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The paper presents some models based on artificial neural networks for particulate matter concentration forecasting. A methodology framework is proposed for selecting the best forecasting model from a set of neural networks models. First, two artificial neural network types (feed forward and radial basis) are analyzed for concentration forecasting of the particulate matter with diameter less than...
Accurate forecasting of fine particulate matter concentration in cities is an important problem that can be solved with efficient methods as those provided by computational intelligence, which apply a data driven approach. An example of such method is given by artificial neural networks that are universal approximators, providing very good solutions to time series forecasting. The paper presents a...
Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security. Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning. In this paper the authors present a method to assess the impact of security issues on the systems availability. A fuzzy model for estimating...
The growing rate of urban and industrial development leads to high levels of air pollution in most countries around the world. Because air pollution has a major impact on human health, monitoring and forecasting of the most important pollutants concentrations are very important. The modelling of the non-linear and complex phenomena associated to air pollution is successfully performed using artificial...
The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters...
The paper presents details on the development of an intelligent system for particulate matter (PM) air pollution monitoring, analysis and forecasting in two pilot cities, Ploiesti and Targoviste, in selected areas near schools, kindergartens and pediatric hospitals. The main purpose of the system is to provide expert early warnings to protect children with health problems. An in-situ PM10, PM2.5 monitoring...
Recent studies on air pollution emphasized particulate matter impact on human health and climate changes. This impact generated a trend for developing research projects which deal with monitoring and forecasting air quality. This paper fits into this trend and presents an ANFIS (adaptive neuro-fuzzy inference system) modelling approach to predict particulate matter concentration for short terms. The...
Cybersecurity of industrial control system is a very complex and challenging research topic, due to the integration of these systems in national critical infrastructures. The control systems are now interconnected in industrial networks and frequently to the Internet. In this context they are becoming targets of various cyber attacks conducted by malicious people such as hackers, script kiddies, industrial...
Usage of wireless sensor networks in various applications such as home automation, intelligent cars and infrastructures, military environment and critical factory monitoring and control is constantly increasing. WSN security is a real challenge for researchers and industry specialists since WSN are now used even in critical environments. In this paper the authors will assess the security risks of...
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