High Fidelity Modelling for High Altitude Long Endurance Solar Powered Aircraft

High Altitude Long Endurance (HALE) platforms are the aerial platforms capable of flying in the stratosphere for long periods of time. This master thesis presents aircraft system identification procedures geared towards such fixed wing platforms where aerodynamic forces and moments are parametrically modelled with so-called stability and control derivatives. The first part of the thesis addresses local System identification procedures intended for controller synthesis at low altitude flights whereas the second part of the thesis deals with a preliminary study on a new global system identification method. The local system identification procedure is based on the two step method, which offers flexibility regarding the aerodynamic structure. Therefore, it is suitable for the development of a system identification tool chain for various fixed wing platforms. Various system identification experiments have been conducted to collect flight test data. The parameters for the estimation of aerodynamic forces and moments are then found through an optimization procedure. Such parameters have been validated using a validation set from flight test data and their applicability for controller synthesis has been demonstrated. Global system identification typically requires the collection of flight test data at multiple points in the flight envelope and often, is combined with extensive Computational Fluid Dynamics (CFD) solutions as well as wind-tunnel experiments. Such an approach is time consuming and costly. This thesis presents a new method to overcome the limitations of the current methodology by applying a Parameter search on VLM-based (Vortex Lattice Method) dynamic simulations of aircraft System identification manoeuvres and correcting the estimated models with available flight test data. The current study shows improvements in fidelity with decrease in Root Mean Squared Error (RMSE) by factor 0.2 and 0.5 for x-axis and z-axis forces in body frame respectively, while reducing the effort for obtaining a model with similar fidelity

Institutions, the state and performance : evidence from South Korea

The purpose of this thesis is to investigate whether State intervention in the market can be a catalyst for economic growth by examining the cases of South Korea. Since Stale intervention in the market typically takes two form; namely, implementing industrial policy and controlling State-owned enterprises (SOEs). I investigate industrial policy as an overall strategy of state intervention. and performance contract and board of directors as governance mechanisms of SOEs. In doing so the thesis contributes to the existing knowledge in the following ways. First, this thesis extends the existing literature on industrial policy by shedding new light on the dynamic nature of industrial policy. That is, industrial policy necessarily changes the market conditions where it ha: been shaped and, therefore, it becomes outdated. which necessitate new policies. Given this, I propose a dynamic framework for successful industrial policy over time and find that South Korean industrial policy over the period 1960-1996 an be explained within the framework. confirming that successful industrial policy. hould be a dynamic and evolutionary process which is responsive to changes in institutional environment. Second. the thesis extends the existing literature on performance contracts (PCs) by examining whether PCs an actually improve the performance or SOEs. From relevant theories, the thesis draws out conditions that 'sensible' PCs measures should meet so as to effectively motivate SOEs to perform better and the use of Total Quality Management (TQM) as a basis for generating specific PC measures. The arguments are then empirically tested using data from the South Korean PC which are built on TQM. The results show that the South Korean PC meets the condition. for 'sensible' measure, and actually improve the performance of the South Korean SOEs. indicating that PC: can improve the performance of OE: where PCs incorporate sensible measures. Finally, this thesis. extends the existing literature on corporate governance by empirically investigating how corporate boards add value to firms in the context of SOEs. and how SOE boards interact with PCs. sing a novel framework that incorporate board process, the thesis. derives empirical evidence that PCs act as substitute for board monitoring. The results indicate that SOEs do adjust their internal governance in response to internal imperatives (the reduced need for monitoring due to the presence of PCs) rather than institutional pressure of PCs for effective monitoring. This implies that regulators should consider this subtitutive effect when they design the governance structure of SOEs

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form. The key insight of our work is that the information matrix, i.e. the inverse of the covariance matrix tends to be sparse in its spectrum. Therefore, dimensionality reduction techniques such as low rank approximations (LRA) can be effectively exploited. To achieve this, we develop a novel sparsification algorithm and derive a cost-effective analytical sampler. As a result, we show that the information form can be scalably applied to represent model uncertainty in DNNs. Our exhaustive theoretical analysis and empirical evaluations on various benchmarks show the competitiveness of our approach over the current methods.Comment: Accepted to the Thirty-seventh International Conference on Machine Learning (ICML) 202

