Use of the smart technology for water quality control: feedback from large-scale experimentation

The main purpose of drinking water network is to ensure safe quality of water to users. However, accidental contamination and malicious attacks in water networks can degrade the water quality. Such critical events threat the human health and lead to harmful diseases. Water utilities are concerned by the control of water quality. Since conventional methods based on laboratory analyses require several days, online control technology presents good opportunity to rapid detection of any water contamination. Devices based on smart technology have been developed for real-time control of the water quality. However, the use of these devices is recent and yet requires investigations. This paper presents feedback of the use of the smart technology in a large-scale experimentation conducted at the campus of Lille University within the European project “SmartWater4Europe”. Two devices are used: S::CAN, which measures various parameters such as Conductivity and Turbidity and EventLab, which measures the variation of the refractive index. This paper presents the implementation of these devices, data storage and management as well as analysis of recorded data.     

Structure and catalytic activity of POSS-stabilized Pd nanoparticles

Palladium nanoparticles stabilized with polyhedral oligomeric silsesquioxanes have been characterized and determined to function as a heterogeneous catalyst. TEM and SEM studies indicate the material comprises 2–5 nm diameter Pd crystallites in a POSS layer, which aggregate to form spherical secondary structures of about 50 nm in diameter. Solid-state NMR reveals that the primary interaction between Pd and POSS is through the alkyl-amino functional groups on the POSS cage, in addition to some weak interactions with the cage itself. This material is observed by microbalance measurements to efficiently catalyze the direct hydrogenation of 1,4-diphenylbutadiyne.     

Robustness and convergence rate of a discrete-time learning control algorithm for a class of nonlinear systems

In this paper, we apply a discrete-time learning algorithm to a class of discrete-time varying nonlinear systems with affine input action and linear output having relative degree one. We investigate the robustness of the algorithm to state disturbance, measurement noise and reinitialization errors. We show that the input and the state variables are always bounded if certain conditions are met. Moreover, we shown that the input error and state error converge uniformly to zero in absence of all disturbances. In addition, we show that, after a finite number of iterations, the convergence rate is exponential in l^∞. A numerical example is added to illustrate the results. Copyright © 1999 John Wiley & Sons, Ltd.     

A map matching approach for train positioning. II. Application andexperimentation

For pt.I see ibid., vol.49, no.2, p.467-75 (2000). In this paper, applications of the map matching algorithm proposed in part I are presented. In particular, steady-state Kalman filters are proposed and applied for filtering the yaw rate and tachometer signals. In addition, experimental results, using a quartz yaw rate sensor and axle encoders aboard a freight train, are included to show the performance of the proposed map matching algorithm     

Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise

Arbitrary high precision output tracking is one of the most desirable control objectives found in industrial applications regardless of measurement errors. The main purpose of this paper is to supply to the iterative learning control (ILC) designer guidelines to select the corresponding learning gain in order to achieve this control objective. For example, if certain conditions are met, then it is necessary for the learning gain to converge to zero in the learning iterative domain. In particular, this paper presents necessary and sufficient conditions for boundedness of trajectories and uniform tracking in presence of measurement noise and a class of random reinitialization errors for a simple ILC algorithm. The system under consideration is a class of discrete-time affine nonlinear systems with arbitrary relative degree and arbitrary number of system inputs and outputs. The state function does not need to satisfy a Lipschitz condition. This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. The resulting tracking output error is shown to converge to zero at a rate inversely proportional to square root of the number of learning iterations in presence of measurement noise and a class of reinitialization errors. Two illustrative numerical examples are presented.     

Optimal selection of the forgetting matrix into an iterative learning control algorithm

A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the optimal forgetting matrix and the learning gain matrix of a P-type iterative learning control (ILC) for linear discrete-time varying systems with arbitrary relative degree. This note shows that a forgetting matrix is neither needed for boundedness of trajectories nor for output tracking. In particular, it is shown that, in the presence of random disturbances, the optimal forgetting matrix is zero for all learning iterations. In addition, the resultant optimal learning gain guarantees boundedness of trajectories as well as uniform output tracking in presence of measurement noise for arbitrary relative degree.     

Stochastic P-type/D-type iterative learning control algorithms

This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. The optimal algorithm is based on minimizing the trace of the input error covariance matrix. The state disturbance, reinitialization errors and measurement errors are considered to be zero-mean white processes. It is shown that if the product of the input-output coupling matrices C ( t + 1 ) B ( t ) is full column rank, then the input error covariance matrix converges to zero in presence of uncorrelated disturbances. Another sub-optimal P-type algorithm, which does not require the knowledge of the state matrix, is also presented. It is shown that the convergence of the input error covariance matrices corresponding to the optimal and sub-optimal P-type and D-type algorithms are equivalent, and all converge to zero at a rate inversely proportional to the number of learning iterations. A transient-response performance comparison, in the domain of learning iterations, for the optimal and sub-optimal P- and D-type algorithms is investigated. A numerical example is added to illustrate the results.     

Hot on the Tail of the Elusive WIMP: Cryogenic Dark Matter Searches in the 21st Century

It is now well established and accepted that the universe has a total density equal to the critical density (Ω=1) and that roughly 25% of that amount is accounted for by non-relativistic particles. That these particles, referred to as Dark Matter, have remained a mystery has served to motivate physicists to design more ingenious and far reaching experiments in an attempt to identify and understand them. This paper will review various ongoing and proposed Dark Matter searches which employ cryogenic techniques to both detect the rare Dark Matter interactions as well as reject the vast number of background events from cosmic ray and radioactive backgrounds. Such experiments are already sensitive to and are able to reject certain models of supersymmetry, and with the increases in sensitivity projected over the next few years may even be able to detect these elusive particles.      

简化Li+电池充电器测试

为了简化测试过程,本文给出了一个电池仿真电路,可加快测试速度,在不带实际电池的情况下实现对锂离子电池充电器的测试。