Averaging analysis of periodically forced fluid networks

For a lumped parameter model of a flow network driven by a periodic generator, we apply averaging to find an approximate solution and analyze its stability. The approximate solution has three parts: mean flow due to the resistive effects of branches, a time-periodic part due to “inductive” effects, and a mean flow average correction due to the interaction of nonlinear and time varying effects. We present an example that may explain the scenario leading to venous diseases. It is shown that the widening of a branch in a venous network leads to an increase in the AC flow and a decrease in the DC flow through that branch, thus increasing the stress on venous valves, and consequently leading to a further increase in the effective width of the vein.     

Stochastic nonlinear stabilization — II: Inverse optimality

After considering the stabilization of a specific class of stochastic nonlinear systems in a companion paper, in this second part, we address the classical question of when is a stabilizing (in probability) controller optimal and show that for every system with a stochastic control Lyapunov function it is possible to construct a controller which is optimal with respect to a meaningful cost functional. Then we return to the problem from Part I and design an optimal backstepping controller whose cost functional includes penalty on control effort and which has an infinite gain margin.     

Achieving IT‐based synergies through regulation‐oriented and consensus‐oriented IT governance capabilities

This study aims at exploring the IT governance capabilities that enable organizations to achieve IT‐based synergies. Following existing work on the contextualization of theories and drawing on the resource‐based view of the firm (RBV), we develop an RBV of IT‐based synergies in two steps. First, we adopt existing context‐specific constructs and relationships from prior work on IT governance capabilities, IT relatedness, and synergies to develop a preliminary contextualization of the RBV. Second, to further refine our theoretical framework, we conduct an exploratory field study that includes interviews with 26 CIOs and other IT executives from 21 multibusiness firms. Our findings suggest that IT governance capabilities lead to IT‐based synergies through IT relatedness and business process relatedness. We found regulation‐oriented IT governance capabilities (IT roles and IT processes) to increase IT relatedness, while consensus‐oriented IT governance capabilities (IT groups and relational capabilities) had a positive effect on business process relatedness. Our results suggest that, in isolation, IT and business process relatedness lead to IT cost synergies, while collectively enabling IT‐induced business synergies. Our study is among the first to treat IT relatedness as an endogenous construct and to explicitly integrate business process relatedness into the IT governance domain. Our context‐specific decomposition of IT governance capabilities helps to better explain their links to IT and business process relatedness. These findings contribute to a better understanding of the tension between IT‐based synergies and business‐IT alignment. Decision‐makers are guided in developing IT governance capabilities to achieve IT‐based synergies.     

Active fault detection and control: Unified formulation and optimal design

A new unified formulation of the active fault detection and control problem for discrete-time stochastic systems and its optimal solution are proposed. The problem formulation stems from the optimal stochastic control problem and includes important special cases: an active detector and controller, an active detector and input signal generator, and an active detector with a given input signal generator. The optimal solution is derived using the so-called closed loop information processing strategy. This strategy respects the influence of the current decision and/or input on the future behavior of the observed system, allows penalizing future wrong decisions, and improves the quality of fault detection. The proposed formulation and obtained solution also provide better understanding of the active fault detection and its relation to the optimal stochastic control. The results are illustrated in numerical examples.     

Computing multiple-output regression quantile regions

A procedure relying on linear programming techniques is developed to compute (regression) quantile regions that have been defined recently. In the location case, this procedure allows for computing halfspace depth regions even beyond dimension two. The corresponding algorithm is described in detail, and illustrations are provided both for simulated and real data. The efficiency of a Matlab implementation of the algorithm¹ is also investigated through extensive simulations.     

