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  • Presentation: 2024-05-27 10:00 F1031, Luleå
    Rydman, Oskar
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.
    Geophysical 3D models of Paleoproterozoic Iron Oxide Apatite mineralization’s and Related Mineral Systems in Norrbotten, Sweden2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The Northern Norrbotten ore district hosts a multitude of Sweden’s mineral deposits including world class deposits such as the Malmberget and Kirunavaara Iron oxide apatite deposits, the Aitik Iron oxide copper gold deposit, and a multitude of smaller deposits. Northern Norrbotten has been shaped by tectonothermal events related to the evolution of the Fennoscandian Shield and is a geologically complex environment. Without extensive rock outcropping and with most drilling localized to known deposits the regional to local scale of mineralization is not fully understood. To better understand the evolution and extent of the mineralization’s cross-disciplinary geosciences must be applied, where geophysical methods allow for interpretations of the deep and non-outcropping subsurface. Common earth modelling is a term describing a joint model derived from all available geoscientific data in an area, where geophysical models provide the framework.This study describes the geophysical modeling of two IOA deposits in Norrbotten, the Malmberget deposit in Gällivare and the Per-Geijer deposit in Kiruna. To better put these two deposits into a semi-regional setting magnetotelluric (MT) measurements have been conducted together with LKAB. LTU and LKAB have measured more than 200 MT stations in the two areas from 2016-2023. These measurements have then been robustly processed into magnetic transfer functions (impedances) for the broadband MT frequency spectrum (1000Hz,1000s). Then, all processed data judged to be of sufficient quality have been used for 3D inversion modelling using the ModEM code. The resulting conductivity/resistivity models reveals the local conductivity structure of the area, believed to be closely tied to the mineralization due to the conductive properties of the iron bearing minerals. Both areas yielded believable models which pinpointed known mineralization’s at surface as conductive anomalies and their connections to deeper regional anomalies.During modelling a robust iteratively re-weighted least square (IRLS) scheme has been implemented in the inversion algorithms. This scheme allows for objective re-weighting of data errors based on the ability for a given model discretization to predict individual datums. This, to better identify measurements which have been contaminated by local electromagnetic noise due to anthropogenic sources (mainly the power grid and railway). Due to the mathematical properties of the scheme, it allows for models which minimizes the L1 data error-norm instead of usual L2 minimization. This has yielded models whit sharper contrasts in resistivity and successfully emphasizes data believed to be reliable. Results indicate that the scheme was implemented successfully and the tradeoffs in data-fit are deemed acceptable.In addition, in the Kiruna study potential field data (magnetic total field and gravimetry) have been 3D modelled for the same area. These data sets have been inversion modelled in 3D using the MR3D-code developed at LTU with partners. Resulting 3D models have then been interpreted collectively both traditionally and with the use of machine learning methods. To guide interpretations more than 100 rock samples have been collected in the area and their petrophysical properties (density, magnetic susceptibility, electrical resistivity) have been measured at LTU. These petrophysical properties have been used to guide the machine learning methods for the 3D models by first using K-mean clustering on normalized petrophysical data and then using the resulting centroid vectors as input for a Gaussian mixture model of the similarly normalized 3D models. Resulting clusters show potential in being able to pick up sharp geological boundaries but expectedly is unable to fully capture geological structures one to one.  

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    The full text will be freely available from 2025-11-27 09:00
  • Presentation: 2024-05-28 10:00 C305, Luleå
    Rodriguez San Miguel, Carlota
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Small-scale Experiments for Blast-induced Damage: Exploring crack propagation through Digital Image Correlation2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Blasting plays a crucial role in several engineering applications, from mining and tunneling to demolition projects. One of the remaining challenges of this process is that it can significantly affect the integrity of the rock mass by inducing damage in the form of cracks. Broadening the understanding of the behavior of the blast-induced cracks is essential for predicting the damage. One way of investigating this issue is through small-scale blasting experiments focused on crack propagation behavior.

    Controlled blasting experiments were conducted on rock-like cylindrical samples charged with Pentaerythritol tetranitrate (PETN) cords. Different blast designs were tested and a method for integrating a Digital Image Correlation (DIC) technique in the analysis was developed. The DIC system was composed of an Ultra High-Speed Camera (UHSC), a light system, and a data acquisition system. The setup was tested in a laboratory and underwent different calibrations before implementing it in the mine, where using explosives during the tests is allowed. The UHSC captured the blasting process regarding crack propagation. To analyze the development of the cracks, DIC technique was employed and results in terms of displacement versus time were measured from the sample surface.

