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

An introduction to nonlinear fault diagnosis with an application to a congested internet router

We show that the open-loop transfer functions and the stability margins may be defined within the recent model-free control setting. Several convincing computer experiments are presented including one which studies the robustness with respect to delays.

https://hal.archives-ouvertes.fr/hal-00966107/document

Stability margins and model-free control: A first look

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/0520.pdf

Revisiting some practical issues in the implementation of model-free control

We are designing, perhaps for the first time, closed-loop fault-tolerant control for uncertain nonlinear systems. Our solution is based on a new algebraic estimation technique of the derivatives of a time signal, which • yields good estimates of the unknown parameters and of the residuals, i.e., of the fault indicators, • is easily implementable in real time, • is robust with respect to a large variety of noises, without any necessity of knowing their statistical properties. Convincing numerical simulations are provided via a popular case-study in the diagnosis community, namely the three-tank system, which may be characterized as a flat hybrid system.

https://hal.inria.fr/inria-00111208/document/

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Papers

An introduction to nonlinear fault diagnosis with an application to a congested internet router

Stability margins and model-free control: A first look

Revisiting some practical issues in the implementation of model-free control

Stability margins and model-free control: A first look

Closed-loop fault-tolerant control for uncertain nonlinear systems