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A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure. By minimizing the distance between the marginal and conditional distributions between the source and the target structures, this article successfully localizes damage states of disparate severities, under varying environmental and operational conditions. The effect of partial and universal domain adaptation—where the number of damage states in the source and target datasets differ—is also explored in order to mimic realistic industrial applications of these methods.
In sandstorms and thunderclouds, turbulence-induced collisions between solid particles and ice crystals lead to inevitable triboelectrification. The charge segregation is usually size dependent, with small particles charged negatively and large particles charged positively. In this work, we perform numerical simulations to study the influence of charge segregation on the dynamics of bidispersed inertial particles in turbulence. Direct numerical simulations of homogeneous isotropic turbulence are performed with the Taylor Reynolds number ${Re}_{\lambda }=147.5$, while particles are subjected to both electrostatic interactions and fluid drag, with Stokes numbers of 1 and 10 for small and large particles, respectively. Coulomb repulsion/attraction is shown to effectively inhibit/enhance particle clustering within a short range. Besides, the mean relative velocity between same-size particles is found to rise as the particle charge increases because of the exclusion of low-velocity pairs, while the relative velocity between different-size particles is almost unaffected, emphasizing the dominant roles of differential inertia. The mean Coulomb-turbulence parameter, ${Ct}_0$, is then defined to characterize the competition between the Coulomb potential energy and the mean relative kinetic energy. In addition, a model is proposed to quantify the rate at which charged particles approach each other and to capture the transition of the particle relative motion from the turbulence-dominated regime to the electrostatic-dominated regime. Finally, the probability distribution function of the approach rate between particle pairs is examined, and its dependence on the Coulomb force is further discussed using the extended Coulomb-turbulence parameter.
In this paper, we investigate low-cost solutions for enabling ground moving target indication applications with multichannel mobile passive radar systems. As known, in order to be competitive with their active counterparts, passive radars are typically characterized by severe constraints in terms of cost, complexity, and compactness, especially when installed on moving platforms. On the one hand, carrying out the computations onboard requires processing techniques as simple as possible. On the other hand, the need for lightweight and compact systems that can be installed on a moving platform requires using a limited number of receiving channels. To meet these requirements, we propose a series of nonadaptive detectors based on multichannel displaced phase center antennas, which allow suppressing the Doppler-spread clutter component without requiring computationally intensive space–time adaptive processing techniques. Moreover, we explore the use of nonuniformly spaced array configurations on receive, which represent a good alternative to conventional uniform linear arrays when a limited number of receiving channels can be implemented. The effectiveness of the proposed processing techniques and antenna design solutions is demonstrated via numerical analysis for the case of a DVB-T-based mobile passive radar system.
The Zakharov equation describes the evolution of weakly nonlinear surface gravity waves for arbitrary spectral shape. For deep-water waves, results from the Zakharov equation are well established. However, for two-dimensional propagation, in intermediate and shallow water, there are problems related to the treatment of apparent singularities in the contribution of the wave-induced set-up to the evolution of the surface gravity waves. More specifically, the kernel in the integral term is characterized by a regular and an apparent singular contribution. Here, we show that the Davey–Stewartson equation can be directly derived from the Zakharov equation, also in the shallow water limit. This result provides guidance on how to treat the singular contribution to the evolution of the action variable. A relevant result that is obtained is that the growth rate obtained from the stability analysis of a plane wave in shallow water does not depend on the singular part of the kernel of the Zakharov equation.
For the dispersion of soluble matter in solvent flowing through a tube as investigated originally by G.I. Taylor, a streamwise dispersion theory is developed from a Lagrangian perspective for the whole process with multi-scale effects. By means of a convected coordinate system to decouple convection from diffusion, a diffusion-type governing equation is presented to reflect superposable diffusion processes with a multi-scale time-dependent anisotropic diffusivity tensor. A short-time benchmark, complementing the existing Taylor–Aris solution, is obtained to reveal novel statistical and physical features of mean concentration for an initial phase with isotropic molecular diffusion. For long times, effective streamwise diffusion prevails asymptotically corresponding to the overall enhanced diffusion in Taylor's classical theory. By inverse integral expansions of local concentration moments, a general streamwise dispersion model is devised to match the short- and long-time asymptotic solutions. Analytical solutions are provided for most typical cases of point and area sources in a Poiseuille tube flow, predicting persistent long tails and skewed platforms. The theoretical findings are substantiated through Monte Carlo simulations, from the initial release to the Taylor dispersion regime. Asymmetries of concentration distribution in a circular tube are certified as originated from (a) initial non-uniformity, (b) unidirectional flow convection, and (c) non-penetration boundary effect. Peculiar peaks in the concentration cloud, enhanced streamwise dispersivity and asymmetric collective phenomena of concentration distributions are illustrated heuristically and characterised to depict the non-equilibrium dispersion. The streamwise perspective could advance our understanding of macro-transport processes of both passive solutes and active suspensions.
