[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

By 2050, the world is expected to generate 340 billion tons of waste annually, increasing drastically from today’s 201 billion tons What a Waste 20: A Global Snapshot of Solid Waste Management to 2050 aggregates extensive solid waste data at the national and urban levels It estimates and projects waste generation to 2030 and 2050 Beyond the core data metrics from waste generation to disposal, the report provides information on waste management costs, revenues, and tariffs; special wastes; regulations; public communication; administrative and operational models; and the informal sector

What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050

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

System Identification I

Exact output control for a family of linear plants with parameter uncertainties

tasks Multivariate statistical models based on principal components are used to detect abnormal situations Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis Results from the DX 09 Diagnostic Challenge shown strong detection properties, whereas the need of further investigations in the diagnostic system

/pdf/faultbuster-data-driven-fault-detection-and-diagnosis-for-3l9h6sil9v.pdf

FaultBuster: data driven fault detection and diagnosis for industrial systems

Fault Tolerant Decentralized Nonlinear MPC for Fleets of Unmanned Marine Vehicles

Adaptation and Learning in Neural Networks Multiple Models Based Control of Mobile Robots

This paper presents two different approaches for developing the residual generation module of a model-based sensor fault detection (FD) system applied to the robot navigation problem. The first approach is based on the structural analysis. The second one exploits the structure of nonlinear geometric control theory to derive the nonlinear analytical redundancy (NLAR) tests for sensor navigation purposes. The robot sensor suite includes at least a Global Positioning System (GPS) antenna, an Inertial Measurement Unit (IMU), and two incremental optical encoders. Analysis of the residuals generated by these two presented methods shown that the structural analysis is the feasible way to treat the navigation sensor fault detection problem, as confirmed by the developed experimental results.

Sauro Longhi

Papers

Exact output control for a family of linear plants with parameter uncertainties

FaultBuster: data driven fault detection and diagnosis for industrial systems

Fault Tolerant Decentralized Nonlinear MPC for Fleets of Unmanned Marine Vehicles

Adaptation and Learning in Neural Networks Multiple Models Based Control of Mobile Robots

Residual generation approaches in navigation sensors fault detection applications