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We consider the problem of optimal reactive power compensation for the minimization of power distribution losses in a smart microgrid. We first propose an approximate model for the power distribution network, which allows us to cast the problem into the class of convex quadratic, linearly constrained, optimization problems. Then, we design a randomized, gossip-like optimization algorithm based on that model. We show how a distributed approach is possible, where agents have a partial knowledge of the problem parameters and state, and can only perform local measurements. For the proposed algorithm, we provide conditions for convergence together with an analytic characterization of the convergence speed. The analysis shows that the best performance can be achieved when we command cooperation among agents that are neighbors in the electric topology. Numerical simulations are included to validate the proposed model and to confirm the analytic results about the performance of the proposed algorithm.

In semiconductor fabrication processes, effectivemanagement of maintenance operations is fundamental todecrease costs associated with failures and downtime. PredictiveMaintenance (PdM) approaches, based on statistical methodsand historical data, are becoming popular for their predictivecapabilities and low (potentially zero) added costs. We presenthere a PdM module based on Support Vector Machines forprediction of integral type faults, that is, the kind of failuresthat happen due to machine usage and stress of equipmentparts. The proposed module may also be employed as a healthfactor indicator. The module has been applied to a frequentmaintenance problem in semiconductor manufacturing industry,namely the breaking of the filament in the ion-source ofion-implantation tools. The PdM has been tested on a realproduction dataset.

This paper considers a variation of the 17$^{\text{th}}$ century problem commonly known as the Newton-Pepys problem, or the John Smith's problem. We provide its solution, interpreting the result in terms of maximum likelihood estimation and Ockham's razor. In addition, we illustrate the practical relevance of these findings for solving size-estimation problems, and in particular for determining the number of agents in a wireless sensor network.

In semiconductor manufacturing plants, monitoring physical properties of all wafers is crucial to maintain good yield and high quality standards. However, such an approach is too costly, and in practice, only few wafers in a lot are actually monitored. Virtual metrology (VM) systems allow to partly overcome the lack of physical metrology. In a VM scheme, tool data are used to predict, for every wafer, metrology measurements. In this paper, we present a VM system for a chemical vapor deposition (CVD) process. On the basis of the available metrology results and of the knowledge, for every wafer, of equipment variables, it is possible to predict CVD thickness. In this work, we propose a VM module based on least angle regression to overcome the problem of high dimensionality and model interpretability. We also present a statistical distance-based clustering approach for the modeling of the whole tool production. The proposed VM models have been tested on industrial production data sets.

In this paper we consider discrete-time positive switched systems, switching among autonomous
subsystems, characterized either by monomial matrices or by circulant matrices. For these two classes of
systems, some interesting necessary and sufficient conditions for (global uniform) asymptotic stability
and stabilizability are provided. Such conditions lead to simple algorithms that allow to easily detect,
under suitable conditions, whether a given positive switched system is not stabilizable.

We describe tradeoffs between the performance in visual decision problems and the control authority that the agent can exercise on the sensing process. We focus on problems of “coverage” (ensuring that all regions in the scene are seen) and “change estimation” (finding and learning an unknown object in an otherwise known and static scene), propose a measure of control authority and empirically relate it to the expected risk and its proxy (conditional entropy of the posterior density). We then show that a “passive” agent can provide no guarantees on performance beyond what is afforded by the priors, and that an “omnipotent” agent, capable of infinite control authority, can achieve arbitrarily good performance (asymptotically).

The paper considers the classification problem using Support Vector Machines, and investigates how to maximally reduce the size of the training set without losing information. Under linearly separable dataset assumptions, we derive the exact conditions stating which observations can be discarded without diminishing the overall information content. For this purpose, we introduce the concept of Potential Support Vectors, i.e., those data that can become Support Vectors when future data become available. Complementary, we also characterize the set of Discardable Vectors, i.e., those data that, given the current dataset, can never become Support Vectors. These vectors are thus useless for future training purposes, and can eventually be removed without loss of information. We then provide an efficient algorithm based on linear programming which returns the potential and discardable vectors by constructing a simplex tableau. Finally we compare it with alternative algorithms available in the literature on some synthetic data as well as on datasets from standard repositories.

