The electrochemical detection of heavy metal ions is reported using an inexpensive portable in-house built potentiostat and epitaxial graphene. Monolayer, hydrogen-intercalated quasi-freestanding bilayer, and multilay...
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The electrochemical detection of heavy metal ions is reported using an inexpensive portable in-house built potentiostat and epitaxial graphene. Monolayer, hydrogen-intercalated quasi-freestanding bilayer, and multilayer epitaxial graphene were each tested as working electrodes before and after modification with an oxygen plasma etch to introduce oxygen chemical groups to the surface. The graphene samples were characterized using X-ray photoelectron spectroscopy, atomic force microscopy, Raman spectroscopy, and van der Pauw Hall measurements. Dose-response curves in seawater were evaluated with added trace levels of four heavy metal salts (CdCl2, CuSO4, HgCl2, and PbCl2), along with detection algorithms based on machine learning and library development for each form of graphene and its oxygen plasma modification. Oxygen plasma-modified, hydrogen-intercalated quasi-freestanding bilayer epitaxial graphene was found to perform best for correctly identifying heavy metals in seawater.
Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagatio...
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Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users' identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.
In this paper a generally applicable approach for forming a continuous explicit piecewise-linear model by separatedly fitting local observations is proposed. This is achieved by connecting local models with suitable h...
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In this paper a generally applicable approach for forming a continuous explicit piecewise-linear model by separatedly fitting local observations is proposed. This is achieved by connecting local models with suitable hyperplanes. Based on this approach two practical algorithms for system identification are designed, which can produce a continuous piecewise-linear model capable of fitting observations to any accuracy predefined. It is shown by numerical simulation that the models obtained can not only fit observations well, but also possess good forecasting performance.
作者：
A. TianoG. MagenesR. SuttonP. CravenDIS
University of Pavia Via Ferrata 1 27100 Pavia Italy Tel:+39 382 505361 - Fax: +39 382 505 373 IAN
National Research Council Via De Marini 6 Genova Italy Marine Technology Division
Institute of Marine Studies University of Plymouth U.K. Drake Circus Plymouth Devon PL4 8AA U.K.
This paper deals with the application of identification methods to the determination of the dynamical behaviour of an UUV (Unmanned Underwater Vehicle). After a concise introduction to the longitudinal equations of th...
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This paper deals with the application of identification methods to the determination of the dynamical behaviour of an UUV (Unmanned Underwater Vehicle). After a concise introduction to the longitudinal equations of the motion, which describe the heave and pitch responses to the action of the control surface deflections and of the thrusters, identification is formulated for a linearized UUV model. The related minimization problem is approached and solved by means of two different random-search methods, respectively based on simulated annealing and on genetic algorithms. The numerical features of such identification methods are discussed and some preliminary promising results are presented, which are obtained by simulation experiments.
This paper outlines a strategy for developing neural network models suitable for nonlinear controller design. The strategy first employs a multiple neural network approach to the dynamic modelling of (minimum phase) a...
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This paper outlines a strategy for developing neural network models suitable for nonlinear controller design. The strategy first employs a multiple neural network approach to the dynamic modelling of (minimum phase) affine systems. The resultant dynamic model is then used for controller design within a differential geometric framework. The focus of the work is to predict a priori whether the nonlinear controller will be stable. Stability checks are used in order to vet candidate models and to influence the neural network training procedure itself.
This paper presents an algorithm for designing a cryptographic system, in which the derivative disproportion functions (key functions) are used. This cryptographic system is used for an operative identification of a d...
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This paper presents an algorithm for designing a cryptographic system, in which the derivative disproportion functions (key functions) are used. This cryptographic system is used for an operative identification of a differential equation describing the movement of quasi-stationary objects. The symbols to be transmitted are encrypted by the sum of at least two of these functions combined with random coefficients. A new algorithm is proposed for decoding the received messages making use of important properties of the derivative disproportion functions. Numerical experiments are reported to demonstrate the algorithm’s reliability and robustness.
Glucose–insulin interactions in the Type I diabetic patient are approximated in an input–output sense using a third–order Volterra series model. Due to the large number of unique coefficients present in a third–or...
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Glucose–insulin interactions in the Type I diabetic patient are approximated in an input–output sense using a third–order Volterra series model. Due to the large number of unique coefficients present in a third–order model, efficient parameter identification methods are developed. Several pruned model structures were examined, and maximum dynamic accuracy was obtained when a linear plus nonlinear diagonal model was employed. Increased steady state accuracy could be obtained by including semi–diagonal and off–diagonal coefficients; however, this increase in static accuracy came at a cost of decreased dynamic accuracy. Furthermore, calculation of semi-diagonal and off-diagonal coefficients requires data acquisition times infeasible for clinical applications. Hence, the linear plus nonlinear diagonal Volterra series model is a well–suited structure for approximating Type I diabetic patient glucose–insulin dynamics using input–output methods.
Several recursive algorithms for parametric identification of discrete time systems derived from M.R.A.S. techniques in a deterministic environment areanalysed. All these algorithms belong to the class of output error...
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Several recursive algorithms for parametric identification of discrete time systems derived from M.R.A.S. techniques in a deterministic environment areanalysed. All these algorithms belong to the class of output error method which have been very little discussed previously in the identification litterature. These algorithms are analysed in the stochastic environment using the O.D.E. method. A summary of the convergence properties of these algorithms is also given for the deterministic case. A comparative evaluation of these algorithms is presented with respect to recursive algorithms belonging to "equation error method" (least squares, extended least squares, approximate maxlirnum likelihood.
Abstract In this contribution we outline an experiment procedure tailored for Model Predictive Control (MPC). The design criterion takes the MPC criterion into account explicitly. The Scenario Approach is used to hand...
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Abstract In this contribution we outline an experiment procedure tailored for Model Predictive Control (MPC). The design criterion takes the MPC criterion into account explicitly. The Scenario Approach is used to handle the fact that there is no explicit expression for the MPC criterion nor to the performance degradation due to the use of an estimated model (due to the constraints). The approach is illustrated on a railcar example.
Fuel consumption of heavy-duty vehicles such as tractors, bulldozers etc. is comparably high due to their scope of operation. The operation settings are usually fixed and not tuned to the environmental factors, such a...
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Fuel consumption of heavy-duty vehicles such as tractors, bulldozers etc. is comparably high due to their scope of operation. The operation settings are usually fixed and not tuned to the environmental factors, such as ground conditions. Yet exactly the ground-to-propelling-unit properties are decisive in energy efficiency. Optimizing the latter would require a means of identifying those properties. This is the central matter of the current study. More specifically, the goal is to estimate the ground conditions from the available measurements, such as drive train signals, and to establish a map of those. The ground condition parameters are estimated using an adaptive unscented Kalman filter. A case study is provided with the actual and estimated ground condition maps. Such a mapping can be seen as a crucial milestone in optimal operation control of heavy-duty vehicles.
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