie utfördes i Stadsteatern för att verifiera lösningens användbarhet, det studerades hur antalet besökare påverkar inomhusklimatet och potentiella risker identifierades. Artikel V och VI jämför prestanda hos moderna djupinlärningsmetoder med avseende på förmåga att prognostisera byggnaders energiförbrukning. Artikel V fokuserar på prestanda hos egenutvecklade prediktiva modeller vilka utvärderades i Stadsteatern och Stadsmuseet, som utgör två olika driftsfall. Artikel VI visar vad kombinationen av prediktiva modeller och digitala tvillingar kan göra för att förbättra byggnaders energiprestanda. Sammanfattningsvis bidrar denna avhandling till bevarande av kulturhistoriskt viktiga byggnader med ett omfattande digitaliseringsramverk. Ett ramverk som integrerar olika digitala teknologier med en holistisk strategi för att möjliggöra smart underhåll av historiska byggnader.

The era of extensive digitalization marked by the fourth industrial revolution has ushered in significant advancements in technologies like automation, artificial intelligence, and the Internet of Things (IoT). These innovations are revolutionizing manufacturing processes. Industry 4.0 (I4.0) and the subsequent Industry 5.0 (I5.0) emerged as comprehensive representations of the physical world in the information world, with goals to establish smart factories and promote human-machine coexistence. However, the implementation of I4.0 and I5.0 applications faces challenges related to engineering effort, interoperability, and efficient service discovery and binding. This thesis seeks to address these challenges by exploring potential strategies to develop an efficient System of Systems (SoS) that comprises individual, autonomous systems collaborating to achieve a shared goal. This research examines methods to enhance the efficacy of SoS by refining its engineering procedures, promoting interoperability between standardized protocols, and employing dynamic adaption mechanisms. It aims to achieve automatic service discovery and interoperability between diverse industrial standards by integrating the Eclipse Arrowhead Framework. This IoT framework facilitates secure and seamless communication and collaboration among devices, machines, and systems. Moreover, this work delves into saving energy consumption in distributed SoS environments. The thesis aims to optimize energy usage patterns, diminish peak loads, and bolster energy distribution and stability. This is achieved through the Demand Response (DR) mechanism combined with the Eclipse Arrowhead framework. The overarching objective is to pave the way for flexible production processes characterized by minimal resource waste, optimized energy consumption, and sustainable solutions. Through this endeavor, the thesis contributes to shaping a more efficient, interoperable, and sustainable manufacturing landscape in the context of Industry 4.0 and beyond.