Robust topology optimization of structures with uncertainties in stiffness – Application to truss structures

A computational strategy is proposed for robust structural topology optimization in the presence of uncertainties with known second order statistics. The strategy combines deterministic topology optimization techniques with a perturbation method for the quantification of uncertainties associated with structural stiffness, such as uncertain material properties and/or structure geometry. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. This in turn leads to significant computational savings when compared with Monte Carlo-based optimization algorithms which involve multiple formations and inversions of the global stiffness matrix. Examples from truss structures are presented to show the importance of including the effect of controlling the variability in the final design. It is also shown that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.     

Loose eigenstructure assignment via rank-one LMI approach with application to transient response shaping in H ∞ control

This article proposes a new regional eigenstructure assignment via rank-one LMI approach. A gain parameter condition for the regional eigenvalue/eigenstructure assignment is newly derived. This assignment condition is easily combined with H _∞ design by means of enhanced LMI characterisation. In the present approach, the desired assignment of closed-loop eigenvalues (i.e. poles) are not previously fixed but constrained into individual assignment regions to bring us more design freedom than classical exact assignment. The regional assignment discussed in this article never falls into an ordinary LMI root clustering because the union of assignment regions is disjoint in general. Each closed-loop eigenvector is also constrained into individual alignment cone. To show the practical use of the extra design freedom brought by regional assignment, a transient response shaping in H _∞ design is also discussed. A useful design algorithm based on linearisation algorithm is proposed.     

A review of time–frequency matched filter design with application to seizure detection in multichannel newborn EEG

This paper presents a novel design of a time–frequency (t–f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn seizure using multichannel EEG signals. The solution reduces to two possible t–f approaches that use a general formulation of t–f matched filters (TFMFs) based on the Wigner–Ville and cross Wigner–Ville distributions, and a third new approach based on the signal ambiguity domain representation; referred to as Radon-ambiguity detector. This contribution defines a general design formulation and then implements it for newborn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t–f kernels in terms of classification accuracy using real newborn EEG signals.Experimental results show that the detection method which uses TFMFs based on the cross Wigner–Ville distribution outperforms other approaches including the existing TFMF-based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distribution, achieve higher detection accuracies compared to the ones which use other reduced-interference t–f kernels.     

Quantitative measure for nonlinear unstable systems based on the region of attraction and its application to designing parameter optimization – inverted pendulum example

Quantitative stability measures for mechanical systems are highly needed. However, only a few such measures have been proposed for nonlinear systems. In this paper, a quantitative measure of stability for nonlinear systems based on the region of attraction (ROA) is proposed, and the measure is applied to parameter optimization of mechanical systems: multi-link inverted pendulum example. Recently, some techniques for calculating ROAs have been suggested; however, obtaining an accurate estimate of a ROA remains computationally demanding. We illustrate two techniques for efficiently estimating the proposed measure and apply them to the design parameter optimization problem for maximizing the stability measure. A number of simulations show the effectiveness of the proposed method.     

Multi-sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models

Electronic Markets - Our knowledge on differences in business model characteristics of thriving and failing Multi-Sided Platforms in competitive B2B networks (B2B-MSP) and potential influences of...     

A fuzzy random resource-constrained scheduling model with?multiple projects and its application to a working procedure in?a?large-scale water conservancy and hydropower construction project

The aim of this paper is to deal with resource-constrained multiple project scheduling problems (rc-mPSP) under a fuzzy random environment by a hybrid genetic algorithm with fuzzy logic controller (flc-hGA), to a large-scale water conservancy and hydropower construction project in the southwest region of China, whose main project is a dam embankment. The objective functions in this paper are to minimize the total project time (that is the sum of the completion time for all projects) and to minimize the total tardiness penalty of multiple projects, which is the sum of penalty costs for all the projects. After describing the problem of the working procedure in the project and presenting the mathematical formulation model of a resource-constrained project scheduling problem under a fuzzy random environment, we give some definitions and discuss some properties of fuzzy random variables. Then, a method of solving solution sets of fuzzy random multiple objective programming problems is proposed. Because traditional optimization techniques could not cope with the rc-mPSP under a fuzzy random environment effectively, we present a new approach based on the hybrid genetic algorithm (hGA). In order to improve its efficiency, the proposed method hybridized with the fuzzy logic controller (flc) concept for auto-tuning the GA parameters is presented. For the practical problems in this paper, flc-hGA is proved the most effective and most appropriate compared with other approaches. The computer generated results validate the effectiveness of the proposed model and algorithm in solving large-scale practical problems.