In past years the Reversed Field Pinch RFX-mod has also been operated as a low current Tokamak to perform experiments of active control of MHD modes particularly harmful to a prospective reactor. The stabilization of m=2, n=1 mode has been achieved for 150 kA plasma currents in circular shape discharges at q(a)<2. In order to test the system capability of stabilizing such modes in improved confinement regimes, the possibility of producing D- shaped plasma discharges has been explored. Preliminary experiments were carried out in open loop in 2011. In the meantime a completely new plasma position and shape control system was designed and its performances simulated with the finite element 2D MHD equilibrium code MAXFEA. According to the simulation results, feedback control of the D- shape configuration was capable of meeting the design requirements. As a first step, the recent experimental campaign in Tokamak configuration was partially dedicated to demonstrate the possibility of a stable feedback controlled operation with an elongated plasma. In the paper the identification of the transfer function between a dedicated Field Shaping (FS) coil current distribution and the plasma elongation, the design of the control system, its implementation and successful testing are described.

Camera positioning units are widely used in surveillance and they are sometimes mounted on floating supports, e.g.
on patrolling ships or buoys. The support motion, in turn, induces an apparent motion in the image plane, which can create troubles to the image processing, especially when a specific feature must be tracked (e.g. a distant ship, getting close to a forbidden area). Low cost devices are often characterized by low frame rate and low image resolution, for which traditional image stabilization techniques usually results to be rather ineffective. Additionally, low-end camera units are usually driven by hybrid stepper motors and, being conceived to work in an harsh environment, they do not mount any optical image stabilization (OIS) system, either in the camera lenses or in the image sensor. In this paper, the image acquired by a pan–tilt camera positing unit mounted on a moving support is stabilized by exploiting the camera attitude information provided by a MEMS-based IMU with an embedded magnetometer. In particular, two independent integral control loops are designed for the pan and tilt motors in order to compensate for the yaw and pitch motions of the support. As for the roll motion, since it relates to an unavailable degree of freedom in the positioning unit, it can be compensated only on the captured image. The proposed solution is experimentally tested on a real device mounted on a moving table actuated by a 6 degrees–of–freedom pneumatic hexapod. Realistic motions are recreated by using the data recordings taken aboard of a patrolling ship and a costal buoy. Experimental results show that the proposed solution is capable of keeping the camera pointing at a fixed target with a good accuracy, thus making higher-level image processing easier and more effective.

Camera positioning units for surveillance applica- tions are often mounted on mobile supports or vehicles. In such circumstances, the motion of the supporting base affects the camera field of view, thus making the task of pointing and tracking a specific target problematic, especially when using low cost devices that are usually not equipped with rapid actuators and fast video processing units. Visual tracking capabilities can be improved if the camera field of view is preliminarily stabilized against the movements of the base. Although some cameras available on the market are already equipped with an optical image stabilization (OIS) system, implemented either in the camera lenses or in the image sensor, these are usually too expensive to be installed on low–end positioning devices.
A cheaper approach to image stabilization consists of stabilizing the camera motion using the motors of the positioning unit and the inertial measurements provided by a low–cost MEMS Inertial Measurement Unit (IMU). This paper explores the feasibility of applying such image stabilization system to a low cost pan–tilt– zoom (PTZ) camera positioning unit driven by hybrid stepper motors (HSMs), in order to aid the task of pointing and tracking of a specific target on the camera image plane. In the proposed solution, a two–level cascaded control structure, consisting of inner inertial stabilizing control loop and an outer visual servoing control loop, is used to control the PTZ unit. Several tests are carried out on a real device mounted on a moving table actuated by a 6 degrees–of–freedom pneumatic hexapod. Realistic motions are recreated by using the data recordings taken aboard of a patrolling ship.

In this paper, we study modeling and identification of stochastic systems by Generalized Factor Analysis models. Although this class of models was originally introduced for econometric purposes, we present some possible  applications of engineering interest. We show that there is a natural connection between Generalized Factor Analysis models and multi-agents systems. The common factor component of the model has an interpretation as a flocking component of the system behaviour.

Models typically used to simulate the influence of atmospheric turbulence on ground telescope observations are usually based on the frozen flow hypothesis. However, the frozen flow model of the atmosphere is valid at time scales of the order of tens/hundreds of milliseconds. This paper generalizes a previous model for turbulence simulation to ensure reliable tests of AO system performance in realistic working conditions. The proposed method relies on the use of two simulation models: First, the part of turbulence that shows a coherent flow at short time scales is simulated by means of a multiscale autoregressive-moving average model, which allows to efficiently simulate (with computational complexity O(n)) the coherent evolution of the turbulence. Secondly, an approach similar to that considered for dynamic textures, is used to simulate aberrations caused by processes that evolve on much longer time scales. The proposed procedure is tested on simulations.

