132571 research outputs found
Sort by
Graphical analysis of agent-based opinion formation models
No full textAgent-based models of opinion formation are becoming increasingly complex, because of their size and of the embedding of several individual psychological traits of the agents, aimed at realistically capturing the multifaceted aspects of social interaction. Therefore, the characterisation of the model properties mostly relies on simulation-based numerical approaches: more techniques are needed to analyse, contrast, and compare the properties of different models. We propose a novel graphical technique, which relies on the Agreement Plot to visualise the evolution of opinion distributions over time, that allows us to unveil behavioural patterns and capabilities of agent-based opinion formation models. Our proposed approach can be used to characterise the relation between global properties of the model evolution and the model features (initial opinion distributions, agent parameters, underlying digraphs), and is here showcased through its application to both seminal and recently proposed opinion formation models.Team Tamas Keviczk
Fine Pointing Assembly for Optical Ground Station: Tip-Tilt removal through feedback control
No full textThe mini optical ground station (MISO), on top of the Aerospace Engineering faculty, serves the purpose of demonstrating Free Space Optical Communication technology. A downlink from low earth orbit imposes pointing requirements beyond what can be achieved by the telescope mount. A Fine Pointing Assembly (FPA), the topic of this thesis, aims to reduce residual pointing error to facilitate coupling into a single-mode fiber architecture. This thesis addresses the design, implementation, and characterization of FPA to fulfill this job in a lab environment. The FPA was built as a feedback control system actuating a fine steering mirror to counteract tip-tilt with feedback from a SWIR camera and the center of the gravity tracking algorithm. The bandwidth of this system is limited by latency from software implementations of the interfaces. The developed FPA removes 76% of the Tip-Tilt disturbance energy and can be improved by mitigation efforts toward latency reduction.Aerospace Engineerin
Proof-of-concept of personalized CIED-derived modeling for ambulatory heart failure monitoring
No full textIntroduction: Heart failure (HF) poses a significant burden on public health. This can be largely attributed to recurrent hospitalizations in consequence of HF decompensation. Detection of early signs of impending fluid retention may facilitate timely medical intervention and thereby prevent hospitalizations. Monitoring of Cardiac Implantable Electronic Devices (CIEDs)-derived parameters has been proposed as promising solution, as the sensor inherent in CIEDs provide the ability to continuously monitor physiological signals. The aim of this study was to develop personalized machine learning (ML) models that can identify upcoming HF decompensation based on CIED-derived parameters. Methods: Two ML models, a support vector classifier (SVC) and an extreme gradient boosting (XGBoost) model, were developed for all patients. Features known to be associated to HF decompensation were extracted from daily CIED data. The output of the models is the daily classification of the patient’s HF status, either ‘stable’ or ‘unstable’. Model performance was evaluated through area under the precision-recall curve (AUPRC). First, the models were tested on a development dataset with leave-one-out cross-validation, and subsequently on an independent test set. Results: In total, for 62 patients two models were developed. The average AUPRC on the independent test set of the XGBoost models was 0.63 ± 0.28 and of the SVC models was 0.57 ± 0.26. Finally, for each patient, the model that resulted in the highest AUPRC was selected. The final models achieved an AUPRC on the independent test set of 0.61 ± 0.28. Conclusion: The findings of this study show promising results for the use of personalized CIED-derived models. However, significant variability in model performance across patients highlight the need for further research.TM30004; 35 ECTSTechnical Medicin
On the scalability of helium-filled soap bubbles for volumetric PIV
No full textThe scalability of experiments using PIV relies upon several parameters, namely illumination power, camera sensor and primarily the tracers light scattering capability. Given their larger cross section, helium-filled soap bubbles (HFSB) allow measurements in air flows over a significantly large domain compared to traditional oil or fog droplets. Controlling their diameter translates into scalability of the experiment. This work presents a technique to extend the control of HFSB diameter by geometrical variations of the generator. The latter expands the more limited range allowed by varying the relative helium-air mass flow rates. A theoretical model predicts the bubble size and production rate, which is verified experimentally by high-speed shadow visualization. The