An optimal dynamic inversion approach for controlling a class of one-dimensional nonlinear distributed parameter systems

Combining the principles of dynamic inversion and optimization theory, a new approach is presented for stable control of a class of one-dimensional nonlinear distributed parameter systems, assuming the availability a continuous actuator in the spatial domain. Unlike the existing approximate-then-design and design-then-approximate techniques, here there is no need of any approximation either of the system dynamics or of the resulting controller. Rather, the control synthesis approach is fairly straight-forward and simple. The controller formulation has more elegance because we can prove the convergence of the controller to its steady state value. To demonstrate the potential of the proposed technique, a real-life temperature control problem for a heat transfer application is solved. It has been demonstrated that a desired temperature profile can be achieved starting from any arbitrary initial temperature profile.

Novel ABO(3) oxides related to perovskite and YAlO3 structure types in the La-B-V-O (B = Ni, Cu) systems

The synthesis, structure and magnetic properties of mixed-metal oxides of ABO(3) composition in the La-B-V-O (B = Ni, Cu) systems are described in the present paper. While the B = Ni oxides adopt GdFeO3-like perovskite structure containing disordered nickel and vanadium at the octahedral B site, La3Cu2VO9 crystallizes in a YAlO3-type structure. A detailed investigation of the superstructure of nominal La3Cu2VO9 by WDS analysis and Rietveld refinement of powder XRD data reveal that the likely composition of the phase is La13Cu9V4O38.5, where the Cu and V atoms are ordered in a root13a(h) (a(h) = hexagonal a parameter of YAlO3-like subcell) superstructure. Magnetic susceptibility data support the proposed superstructure consisting of triangular Cu-3 clusters. At low temperatures, the magnetic moment corresponds to S = 1/2 per Cu-3 cluster, while at high temperatures the behavior is Curie-Weiss like, showing S = 1/2 per copper. The present work reveals the contrasting behavior of La-Cu-V-O and La-Ni-V-O systems: while a unique line-phase related to YAlO3 structure is formed around La3Cu2VO9 Composition in the copper system, a continuous series of perovskite-GdFeO3 solid solutions, LaNi1-xVxO3 for 0 less than or equal to x less than or equal to 1/3 seems to be obtained in the nickel system, where the oxidation state of nickel varies from 3+ to 2+.

On Convergence of Differential Evolution Over a Class of Continuous Functions With Unique Global Optimum

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

Time variant reliability model updating in instrumented dynamical systems based on Girsanov's transformation

The problem of updating the reliability of instrumented structures based on measured response under random dynamic loading is considered. A solution strategy within the framework of Monte Carlo simulation based dynamic state estimation method and Girsanov's transformation for variance reduction is developed. For linear Gaussian state space models, the solution is developed based on continuous version of the Kalman filter, while, for non-linear and (or) non-Gaussian state space models, bootstrap particle filters are adopted. The controls to implement the Girsanov transformation are developed by solving a constrained non-linear optimization problem. Numerical illustrations include studies on a multi degree of freedom linear system and non-linear systems with geometric and (or) hereditary non-linearities and non-stationary random excitations.

Time variant reliability model updating in instrumented dynamical systems based on Girsanov’s transformation

The problem of updating the reliability of instrumented structures based on measured response under random dynamic loading is considered. A solution strategy within the framework of Monte Carlo simulation based dynamic state estimation method and Girsanov’s transformation for variance reduction is developed. For linear Gaussian state space models, the solution is developed based on continuous version of the Kalman filter, while, for non-linear and (or) non-Gaussian state space models, bootstrap particle filters are adopted. The controls to implement the Girsanov transformation are developed by solving a constrained non-linear optimization problem. Numerical illustrations include studies on a multi degree of freedom linear system and non-linear systems with geometric and (or) hereditary non-linearities and non-stationary random excitations.

A Continuous Electrical Conductivity Model for Monolayer Graphene From Near Intrinsic to Far Extrinsic Region

We present a closed-form continuous model for the electrical conductivity of a single layer graphene (SLG) sheet in the presence of short-range impurities, long-range screened impurities, and acoustic phonons. The validity of the model extends from very low doping levels (chemical potential close to the Dirac cone vertex) to very high doping levels. We demonstrate complete functional relations of the chemical potential, polarization function, and conductivity with respect to both doping level and temperature (T), which were otherwise developed for SLG sheet only in the very low and very high doping levels. The advantage of the continuous conductivity model reported in this paper lies in its simple form which depends only on three adjustable parameters: the short-range impurity density, the long-range screened impurity density, and temperature T. The proposed theoretical model was successfully used to correlate various experiments in the midtemperature and moderate density regimes.

Simultaneous perturbation methods for adaptive labor staffing in service systems

We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

Minimum-Error-Probability CFO Estimation for Multiuser MIMO-OFDM Systems

We consider carrier frequency offset (CFO) estimation in the context of multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems over noisy frequency-selective wireless channels with both single- and multiuser scenarios. We conceived a new approach for parameter estimation by discretizing the continuous-valued CFO parameter into a discrete set of bins and then invoked detection theory, analogous to the minimum-bit-error-ratio optimization framework for detecting the finite-alphabet received signal. Using this radical approach, we propose a novel CFO estimation method and study its performance using both analytical results and Monte Carlo simulations. We obtain expressions for the variance of the CFO estimation error and the resultant BER degradation with the single- user scenario. Our simulations demonstrate that the overall BER performance of a MIMO-OFDM system using the proposed method is substantially improved for all the modulation schemes considered, albeit this is achieved at increased complexity.

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based lookahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

Morphological and functional characterisation of the burrow systems of six earthworm species (Lumbricidae)

Earthworm burrow systems are generally described based on postulated behaviours associated with the three ecological types. In this study, we used X-ray tomography to obtain 3D information on the burrowing behaviour of six very common anecic (Aporrectodea nocturna and Lumbricus terrestris) and endogeic (Aporrectodea rosea, Allolobophora chlorotica, Aporrectodea caliginosa, Aporrectodea icterica) earthworm species, introduced into repacked soil cores for 6 weeks. A simple water infiltration test, the Beerkan method, was also used to assess some functional properties of these burrow systems. Endogeic worms make larger burrow systems, which are more highly branched, less continuous and of smaller diameter, than those of anecic worms. Among the anecic species, L. terrestris burrow systems are shorter (9.2 vs 21.2 m) with a higher number (14.5 vs 23.5) of less branched burrows (12.2 vs 20.2 branches m(-1)), which are also wider (7.78 vs 5.16 mm) than those of A. nocturna. In comparison, the burrow systems made by endogeic species appeared similar to each other. However, A. rosea burrows were short and narrow, whereas A. icterica had a longer burrow system (15.7 m), more intense bioturbation intensity (refilled macropores or soil lateral compaction around them) and thus a greater number of burrows. Regarding water infiltration, anecic burrow systems were far more efficient due to open burrows linking the top and bottom of the cores. For endogeic species, we observed a linear relationship between burrow length and the water infiltration rate (R (2) = 0.49, p < 0.01). Overall, the three main characteristics significantly influencing water infiltration were burrow length, burrow number and bioturbation volume. This last characteristic highlighted the effect of burrow refilling by casts.