Motivated by the increasing importance of Adap- tive Optics (AO) systems for improving the real resolution of large ground telescopes, and by the need of testing the AO system performance in realistic working conditions, in this paper we address the problem of simulating the turbulence effect on ground telescope observations at high resolution. The multiscale approach presented here generalizes that in [3]: First, a relevant computational time reduction is obtained by exploiting a local spatial principal component analysis (PCA) representation of the turbulence. Furthermore, differently from [3], the turbulence at low resolution is modeled as a moving average (MA) process. While in [3] the wind velocity was restricted to be directed along one of the two spatial axes, the approach proposed here allows to evolve the turbulence indifferently in all the directions. In our simulations the pro- posed procedure reproduces with good accuracy the theoretical statistical characteristics of the turbulent phase.

Motivated by the increasing importance of adaptive optics (AO) systems for improving the real resolution of large ground telescopes, and by the need of testing the AO system performance in realistic working conditions, in this paper we address the problem of simulating the turbulence effect on ground telescope observations at high resolution. The procedure presented here generalizes the multiscale stochastic approach introduced in our earlier paper [Appl. Opt. 50, 4124 (2011)], with respect to the previous solution, a relevant computational time reduction is obtained by exploiting a local spatial principal component analysis (PCA) representation of the turbulence. Furthermore, the turbulence at low resolution is modeled as a moving average (MA) process, while previously [Appl. Opt. 50, 4124 (2011)] the wind velocity was restricted to be directed along one of the two spatial axes, the use of such MA model allows the turbulence to evolve indifferently in all the directions. In our simulations, the proposed procedure reproduces the theoretical statistical characteristics of the turbulent phase with good accuracy.

We revisit the classic slow coherency and area
aggregation approach to model reduction in power networks.
The slow coherency approach is based on identifying sparsely
and densely connected areas of a network, within which
all generators swing coherently. A time-scale separation and
singular perturbation analysis then results in a reduced loworder
system, where coherent areas are collapsed into aggregate
variables. Here, we study the application of slow coherency
and area aggregation to first-order consensus systems and
second-order power system swing dynamics. We unify different
theoretic approaches and ideas found throughout the literature,
we relax some technical assumptions, and we extend existing
results. In particular, we provide a complete analysis of the
second-order swing dynamics – without restrictive assumptions
on the system damping. Moreover, we identify the reduced
aggregate models as generalized first or second-order Laplacian
flows with multiple time constants, aggregate damping and
inertia matrices, and possibly adverse interactions.

Abstract—The aim of this paper is to introduce and characterize
observability and reconstructibility properties for Boolean
networks and Boolean control networks, described according to
the algebraic approach proposed by D. Cheng and co-authors in
the series of papers [3], [6], [7] and in the recent monography
[8]. A complete characterization of these properties, based both
on the Boolean matrices involved in the network description and
on the corresponding digraphs, is provided. Finally, the problem
of state observer design for reconstructible BNs and BCNs is
addressed, and two different solutions are proposed.

In large ground telescopes the Adaptive Optics (AO) system aims at compensating the atmosphere effect on telescope measurements, and, the use of optimal filtering is fundamental for such task. This work is motivated by two important characteristics of new AO systems: on one hand, because of the request of very high measurement resolutions, the size of new telescopes, and of their sensors, is quickly increasing in the last decades, thus imposing to the AO systems the analysis of larger amount of data. On the other hand, the optimal filter has to be periodically updated according to temporal changes in atmosphere characteristics. Hence, it is of fundamental importance the use of computationally efficient algorithms for the update of the optimal filter gain.
This paper proposes some changes to a recently presented method for the efficient computation, in the frequency domain, of the Kalman gain for large AO systems [15]. The proposed changes, which mainly aim at correcting some issues due to the conversion spatial–frequency domain, and viceversa, allow to compute a better approximation of the optimal Kalman gain, and, consequently, significantly improve the performance of the AO system.