Virtual Mechatronic/Robotic laboratory – A step further in distance learning

The implementation of the distance learning and e-learning in technical disciplines (like Mechanical and Electrical Engineering) is still far behind the grown practice in narrative disciplines (like Economy, management, etc.). This comes out from the fact that education in technical disciplines inevitably involves laboratory exercises and this fact drastically increases the complexity of a potential e-learning system. New approach and new specific knowledge are needed to develop such a system. We expect to meet the requirements of distance learning by developing the software-based laboratory exercises, i.e., a virtual laboratory. To fully substitute a physical system like laboratory equipment, one must emulate its full dynamics. The mathematical model in the form of differential equations will be applied to calculate dynamics and provide the data that would otherwise be measured on a physical system – this means simulation.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Stochastic source seeking for nonholonomic unicycle

We apply the recently introduced method of stochastic extremum seeking to navigate a nonholonomic unicycle towards the maximum of an unknown, spatially distributed signal field, using only the measurement of the signal at the vehicle’s location but without the measurement of the vehicle’s position. Keeping the forward velocity constant and controlling only the angular velocity, we design a stochastic source seeking control law which employs excitation based on filtered white noise, rather than sinusoidal perturbations used in the existing work. We study stability with the help of stochastic averaging theorems that we recently developed for general nonlinear continuous-time systems with stochastic perturbations. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source. We characterize the convergence speed explicitly and provide design guidelines for maximizing it, as well as for minimizing the residual set near the source. We present a detailed simulation study, including a study of the effect of saturation on the steering input.     

Boundary control and estimation of reaction–diffusion equations on the sphere under revolution symmetry conditions

ABSTRACT

Recently, the problem of designing boundary controllers and observers for unstable linear constant-coefficient reaction–diffusion equation on N-balls has been solved by means of the backstepping method. However, the extension of these results to spatially varying coefficients is far from trivial. This work deals with radially varying reaction coefficients under revolution symmetry conditions on a sphere (the three-dimensional case). Under these conditions, the equations become singular in the radius. When applying the backstepping method, a similar type of singularity appears in the backstepping control and observer kernel equations. However, with a simple scaling transformation, we are able to reduce the singular equation to a regular equation, which turns out to be the same kernel equations appearing when using the one-dimensional backstepping method. In addition, the scaling transformation allows us to prove stability in the H ¹ space.     

Library of sophisticated functions for analysis of nuclear spectra

In the paper we present compact library for analysis of nuclear spectra. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting. The functions can process one- and two-dimensional spectra. The software described in the paper comprises a number of conventional as well as newly developed methods needed to analyze experimental data.

Program summary

Program title: SpecAnalysLib 1.1Catalogue identifier: AEDZ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEDZ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 42 154No. of bytes in distributed program, including test data, etc.: 2 379 437Distribution format: tar.gzProgramming language: C++Computer: Pentium 3 PC 2.4 GHz or higher, Borland C++ Builder v. 6. A precompiled Windows version is included in the distribution packageOperating system: Windows 32 bit versionsRAM: 10 MBWord size: 32 bitsClassification: 17.6Nature of problem: The demand for advanced highly effective experimental data analysis functions is enormous. The library package represents one approach to give the physicists the possibility to use the advanced routines simply by calling them from their own programs. SpecAnalysLib is a collection of functions for analysis of one- and two-parameter γ-ray spectra, but they can be used for other types of data as well. The library consists of sophisticated functions for background elimination, smoothing, peak searching, deconvolution, and peak fitting.Solution method: The algorithms of background estimation are based on Sensitive Non-linear Iterative Peak (SNIP) clipping algorithm. The smoothing algorithms are based on the convolution of the original data with several types of filters and algorithms based on discrete Markov chains. The peak searching algorithms use the smoothed second differences and they can search for peaks of general form. The deconvolution (decomposition - unfolding) functions use the Gold iterative algorithm, its improved high resolution version and Richardson-Lucy algorithm. In the algorithms of peak fitting we have implemented two approaches. The first one is based on the algorithm without matrix inversion - AWMI algorithm. It allows it to fit large blocks of data and large number of parameters. The other one is based on the calculation of the system of linear equations using Stiefel-Hestens method. It converges faster than the AWMI, however it is not suitable for fitting large number of parameters.Restrictions: Dimensionality of the analyzed data is limited to two.Unusual features: Dynamically loadable library (DLL) of processing functions users can call from their own programs.Running time: Most processing routines execute interactively or in a few seconds. Computationally intensive routines (deconvolution, fitting) execute longer, depending on the number of iterations specified and volume of the processed data.