    The described experiments integrate a novel analysis approach to the results from the DIC technique and propose a way of interpreting the outcomes regarding crack development in terms of velocity. While developing the methodology, the pre-processing of the data (UHSC images) was shown to enhance the DIC analysis and affect the further post-processing of the results. The presented methodology proposes a human-independent procedure of analysis that can help to differentiate the displacement of the crack along its time. Nevertheless, a visual analysis of the results was performed to complement the results and try to broaden the understanding of the crack development process.

    The DIC results indicated a nonconstant crack propagation velocity while the development patterns were interpreted to match previous literature. The experimental studies confirmed the radial propagation behavior surrounding the blasthole in the single borehole test, while the two borehole configurations show to influence the crack propagation direction and interconnection.

    This work describes small-scale experiments that provide meaningful insights in crack propagation and how the different blast design parameters can affect their development. The findings of this study could be useful as an input of a predictive tool to assess blast-induced crack initiation and development.

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  • Presentation: 2024-05-29 09:00 F341, Luleå
    Damigos, Gerasimos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Ericsson AB.
    Towards 5G-Enabled Intelligent Machines2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

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  • Presentation: 2024-06-05 10:00 E632, Luleå
    Foorginezhad, Sahar
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    CO2 Capture through Integration of Aqueous and Immobilized Deep Eutectic Solvents2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The growing global concern over rising CO2 emissions and its significant impact on climate change highlight the urgent need for efficient CO2 capture technologies. Among the array of techniques employed for this purpose, chemical absorption stands out, characterized by high capture capacity, promising efficiency, and versatile applicability. In this context, Ionic liquids (ILs) and their analogs, deep eutectic solvents (DES), have emerged as promising alternatives to conventional solvents due to their low vapor pressures, high thermal stability, and chemical tunability. However, they also face challenges of high viscosity and cost. Studies have identified two promising strategies to address these limitations: (i) using low-viscous solvents to mix with ILs/DESs and (ii) immobilizing ILs/DESs over a large surface (solid porous materials) to develop composites. The goal of this thesis was to integrate these two strategies to develop an innovative sorbent with enhanced CO2 capture capacity while improving kinetics. The main progress achieved in this thesis is as follows:

    In the first part, ILs/DESs were screened from the properties where a literature survey was combined with COSMO-RS for different IL/DES-based technologies. One DES was selected. To study the CO2 capture using immobilized ILs/DESs, porous adsorbents were evaluated based on a literature survey by considering the surface area, pore size/volume, stability, availability, and price. Mesoporous silica was selected as a suitable substrate for immobilization. 

    In the second part, a range of aqueous DESs was developed based on the molar ratio of DES components and water content for CO2 capture. Then, the CO2 capture capacity and viscosity of aqueous DESs (before and after absorption) were systematically evaluated and compared with a commercial absorbent. An optimal solution was then selected to achieve a balance between higher CO2 capture capacity and lower viscosity. Additionally, absorption kinetics, recyclability, and the effect of temperature on CO2 capture capacity were studied.

    In the third part, studies were directed towards the novel strategy, i.e., developing sorbent via integrating aqueous and immobilized DESs, i.e., slurry. To this end, DES was immobilized into mesoporous silica at different loadings and mixed with the aqueous DES. Their CO2 capture capacity was measured and the optimal slurry, selected based on CO2 capture performance, underwent further analysis to evaluate kinetics, recyclability, and temperature effect on performance and obtained results were compared with a commercial solvent. 

    The full text will be freely available from 2024-12-01 09:00
  • Presentation: 2024-06-17 09:00 A117, Luleå
    Saucedo, Mario Alberto Valdes
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards human-inspired perception in robotic systems by leveraging computational methods for semantic understanding2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis presents a recollection of developments and results towards the research of human-like semantic understanding of the environment for robotics systems. Achieving a level of understanding in robots comparable to humans has proven to be a significant challenge in robotics, although modern sensors like stereo cameras and neuromorphic cameras enable robots to perceive the world in a manner akin to human senses, extracting and interpreting semantic information proves to be significantly inefficient by comparison. This thesis explores different aspects of the machine vision field to level computational methods in order to address real-life challenges for the task of semantic scene understanding in both everyday environments as well as challenging unstructured environments. 

    The works included in this thesis present key contributions towards three main research directions. The first direction establishes novel perception algorithms for object detection and localization, aimed at real-life deployments in onboard mobile devices for %perceptually degraded unstructured environments. Along this direction, the contributions focus on the development of robust detection pipelines as well as fusion strategies for different sensor modalities including stereo cameras, neuromorphic cameras, and LiDARs. 

