Interactive Intelligence: Multimodal AI for Real-Time Interaction Loop towards Attentive E-Reading

E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...

Application of additive manufacturing in vascular self-healing cementitious materials

Self-healing concrete has great potential to enhance the durability of concrete structures without significantly increasing the initial costs. Among the self-healing approaches, vascular self-healing cementitious composite is capable of supplying healing agents to the cracked region in a continuous way or multiple times. However, the use of...

Modelling of coupled reservoir and wellbore for energy transition

Borehole operations play a crucial role in managing various subsurface activities related to energy, including energy storage, geothermal energy production, CO2 sequestration, oil and gas extraction, wastewater disposal, and thermal recovery processes. In recent times, intelligent well technologies, such as long deviated multi-lateral wells...

On developers’ practices for hazard diagnosis in machine learning systems

Machine learning (ML) is an artificial intelligence technology that has a great potential for being adopted in various sectors of activities. Yet, it is now also increasingly recognized as a hazardous technology. Failures in the outputs of an ML system might cause physical or social harms. Besides, the development and deployment of an ML system...

Big slopes, little data: data-driven nowcasting of deep-seated landslide deformation

Landslides are a major geohazard in hilly and mountainous environments. We focus on slow-moving, deep-seated landslides that are characterized by gradual, non-catastrophic deformations of millimeters to decimeters per year and cause extensive economic damage. To assess their potential impact and for the design of mitigation solutions, a detailed...

Interaction-Aware Motion Planning in Crowded Dynamic Environments

Autonomous robots will profoundly impact our society, making our roads safer, reducing labor costs and carbon dioxide (CO2) emissions, and improving our life quality. However, to make that happen, robots need to navigate among humans, which is extremely difficult. Firstly, humans do not explicitly communicate their intentions and use intuition...

Less Machine (=) More Vision: Approaches towards Practical and Efficient Machine Vision with Applications in Face Analysis

Machines that interact with humans can do so better if they can also visually understand us, but they have limited resources to do so. The main topic of this dissertation is contrasting the use of resources by machine vision systems against the accuracy obtained by them. This thesis focuses on reducing the need for data, memory, and computation...

Nonlinear dynamic atomic force microscopy

Most physical phenomena be it mechanical, chemical or biological are inherently nonlinear in nature. In fact, it is the linear phenomenon that is the exception rather than the rule. By harnessing these nonlinearities one can obtain far greater information about the underlying physics and develop more sensitive and efficient devices. This is...

Microseismic event detection and localization: A migration-based and machine-learning approach using full waveforms

When humans started started exploiting the abundant underground natural resources the Earth has to offer such as hydrocarbons, minerals and heat, we started to experience earthquakes that are related to this exploitation, so called induced earthquakes. Under certain conditions those can damage local infrastructure. However, most events are weak...

A data-driven and machine-learning study on microstructure-property relations in steel

Multi-phase metallic materials such as Advanced High-Strength Steels (AHSS) are of great importance in a wide variety of high-tech industries due to their higher strength compared to conventional (mild) forming steels. The higher strength leads to various advantages in weight, safety and environmental friendliness. In order to develop new AHHS...

Towards Self-Sufficient High-Rises: Performance Optimisation using Artificial Intelligence

Population growth and urbanisation trends bring many consequences related to the increase in global energy consumption, CO2 emissions and a decrease in arable land per person. High‑rises have been one of the inevitable buildings of metropoles to provide extra floor space since the early examples in the 19th century. Therefore, optimisation of...

Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and...

On domain-adaptive machine learning

Artificial intelligence, and in particular machine learning, is concerned with teaching computer systems to perform tasks. Tasks such as autonomous driving, recognizing tumors in medical images, or detecting suspicious packages in airports. Such systems learn by observing examples, i.e. data, and forming a mathematical description of what types...

Exploring the role of system operation modes in failure analysis in the context of first generation cyber-physical systems

Typically, emerging system failures have a strong impact on the performance of industrial systems as well as on the efficiency of their operational and servicing processes. Being aware of these, maintenance and repair researchers have developed multiple failure detection and diagnosis techniques that allow early recognition of system or...

Revisiting Urban Dynamics through Social Urban Data: Methods and tools for data integration, visualization, and exploratory analysis to understand the spatiotemporal dynamics of human activity in cities

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and...