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form. The key insight of our work is that the information matrix, i.e. the inverse of the covariance matrix tends to be sparse in its spectrum. Therefore, dimensionality reduction techniques such as low rank approximations (LRA) can be effectively exploited. To achieve this, we develop a novel sparsification algorithm and derive a cost-effective analytical sampler. As a result, we show that the information form can be scalably applied to represent model uncertainty in DNNs. Our exhaustive theoretical analysis and empirical evaluations on various benchmarks show the competitiveness of our approach over the current methods

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment.Comment: Accepted on CoRL202

Warmth suppresses and desensitizes damage-sensing ion channel TRPA1

<p>Abstract</p> <p>Background</p> <p>Acute or chronic tissue damage induces an inflammatory response accompanied by pain and alterations in local tissue temperature. Recent studies revealed that the transient receptor potential A1 (TRPA1) channel is activated by a wide variety of substances that are released following tissue damage to evoke nociception and neurogenic inflammation. Although the effects of a noxious range of cold temperatures on TRPA1 have been rigorously studied, it is not known how agonist-induced activation of TRPA1 is regulated by temperature over an innocuous range centred on the normal skin surface temperature. This study investigated the effect of temperature on agonist-induced currents in human embryonic kidney (HEK) 293 cells transfected with rat or human TRPA1 and in rat sensory neurons.</p> <p>Results</p> <p>Agonist-induced TRPA1 currents in HEK293 cells were strongly suppressed by warm temperatures, and almost abolished at 39°C. Such inhibition occurred when TRPA1 was activated by either electrophilic or non-electrophilic agonists. Warming not only decreased the apparent affinity of TRPA1 for mustard oil (MO), but also greatly enhanced the desensitization and tachyphylaxis of TRPA1. Warming also attenuated MO-induced ionic currents in sensory neurons. These results suggest that the extent of agonist-induced activity of TRPA1 may depend on surrounding tissue temperature, and local hyperthermia during acute inflammation could be an endogenous negative regulatory mechanism to attenuate persistent pain at the site of injury.</p> <p>Conclusion</p> <p>These results indicate that warmth suppresses and desensitizes damage-sensing ion channel TRPA1. Such warmth-induced suppression of TRPA1 may also explain, at least in part, the mechanistic basis of heat therapy that has been widely used as a supplemental anti-nociceptive approach.</p

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment

Learning Fluid Flow Visualizations From In-Flight Images With Tufts

To better understand fluid flows around aerial systems, strips of wire or rope, widely known as tufts, are often used to visualize the local flow direction. This letter presents a computer vision system that automatically extracts the shape of tufts from images, which have been collected during real flights of a helicopter and an unmanned aerial vehicle (UAV). As images from these aerial systems present challenges to both the model-based computer vision and the end-to-end supervised deep learning techniques, we propose a semantic segmentation pipeline that consists of three uncertainty-based modules namely, (a) active learning for object detection, (b) label propagation for object classification, and (c) weakly supervised instance segmentation. Overall, these probabilistic approaches facilitate the learning process without requiring any manual annotations of semantic segmentation masks. Empirically, we motivate our design choices through comparative assessments and provide real-world demonstrations of the proposed concept, for the first time to our knowledge

Bayesian Active Learning for Sim-to-Real Robotic Perception

While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an efficient acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we employ deep Bayesian active learning to minimize manual annotation efforts and devise an autonomous learning paradigm to select the data that is considered useful for the human expert to annotate. To achieve this, a Bayesian Neural Network (BNN) object detector providing reliable un- certainty estimates is adapted to infer the informativeness of the unlabeled data. Furthermore, to cope with misalignments of the label distribution in uncertainty-based sampling, we develop an effective randomized sampling strategy that performs favorably compared to other complex alternatives. In our experiments on object classification and detection, we show benefits of our approach and provide evidence that labeling efforts can be reduced significantly. Finally, we demonstrate the practical effectiveness of this idea in a grasping task on an assistive robot