overall range of HFSB produced in a stable (bubbling) regime varies from 0.16 to 2.7 mm. Imaging by light scattering of such tracers is also investigated, in view of controversies in the literature on whether diffraction or geometrical imaging dominate the imaging regime. The light scattered by scaled HFSB tracers is imaged with a high-speed camera orthogonal to the illumination. Both the total energy collected on the sensor for a single tracer, as well as its peak intensity, are found to preserve scaling with the square of the diameter at object magnification of 10–1 or below, typical of PIV experiments. For large-scale volumetric applications, it is shown that varying the bubble diameter allows increasing both the measurement domain as well as the working distance of the imagers at 10 m and beyond. A scaling rule is proposed for the latter.Aerodynamic
Design, Construction and Modeling of an Experimental Setup for the long term Eco-hydrological behavior of a Live Pole Drain
No full textLive Pole Drains (LPDs) are a plant-based drainage system used to drain natural slopes and prevent shallow gully erosion. LPDs are a Nature-based Solution built by placing a live fascine in a shallow ditch or gully along the slope direction, allowing moderate fluxes of surface runoff or seepage to infiltrate and high water fluxes to be conveyed along the fascine without further eroding the slope. Despite their practical implementation, the transient and long-term eco-hydrological behavior of LPDs is not well understood. We aim to better understand the LPD’s water balance, the seasonal and life-span changes in hydrological behavior, as well as the impact of an LPD on surface runoff water quality. To this end, we built and instrumented an artificial slope with full-scale LPDs in an open-air lab (OAL) at TU Delft. The design of the setup and the monitoring plan of the LPDs were developed in collaboration with Glasgow Caledonian University with insights from the construction and monitoring of three LPDs at different growth stages in their OAL on the east coast of Scotland. Herein, the design and possible research experiments that can be performed over the next 5 years are presented, generating a data set to further develop and validate hydrological modeling of LPDs. We expect this long-term demonstrative setup to generate interest and facilitate a more comprehensive understanding of LPD functions, ultimately leading to the incorporation of LPD design and maintenance standards in engineering toolboxes for slope and gully stabilization.Sponge CampusWater Managemen
Learning Interpretable Reduced-order Models for Jumping Quadrupeds
No full textThis work introduces a novel methodology for the development of interpretable reduced-order dynamic models specifically tailored for jumping quadruped robots. Leveraging Symbolic Regression combined with autoencoder neural networks, the framework autonomously derives symbolic equations from data and fundamental physics principles capturing the complex dynamics of jumping actions with high fidelity. This approach significantly reduces model complexity while enhancing interpretability, facilitating deeper insights for legged robotic applications. The efficacy and accuracy of the proposed models are validated through comprehensive experimental studies, marking a substantial advancement in the design of agile and efficient legged robots. This research demonstrates the outperformance of a learned 2D model compared to existing template models such as the ASLIP. Also, an analysis of the dimensionality of the learned model is conducted showing the overarching tradeoff between accuracy and complexity. The method is validated on different simulated quadrupeds and an actual hardware robot.Mechanical Engineering | Vehicle Engineering | Cognitive Robotic
Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures
No full textData-driven methodologies have found increasing usage in the last decade for remaining useful life (RUL) prognostics of composite materials utilizing structural health monitoring (SHM) data. Of particular interest is the reliable RUL prediction in cases where the end-of-life is not in between the extreme values within the testing dataset. For example, when unexpected phenomena that severely compromise the structural integrity occur during the service life. Such cases are often referred as outliers and the RUL prognosis based on a data-driven model that learns from past data is often erroneous. This study addresses this challenge by proposing a new stochastic model; the Similarity Learning Hidden Semi Markov Model (SLHSMM), an extension of the Non-Homogenous Hidden Semi Markov Model (NHHSMM). Through the utilization of a nonparametric discrete distribution, which characterizes the similarity between the testing structure and the training structures, a dynamic re-estimation process is employed. This process assigns higher importance to the training structures that display greater similarity to the testing one. As a result, the estimated parameters effectively capture the specific characteristics of the testing structure. The training and testing SHM data sets consist of strain measurements collected from a case study where carbon–epoxy single-stringered panels, are subjected to constant, variable, and random amplitude fatigue loading until failure. RUL estimations from the SLHSMM, the NHHSMM, and the Gaussian Process Regression (GPR) are compared. The SLHSMM clearly outperforms its classical counterpart and GPR providing more accurate outlier and inlier prognostics, demonstrating its capability to adapt to unexpected phenomena and integrate unforeseen data into a prognostic platform.Structural Integrity & Composite