The transition to renewable energy is vital and fast-paced, but how do we choose which technologies to drive this energy transition? This timely book provides everyone interested in the renewable energy transition with an introduction to and technical foundation for understanding modern energy technology. It traces everyday power generation through history, from the Industrial Revolution to today. It examines the use of wood, coal, oil, natural gas, hydro, and nuclear to produce energy, before discussing renewable energy sources such as biomass, photovoltaics, concentrated solar power, wind, wave, and geothermal. The book examines to what extent and how each technology can contribute to a clean, green infrastructure. The Truth About Energy explains the science and engineering of energy to help everyone understand and compare current and future advances in renewable energy, providing the context to critically examine the different technologies that are competing in a fast-evolving engineering, political, and economic landscape.
There are many exercises included at the ends of chapters in Parts I and II of book. This appendix provides brief solutions or at least answers to most of these exercises.
This paper proposes a composite non-singular fast terminal sliding mode attitude control scheme based on a reduced-order extended state observer for the stratospheric airship’s attitude system affected by multiple disturbances. First, the feedback linearisation method is applied to address the nonlinearity of the attitude motion model and achieve decoupling of the model in three channels. Second, the overall disturbances, encompassing airship parameter perturbations and external disturbances, are treated as an aggregate. A reduced-order extended state observer is designed for each channel to formulate a composite non-singular fast terminal sliding mode surface. In the control design phase, the hyperbolic sine function is adopted as replacement for the sign function to ensure the continuity of the control signal. The estimated disturbances are incorporated in the control law design to directly offset the effects of multiple disturbances on the attitude motion of the airship. Third, based on Lyapunov theory, it has been proven that the control law can drive the attitude tracking error to converge to zero within a finite time. Simulation results demonstrate that the proposed control scheme exhibits favorable disturbance rejection capability, as well as higher tracking accuracy and faster response speed.
We begin our journey into state estimation by considering systems that can be modelled using linear equations corrupted by Gaussian noise. While these linear-Gaussian systems are severe approximations of real robots, the mathematics are very amenable to straightforward analysis. We discuss the difference between Bayesian estimation and maximum a posteriori estimation in the context of batch trajectory estimation; these two approaches are effectively the same for linear systems, but this contrast is crucial to understanding the results for nonlinear systems later on. After introducing batch trajectory estimation, we show how the structure of the problem gives rise to sparsity in our equations that can be exploited to provide a very efficient solution. Indeed, the famous Rauch-Tung-Striebel smoother (whose forward pass is the Kalman filter) is equivalent to solving the batch trajectory problem. Several other avenues to the Kalman filter are also explored. Although much of the book focusses on discrete-time motion models for robots, we show how to begin with continuous-time models as well; in particular, we make the connection that batch continuous-time trajectory is an example of Gaussian process regression, a popular tool from machine learning.
This appendix contains a few extra derivations relating to rotations and poses that may be of interest to some enthusiastic readers. In particular, the eigen/Jordan decomposition of rotations and poses provides some deeper insight into these quantities that are ubiquitous in robotics.
Typical robots not only translate in the world but also rotate. This chapter serves a primer on three-dimensional geometry introducing such important geometric concepts as vectors, reference frames, coordinates, rotations, and poses (rotation and translation). We introduce kinematics, how geometry changes over time, with an eye towards describing robot motion models. We also present several common sensor models using our three-dimensional tools: camera, stereo camera, lidar, and inertial measurement unit.
