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1. Negative value of information in systems’ maintenance

   Pozzi, M; Malings, C; Minca, AC (August 2017, Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure 12th Int. Conf. on Structural Safety and Reliability, Vienna, Austria, 6–10 August 2017)

   The value of information (VoI) provides a rational metric to assess the impact of data in decision processes, including maintenance of engineering systems. According to the principle that “information never hurts”, VoI is guaranteed to be non-negative when a single agent aims at minimizing an expected cost. However, in other contexts such as non-cooperative games, where agents compete against each other, revealing a piece of information to all agents may have a negative impact to some of them, as the negative effect of the competitors being informed and adjusting their policies surpasses the direct VoI. Being aware of this, some agents prefer to avoid having certain information collected, when it must be shared with others, as the overall VoI is negative for them. A similar result may occur for managers of infrastructure assets following the prescriptions of codes and regulations. Modern codes require the probability of some failure events be below a threshold, so managers are forced to retrofit assets if that probability is too high. If the economic incentive of those agents disagrees with the code requirements, the VoI associated with tests or inspections may be negative. In this paper, we investigate under what circumstance this happens, and how severe the effects of this issue can be. 

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2. Built Environment and Infrastructure Monitoring, Assessment, and Maintenance

   https://doi.org/10.1061/9780784482445.024  

   Zhu, Runhe; Becerik-Gerber, Burcin; Lucas, Gale; Southers, Erroll; Pynadath, David V. (June 2019, ASCE International Conference on Computing in Civil Engineering 2019)
3. Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

   https://doi.org/10.1177/14759217211035532  

   Yang, Kang; Kim, Sungwon; Yue, Rongting; Yue, Haotian; Harley, Joel B. (August 2021, Structural Health Monitoring)

   null (Ed.)

   Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions. 

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4. Non-parametric Models for Long-term Autonomy

   Acshi Haggenmiller (July 2022, none)

   In this thesis we propose novel estimation techniques for localization and planning problems, which are key challenges in long-term autonomy. We take inspiration in our methods from non-parametric estimation and use tools such as kernel density estimation, non-linear least-squares optimization, binary masking, and random sampling. We show that these methods, by avoiding explicit parametric models, outperform existing methods that use them. Despite the seeming differences between localization and planning, we demonstrate in this thesis that the problems share core structural similarities. When real or simulation-sampled measurements are expensive, noisy, or high variance, non-parametric estimation techniques give higher-quality results in less time. We first address two localization problems. In order to permit localization with a set of ad hoc-placed radios, we propose an ultra-wideband (UWB) graph realization system to localize the radios. Our system achieves high accuracy and robustness by using kernel density estimation for measurement probability densities, by explicitly modeling antenna delays, and by optimizing this combination with a non-linear least squares formulation. Next, in order to then support robotic navigation, we present a flexible system for simultaneous localization and mapping (SLAM) that combines elements from both traditional dense metric SLAM and topological SLAM, using a binary "masking function" to focus attention. This masking function controls which lidar scans are available for loop closures. We provide several masking functions based on approximate topological class detectors. We then examine planning problems in the final chapter and in the appendix. In order to plan with uncertainty around multiple dynamic agents, we describe Monte-Carlo Policy-Tree Decision Making (MCPTDM), a framework for efficiently computing policies in partially-observable, stochastic, continuous problems. MCPTDM composes a sequence of simpler closed-loop policies and uses marginal action costs and particle repetition to improve cost estimates and sample efficiency by reducing variance. Finally, in the appendix we explore Learned Similarity Monte-Carlo Planning (LSMCP), where we seek to enhance the sample efficiency of partially observable Monte Carlo tree search-based planning by taking advantage of similarities in the final outcomes of similar states and actions. We train a multilayer perceptron to learn a similarity function which we then use to enhance value estimates in the planning. Collectively, we show in this thesis that non-parametric methods promote long-term autonomy by reducing error and increasing robustness across multiple domains. 

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5. Long-term geospace climate monitoring

   https://doi.org/10.3389/fspas.2023.1139230  

   Zhang, Shun-Rong; Cnossen, Ingrid; Laštovička, Jan; Elias, Ana G.; Yue, Xinan; Jacobi, Christoph; Yue, Jia; Wang, Wenbin; Qian, Liying; Goncharenko, Larisa (February 2023, Frontiers in Astronomy and Space Sciences)

   Climate change is characterized by global surface warming associated with the increase of greenhouse gas population since the start of the industrial era. Growing evidence shows that the upper atmosphere is experiencing appreciable cooling over the last several decades. The seminal modeling study by Roble and Dickinson (1989) suggested potential effects of increased greenhouse gases on the ionosphere and thermosphere cooling which appear consistent with some observations. However, several outstanding issues remain regarding the role of CO 2 , other important contributors, and impacts of the cooling trend in the ionosphere and thermosphere: for example, (1) what is the regional variability of the trends? (2) the very strong ionospheric cooling observed by multiple incoherent scatter radars that does not fit with the prevailing theory based on the argument of anthropogenic greenhouse gas increases, why? (3) what is the effect of secular changes in Earth’s main magnetic field? Is it visible now in the ionospheric data and can it explain some of the regional variability in the observed ionospheric trends? (4) what is the impact of long-term cooling in the thermosphere on operational systems? (5) what are the appropriate strategic plans to ensure the long-term monitoring of the critical space climate? 

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