Road vehicle detection is a significant task for tackling traffic issues in intelligent transportation systems (ITS). In this paper, we introduce ultra-weak fiber Bragg grating (UWFBG) sensing technology for road vehi...
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Road vehicle detection is a significant task for tackling traffic issues in intelligent transportation systems (ITS). In this paper, we introduce ultra-weak fiber Bragg grating (UWFBG) sensing technology for road vehicle flow, vehicle axle and wheelbase detection. We carry out pilot installation of UWFBG sensing optic cables in the asphalt base of the road in a new-built expressway. As opposed to the conventional linear installation along the road running direction, the deployment of cables is mainly perpendicular to the road. Using the acquired vehicle data, we analyze signal characteristics and propose real-time data processing algorithms based on identification and classification of peaks/valleys of vehicle waveforms as well as cross-correlation calculation. We test the proposed method in different scenes. Test results show that the calculated wheelbases achieve good accuracy (relative errors are <1 %) and the algorithms can well distinguish individual vehicle in complex vehicle conditions.
This paper discusses the algorithms used for diagnostics and automatic adjustment of the parameters for non-destructive monitoring of the properties of rolled metal at the Vyksa Production Site. We describe the result...
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This paper discusses the algorithms used for diagnostics and automatic adjustment of the parameters for non-destructive monitoring of the properties of rolled metal at the Vyksa Production Site. We describe the results obtained, in addition to the levels of monitoring for the status of the certification system.
This paper is a part of a project aiming to develop supervisor and monitoring devices for embedded systems in airplanes and vehicles. It focuses on the reliability of these systems and establishes a monitoring framewo...
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Introduction: Non-invasive telemonitoring programmes detecting deterioration of heart failure are increasingly used in heart failure care. Aim: The aim of this study was to compare different monitoring algorithms used...
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Introduction: Non-invasive telemonitoring programmes detecting deterioration of heart failure are increasingly used in heart failure care. Aim: The aim of this study was to compare different monitoring algorithms used in non-invasive telemonitoring programmes for patients with chronic heart failure. Methods: We performed a systematic literature review in MEDLINE (PubMed) and Embase to identify published reports on non-invasive telemonitoring programmes in patients with heart failure aged over 18 years. Results: Out of 99 studies included in the study, 20 (20%) studies described the algorithm used for monitoring worsening heart failure or algorithms used for titration of heart failure medication. Most frequently used biometric measurements were bodyweight (96%), blood pressure (85%) and heart rate (61%). algorithms to detect worsening heart failure were based on daily changes in bodyweight in 20 (100%) studies and/or blood pressure in 12 (60%) studies. In 12 (60%) studies patients were contacted by telephone in the case of measurements outside thresholds. Conclusion: Only one in five studies on telemonitoring in chronic heart failure reported the algorithm that was used to detect worsening heart failure. Standardised description of the telemonitoring algorithm can expedite the identification of key components in telemonitoring algorithms that allow accurate prediction of worsening heart failure.
In spatially located, large scale systems, time and space dynamics interact and drives the behaviour. Examples of such systems can be found in many smart city applications and Cyber-Physical Systems. In this paper we ...
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In spatially located, large scale systems, time and space dynamics interact and drives the behaviour. Examples of such systems can be found in many smart city applications and Cyber-Physical Systems. In this paper we present the Signal Spatio-Temporal Logic (SSTL), a modal logic that can be used to specify spatio-temporal properties of linear time and discrete space models. The logic is equipped with a Boolean and a quantitative semantics for which efficient monitoring algorithms have been developed. As such, it is suitable for real-time verification of both white box and black box complex systems. These algorithms can also be combined with stochastic model checking routines. SSTL combines the until temporal modality with two spatial modalities, one expressing that something is true somewhere nearby and the other capturing the notion of being surrounded by a region that satisfies a given spatio-temporal property. The monitoring algorithms are implemented in an open source Java tool. We illustrate the use of SSTL analysing the formation of patterns in a Turing Reaction-Diffusion system and spatio-temporal aspects of a large bike-sharing system.
We address the specification and verification of spatio-temporal behaviours of complex systems, extending Signal Spatio-Temporal Logic (SSTL) with a spatial operator capable of specifying topological properties in a d...
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ISBN:
(纸本)9783319238203;9783319238197
We address the specification and verification of spatio-temporal behaviours of complex systems, extending Signal Spatio-Temporal Logic (SSTL) with a spatial operator capable of specifying topological properties in a discrete space. The latter is modelled as a weighted graph, and provided with a boolean and a quantitative semantics. Furthermore, we define efficient monitoring algorithms for both the boolean and the quantitative semantics. These are implemented in a Java tool available online. We illustrate the expressiveness of SSTL and the effectiveness of the monitoring procedures on the formation of patterns in a Turing reaction-diffusion system.
In this paper we discuss the negative impact on monitoring algorithms of working with imprecisely dated data. Two examples from the world of the oil & gas industry are presented and serve to illustrate that this p...
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In this paper we discuss the negative impact on monitoring algorithms of working with imprecisely dated data. Two examples from the world of the oil & gas industry are presented and serve to illustrate that this problem can be of practical importance. First analytical results show that when signals with significant time variations are monitored, the impact of dating of measurements can be as troublesome (or even worse) than measurement noises. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this article we expose typical examples of systems from the oil industry having variable delays. The root causes of the variability can be the transport phenomena, the clocks mis-synchronisation in the employed inf...
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In this article we expose typical examples of systems from the oil industry having variable delays. The root causes of the variability can be the transport phenomena, the clocks mis-synchronisation in the employed information technology, or the transmission of waves in surrounding medium. We discuss these problems and sketch solutions.
In this paper we discuss the negative impact on monitoring algorithms of working with imprecisely dated data. Two examples from the world of the oil & gas industry are presented and serve to illustrate that this p...
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In this paper we discuss the negative impact on monitoring algorithms of working with imprecisely dated data. Two examples from the world of the oil & gas industry are presented and serve to illustrate that this problem can be of practical importance. First analytical results show that when signals with significant time variations are monitored, the impact of dating of measurements can be as troublesome (or even worse) than measurement noises.
The quality of data collected by air pollution monitoring networks is often affected by inaccuracies and missing data problems, mainly due to breakdowns and/or biases of the measurement instruments. In this paper we p...
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The quality of data collected by air pollution monitoring networks is often affected by inaccuracies and missing data problems, mainly due to breakdowns and/or biases of the measurement instruments. In this paper we propose a statistical method to detect, as soon as possible, biases in the measurement devices, in order to improve the quality of collected data on line. The technique is based on the joint use of stochastic modelling and statistical process control algorithms. This methodology is applied to the mean hourly ozone concentrations recorded from one monitoring site of the Bologna urban area network. We set up the monitoring algorithm through Monte Carlo simulations in such a way to detect anomalies in the data within a reasonable delay. The results show several out of control signals that may be caused by problems in the measurement device. Copyright (C) 2000 John Wiley & Sons, Ltd.
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