In current and next generation of ground tele- scopes, Adaptive Optics (AO) are employed to overcome the detrimental effects induced by the presence of atmospheric turbulence, that strongly affects the quality of data transmission and limits the actual resolution of the overall system. The analysis as well as the prediction of the turbulent phase affecting the light wavefront is therefore of paramount importance to guarantee the effective performance of the AO solution.
In this work, a layered model of turbulence is proposed, based on the definition of a Markov-Random-Field whose pa- rameters are determined according to the turbulence statistics. The problem of turbulence estimation is formalized within the stochastic framework and conditions for the identifiability of the turbulence structure (numbers of layers, energies and velocities) are stated. Finally, an algorithm to allow the layer detection and characterization from measurements is designed. Numerical simulations are used to assess the proposed procedure and validate the results, confirming the validity of the approach and the accuracy of the detection.

In this note we rst characterize the periodic trajectories (or, equivalently, the limit cycles) of a Boolean network, and their global attractiveness. We then investigate under which conditions all the trajectories of a Boolean control network may be forced to converge to the same periodic trajectory. If every trajectory can be driven to such a periodic trajectory, this is possible by means of a feedback control law.

Following the completion in May 2011 of the shutdown for the installation of the beryllium wall and the tungsten divertor, the first set of JET campaigns have addressed the investigation of the retention properties and the development of operational scenarios with the new plasma-facing materials. The large reduction in the carbon content (more than a factor ten) led to a much lower Z_eff (1.2–1.4) during L- and H-mode plasmas, and radiation during the burn-through phase of the plasma initiation with the consequence that breakdown failures are almost absent. Gas balance experiments have shown that the fuel retention rate with the new wall is substantially reduced with respect to the C wall. The re-establishment of the baseline H-mode and hybrid scenarios compatible with the new wall has required an optimization of the control of metallic impurity sources and heat loads. Stable type-I ELMy H-mode regimes with H_98,y2 close to 1 and ?_N ~ 1.6 have been achieved using gas injection. ELM frequency is a key factor for the control of the metallic impurity accumulation. Pedestal temperatures tend to be lower with the new wall, leading to reduced confinement, but nitrogen seeding restores high pedestal temperatures and confinement. Compared with the carbon wall, major disruptions with the new wall show a lower radiated power and a slower current quench. The higher heat loads on Be wall plasma-facing components due to lower radiation made the routine use of massive gas injection for disruption mitigation essential.

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the predictionof ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

Tissue engineering of autologous lung tissue aims to become a therapeutic alternative to transplantation. Efforts published so far in creating scaffolds have used harsh decellularization techniques that damage the extracellular matrix (ECM), deplete its components and take up to 5 weeks to perform. The aim of this study was to create a lung natural acellular scaffold using a method that will reduce the time of production and better preserve scaffold architecture and ECM components. Decellularization of rat lungs via the intratracheal route removed most of the nuclear material when compared to the other entry points. An intermittent inflation approach that mimics lung respiration yielded an acellular scaffold in a shorter time with an improved preservation of pulmonary micro-architecture. Electron microscopy demonstrated the maintenance of an intact alveolar network, with no evidence of collapse or tearing. Pulsatile dye injection via the vasculature indicated an intact capillary network in the scaffold. Morphometry analysis demonstrated a significant increase in alveolar fractional volume, with alveolar size analysis confirming that alveolar dimensions were maintained. Biomechanical testing of the scaffolds indicated an increase in resistance and elastance when compared to fresh lungs. Staining and quantification for ECM components showed a presence of collagen, elastin, GAG and laminin. The intratracheal intermittent decellularization methodology could be translated to sheep lungs, demonstrating a preservation of ECM components, alveolar and vascular architecture. Decellularization treatment and methodology preserves lung architecture and ECM whilst reducing the production time to 3 h. Cell seeding and in vivo experiments are necessary to proceed towards clinical translation.

We consider the problem of controlling two agents with competitive objectives. Agents are modelled as linear discrete time systems, and collect each other’s state information without delays. The competitive problem is  formulated in a receding horizon framework, where each agent’s controllers are computed by minimizing a linear, quadratic cost function which depends on both agents’ states. The two agents specify their state tracking objective in a coordinated or competitive manner. We do not consider state constraints. The simplicity of our framework allows us to provide the following results analytically: 1) When agents compete, their states converge to an equilibrium trajectory where the steady state tracking error is finite. 2) Limit-cycles cannot occur. Numerical simulations and experiments done with a LEGO mindstorm multiagent platform match our analytical results.

