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Thia chapter considers methods for both regression and classification based on Gaussian process, a stochastic process with Gaussian distribution, of which the mean vector and covariance matrix can be obtained based on the labeled samples in the training set. The resulting Gaussian process serves as a nonlinear regression function that fits the given dataset. This function can be treated as the probability for data samples' the class identity and used for classificationas as shown before. This Gaussian process approach also has some two advantages: first, the certainty (or confidence) of the regression or classification result can be quantitatively measured; second proper tradeoff between overfitting and underfitting can be made by adjusting a parameter for the covariance of the Gaussian process model.
The emergence of large-scale spatial modulations of turbulent channel flow, as the Reynolds number is decreased, is addressed numerically using the framework of linear stability analysis. Such modulations are known as the precursors of laminar–turbulent patterns found near the onset of relaminarisation. A synthetic two-dimensional base flow is constructed by adding finite-amplitude streaks to the turbulent mean flow. The streak mode is chosen as the leading resolvent mode from linear response theory. In addition, turbulent fluctuations can be taken into account or not by using a simple Cess eddy viscosity model. The linear stability of the base flow is considered by searching for unstable eigenmodes with wavelengths larger than the base flow streaks. As the streak amplitude is increased in the presence of the turbulent closure, the base flow loses its stability to a large-scale modulation below a critical Reynolds-number value. The structure of the corresponding eigenmode, its critical Reynolds number, its critical angle and its wavelengths are all fully consistent with the onset of turbulent modulations from the literature. The existence of a threshold value of the Reynolds number is directly related to the presence of an eddy viscosity, and is justified using an energy budget. The values of the critical streak amplitudes are discussed in relation with those relevant to turbulent flows.
This chapter is solely dedicated to reinforcement learning (RL), one of the three main learning paradigms covered in the book (together with regression and classification). The goal of RL is for an agent to learn from and respond to its environment modeled as a Markov decision process (MDP), by following a set of policies to take the best action at each state of the MDP, in order to receive the maximum total accumulated reward. The utmost goal is to come up with the optimal policy in terms of the best action to take at each state. Different from all optimization problems previously considered for maximizing (or minimizing) certain objective functions, RL achieves its goal by the general method of dynamic programming (while linear and quadratic programmings are for constrained optimization), which solves a complex problem by breaking it up and solving a set of subproblems recursively. Specifically, the main method for RL is the Q-learning algorithm which finds the optimal policy that takes the best action selected based on the expected values of the total reward at all states and all actions at each state. Toward to end of the chapter, various more advanced versions of RL are briefly discussed based on some previously learned methods such as neural networks and deep learning.
The goal of this chapter is to prepare for the future discussion of various artificial neural network (ANN) learning algorithms by introducing some basic concepts in neural networks and some biologically inspired examples the Habbian and Hopfield networks to illustrate how an ANN based on some simple learning rule can achieve meaningful results, although they are not actually widely used in machine learning practice. Specifically, the behavior of the Hebbian learning network mimics the associative nature of brain, as a simple model of associative memory, and the Hopfield network further shows how a pattern can be stored and then recalled based on a noisy and imcomplete copy of itself, a function that is commenly demonstratedof the brain.
Confidently analyze, interpret and act on financial data with this practical introduction to the fundamentals of financial data science. Master the fundamentals with step-by-step introductions to core topics will equip you with a solid foundation for applying data science techniques to real-world complex financial problems. Extract meaningful insights as you learn how to use data to lead informed, data-driven decisions, with over 50 examples and case studies and hands-on Matlab and Python code. Explore cutting-edge techniques and tools in machine learning for financial data analysis, including deep learning and natural language processing. Accessible to readers without a specialized background in finance or machine learning, and including coverage of data representation and visualization, data models and estimation, principal component analysis, clustering methods, optimization tools, mean/variance portfolio optimization and financial networks, this is the ideal introduction for financial services professionals, and graduate students in finance and data science.
Applications of cryptography are plenty in everyday life. This guidebook is about the security analysis or 'cryptanalysis' of the basic building blocks on which these applications rely. Rather than covering a variety of techniques at an introductory level, this book provides a comprehensive and in-depth treatment of linear cryptanalysis. The subject is introduced from a mathematical point of view, providing an overview of the most influential papers on linear cryptanalysis and placing them in a consistent framework based on linear algebra. A large number of examples and exercises are included, drawing upon practice as well as theory. The book is accessible to students with no prior knowledge of cryptography. It covers linear cryptanalysis starting from the basics, including linear approximations and trails, correlation matrices, automatic search, key-recovery techniques, up to advanced topics, such as multiple and multidimensional linear cryptanalysis, zero-correlation approximations, and the geometric approach.
