A risk-based sensor placement methodology☆

Lee, Ronald W.; Kulesz, James J.

doi:10.1016/j.jhazmat.2008.01.111

Cited by 28 publications

(3 citation statements)

References 15 publications

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“…This processing needs to traverse within parameter space, which is a typical optimization solution with a large number of calculations. To simplify the procedure, we take the method used in [29], and pseudo-code is shown in Table 1. The original picture was divided by small grids with the size of 16 × 9 pixels, a rectangular part of the image made up of adjacent grids is used as a new overlay for each iteration.…”

Section: Simulation and Experiments Resultsmentioning

confidence: 99%

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Zhang

et al. 2020

IEEE Access

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Surveillance cameras are widely installed at public places around the world, and the video surveillance system plays an un-substitutable role in police work, especially in case investigation. The problem regarding the effectiveness and rationality of the video surveillance system comes into being in terms of its high demand for investment and rising public concern of over-construction potentially. To answer the question, it ought to establish mode and metrics for measuring effectiveness in theory. This article argued that the police video surveillance system is preferably a sensor network than a Physical Protect System (PPS) because its main feature is to provide the police officers with the visual information they need. Once the police cannot receive sufficient information from the system, decisions of public security are given based on limited or misleading information, and there may be some potential risks remained. Such risks of public security are not directly relevant to the integrity and value of the assets but the uncertainty of decisionmaking, which is different from the one of traditional PPS. In this paper, we proposed an entropy model for measuring the uncertainty based on attributions of video surveillance for law enforcement. Public security risk was divided into three types within the model according to the source of the risk, such as fixed targets (or restricted areas), moving objects, and video information quality. We verified the validity of the model by the simulation experiment of camera field optimization and discussed further work.

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Section: Simulation and Experiments Resultsmentioning

confidence: 99%

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Zhang

et al. 2020

IEEE Access

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“…In the problem of optimal SHM system design, the utility of the sensor network is quantified by the expected Shannon information gain [ 18 ], and the unit is represented by each measurement. Applications of this law to sensor network optimization in different engineering fields can also be found in [ 74 , 75 , 76 ].…”

Section: Results: Application To the Monitoring Of A Tall Buildingmentioning

confidence: 99%

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

Capellari

Chatzi

Mariani

2018

Sensors

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Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

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“…The objective is to maximize the detection coverage in the geographic area using CFD simulations. Optimal allocation scheme normally depends on the type of sensor and several stopping criteria have been used to declare success of the allocation (Lee and Kulesz, 2008). Gas sensors or detectors are devices detecting concentrations of flammable or toxic products and they can be either fixed or portable and either point or open-path.…”

Section: Approach For Sensors Allocationmentioning

confidence: 99%

A CFD-based approach for gas detectors allocation

Vázquez-Román

Díaz‐Ovalle

Quiroz-Pérez

et al. 2016

Journal of Loss Prevention in the Process Industries

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Accidental gas releases are detected by allocating sensors in optimal places to prevent escalation of the incident. Gas release effects are typically assessed based on calculating the dispersion from releasing points. In this work, a CFD-based approach is proposed to estimate gas dispersion and then to obtain optimal gas sensors allocation. The Ansys-Fluent commercial package is used to estimate concentrations in the open air by solving the governing equations of continuity, momentum, and energy combined with the realizable κ-ε model for turbulence viscosity effects and species convection-diffusion. CFD dynamic simulations are carried out for potential gas leaks, assuming worst-case scenarios with F-stability and 2 m/s wind speed during a 4min releasing period and considering 8 wind directions. The result is a scenario-based methodology to allocate gas sensors supported on fluid dynamics models. The three x-y-z geographical coordinates for the sensor allocation are included in this analysis. To highlight the methodology, a case study considers releases from a large container surrounded by different types of geometric units including sections with high obstacles, low obstacles, and no obstacles. A non-redundant set of perfect sensors are firstly allocated to cover 100% detection for all simulations releases. The benefits of redundant detection via a MooN voting arranging scheme is also discussed. Numerical results demonstrate the capabilities of CFD simulations for this application and highlight the dispersion effects through obstacles with different sizes.

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A risk-based sensor placement methodology☆

Cited by 28 publications

References 15 publications

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

A CFD-based approach for gas detectors allocation

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