The integration of renewable energy sources in power systems requires a well-balanced
control to guarantee system stability in view of fast
uctuating power injections. We present
several conditions on the stability reserve of a power system in terms of the region of attraction
of its steady state that can be used to design and evaluate such controllers. The power system
is modeled by coupled swing equations. The region of attraction of this nonlinear systems is
determined based on Lyapunov theory and Barbalat's lemma. The resulting conditions provide
both 2-norm and 1-norm regions of attractions. The dierent conditions dier, e.g., in their
conservatism and in the required knowledge of the power system parameters.

In this work the design and implementation of an application to track multiple agents in a indoor Wireless Sensor Actor Network (WSAN) is proposed. We developed a tracking algorithm that falls into the category of the radio frequency localization/tracking methods, that exploit the strength of the wireless communications among fixed and mobile agents to establish the position of the mobile ones. The algorithm resorts to an Extended Kalman Filter to process the agents measurements and reach a desired level of tracking performance. The tracking application, namely Teseo, is composed by a low-level NesC management software for the agents side and a Java graphical interface provided to users connected to mobile agents. A detailed description of the operations performed by Teseo is given, accompanied both by simulations to validate the tracking algorithm and experiments on a real testbed to test Teseo.

Measurements of large ground telescopes are af- fected by the presence of the terrestrial atmospheric turbulence: local changes of the atmospheric refraction index (e.g. due to wind and temperature variations) cause a non flat surface of the wavefront of light beams incoming on the telescope, thus degrading the quality of the observed images. Adaptive Optics (AO) systems are of fundamental importance to reduce such atmospheric influence on ground telescopes and thus to obtain high resolution observations. The goal of the AO system is that of estimating and compensating the atmospheric turbulence effect by properly commanding a set of deformable mirrors.
Because of delays in the closed loop system, the Kalman filter plays an important role in ensuring an effective control perfor- mance by providing good atmosphere predictions. However, the need of periodically updating the Kalman filter gain because of changes in the atmosphere characteristics, the increase of telescopes and sensors resolutions and the high sampling rate impose quite strict restrictions to the computational load for computing the Kalman gain.
Motivated by the above considerations, some strategies have been recently considered in the system theory and astronomical communities for the efficient computation of the Kalman gain for large AO systems. Specifically, this paper presents some changes to a recently proposed procedure: the proposed approach, which exploits some results in the control theory of distributed systems, computes an approximation of the optimal gain in the frequency domain exploiting the spatial homogeneity of the system. Then, the control strategy takes advantage of some information on the turbulent phase dynamic, that is estimated from the turbulence measurements. Performances of the proposed method are investigated in some simulations.

Semiconductor fabrication involves several sequentialprocessing steps with the result that critical productionvariables are often affected by a superposition of affects overmultiple steps. In this paper a Virtual Metrology (VM) systemfor early stage measurement of such variables is presented;the VM system seeks to express the contribution to theoutput variability that is due to a defined observable partof the production line. The outputs of the processed systemmay be used for process monitoring and control purposes. Asecond contribution of this work is the introduction of ElasticNets, a regularization and variable selection technique for themodelling of highly-correlated datasets, as a technique for thedevelopment of VM models. Elastic Nets and the proposed VMsystem are illustrated using real data from a multi-stage etchprocess used in the fabrication of disk drive read/write heads.

In this paper two-dimensional (2D) discrete behaviors, dened on the grid Z+ 
Z and having the time as (rst) independent variable, are investigated. For these behaviors, by
emphasizing the causality notion that is naturally associated with the time variable, we introduce
two new concepts of controllability. Algebraic characterizations of time-controllability and of zero-
time-controllability are provided, and it is shown that behaviors endowed with these properties admit
special decompositions. Next, the dead-beat control (DBC) problem and the concept of admissible
DBC are introduced and related to the zero-time-controllability property. Dierently from what
happens with one-dimensional behaviors, zero-time-controllability does not ensure the existence of
regular DBC's, and stronger algebraic properties need to be imposed on the behavior. Finally,
necessary and sucient conditions for the existence of a DBC that makes the resulting behavior
both strongly autonomous and nilpotent are provided.