The electrohydrodynamic force of a surface dielectric barrier discharge (SDBD) has been well-developed for flow control applications during recent decades. In the present paper, a geometrical modification of the SDBD plasma actuator has been applied to induce a vectorised normal flow at the trailing edge of a NACA0015 aerofoil. The pitot-tube velocity measurements of the normal jet along its propagation direction revealed formation of vortices at the centre of the electrode distance played a role in flow control authority of the jet. The aerodynamic operation of the double-SDBD structure as a virtual flap was assessed versus a single counter-flow jet of a floating structure at pre- and post-stall angles of attack at low Reynolds numbers. It was found that at small angles of attack, the steady counter-flow gives the most effectiveness of lift enhancement in low velocity, whereas in the higher velocity the unsteady one results in more efficacy. The efficiency of both steady and unsteady normal jets increased considerably at high angles such that a lift coefficient improvement of 38% was achieved at $\alpha = 14^\circ $. In the higher velocity, the plasma induced vertical flow acts like a Gurney flap, causing lift increase at high angles by affecting the vortical structures at the trailing edge. Evaluating the obtained results recommended employment of the induced normal flow as a virtual flap at high angles of attack in the unsteady actuation mode.
In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.
We propose a novel multiple-scale spatial marching method for flows with slow streamwise variation. The key idea is to couple the boundary region equations, which govern large-scale flow evolution, with local exact coherent structures that capture the small-scale dynamics. This framework is consistent with high-Reynolds-number asymptotic theory and offers a promising approach to constructing time-periodic finite-amplitude solutions in a broad class of spatially developing shear flows. As a first application, we consider a non-uniformly curved channel flow, assuming that a finite-amplitude travelling-wave solution of plane Poiseuille flow is sustained at the inlet. The method allows for the estimation of momentum transport and highlights the impact of the inlet condition on both the transport properties and the overall flow structure. We then consider a case with gradually decreasing curvature, starting with Dean vortices at the inlet. In this setting, small external oscillatory disturbances can give rise to subcritical self-sustained states that persist even after the curvature vanishes.
In the paper, we consider a two-dimensional free-surface flow past a single point vortex in fluid of infinite depth. The flow moves from left to right with uniform speed $c$ far upstream and is subject to the downward acceleration $g$ of gravity. A point vortex of circulation $\varGamma$ is located at depth $H$. The positive direction of circulation is counterclockwise. The flow is characterised by two dimensionless parameters which are the dimensionless vortex circulation $\gamma =\varGamma /(\textit{cH}\,)$ and the Froude number $ \textit{Fr}=c/\sqrt {gH}$. The goal of the paper is to find the solutions of the solitary wave type with one or several crests on the free surface. These solutions are waveless far downstream and have a vertical line of symmetry. We have established that for a fixed Froude number $ \textit{Fr}\le 0.8$, there exists a finite set of positive $\gamma$ for which the solutions of the solitary wave type occur.
Granular flow down an inclined plane is ubiquitous in geophysical and industrial applications. On rough inclines, the flow exhibits Bagnold’s velocity profile and follows the so-called $\mu (I)$ local rheology. On insufficiently rough or smooth inclines, however, velocity slip occurs at the bottom and a basal layer with strong agitation emerges below the bulk, which is not predicted by the local rheology. Here, we use discrete element method simulations to study detailed dynamics of the basal layer in granular flows down both smooth and rough inclines. We control the roughness via a dimensionless parameter, $R_a$, varied systematically from 0 (flat, frictional plane) to near 1 (very rough plane). Three flow regimes are identified: a slip regime ($R_a \lesssim 0.45$) where a dilated basal layer appears, a no-slip regime ($R_a \gtrsim 0.6$) and an intermediate transition regime. In the slip regime the kinematics profiles (velocity, shear rate and granular temperature) of the basal layer strongly deviate from Bagnold’s profiles. General basal slip laws are developed that express the slip velocity as a function of the local shear rate (or granular temperature), base roughness and slope angle. Moreover, the basal layer thickness is insensitive to flow conditions but depends somewhat on the interparticle coefficient of restitution. Finally, we show that the rheological properties of the basal layer do not follow the $\mu (I)$ rheology, but are captured by Bagnold’s stress scaling and an extended kinetic theory for granular flows. Our findings can help develop more predictive granular flow models in the future.
