[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

The use of ambient assisted living technology, namely technology to improve the quality of life of people at home, is becoming a common trait of modern society. This technology, however, is difficult to be completely defined and classified, since it addresses many different human needs ranging from the physiological sphere to the psychological and social ones. In this chapter we focus on personal monitoring and data acquisition in smart homes, and propose the results of our research activities in the form of the description of three functional prototypes, each one addressing a specific need: an environmental monitoring system to measure the respiratory rate, a domotic architecture for both comfort assessment and user indoor localization, and a device for supporting mobility indoors. Each prototype description is followed by an experimental analysis and, finally, by considerations suggesting possible future developments in the very near future.

Personal monitoring and health data acquisition in smart homes

Robust continuous-time tracking and regulation for multirate sampled-data systems

/pdf/comparative-analysis-of-mobile-robot-localization-methods-20d6nmah67.pdf

Comparative Analysis of Mobile Robot Localization Methods Based On Proprioceptive and Exteroceptive Sensors

Population aging phenomena are increasing the attention to safety aspects for Elderly in care homes and hospitals: individuals that can autonomously evacuate should be helped during the evacuation by providing specific devices to them. Our activities are aimed by the design of “guidance” system for these categories, and to inquiry their impact on people motion. One of the most important evacuation steps is represented by the pre-movement phase: after the fire alarm sound, individuals continue to spend time in activities not directly connected to the evacuation. This phase could be very long. This work proposes an interactive system for the pre-movement time based on an experimental analysis of pre-movement time behaviors. The system is composed of an individual wearable device: the Zig-Bee-based localization module identifies people’s positions after the alarm and understands whether they are motionless; the interactive module gives a personal stimulus to the latecomers. Technical requirements are evaluated. The effectiveness of the system is investigated through simulations: up to 30 % reduction in total evacuation time can be obtained.

Sauro Longhi

Papers

Personal monitoring and health data acquisition in smart homes

Robust continuous-time tracking and regulation for multirate sampled-data systems

Comparative Analysis of Mobile Robot Localization Methods Based On Proprioceptive and Exteroceptive Sensors

Evacuation Aid for Elderly in Care Homes and Hospitals: An Interactive System for Reducing Pre-movement Time in Case of Fire

Experimental results of Discrete Time Variable Structure Control for Dynamic Positioning of Marine Surface Vessels