Set-membership state estimators based on cooperative output injections

To estimate validated bounds on the actual state vector of uncertain non-linear systems, cooperative output injections methods are proposed in this contribution. The aim of the output injections is to design set-membership state estimators that ensure the order-preserving property between the lower, actual and upper state trajectories. Based on a special sensors placement, continuous-time and event-triggered output injections are proposed to cope with the conservatism of the classical bounding system methods. Furthermore, based on some properties of monotone dynamical systems, the convergence of the proposed set-membership state estimators is shown. It is worth pointing out that the proposed set-membership state estimation method allows one: on one hand, to avoid the conservatism related to the use of similarity transformations usually required in the framework of interval observer design approaches, and on the other hand, to circumvent the pessimism accumulation related to the wrapping effect of set-valued iterative numerical schemes.

Deep Inhalation Eliminates Spatial Wrapping

This article is to provide evidence that deep inhalation can diminish spatial wrapping. Observation that provides further supporting evidence to our previous hypothesis that assumes inactive ‘peripheral brain’ can effectively eliminate the wrapping effect. This might be because the possible forcible reduction in the amount of oxygenated hemoglobin in the brain during the intended inhalation; in turn, different parts in the neocortex might be partially deactivated. Deep exhalations, however, return the perception of warping to its normal state of perception, possibly because of the oxygen-rich blood had returned back to the brain. Deep breathing therefore produces a perceptual rivalry; real perception (deep inhalation) versus illusive perception (deep exhalation).

Under the Hood of a Stand-Alone Lagrangian Reachability Tool

Tool presentation: We present work in progress on a stand-alone implementation of Lagrangian reachability, a recently introduced over-approximation technique for nonlinear continuous systems. Unlike the previous prototype, the current implementation does not depend on the over-approximation tool CAPD, and invokes an improved Lohner’s QR method to tame the infamous wrapping effect.

Study on Structure and Property of Polyester/Cotton/Silk Three-Component Sirofil Spun Composite Yarn

In this paper, by comparing the properties of polyester/cotton/silk three-component Sirofil composite yarn and traditional cotton yarn, polyester/cotton core-spun yarn, silk/cotton two-component Sirofil composite yarn, the structural characteristics of three-component Sirofil composite yarn and the relationship between its structure and property were explored. There was the inhibition effect of cotton fiber on polyester filament, so the frictional and cohesive force between cotton fiber and polyester filament could be enhanced. The tenacity of polyester filament and cotton fiber were very good and the wrapping effect of raw silk could enhance the cohesive force between cotton fibers, cotton fiber and polyester filament. Therefore, the strength of composite yarn was obviously increased and the abrasive resistance and anti-stripping wool property were also greatly enhanced. Secondly, hairiness between raw silk and fiber strand was very easily captured by raw silk and there was the wrapping effect of raw silk on fiber strand in the composite spinning, so some hairiness was covered by raw silk, which could decrease the length of hairiness or eliminate hairiness. Furthermore, much more uniform raw silk and polyester filaments were added, which could increase the yarn evenness of Sirofil composite yarn.

ON NAIVE TAYLOR MODEL INTEGRATION METHOD

Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an effective tool for reducing both the dependency problem and the wrapping effect. By construction, Taylor model methods appear particularly suitable for integrating nonlinear ODEs. In this paper, we analyze Taylor model based integration of ODEs and compare Taylor model with traditional enclosure methods for IVPs for ODEs. More advanced Taylor model integration methods are discussed in the algorithm (1). For clarity, we summarize the major steps of the naive Taylor model method as algorithm 1.