Autonomous manoeuvre decision-making is essential for enhancing the survivability and operational effectiveness of unmanned aerial vehicles in high-risk and dynamic air combat scenarios. To address the limitations of traditional air combat decision-making methods in dealing with complex and rapidly changing environments, this paper proposes an autonomous air combat decision-making algorithm based on hybrid temporal difference error-reward prioritised experience replay with twin delayed deep deterministic policy gradient. This algorithm constructs a closed-loop learning system from environmental interaction to policy optimisation, addressing the key challenges of slow convergence and insufficient identification of critical tactical decisions in autonomous air combat. A hybrid priority metric leveraging reward backpropagation and temporal difference error filter is introduced to optimise the learning of high-value experiences while balancing sample diversity and the reuse of critical experiences. To reduce excessive trial and error in the initial phase, an integrated reward function combining task rewards and auxiliary guidance rewards is designed using the reward reshaping method to guide the agent on how to choose a manoeuvre strategy. Based on the established three-dimensional close-range air combat game model, simulation validations were conducted for both basic manoeuvre and expert system engagements. The results demonstrate that the proposed autonomous air combat manoeuvre decision-making algorithm exhibits higher learning efficiency and convergence stability. It can rapidly identify high-value manoeuvres and effectively formulate rational yet superior tactical strategies in the face of complex battlefield scenarios, demonstrating obvious benefits in enhancing combat effectiveness and tactical adaptability.
The flow past a $6:1$ prolate spheroid at a moderate pitch angle $\alpha =10^\circ$ is investigated with a focus on the turbulent wake in a high-fidelity large eddy simulation (LES) study. Two length-based Reynolds numbers, ${\textit{Re}}_L=3\times 10^4$ and $9\times 10^4$, and four Froude numbers, ${\textit{Fr}} = \infty \text{(unstratified)}, 6, 1.9 \text{ and }1$, are selected for the parametric study. Spectral proper orthogonal decomposition (SPOD) analysis of the flow reveals the leading coherent modes in the unsteady separated flow at the tail of the body. At the higher ${\textit{Re}}_L=9\times 10^4$, a high-frequency spanwise flapping of shear layers on either side of the body is observed in the separated boundary layer for all cases. The flapping does not perturb the lateral symmetry of the wake. At ${\textit{Fr}}=\infty$, a low-frequency oscillating laterally asymmetric mode, which is found in addition to the shear-layer mode, leads to a sidewise unsteady lateral load. All temporally averaged wakes at ${\textit{Re}}=9\times 10^4$ are found to be spanwise symmetric in the mean as opposed to the lower ${\textit{Re}}=3\times 10^4$, at which the ${\textit{Fr}}=\infty \text{ and }6$ wakes exhibit asymmetry. The turbulent kinetic energy (TKE) budget is compared among cases. Here, ${\textit{Fr}}=\infty$ exhibits higher production and dissipation compared with ${\textit{Fr}}=6 \text{ and }1.9$. The streamwise vortex pair in the wake induces a significant mean vertical velocity ($U_z$). Therefore, in contrast to straight-on flow, the terms involving gradients of $U_z$ matter to TKE production. Buoyancy reduces $U_z$ and also the Reynolds shear stresses involving $u^{\prime}_z$. Through this indirect mechanism, buoyancy exerts control on the wake TKE budget, albeit being small relative to production and dissipation. Buoyancy, through the baroclinic torque, is found to qualitatively affect the streamwise vorticity. In particular, the primary vortex pair is extinguished in the intermediate wake and two new vortex pairs form with opposite-sense circulation relative to the primary.
This paper investigates the transient characteristics of uniform momentum zones (UMZs) in a rapidly accelerating turbulent pipe flow using direct numerical simulation datasets starting from an initial friction Reynolds number ($Re_{\tau 0}) = 500$ up to a final friction Reynolds number ($Re_{\tau 1}) = 670$. Instantaneous UMZs are identified following the identification methodology proposed by Adrian et al. (2000 J. Fluid Mech. vol. 422, pp. 1–54). The present results reveal that, as the flow rapidly accelerates, the average number of UMZs drops. However, as the flow recovers, it is regained. This result is complemented by the temporal evolution of the average number of internal shear layers. The temporal evolution of UMZs reveals that UMZs sustain their hierarchical flow arrangement with slower zones near the wall and faster zones away from the wall throughout the rapid turbulent flow acceleration. The results show that UMZs speed up during the inertial and pre-transition phases, and progressively slow down during the transition and core-relaxation stages. It is also revealed that UMZs near the wall respond first to flow instability and show earlier signs of recovery based on UMZ kinematic results. Finally, the dominant quadrant behaviour of Reynolds shear stress within UMZs has been investigated. It is found that, prior to the flow excursion, the UMZs nearest to the wall are always $Q2$ dominated, while the rest of the UMZs are always $Q4$ dominated. This behaviour is detected to not change during and after the flow excursion, suggesting that this is a characteristic behaviour of UMZs in accelerating turbulent wall-bounded flows.
