Evolution of cooperation in lattice population with adaptive interaction intensity

We study the evolutionary Prisoner’s Dilemma game among individuals endowed with adaptively interaction intensity. Individuals adjust their interaction intensity according to the rules “payoff increase-high intensity, payoff decrease-low intensity”: if an individual’s payoff increases compared with that in the previous generation, he raises his interaction intensity; otherwise, he reduces the probability of interaction. We find that if individuals can adjust their interaction intensity with a proper scale, cooperation can be promoted. Interestingly, individuals with low interaction intensity usually hold the boundary of cooperator cluster. Such spatial distribution can alleviate the exploitation from defectors to cooperators since the interaction between cooperators and defectors is weakened. We hope our work can yield some insight into investigation of the evolution of cooperation in structured population.     

Local adaptive sampling for wireless sensor network powered by energy harvesting

For a sensor network, energy limitation is always a key factor to affect the continuous work of a sensor node. A good idea is harvesting energy from the environment to support the node to work continuously. However, energy from environment is varied with time, weather and season. So in order to use the varied environment energy, it is necessary to find a way to achieve real-time monitoring and adaptive working. In this paper, an algorithm called “Adaptive Sampling” was proposed to adapt the sample mode to the present energy condition. Simulation proves that the algorithm can make the CO₂ sensor flexibly achieve adaptive sampling under different energy condition with the least MSE 2.7767. This algorithm can be widely used in wireless sensor network power by energy harvesting for local adaptive sampling.     

Performance-based design and analysis of flexible composite propulsors

Advanced composite propellers, turbines, and jet engines have become increasingly popular in part because of their ability to provide improved performance over traditional metallic rotors through exploitation of the intrinsic bend–twist coupling characteristics of anisotropic composite materials. While these performance improvements can be significant from a conceptual perspective, the load-dependent deformation responses of adaptive blades make the design of these structures highly non-trivial. Hence, it is necessary to understand and predict the dependence of the deformations on the geometry, material constitution, and fluid–structure interaction responses across the entire range of expected loading conditions.The objective of this work is to develop a probabilistic performance-based design and analysis methodology for flexible composite propulsors. To demonstrate the method, it is applied for the design and analysis of two (rigid) metallic and (flexible) composite propellers for a twin-shafted naval combatant craft. The probabilistic operational space is developed by considering the variation of vessel thrust requirements as a function of the vessel speed and wave conditions along with the probabilistic speed profiles. The performance of the metallic and composite propellers are compared and discussed. The implications of load-dependent deformations of the flexible composite propeller on the operating conditions and the resulting performance with respect to propeller efficiency, power demand, and fluid cavitation are presented for both spatially uniform and varying flows. While the proposed framework is demonstrated for marine propellers, the methodology can be generally applied for any marine, aerospace, or wind energy structure that must operate in a wide range of loading conditions over its expected life.     

Analysis of the bandwidth of resonant spectra for tilted fiber grating

Based on the coupled-mode equations, the formula of null-null bandwidth for tilted fiber grating is deduced in this paper. Numerical simulations and theoretical analysis of the effects of the tilt angle on bandwidth and maximum reflectivity are performed. Furthermore, under the maximum reflection of 95%, the relation of bandwidth with the tilt angle, modulation amplitude, and the needed length of grating are demonstrated in two three-dimensional diagrams. It reveals that the grating tilt can obviously narrow the bandwidth, especially in the case of large modulation amplitude and grating length, which is meaningful to its application in optical communications and fiber sensing.     

Adaptive,Delayed-Acceptance MCMC for Targets With Expensive Likelihoods

When conducting Bayesian inference, delayed-acceptance (DA) Metropolis–Hastings (MH) algorithms and DA pseudo-marginal MH algorithms can be applied when it is computationally expensive to calculate the true posterior or an unbiased estimate thereof, but a computationally cheap approximation is available. A first accept-reject stage is applied, with the cheap approximation substituted for the true posterior in the MH acceptance ratio. Only for those proposals that pass through the first stage is the computationally expensive true posterior (or unbiased estimate thereof) evaluated, with a second accept-reject stage ensuring that detailed balance is satisfied with respect to the intended true posterior. In some scenarios, there is no obvious computationally cheap approximation. A weighted average of previous evaluations of the computationally expensive posterior provides a generic approximation to the posterior. If only the k-nearest neighbors have nonzero weights then evaluation of the approximate posterior can be made computationally cheap provided that the points at which the posterior has been evaluated are stored in a multi-dimensional binary tree, known as a KD-tree. The contents of the KD-tree are potentially updated after every computationally intensive evaluation. The resulting adaptive, delayed-acceptance [pseudo-marginal] Metropolis–Hastings algorithm is justified both theoretically and empirically. Guidance on tuning parameters is provided and the methodology is applied to a discretely observed Markov jump process characterizing predator–prey interactions and an ODE system describing the dynamics of an autoregulatory gene network. Supplementary material for this article is available online.     

Adaptive-observer-based output-constrained tracking of a class of arbitrarily switched uncertain non-affine nonlinear systems

This paper addresses an adaptive output-feedback tracking problem of arbitrarily switched pure-feedback nonlinear systems with time-varying output constraints and unknown control directions. In this work, the tracking problem of switched non-affine nonlinear systems with output constraints is transformed into the stabilization problem of switched unconstrained affine systems. The main contribution of this paper is to present a universal formula for constructing an adaptive state-observer-based tracking controller with only two adaptive parameters by using the common Lyapunov function method. These adaptive parameters in the proposed control scheme are derived using the function approximation technique and a priori knowledge of the signs of control gain functions is not required. The theoretical analysis is presented for the Lyapunov stability and the constraint satisfaction of the resulting closed-loop system in the presence of arbitrary switchings.     

Interaction Model and Model Selection for Function-on-Function Regression

Regression models with interaction effects have been widely used in multivariate analysis to improve model flexibility and prediction accuracy. In functional data analysis, however, due to the challenges of estimating three-dimensional coefficient functions, interaction effects have not been considered for function-on-function linear regression. In this article, we propose function-on-function regression models with interaction and quadratic effects. For a model with specified main and interaction effects, we propose an efficient estimation method that enjoys a minimum prediction error property and has good predictive performance in practice. Moreover, converting the estimation of three-dimensional coefficient functions of the interaction effects to the estimation of two- and one-dimensional functions separately, our method is computationally efficient. We also propose adaptive penalties to account for varying magnitudes and roughness levels of coefficient functions. In practice, the forms of the models are usually unspecified. We propose a stepwise procedure for model selection based on a predictive criterion. This method is implemented in our R package FRegSigComp. Supplemental materials are available online.     

Coherent beam combination of an optical array using adaptive fiber optics collimators

A novel adaptive-fiber-optics-collimator (AFOC) compensating both piston-type and tip/tilt-type phase errors of output beam is introduced, and has been employed in experiments of coherent beam combination (CBC) of a delta distributed fiber array. Feedback control is realized using stochastic parallel gradient descent (SPGD) algorithm. Excellent CBC effect has been achieved when piston and tip/tilt errors among beamlets corrected. The necessity of wavefront tip/tilt control in CBC is verified. Experimental results exhibit great potential applications of this kind of AFOC in fiber amplifier arrays.