7,892 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Resilience and food security in a food systems context

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    This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners

    Modified Theories of Gravity and Cosmological Applications

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    This reprint focuses on recent aspects of gravitational theory and cosmology. It contains subjects of particular interest for modified gravity theories and applications to cosmology, special attention is given to Einstein–Gauss–Bonnet, f(R)-gravity, anisotropic inflation, extra dimension theories of gravity, black holes, dark energy, Palatini gravity, anisotropic spacetime, Einstein–Finsler gravity, off-diagonal cosmological solutions, Hawking-temperature and scalar-tensor-vector theories

    Designing and Expanding Electrical Networks – Complexity and Combinatorial Algorithms

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    The transition from conventional to renewable power generation has a large impact on when and where electricity is generated. To deal with this change the electric transmission network needs to be adapted and expanded. Expanding the network has two benefits. Electricity can be generated at locations with high renewable energy potentials and then transmitted to the consumers via the transmission network. Without the expansion the existing transmission network may be unable to cope with the transmission needs, thus requiring power generation at locations closer to the energy demand, but at less well-suited locations. Second, renewable energy generation (e.g., from wind or solar irradiation) is typically volatile. Having strong interconnections between regions within a large geographical area allows to the smooth the generation and demand over that area. This smoothing makes them more predictable and the volatility of the generation easier to handle. In this thesis we consider problems that arise when designing and expanding electric transmission networks. As the first step we formalize them such that we have a precise mathematical problem formulation. Afterwards, we pursue two goals: first, improve the theoretical understanding of these problems by determining their computational complexity under various restrictions, and second, develop algorithms that can solve these problems. A basic formulation of the expansion planning problem models the network as a graph and potential new transmission lines as edges that may be added to the graph. We formalize this formulation as the problems Flow Expansion and Electrical Flow Expansion, which differ in the flow model (graph-theoretical vs. electrical flow). We prove that in general the decision variants of these problems are NP\mathcal{NP}-complete, even if the network structure is already very simple, e.g., a star. For certain restrictions, we give polynomial-time algorithms as well. Our results delineate the boundary between the NP\mathcal{NP}-complete cases and the cases that can be solved in polynomial time. The basic expansion planning problems mentioned above ignore that real transmission networks should still be able to operate if a small part of the transmission equipment fails. We employ a criticality measure from the literature, which measures the dynamic effects of the failure of a single transmission line on the whole transmission network. In a first step, we compare this criticality measure to the well-used N−1N-1 criterion. Moreover, we formulate this criticality measure as a set of linear inequalities, which may be added to any formulation of a network design problem as a mathematical program. To exemplify this usage, we introduce the criticality criterion in two transmission network expansion planning problems, which can be formulated as mixed-integer linear programs (MILPs). We then evaluate the performance of solving the MILPs. Finally, we develop a greedy heuristic for one of the two problems, and compare its performance to solving the MILP. Microgrids play an important role in the electrification of rural areas. We formalize the design of the cable layout of a microgrid as a geometric optimization problem, which we call Microgrid Cable Layout. A key difference to the network design problems above is that there is no graph with candidate edges given. Instead, edges and new vertices may be placed anywhere in the plane. We present a hybrid genetic algorithm for Microgrid Cable Layout and evaluate it on a set of benchmark instances, which include a real microgrid in the Democratic Republic of the Congo. Finally, instead of expanding electrical networks one may place electric equipment such as FACTS (flexible AC transmission system). These influence the properties of the transmission lines such that the network can be used more efficiently. We apply a model of FACTS from the literature and study the problem whether a given network with given positions and properties of the FACTS admits an electrical flow provided that FACTS are set appropriately. We call such a flow a FACTS flow. In this thesis we prove that in general it is NP\mathcal{NP}-complete to determine whether a network admits a FACTS flow, and we present polynomial-time algorithms for two restricted cases

    Asymptotically Faster Quantum Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

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    The CONGEST and CONGEST-CLIQUE models have been carefully studied to represent situations where the communication bandwidth between processors in a network is severely limited. Messages of only O(log(n))O(log(n)) bits of information each may be sent between processors in each round. The quantum versions of these models allow the processors instead to communicate and compute with quantum bits under the same bandwidth limitations. This leads to the following natural research question: What problems can be solved more efficiently in these quantum models than in the classical ones? Building on existing work, we contribute to this question in two ways. Firstly, we present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O~(n1/4)\tilde{O}(n^{1/4}) rounds of communication and O~(n9/4)\tilde{O}(n^{9/4}) messages, where nn is the number of nodes in the network. The algorithms thus achieve a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithmic frameworks with quantum subroutines. An existing framework for using distributed version of Grover's search algorithm to accelerate triangle finding lies at the core of the asymptotic speedup. Secondly, we carefully characterize the constants and logarithmic factors involved in our algorithms as well as related algorithms, otherwise commonly obscured by O~\tilde{O} notation. The analysis shows that some improvements are needed to render both our and existing related quantum and classical algorithms practical, as their asymptotic speedups only help for very large values of nn.Comment: 23 pages, 0 figure

    In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

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    Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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