For smectic C* (SmC*) liquid crystals, configured in a bookshelf-type geometry between two horizontal parallel plates, with the bottom plate fixed and the top plate free to move, it is known from experiment that pumping can occur when an electric field is applied, i.e. an upward movement of the top plate through mechanical vibrations when the electric field is suddenly reversed. In this paper we revisit an earlier mathematical model for fast electric field reversal by removing an assumption made there on the velocity field; instead, we arrive at a time-dependent, two-dimensional squeeze-film model, which can ultimately be formulated in terms of a highly nonlinear integro-differential equation. Subsequent analysis leads to an unexpected solvability condition involving the five SmC* viscosity coefficients regarding the existence and uniqueness of solutions. Furthermore, we find that, when solutions do exist, they imply that the plate can move down as well as up, with the final resting position turning out to be dependent on the initial conditions; this is in stark contrast to the results of the earlier model.
Permanent gravity waves propagating in deep water, spanning amplitudes from infinitesimal to their theoretical limiting values, remain a classical yet challenging problem due to its inherent nonlinear complexities. Traditional analytical and numerical methods encounter substantial difficulties near the limiting wave condition due to singularities at sharp wave crests. In this study, we propose a novel hybrid framework combining the homotopy analysis method (HAM) with machine learning (ML) to efficiently compute convergent series solutions of Stokes waves in deep water for arbitrary wave amplitudes from small to theoretical limiting values, which show excellent agreement with established benchmarks. We introduce a neural network trained using only 20 representative cases whose series solution are given by means of HAM, which can rapidly predict series solutions across arbitrary steepness levels, substantially improving computational efficiency. Additionally, we develop a neural network to gain the inverse mapping from the conformal coordinates $(\theta , r)$ to the physical coordinates $(x,y)$, facilitating explicit and intuitive representations of series solutions in physical plane. This HAM–ML hybrid framework represents a powerful and efficient approach to compute convergent series in a whole range of physical parameters for water waves with arbitrary wave height including even limiting waves. In this way we establish a new paradigm to quickly obtain convergent series solutions of complex nonlinear systems for a whole range of physical parameters, thereby significantly broadening the scope of series solutions that can be easily gained by means of HAM even for highly nonlinear problems in science and engineering.
Magneto-gravity-precessional instability, which results from the excitation of resonant magneto-inertia-gravity (MIG) waves by a background shear generic to precessional flows, is addressed here. Two simple background precession flows, that of Kerswell (1993 Geophys. Astrophys. Fluid Dyn. vol 72, no. 1–4, pp. 107–144), and that of Mahalov (1993 Phys. Fluids A: Fluid Dyn. vol. 5, no. 4, pp. 891–900), are considered. We analytically perform an asymptotic analysis to order ${ O}(\varepsilon ),$ where $\varepsilon$ denotes the Poincaré number, i.e. the precession parameter, and determine the maximum growth rate of the destabilizing subharmonic resonances of MIG waves: that between two fast modes, that between two slow modes and that between a fast mode and a slow mode (mixed modes). The domains of the $(K_0 B_0/\varOmega _0, N/\varOmega _0)\hbox{-}$plane for which this instability operates are identified, where $1/K_0$ denotes a characteristic length scale, $B_0$ is the unperturbed Alfvén velocity, $\varOmega _0$ is the rotation rate and $N$ denotes the Brunt–Väisälä frequency. We demonstrate that the $N\rightarrow 0$ limit is, in fact, singular (discontinuous). At large $K_0B_0/\varOmega _0,$ stable stratification acts to suppress the destabilizing resonance between two fast modes as well as that between two slow modes, whereas it revives the destabilizing resonance between a fast mode and a slow mode provided $N\lt \varOmega _0,$ because, without stratification, the maximal growth rate of this instability approaches zero as $K_0B_0/\varOmega _0\rightarrow +\infty .$ This would be relevant for the generation of the mean electromotive force, and hence, the $\alpha \hbox{-}$effect in helical magnetized precessional flows under weak stable stratification. Diffusive effects on the instability is considered in the simple case where the magnetic and thermal Prandtl numbers are both equal to one.
We examine the linear stability of a shear flow driven by wind stress at the free surface and rotation at the lower boundary, mimicking oceanic flows influenced by surface winds and the Earth’s rotation. The linearised eigenvalue problem is solved using the Chebyshev spectral collocation method and a long-wave asymptotic analysis. Our results reveal new long-wave instability modes that emerge for non-zero rotational Reynolds numbers. It is observed that the most unstable mode, characterised by the lowest critical parameters, corresponds to long-wave spanwise disturbances with vanishing streamwise wavenumber. The asymptotic analysis, which shows excellent agreement with numerical results, analytically confirms the existence of this instability. Thus, the present study demonstrates the hitherto unreported combined influence of wind stress and the Earth’s rotation on ocean dynamics.