The effect of working fluid and compressibility on the optimal solidity of axial turbine cascades
No full textThe blade solidity, namely the blade chordtopitch ratio, largely affects the fluiddynamic performance of turbomachinery. For turbomachines operating with air or steam, the optimal value of the solidity which maximizes the efficiency is estimated with empirical correlations such as the ones proposed by Zweifel (1945) and Traupel (1966). However, if the turbomachine operates with unconventional fluids, the accuracy of these correlations becomes questionable. Examples of such working fluids are the nonideal (dense) vapors of organic compounds (e.g., hydrocarbons, siloxanes) used to operate organic Rankine cycle (ORC) power systems. This study investigates the effect of both the working fluid and the flow compressibility on the optimum pitchtochord ratio of turbine stages. A first principle model for the profile losses is developed for this purpose. Charts providing the optimal pitchtochord ratio for unconventional turbine stages are then provided. Numerical simulations of the flow over a turbine stator cascade have been conducted to validate the model results and evaluate the influence of both working fluid, flow compressibility, and solidity value on the loss breakdown. The results show that the optimal solidity of turbine cascades value significantly increases with the flow compressibility. Therefore, models providing the optimal solidity based on the estimate of passage loss only are not suited for unconventional turbines.Flight Performance and Propulsio
Digital Slot Machines: Social Media Platforms as Attentional Scaffolds
No full textIn this paper we introduce the concept of attentional scaffolds and show the resemblance between social media platforms and slot machines, both functioning as hostile attentional scaffolds. The first section establishes the groundwork for the concept of attentional scaffolds and draws parallels to the mechanics of slot machines, to argue that social media platforms aim to capture users’ attention to maximize engagement through a system of intermittent rewards. The second section shifts focus to the interplay between emotions and attention, revealing how online attentional capture through emotionally triggering stimuli leads to distraction. The final section elucidates the collective implications of scaffolding attention through social media platforms. The examination of phenomena such as emotional contagion and the emergence of group emotions underscores the transition from individual experiences to shared collective outcomes. Employing online moral outrage as a case study, we illustrate how negative emotions serve as scaffolds for individuals’ attention, propagate within social groups, and give rise to collective attitudes.Ethics & Philosophy of Technolog
Model-based approach for the automatic inclusion of production considerations in the conceptual design of aircraft structures
No full textIncluding production considerations in the early design stages of aircraft structures is challenging. Production information is mostly known by experts and rarely formally documented such that it can be effectively used during the design process. Producibility is mostly considered after completing the design, resulting in increased cost and development time due to the late discovery of production issues. This paper presents a new model, called the Manufacturing Information Model (MIM), which supports the automatic inclusion of production considerations into the design process. The MIM provides a single source of truth and a generic structure to capture and organize production-related information in a product system. Furthermore, it provides compatibility analyses to automatically warn for or exclude infeasible designs. Analysis tools use the information stored within the MIM to calculate the mass, costs, and production rate of the product. To show the functionalities of the MIM, it has been applied to the conceptual design of a wing box at a Tier 1 company. This use case shows how the MIM supports trade-off decisions, as it allows for the identification of trends and the ranking of different manufacturing concepts. Overall, the MIM provides a structured and formal approach to include production information in the conceptual design, improving the decision-making process.Flight Performance and Propulsio