[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

In this editorial, we provide an overview of the content of the Special Issue on “Smart Homes”. The aim of this Special Issue is to provide a comprehensive collection of some of the current state-of-the-art technologies in the context of smart homes, together with new advanced theoretical and technological solutions that enable smart technology diffusion into homes.

https://www.mdpi.com/1424-8220/19/4/836/pdf

Special Issue on “Smart Homes”: Editors’ Notes

In asymptotic tracking of a continuous-time plant using digital control, the undesirable ripple which may arise between sampling instants may become unacceptable, and should be avoided; this is achieved if a continuous-time internal model of reference signals is included in the forward path of the feedback control system. In this paper, a method for obtaining such a continuous-time internal model for sinusoidal-exponential reference signals and disturbance functions containing a linear function of time as a factor, is derived for the case when the only uncertainties about the description of the plant concern the values of some "physical" parameters. Such a method allows the control requirements to be robustly satisfied, at least in a neighbourhood of the nominal "physical" parameters of the plant to be controlled and, in particular, a continuous-time null steady-state error response to be guaranteed for all the values of the physical parameters in such a neighbourhood of the nominal ones-together with a ripple-free dead-beat error convergence at the nominal parameters. This can be obtained under conditions which are weaker than the Davison one, and are expressed in terms of the description of the original continuous-time plant. >

Robust ripple-free tracking and regulation under uncertainties of physical parameters for ramp-exponential signals

Prospective ISO 22400 for the challenges of human-centered manufacturing

The study and analysis of motorcycle’s critical driving situations, falls and accidents, are difficult to be described analitically because of the simultaneity of different and complex phenomena which make the dynamics quite elaborate. However, the controllers for active safety systems of motorcycles cannot be synthesized regardless by a suitable analytic model of the vehicle dynamic. In this paper, an analytic model able to describe the strong nonlinearities conveyed by the complex dynamic of a two-wheeled vehicle in curve, is presented. The model takes into account the coupling between the in-plane and out-of-plane dynamics which is accounted for the system nonlinear behaviour, the rear traction and the nonlinear features of the tyres dynamics. The major steps taken to derive such a model are described. Two sets of motion equations with different levels of accuracy have been developed making use of two different linearizations. The first one is obtained as linearization with respect to the roll and the steer angle around the vertical position, while the second is given by the linearization with respect to the steer angle only. A comparison between the two models for a given initial condition have been made by simulating a real scenario representing a critical vehicle condition, the lowside fall. Results show how these two models have no substantial differences in the description of the low side major dynamics making the simpler model a feasible choice for a model-based design of motorcycle’s control system.

/pdf/comparison-of-dynamical-behaviours-of-two-motorcycle-s-2xhahj69zz.pdf

Comparison of Dynamical Behaviours of Two Motorcycle’s Models in the Simulation of Lowside Fall

For a given family of linear, continuous-time parameter-dependent plants, the authors consider the problem of designing a controller which assures the exact tracking of an external reference signal. The solution is provided in terms of a linear periodic controller acting on a sampled-data control system structure. >

Sauro Longhi

Papers

Special Issue on “Smart Homes”: Editors’ Notes

Robust ripple-free tracking and regulation under uncertainties of physical parameters for ramp-exponential signals

Prospective ISO 22400 for the challenges of human-centered manufacturing

Comparison of Dynamical Behaviours of Two Motorcycle’s Models in the Simulation of Lowside Fall

Simultaneous tracking for a family of linear plants