Some argue that any effort to limit capitalist domination of media is an underhanded backdoor effort to eliminate capitalism. It is true that for those who are socialists or who are critical of capitalism, having a commercial media system is problematic; the track record is that such a system will not have much enthusiasm for hard-edged social criticism, especially from the Left. But even those who believe in the legitimacy of capitalism need not believe that media should be the sole province of capitalists. Indeed, they should not, if they value democracy. Market economies can survive, arguably even prosper, without having U.S.-style commercial media systems. The real question is whether democracy can survive as anything more than a fig leaf covering concentrated private power. As for the argument that new digital technologies render regulation-!J meaning regulation that might impede private interests, not regulation that assists them-obsolete, it does not even pass rudimentary analysis. Merely being able to launch a Web site is wonderful and it has opened up the media system, but what is striking is how little effect it has had on the commercial media system so far. Not a single new commercially viable media content concern has been introduced on the Internet. The power of the market trumps the magic of the technology. If we want the Internet to spawn a new generation of viable media content providers, we cannot look to the market to produce that outcome. It will require explicit policies to generate such an outcome. Moreover, however one might want the Internet to develop, policy decisions will go a long way in pointing it in its ultimate direction. Global trade agreements, intellectual property laws and regulations, traditional media subsidies and regulations, and the like will be decisive in ,determining the future of the Internet. As with media writ large, the question is not whether we want regulation, but what type of regulation we want. It is worth noting as an addendum, that the Internet itself is a tremendous testament to public sector policymaking and cooperative economic and social activity. The Internet would never have developed if left to the market. The same is true of many other innovations in communication. in short, the public sector has a cornerstone role in communication, and fundamental interests to protect. This also suggests that the positive economic benefits from devel~ oping the media-information sector can, shou.ld, and do come from signifi~t public sector involvement. 20

Rich Media, Poor Democracy: Communication Politics in Dubious Times

News and News Sources: A Critical Introduction

In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the ad...

https://journals.sagepub.com/doi/pdf/10.1177/1475921718804132

A novel deep learning-based method for damage identification of smart building structures:

This paper presents the performance of a new magnetorheological elastomer-based semi-active/passive variable stiffness and damping isolator (VSDI) in a scaled building system. The force of the VSDI can be controlled in real time by varying the applied magnetic field. To demonstrate the performance of the VSDI, four prototypes are built and utilized in a scaled three-story building. A Lyapunov-based control strategy is employed and it is demonstrated that it works well for the scaled building system under the scaled El Centro earthquake motion. Experimental results show that the VSDIs significantly reduce the acceleration and relative displacement of the building floors.

https://iopscience.iop.org/article/10.1088/0964-1726/23/4/045014/pdf

Performance of a new magnetorheological elastomer isolation system

Machine learning in the Internet of Things: Designed techniques for smart cities

This paper is focused on the development of a damage detection indicator that combines a data driven baseline model (reference pattern obtained from the healthy structure) based on principal component analysis (PCA) and multivariate hypothesis testing. More pre- cisely, a test for the plausibility of a value for a normal population mean vector is performed. The results indicate that the test is able to accurately clasify random samples as healthy or not.

Detection of structural changes through principal component analysis (PCA) and multivariate statistical inference

Magnetorheological (MR) dampers are devices that can be use d for vibration re- duction in structures. However, to use these devices in an ef fective way, a precise modeling is required. In this sense, in this paper we consider a modified p arameter identification method of large-scale magnetorheological dampers which are repre sented using the normalized Bouc- Wen model. The main benefit of the proposed identification alg orithm is the accuracy of the parameter estimation. The validation of the parameter iden tification method has been carried out using a black box model of an MR damper in a smart base-isol ated benchmark building. Magnetorheological dampers are used in this numerical plat form both as isolation bearings as well as semiactive control devices.

Parameter identification of large-scale magnetorheological dampers in a benchmark building

Force-derivative feedback semi-active control of base-isolated buildings using large-scale MR fluid dampers

Structural Health Monitoring is a growing area of interest given the benefits obtained from its use This area includes different tasks in the damage identification process, among them, the most important is the damage detection at an early stage which enables to increase the security in mechanisms and systems, reducing risks and avoiding accidents As a contribution in this topic, this work presents a data-driven methodology for the detection and classification of damages by using multivariate data driven approaches and machine learning algorithms which are validated and compared by using data from real structures in order to determine its behavior In the methodology, PCA (Principal component analysis) and some pre-processing steps are used as the mechanisms to reduce data and build the features vector with relevant information about the different states of the structures under test This methodology is validated by using some aluminum plates which are instrumented and inspected by means of PZT transducers attached to them and working in in several actuation phases Results show a properly damage detection and classification of different simulated and real-damages

Francesc Pozo Montero

Papers

Detection of structural changes through principal component analysis (PCA) and multivariate statistical inference

Parameter identification of large-scale magnetorheological dampers in a benchmark building

Force-derivative feedback semi-active control of base-isolated buildings using large-scale MR fluid dampers

Structural damage detection and classification based on machine learning algorithms

Structural health monitoring and condition monitoring applications: sensing, distributed communication and processing