400 research outputs found

    Closed-Loop Model Identification and MPC-based Navigation of Quadcopters: A Case Study of Parrot Bebop 2

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    The growing potential of quadcopters in various domains, such as aerial photography, search and rescue, and infrastructure inspection, underscores the need for real-time control under strict safety and operational constraints. This challenge is compounded by the inherent nonlinear dynamics of quadcopters and the on-board computational limitations they face. This paper aims at addressing these challenges. First, this paper presents a comprehensive procedure for deriving a linear yet efficient model to describe the dynamics of quadrotors, thereby reducing complexity without compromising efficiency. Then, this paper develops a steady-state-aware Model Predictive Control (MPC) to effectively navigate quadcopters, while guaranteeing constraint satisfaction at all times. The main advantage of the steady-state-aware MPC is its low computational complexity, which makes it an appropriate choice for systems with limited computing capacity, like quadcopters. This paper considers Parrot Bebop 2 as the running example, and experimentally validates and evaluates the proposed algorithms

    Optimal Asset-Liability Management for Defned Beneft Pension Fund Under Stochastic Correlation

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    We consider a second pillar pension fund problem relying on a multi-stage stochastic asset-liability management (ALM) model which is specified with an asset universe including money-market, fixed-income, inflation-linked bond as well as equity and commodity. The current value of liability is determined under the assumptions of constant pension fund future pension payments and their current market value (current fund obligation) under assumption of constant pension fund population by discounting all future pension payments. Pension payments are random and determined by the evolution of the population and by inflation. Over a long-term horizon discount rates will also fluctuate and derive the evaluation of the fund liabilities. The pension manager will seek an optimal investment strategy to fund all liabilities and generate the surplus. We present an extension of a scenario tree generation procedure to include stochastic correlations among asset classes and test whether, as claimed by several authors, such extension is effective during crises periods, when correlation clustering is commonly claimed to affect the markets and reduce significantly the effectiveness of portfolio diversification. We test the sensitivity of the first-stage implementable decision to alternative assumptions on the returns’ correlations and their impact on the portfolio terminal distribution during a crisis period. The funding ratio (FR) is the ratio of the portfolio assets to the liabilities. A pension funds, primary aim is to assess the FR at every decision stage over time. The pension fund’s manager wishes to have sufficient liquidity and to control interest and inflation rate risks with a minimum return guarantee. Asset returns are defined with respect to a risk exposure captured by the concept of risk capital, recently introduced in modern pension systems and which is becoming a standard in Institutional ALM and in particular in pension fund ALM. In this thesis the elements of a real-world case problem are discuss and results presented over a 10-year horizon with the pension fund economic and financial constraints. Focusing on a period, between 2009-2011, of increasing markets’ volatility, we analyze the effectiveness of a long-term, discrete dynamic investment strategy under an assumption of stochastic correlation. The method relies on the definition of a probability space generated through Monte Carlo simulation and the implementation of a scenario generation scheme with a Dynamic Conditional Correlation (DCC) model. We consider a defied benefit (DB) pension fund problem: under a DB scheme benefits are defined in terms of percentage of last year salaries. The liability of pension fund is also called defined benefit obligation (DBO) under such assumption. stressed funding condition will arises when assets value decreases and liability value increase. The analysis of pension funds market perspectives is strictly related with evolution of the funding ratio. The collected evidence supports the inclusion of stochastic correlation between asset returns during the recent European financial crisis. Over a three year backtesting period which includes the 2009-2011 sovereign crisis, the introduced extension is shown to generate an effective hedge to positive risk premium

    Top-k overlapping densest subgraphs: Approximation and complexity

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    A central problem in graph mining is finding dense subgraphs, with several applications in different fields, a notable example being identifying communities. While a lot of effort has been put in the problem of finding a single dense subgraph, only recently the focus has been shifted to the problem of finding a set of densest subgraphs. An approach introduced to find possible overlapping subgraphs is the Top-k-Overlapping Densest Subgraphs problem. Given an integer k ≥ 1 and a parameter λ > 0, the goal of this problem is to find a set of k densest subgraphs that may share some vertices. The objective function to be maximized takes into account the density of the subgraphs and the distance between subgraphs in the solution (multiplied by λ). The Top-k-Overlapping Densest Subgraphs problem has been shown to admit a 1⁄10-factor approximation algorithm. Furthermore, the computational complexity of the problem has been left open. In this paper, we present contributions concerning the approximability and the computational complexity of the problem. For the approximability, we present approximation algorithms that improve the approximation factor to 1⁄2, when k is smaller than the number of vertices in the graph, and to 2/3, when k is a constant. For the computational complexity, we show that the problem is NP-hard even when k = 3

    Random assignments with uniform preferences: An impossibility result

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    Agents have uniform preferences if a weakly decreasing utility function determines each agent's preference ranking over the same order of alternatives. We show that the impossibility in the random assignment problem between strategyproofness, ordinally efficiency, and fairness in the sense of equal division lower bound, prevails even if agents have uniform preferences. Furthermore, it continues to hold even if we weaken the strategyproofness to upper-contour strategyproofness, or the ordinal efficiency to robust ex-post Pareto efficiency

