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Hybrid Soft Actor-Critic and Incremental Dual Heuristic Programming Reinforcement Learning for Fault-Tolerant Flight Control
No full textRecent advancements in fault-tolerant flight control have involved model-free offline and online Reinforcement Learning (RL) algorithms in order to provide robust and adaptive control to autonomous systems. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP) and Soft Actor-Critic (SAC), this research proposes a hybrid SAC-IDHP framework aiming to combine adaptive online learning from IDHP with the high complexity generalization power of SAC in controlling a fully coupled system. The hybrid framework is implemented into the inner loop of a cascaded altitude controller for a high-fidelity, six-degree-of-freedom model of the Cessna Citation II PH-LAB research aircraft. Compared to SAC-only, the SAC-IDHP hybrid demonstrates an improvement in tracking performance of 0.74%, 5.46% and 0.82% in nMAE for nominal case, longitudinal and lateral failure cases respectively. Random online policy initialization is eliminated due to identity initialization of the hybrid policy, resulting in an argument for increased safety. Additionally, robustness to biased sensor noise, initial flight condition and random critic initialization is demonstrated.Control & Simulatio
Fiber-Optic Pedicle Probes to Advance Spine Surgery through Diffuse Reflectance Spectroscopy
No full textDiffuse Reflectance Spectroscopy (DRS) can provide tissue feedback for pedicle screw placement in spine surgery, yet the integration of fiber optics into the tip of the pedicle probe, a device used to pierce through bone, is challenging, since the optical probing depth and signal-to-noise ratio (SNR) are affected negatively compared to those of a blunt DRS probe. Through Monte Carlo simulations and optical phantom experiments, we show how differences in the shape of the instrument tip influence the acquired spectrum. Our findings demonstrate that a single bevel with an angle of 30∘ offers a solution to anticipate cortical breaches during pedicle screw placement. Compared to a blunt probe, the optical probing depth and SNR of a cone tip are reduced by 50%. The single bevel tip excels with 75% of the optical probing depth and a SNR remaining at approximately ⅔, facilitating the construction of a surgical instrument with integrated DRS.Medical Instruments & Bio-Inspired Technolog
Reliability Based Design Optimization on Quay Walls by re-calculating partial factors
No full textIn the years to come, the Netherlands will face a substantial challenge as over 1,500 kilometers of aging quay walls and sheet pile walls approach the end of their technical lifespan. Infrastructure managers anticipate that the necessary replacements will necessitate investments amounting to billions of euros. Moreover, this task carries a significant environmental footprint, notably in terms of CO2 emissions. The construction work required for these replacements will also result in disruptions and reduced accessibility, inconveniencing users.This study addresses two pivotal aspects. Firstly, it focuses on enhancing the design aspects of new structures and optimizing costs, with a specific focus exploring how these enhancements can ease the financial challenges faced by infrastructure managers. Secondly, it investigates the safety of existing structures and explores ways to maximize their loadbearing capacity while maintaining safety standards. The expected outcomes of this study promise improved design aspects, cost-efficiency, and enhanced safety measures.Quay walls can fail due to various mechanisms. This research investigates three primary causes: yielding of soil, yielding of quay wall and anchor yielding. Quay walls illustrate the complexities of soil-structure interaction. To address this, models were developed in both Plaxis and D-Sheet Piling. D-Sheet Piling was the preferred choice due to its computational speed. The reliability analysis was conducted with Probabilistic Toolkit. Considering the calculation methods, First Order Reliability Method (FORM) was employed, emphasizing in efficient computational results in contrast to the Monte-Carlo approach.In the first aspect, the partial factors were recalculated and compared them with the existing EC partial factor approach. To optimize the current design methodology, the retaining height of the structure was adjusted based on its reliability index. Additionally, the maximum anchor force required was re-evaluated for the structure. This procedure has been conducted for two scenarios, considering and not considering model uncertainty.Furthermore, an analysis was conducted to understand how altering the retaining height can lead to reduced steel usage, subsequently impacting costs and CO2 emissions. In the second aspect, it was pursued to enhance the structure’s performance by introducing a factor "n" across four distinct scenarios: 1. Simultaneously increasing all loads. 2. Increasing the surcharge loads on the terrain. 3. Increasing the bollard load. 4. Raising the final excavation level in front of the quay wall. While this study aligns with the extensive body of research in the field of civil engineering, It seeks to offer a new and sustainable approach on understanding quay wall design, focusing specifically on the designers’ viewpoint. Through the exploration of innovative design frameworks and approaches, this research seeks to make a valuable contribution to the long-term sustainability of quay wall structures. It aims to redefine our approach to accessibility and safety in these crucial structures. The comprehensive investigations conducted throughout this study provide an enhanced comprehension of quay wall design, reliability, and the optimization of performance.Geo-Engineerin