In numerical studies of thermal convection that includes a layer of lighter surface fluid, the light fluid naturally forms clusters that bulge downward at downwelling sites. A curious result is that in some cases, the clusters have maximum bulging downward near the sides of the cluster instead of a single bulge downward centred above the downwelling. The fluid mechanics leading to this ‘double bulge’ formation is analysed. To accomplish this, a simplified model replaces the thermally driven convection cells with driving cells with a fixed speed. Adding a layer of dense fluid on the bottom to the previous configuration leads to bulges along the top and bottom. More importantly, this allows a new scaling that reduces the number of governing parameters from four to three and even to two in this study. The mechanism for the double bulges comes from buoyancy of the clusters. This produces localized vorticity at the sides of the cluster that has the opposite sign of the driving cells. When this vorticity is approximately the same order of magnitude as the driving cell vorticity, a divergence in the middle of each cluster leads to the double bulges. The effect can be so great that the underlying flow cells are tilted so that vertical motion is reversed under the middle of each bulge.
This paper outlines the results of particle-in-cell simulations of a relativistic magnetron with six cavities and a transparent cathode configuration. Excitation of the π mode in the interaction region was attained, which in turn led to $\textrm{TE}_{11}$ mode emission of microwaves to the waveguide. This mode transformation was achieved with a non-symmetric diffraction output, consisting of four large and two small tapered cavities. Simulations were performed with a voltage across the anode-cathode gap varying from 164 to 356 kV, and axial magnetic field strengths between 0.24 and 0.34 T. Maximum efficiency of 37% was obtained with a peak output power of 590 MW, having a voltage of 261 kV and a magnetic field of 0.30 T. Furthermore, a frequency of 2.57 GHz and a rise time of microwaves at the waveguide of 15 ns were demonstrated. The electron leakage current was shown to decrease from ∼10$\%$ to less than $1\%$ when employing a longer interaction region, while still exhibiting good performance. Additionally, we show that there is an optimal range of voltages given a magnetic field, for which π mode excitation with high efficiency is attained.
The final technical chapter returns to the idea of representing a robot trajectory as a continuous function of time, only now in three-dimensional space where the robot may translate and rotate. We provide a method to adapt our earlier continuous-time trajectory estimation to Lie groups that is practical and efficient. The chapter serves as a final example of pulling together many of the key ingredients of the book into a single problem: continuous time estimation as Gaussian process regression, Lie groups to handle rotations, and simultaneous localization and mapping.
Nonlinear systems provide additional challenges for robotic state estimation. We provide a derivation of the famous extended Kalman filter (EKF) and then go on to study several generalizations and extensions of recursive estimation that are commonly used: the Bayes filter, the iterated EKF, the particle filter, and the sigmapoint Kalman filter. We return to batch estimation for nonlinear systems, which we connect more deeply to numerical optimization than in the linear-Gaussian chapter. We discuss the strengths and weaknesses of the various techniques presented and then introduce sliding-window filters as a compromise between recursive and batch methods. Finally, we discuss how continuous-time motion models can be employed in batch trajectory estimation for nonlinear systems.
Rotational state variables are a problem for our estimation tools from earlier chapters, which all assumed the state to be estimated was a vector in the sense of linear algebra. Rotations cannot be globally described as vectors and as such must be handled with care. This chapter re-examines rotations as an example of a Lie group, which has many useful properties despite not being a vector space. The main takeaway of the chapter is that in estimation we can use the Lie group structure to adapt our estimation tools to work with rotations, and by association poses. The key is to consider small perturbations to rotations in the group's Lie algebra in order to make two tasks easier to handle: performing numerical optimization and representing uncertainty. The chapter can also serve as a useful reference for readers already familiar with the content.
Following on the heels of the chapter on nonlinear estimation, this chapter focusses on some of the common pitfalls and failure modes of estimation techniques. We begin by discussing some key properties that we would like healthy estimators to have (i.e., unbiased, consistent) and how to measure these properties. We delve more deeply into biases and discuss how in some cases we can fold bias estimation right into our estimator, while in other cases we cannot. We touch briefly on data association (matching measurements to the right parts of models) and how to mitigate the effect of outlier measurements using robust estimation. We close with some methods to determine good measurement covariances for use in our estimators.