    Real-Time Structure and Object Aware Semantic SLAM

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    Simultaneous Localization And Mapping (SLAM) is one of the fundamental problems in mobile robotics and addresses the reconstruction of a previously unseen environment while simultaneously localising a mobile robot with respect to it. For visual-SLAM, the simplest representation of the map is a collection of 3D points that is sparse and efficient to compute and update, particularly for large-scale environments, however it lacks semantic information and is not useful for high-level tasks such as robotic grasping and manipulation. Although methods to compute denser representations have been proposed, these reconstructions remain equivalent to a collection of points and therefore carry no additional semantic information or relationship. Man-made environments contain many structures and objects that carry high-level semantics and can potentially act as landmarks of a SLAM map, while encapsulating semantic information as opposed to a set of points. For instance, planes are good representations for feature deprived regions, where they provide information complimentary to points and can also model dominant planar layouts of the environment with very few parameters. Furthermore, a generic representation for previously unseen objects can be used as a general landmark that carries semantics in the reconstructed map. Integrating visual semantic understanding and geometric reconstruction has been studied before, however due to various reasons, including high- level geometric entities in the SLAM framework has been restricted to a slow, offline structure-from-motion context, or high-level entities merely act as regulators for points in the map instead of independent landmarks. One of those critical reasons is the lack of proper mathematical representation for high-level landmarks and the other main reasons are the challenge of detection and tracking of these landmarks and formulating an observation model – a mapping between corresponding image observable quantities and estimated parameters of the representations. In this work, we address these challenges to achieve an online real-time SLAM framework with scalable maps consisting of both sparse points and high-level structural and semantic landmarks such as planes and objects. We explicitly target real-time performance and keep that as a beacon which influences critically the representation choice and all the modules of our SLAM system. In the context of factor graphs, we propose novel representations for structural entities as planes and general unseen and not-predefined objects as bounded dual quadrics that decompose to permit clean, fast and effective real-time implementation that is amenable to the nonlinear leastsquare formulation and respects the sparsity pattern of the SLAM problem. In this representation we are not concerned with high-fidelity reconstruction of individual objects, but rather to represent the general layout and orientation of objects in the environment. Also the minimal representations of planes is explored leading to a representation that can be constructed and updated online in a least-squares framework. Another challenge that we address in this work is to marry high-level landmark detections based on deep-learned frameworks, with geometric SLAM systems. Due to the recent success of CNN-based object detections and also depth and surface normal estimations from single image, it is feasible now to detect and estimate these semantic landmarks from single RGB images, therefore leading us seamlessly from RGB-D SLAM system to pure monocular SLAM thanks to the real-time predictions of the trained CNN and appropriate representations. Furthermore, to benefit from deep-learned priors, we incorporate high-fidelity single-image reconstructions and hallucinations of objects on top of the coarse quadrics to enrich the sparse map semantically, while constraining the shape of the coarse quadrics even more. Pertinent to our beacon, proposed landmark representations in the map also provide the potential for imposing additional constraints and priors that carry crucial semantic information about the scene, without incurring great extra computational cost. In this work, we have explored and proposed constraints such as priors on the extent and shape of the objects, point-plane regularizer, plane-plane (Manhattan assumption), and plane-object (supporting affordance) constraints. We evaluate our proposed SLAM system extensively using different input sensor modalities from RGB-D to monocular in almost all publicly available benchmarks both indoors and outdoors to show its applicability as a general-purpose SLAM solution. The extensive experiments show the efficacy of our SLAM through different comparisons and ablation studies including high-level structures and objects with imposed constraints among them in various scenarios. In particular, the estimated camera trajectories have been improved significantly in varied sequences of visual SLAM datasets and also our own captured sequences with UR5 robotic arm equipped with a depth camera. In addition to more accurate camera trajectories, our system yields enriched sparse maps with semantically meaningful planar structures and generic objects in the scene along with their mutual relationshipsThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

    Cement-Based Mortar Panels Reinforced with Recycled Steel Fibers in Flexural Strengthening of Concrete Beams

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    The effectiveness of a strengthening technique devised for the concrete beams subjected to bending is presented in this study, where recycled-steel fiber-reinforced mortar (RSFRM) panels are used as an eco-friendly replacement for ordinary steel fibers. Different mix designs for RSFRM are first investigated experimentally by testing 160 × 400 × 400 mm3 notched beam-like specimens in 3-point bending, while 100 × 100 × 100 mm3 cubes are tested in compression, to optimize the mix design. Finite element (FE) analyses are carried out on strengthened and non-strengthened beams to investigate the effectiveness of the proposed strengthening technique based on RSFRM panels. Starting from the tests on notched beams, an inverse FE analysis is used to optimize the RSFRM’s parameters to be implemented into the numerical model. The results show that applying RSFRM panels not only markedly increases the load-bearing capacity of the beams (up to 3.19 times with 3% of fibers by volume), but also changes their fracture mechanism from brittle to ductile fracture

    Effect of operational parameters and internal recycle on rhenium solvent extraction from leach liquors using a mixer-settler

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    AbstractThe extraction of rhenium from molybdenite roasting dust leach solution was performed using a mixer-settler extractor by tributyl phosphate (TBP) diluted in kerosene as the extractant. In the single-stage extraction experiments, effect of the aqueous to organic phase ratios, Qa/Qo, and the number of extraction stages, N, on the rhenium extraction was studied. It was found that using the phase ratio of 1:1 in a two-stage extraction, 87.5% depletion of rhenium was obtained. The comparison of experimental results with the continuous co-current extraction showed a good agreement. The effect of internal recycle of organic phase was investigated in the phase ratio of 1:1 by changing the flow rate ratio of recycle-to-fresh organic phase, Qro/Qfo. The optimum performance was achieved in the phase ratio, Qro/Qfo, equal to 3:7. It was found that improvement in the performance of the mixer-settler for the rhenium-TBP system can be obtained in the phase ratio of 1:1when Qro/Qfo = 3:7
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