    The second research direction establishes a computational method for levering semantic information into meaningful knowledge representations to enable human-inspired behaviors for the task of traversability estimation for reactive navigation. The contribution presents a novel decay function for traversability soft image generation based on exponential decay, by fusing semantic and geometric information to obtain density images that represent the pixel-wise traversability of the scene. Additionally, it presents a novel Encoder-Decoder lightweight network architecture for coarse semantic segmentation of terrain, integrated with a memory module based on a dynamic certainty filter.

    Finally, the third research direction establishes the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information.The research thus presents an approach to meaningfully incorporate unobserved objects as nodes into an incomplete 3D scene graph using the proposed method Computation of Expectation based on Correlation Information (CECI), to reasonably approximate the probability distribution of the scene by learning histograms from available training data. Extensive simulations and real-life experimental setups support the results and assumptions presented in this work.

    The full text will be freely available from 2024-05-27 09:00
  • Presentation: 2024-06-18 10:00 A109, Luleå
    Tariq, Muhammad
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    LHD operations in sublevel caving mines: a productivity perspective2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Mining is a high-risk industry, so efficiency and safety are key priorities. As mines continue to go deeper and exploit low-grade deposits, bulk mining methods, such as sublevel caving (SLC), have become increasingly important. SLC is suitable for massive steeply dipping ore bodies and is known for its high degree of mechanisation, productivity, and low operational cost. Moreover, technological developments and mechanisation have allowed these methods to be applied at greater depths. In modern mechanised mines Load haul dump (LHD) machines are central to achieving the desired productivity. Therefore, automation of LHDs and their increasing use in mines make it crucial to understand the performance of these machines in actual mining environments. The aim of this research was to understand the differences in the productivity of semiautonomous and manual LHDs and identify how external factors impact the performance of these machines in SLC operations. The research also investigated how LHD operator training could improve the loading efficiency.

    Performance data for semi-autonomous and manual LHDs were collected from LKAB’s Kiirunavaara mine’s central database, GIRON. These data were used to compare cycle times and payloads of semi-autonomous and manual LHDs. The data were filtered and sorted so that only data where both machine types were operating in the same area (crosscut, ring, and ore pass) were used. To understand the impact of external factors, data on the occurrence of boulders were collected from LKAB’s Malmberget mine by recording videos of LHD buckets, while the data on operator training were obtained by performing baseline mapping and conducting a questionnaire study with the LHD operators at LKAB’s Kiirunavaara mine.

    The results of the comparative analysis of manual and semi-autonomous LHDs showed the mean payload was 0.34 tonnes higher for manual LHD machines. However, the differences were not consistent across different areas of the mine. Similarly, when comparing the cycle times, in 57% of the studied area, manual LHDs had lower cycle time, while the opposite was true in the remaining 43% of the areas. Therefore, the differences in cycle time and payload due to mode of operation are not conclusive, meaning that one machine type does not completely outperform the other. This highlights the importance of understanding the external factors that cause such differences. Moreover, the findings emphasize the need to upgrade LHD operator training based on pedagogical principles and the inclusion of new technologies to enhance loading efficiency and increase overall productivity.

    The full text will be freely available from 2024-05-28 09:00
    The full text will be freely available from 2025-11-30 12:00
  • Presentation: 2024-06-20 09:00 A193, Skellefteå
    Kabir, Sami
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Novel Explainable Belief Rule-Based Prediction Framework under Uncertainty2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Traditional Machine Learning (ML) and Deep Learning (DL) models provide very accurate predictions because of their intricate mathematical operations. However, these models do not explain the reasons in support of the predictive outputs. Therefore, there is no trust between humans and AI when it produces such obtuse results. The widespread adoption of AI models heavily relies on the trust that humans place in the decisions made by AI. This trust holds great importance in safety-critical sectors like healthcare, autonomous vehicles, and the energy domain. Several post-hoc tools can help elucidate the results of AI models. The use of training datasets rather than domain knowledge renders such an explanation as a proxy. A misleading explanation will be produced by a biased training dataset. In contrast, an end user is more likely to believe an explanation that is grounded in domain knowledge. Motivated by this, we propose a novel framework, consisting of Belief Rule Based Expert System (BRBES), to predict output and explain it with reference to domain knowledge. BRBES effectively utilizes its rule base to represent domain knowledge and adeptly handles uncertainty resulting from a lack of information. We also fine-tune the parameters and structure of BRBES to enhance the accuracy of the prediction. Therefore, the output of our proposed framework is not only accurate, but also easily explainable.

    This licentiate thesis delves into the challenges and opportunities surrounding eXplainable ArtificialIntelligence (XAI) in order to provide a comprehensive understanding of AI output. It presents a new XAI framework that can effectively predict an output and provide explanations based on domain knowledge. In addition, it shows how effective BRBES integration can be by integrating it with a deep learning model to forecast air quality phenomenon using ground and satellite data.

