다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

Real and Complex Analysis

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.

Systematic and multifactor risk models revisited

Multi-red controller for router fault accommodation

"Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanation via "model-free control" and the corresponding "intelligent" controllers [Fliess M., Join C. (2013). Int. J. Contr., 86, 2228-2252], which are already successfully applied in many concrete situations. As a byproduct this setting provides also a slightly different presentation of homeostasis, or "exact adaptation," where the working conditions are assumed to be "mild." Several convincing, but academic, computer simulations are provided and discussed.

/pdf/dynamic-compensation-and-homeostasis-a-feedback-control-a8re28sq9x.pdf

Dynamic compensation and homeostasis: a feedback control perspective

Comparison of several types of differentiation algorithms has been performed in the paper Wang, Zheng, Efimov, and Perruquetti (2018) with application to a robotic blimp altitude control Unfortunately, one of the differentiation methods, the algebraic differentiator, has not been applied properly The clarifications and the new comparison results are given in this note

/pdf/comments-on-differentiator-application-in-altitude-control-3ql5vef0tw.pdf

Comments on ‘Differentiator application in altitude control for an indoor blimp robot’

In this paper, a sensor fault diagnosis and accommodation method is developed in nonlinear case. This method makes possible the sensor faults compensation. Its principle is based on the on-line detection, localization and estimation of a faulty element when a fault occurs on a nonlinear system. Then, a new control law is computed in order to compensate the fault effect on the system. The performances of the method are tested in simulation on a pilot plant: the three-tank benchmark.

Cédric Join

Papers

Systematic and multifactor risk models revisited

Multi-red controller for router fault accommodation

Dynamic compensation and homeostasis: a feedback control perspective

Comments on ‘Differentiator application in altitude control for an indoor blimp robot’

Sensor fault diagnosis and accommodation in nonlinear system