Techno-Optimism and its Impact on Civil Servant Decision Making: A Case Study and Insights into AI Case Routing for Welfare Benefits Allocation
No full textIn our thesis, we conducted an in-depth exploration of the integration of Artificial Intelligence (AI) in public sector decision-making, focusing particularly on AI case routing for welfare benefits allocation. Initially, our research was guided by a techno-optimistic viewpoint, presuming that municipalities would inherently benefit from AI adoption. However, as our study progressed, we encountered the complexities and challenges inherent in the practical implementation of AI in public services.We employed the Design Science Research methodology, utilizing a diverse array of methods including literature reviews, case studies, expert interviews, and system safety analysis. Our objective was to develop a method-type artifact to enhance decision-making in civil services, with a special emphasis on the application of AI in welfare benefits allocation. Our findings indicated that AI implementation in the public sector is highly context-dependent, requiring a tailored approach that addresses various technical, organizational, and cultural barriers.Throughout our research, we identified critical factors such as the need for transparency, fairness, and accountability in AI systems. We recognized the importance of adopting a balanced perspective that considers both AI and non-AI solutions. Our study highlighted the potential benefits of AI in public services, such as increased efficiency and improved quality of decision-making. However, we also noted significant challenges in ensuring system safety and maintaining ethical standards.Through expert interviews and a detailed system safety analysis, we emphasized the necessity of establishing clear rules for the responsible use of AI case routing and addressing the associated risks. Our research concluded that AI might not always be the most suitable approach, particularly if safety concerns and ethical considerations outweigh the potential benefits.In summary, our thesis underscores the need for a critical and balanced approach to the integration of AI in public services. We advocate for a holistic strategy that involves collaboration among government, academia, industry, and civil society. This approach is vital to harness the potential benefits of AI while mitigating risks and ensuring the well-being of society.Engineering and Policy Analysi
Rapid Aerostructural Optimization of Wing-Propeller Systems
No full textPropeller-wing configurations are expected to return to the aviation industry due to their high propulsive efficiency and applicability in urban and regional air mobility. A knowledge gap exists around wing-propeller optimization because of the complexity of the propeller-wing system and the absence of a computationally efficient way to assess the coupled system. This paper addresses this gap by providing and validating a computationally efficient, mid-fidelity framework. The paper presents optimization results and recommendations for future iterations of the framework. The TU Delft PROWIM propeller is optimized with the framework, comparing sequential isolated optimization, trim optimization, and fully coupled optimization. The studies gives a conservative estimate of the efficiency gains that can be achieved by using coupled optimization, as compared to isolated optimization. Lastly, recommendations are given for future studies, such as including a battery weight model and including swirl velocities. It is expected that such model additions will affect the optimization results, and further emphasize the importance of coupled aerostructural optimization.Flight Performance and Propulsio
A review and perspective on hybrid modeling methodologies
No full textThe term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the capabilities of hybrid models often seem underrated, misunderstood, and disregarded by other disciplines as “simply combining some models” or maybe it has gone unnoticed at all. In fact, hybrid modeling could become an enabling technology in various areas of research and industry, such as systems and synthetic biology, personalized medicine, material design, or the process industries. Thus, a systematic investigation of the hybrid model properties is warranted to scoop the full potential of machine learning, reduce experimental effort, and increase the domain in which models can predict reliably.ChemE/Product and Process Engineerin
Applications of Dynamic Covalent Bonds in Chemical Reaction Networks