    This thesis presents four significant contributions. First, we conduct a comprehensive examination ofthe existing literature on XAI, delving into the numerous challenges and opportunities it offers. Extensive research has been conducted to explore the definition, classification, and practical use of XAI. In addition, this complex study highlights the significance of the user interface in effectively communicating explanations in a way that is comprehensible to humans. It also brings up the tradeoff between explainability and accuracy in XAI. Secondly, we present an innovative explainable BRBES (eBRBES) model that offers accurate predictions of building energy consumption phenomenon while providing insightful explanations based on domain knowledge. As part of eBRBES, we also present a novel Belief Rule Based adaptive Balance Determination (BRBaBD) algorithm to assess the optimal balance between explainability and accuracy. Thirdly, we propose a mathematical model to integrate BRBES with the Convolutional Neural Network (CNN). We leverage the domain knowledge of BRBES, and hidden data patterns discovered by CNN with this integrated approach. We predict air quality withthis integrated model using outdoor ground images and sensor data. Fourth, we integrate two-layer BRBES with CNN to monitor air quality using satellite images, and relevant environmental parameters, such as cloud, relative humidity, temperature, and wind speed. The two-layer BRBES showcases the strength of BRBES in conducting reasoning across multiple layers.

    Based on the research findings of this thesis applied on two different phenomena, it can be argued that utilizing a belief rule-based framework can enhance predictability with greater clarity and precision. 

    The full text will be freely available from 2024-05-30 09:00
    The full text will be freely available from 2025-11-30 12:00
  • Presentation: 2024-06-20 10:00 E632, Luleå
    Giacomini, Enrico
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Investigating Aerodynamic Challenges for Rotorcraft Airfoil in the Martian Athmosphere2024Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Over the past decade, there has been a considerable increase in space exploration efforts, driving the need for new methods to examine planets and other celestial bodies. The current trend involves designing spacecraft capable of surveying surfaces from elevated positions, with drones proving to be more suited for the task. . The focus of space missions has primarily been on exploring Mars, as evidenced by the pioneering flight of the Ingenuity helicopter in 2021. The Martian environment poses significant aerodynamic challenges due to its thin atmosphere and low pressure, complicating drone flight. The generation of lift is problematic owing to the scant atmosphere and the restricted dimensions required for space missions, resulting in low-chord Reynolds number flows. Despite the reduction in skin friction drag due to lower viscosity, the decrease in airfoil efficiency is significantly compromised, with only a partial counterbalance by the reduced gravitational pull. Two main challenges must be addressed: low chord-based Reynolds number flows and Martian dust. The former results in the formation of Laminar Separation Bubbles (LSB), severely impairing the aerodynamic efficiency of the airfoil. Concurrently, the accumulation of dust particles on the airfoil’s surface significantly affects its performance, altering its geometry and surface roughness. Thus, it is crucial to accurately determine the presence and location of both separation bubbles and particle deposition to predict performance degradation. \\This thesis presents a comprehensive survey on drones for planetary exploration and an analysis conducted on a cambered plate with 6$\%$ camber and 1$\%$ thickness, ideal for the types of flows considered. The studies are carried out for Reynolds number flows, namely 20,000 and 50,000, to observe the effects of rotor and airfoil dimensions. The computational study is performed using ANSYS Fluent, utilising a two-dimensional CFD model with a C-type mesh and the gamma-Re ($\gamma-Re_{\theta}$) transition model, which aids in capturing the behaviour of these flow regimes. Additionally, for the dust study, two phases are created: a primary phase, the atmosphere, and a secondary phase, the dust particles. The volume fraction of particles is assumed to be small enough to imply that the primary phase influences the secondary, but not vice versa (one-way coupling). To assess particle adhesion, a deposition model has been developed to check for the deposition of dust particles, working in conjunction with the Discrete Phase Modelling (DPM), which simulates the trajectory of particles within the control volume. The deposition model comprises a particle transport model, which accounts for the forces acting on the particles, and a particle-wall interaction model, which determines the particles' rebound or adhesion. The results are presented and discussed at the end of the thesis, along with a brief discussion of future studies focusing on alternative assumptions for dust modelling.

    The full text will be freely available from 2024-05-30 09:00
  • Presentation: 2024-09-10 10:00 E632, Luleå
    Wiklund, Viktor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering. Boliden Mineral AB.
    On Cone Penetration Tests in Tailings: The need for a calibration chamber2024Licentiate thesis, comprehensive summary (Other academic)