No full textNature has inspired countless researchers in their quest to understand the phenomena we observe and utilise their findings to develop new technologies. This becomes especially apparent in systems chemistry, which heavily draws inspiration from natural systems in its pursuit for the understanding and development of chemical reaction networks (CRNs) with interesting properties. Today, CRNs play a big role in many sensors, amplification systems, transient materials, and more. Despite major advances in the field of CRNs, there is still a need for additional robust, versatile chemistries to allow for more diverse applications, both within systems chemistry and in other fields beyond, such as material science. This thesis aims to explore new applications of Dynamic Covalent Chemistry (DCvC)—typically utilised to make self-healing materials—in CRNs to allow for new applications drawing from the versatile chemistry used in DCv systems.ChemE/Advanced Soft Matte
Bi-sinusoidal light stimulation reveals an enhanced response power and reduced phase coherence at the visual cortex in migraine
No full textIntroduction: Migraine is associated with enhanced visual sensitivity during and outside attacks. Processing of visual information is a highly non-linear process involving complex interactions across (sub)cortical networks. In this exploratory study, we combined electroencephalography with bi-sinusoidal light stimulation to assess non-linear features of visual processing in participants with migraine.Methods: Twenty participants with migraine (10 with aura, 10 without aura) and ten non-headache controls were measured (outside attacks). Participants received bi-sinusoidal 13 + 23 Hz red light visual stimulation. Electroencephalography spectral power and multi-spectral phase coherence were compared between groups at the driving stimulation frequencies together with multiples and combinations of these frequencies (harmonic and intermodulation frequencies) caused by non-linearities.Results: Only at the driving frequency of 13 Hz higher spectral power was found in migraine with aura participants compared with those with migraine without aura and controls. Differences in phase coherence were present for 2nd, 4th, and 5th-order non-linearities in those with migraine (migraine with and without aura) compared with controls. Bi-sinusoidal light stimulation revealed evident non-linearities in the brain’s electroencephalography response up to the 5th order with reduced phase coherence for higher order interactions in interictal participants with migraine.Discussion: Insight into interictal non-linear visual processing may help understand brain dynamics underlying migraine attack susceptibility. Future research is needed to determine the clinical value of the results.Biomechatronics & Human-Machine Contro
Polyhydroxyalkanoate production in a biofilm by mixed culture phototrophic bacteria
No full textAnoxygenic purple phototrophic bacteria (PPB) were utilised in an 80 L biofilm flat plate photobioreactor to generate polyhydroxyalkanoate (PHA) over 44 cycles, with acetate as feed. Over the cycles, net PHA yield (growth + accumulation) averaged 21% while accumulation yield averaged 55%. Average PHA content was 35 wt% volatile solids (VS), with the majority (>80%) being harvested from the biofilm at 100 gTotal solids (TS) L−1. The PPB microbial population averaged 45% of total population. Detailed cycle studies indicated that PHA content (and yield) peaked at 0.5–1 d into the accumulation stage (peak of 53 wt% VS), suggesting that cycle time optimisation could improve both yield and selection of PHA accumulators. The resulting polymeric material was comprised of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with 95.5 mol% 3-hydroxybutyrate and 4.5 mol% 3-hydroxyvalerate content, but the molecular weight, at >1090 kDa, is unusually high for extracted, microbially produced PHA and a feature of this work.BT/Environmental Biotechnolog
Multi-feature-based Automatic Targetless Camera-LiDAR Extrinsic Calibration
No full textIn autonomous driving, environmental perception, crucial for navigation and decision-making, depends on integrating data from multiple sensors like cameras and LiDAR. Camera-LiDAR fusion combines detailed imagery with precise depth, improving environmental awareness. Effective data fusion requires accurate extrinsic calibration to align camera and LiDAR data under one coordinate system. We aim to calibrate the camera and LiDAR extrinsic automatically and without specific targets. Targetless, non-automated calibration methods are time-consuming and labor-intensive. Existing advanced methods have proven that automatic calibration methods based on edge features are effective, and most focus on the extraction and matching of single features. The proposed method matches 2D edges from LiDAR's multi-attribute density map with image-derived intensity gradient and semantic edges, facilitating 2D-2D edge registration. We innovate by incorporating semantic feature and addressing random initial setting through the PnP problem of centroid pairs, enhancing the convergence of the objective function. We introduce a weighted multi-frame averaging technique, considering frame correlation and semantic importance, for smoother calibration. Tested on the KITTI dataset, it surpasses four current methods in single-frame tests and shows more robustness in multi-frame tests than MulFEAT.Our algorithm leverages semantic information for extrinsic calibration, striking a balance between network complexity and robustness. Future enhancements may include using machine learning to convert sparse matrices to dense formats for improved optimization efficiency.Electrical Engineering | Circuits and System
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