Publications Lists     [ all BibTeX ]  

We consider the problem of controlling a smart lighting system of multiple luminaires with collocated occupancy and light sensors. The objective is to attain illumination levels higher than specified values (possibly changing over time) at the workplace by adapting dimming levels using sensor information, while minimizing energy consumption. We propose to estimate the daylight illuminance levels at the workplace based on the daylight illuminance measurements at the ceiling. More specifically, this daylight estimator is based on a model built from data collected by light sensors placed at workplace reference points and at the luminaires in a training phase. Three estimation methods are considered: Regularized least squares, locally weighted regularized least squares, and cluster-based regularized least squares. This model is then used in the operational phase by the lighting controller to compute dimming levels by solving a linear programming problem, in which power consumption is minimized under the constraint that the estimated illuminance is higher than a specified target value. The performance of the proposed approach with the three estimation methods is evaluated using an open-office lighting model with different daylight conditions. We show that the proposed approach offers reduced under-illumination and energy consumption in comparison to existing alternative approaches.

Recently, different communities in computer science, telecommunication and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy combines the statistical properties of the network traffic with the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard, called Energy Efficient Ethernet (EEE). This strategy exploits the possibility of predicting the incoming traffic from the analysis of the current data flow, which typically presents a self-similar behavior. Based on the prediction, Ethernet links can then be put in a low power consumption state for the intervals of time in which traffic is expected to be of low intensity. Theoretical bounds are derived that detail how the performance figures depend on the parameters of the designed strategy and scale with respect to the traffic load. Furthermore, simulations results, based on both real and synthetic traffic traces, are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.

Smart production monitoring is a crucial activity in advanced manufacturing for quality, control and maintenance purposes. Advanced Monitoring Systems aim to detect anomalies and trends; anomalies are data patterns that have different data characteristics from normal instances, while trends are tendencies of production to move in a particular direction over time. In this work, we compare state-of-the-art ML approaches (ABOD, LOF, onlinePCA and osPCA) to detect outliers and events in high-dimensional monitoring problems. The compared anomaly detection strategies have been tested on a real industrial dataset related to a Semiconductor Manufacturing Etching process

Advanced Monitoring Systems are fundamental in advanced manufacturing for control, quality and maintenance purposes. Nowadays, with the increasing availability of data in production and equipment, the need for high-dimensional Anomaly Detection techniques is thriving; anomalies are data patterns that have different data characteristics from normal production instances and that may be associated with faults or drifts in production. Tools for dealing with high-dimensional monitoring problems are provided by Machine Learning: in this paper, we test the performance of a state-of-the-art anomaly detection technique, called Isolation Forest, on a real industrial dataset related to Etching, one of the most important semiconductor manufacturing process. The monitoring has been performed exploiting Optical Spectroscopy Data.

In this paper, we study the problem of real-time optimal distributed partitioning for perimeter patrolling in the context of multicamera networks for surveillance, where each camera has limited mobility range and speed, and the communication is unreliable. The objective is to coordinate the cameras in order to minimize the time elapsed between two different visits of each point of the perimeter. We address this problem by casting it into a convex problem in which the perimeter is partitioned into nonoverlapping segments, each patrolled by a camera that sweeps back and forth at the maximum speed. We then propose an asynchronous distributed algorithm that guarantees that these segments cover the whole patrolling perimeter at any time and asymptotically converge to the optimal centralized solution under reliable communication. We finally modify the proposed algorithm in order to attain the same convergence and covering properties even in the more challenging scenario, where communication is lossy and there is no channel feedback, i.e., the transmitting camera is not aware whether a packet has been received or not by its neighbors.

In this paper we study cluster synchronization in a network of Kuramoto oscillators, where groups of oscillators evolve cohesively and at different frequencies from the neighboring oscillators. Synchronization is critical in a variety of systems, where it enables complex functionalities and behaviors. Synchronization over networks depends on the oscillators’ dynamics, the interaction topology, and coupling strengths, and the relationship between these different factors can be quite intricate. In this work we formally show that three network properties enable the emergence of cluster synchronization. Specifically, weak inter-cluster connections, strong intra-cluster connections, and sufficiently diverse natural frequencies among oscillators belonging to different groups. Our approach relies on system-theoretic tools, and is validated with numerical studies.

Standard hexarotors are often mistakenly considered ‘by definition’ fail-safe multi-rotor platforms because of the two additional propellers when compared to quadrotors. However this is not true, in fact, a standard hexarotor cannot statically hover with ‘only’ five propellers. In this paper we provide a set of new general algebraic conditions to ensure static hover for any multi-rotor platform with any number of generically oriented rotors. These are elegantly formulated as the full-rankness of the control moment input matrix, and the non-orthogonality between its null-space and the row space of the control force input matrix. Input saturations and safety margins are also taken into account with an additional condition on the null-space of control moment input matrix. A deep analysis on the hoverability properties is then carried out focusing on the propeller loss in a hexarotor platform. Leveraging our general results we explain why a standard hexarotor is not robust and how it can be made robust thanks to a particular tilt of the rotors. We finally propose a novel cascaded controller based on a preferential direction in the null-space of the control moment input matrix for the large class of statically hoverable multi-rotors, which goes far beyond standard platforms, and we apply this controller to the case of failed tilted hexarotor.

Virtual Metrology is one of the most prominentAdvanced Process Control applications in SemiconductorManufacturing. The goal of Virtual Metrology is to provideestimations of quantities that are important for production andto assess process quality, but are costly or impossible to bemeasured. Virtual Metrology solutions are based on MachineLearning approaches. The bottleneck of developing VirtualMetrology solutions is generally the feature extraction phase thatcan be time-consuming, and can deeply affect the estimationperformance. In particular, in presence of data with additionaldimensions, such as time, feature extraction is typicallyperformed by means of heuristic approaches that may pickfeatures with poor predictive capabilities. In this work, wepropose the usage of modern Deep Learning approaches tobypass manual feature extraction and to provide highperformanceautomatic Virtual Metrology modules. Theproposed methodology is tested on a real industrial datasetrelated to Etching. The dataset at hand contains OpticalEmission Spectroscopy data and it is paradigmatic of the featureextraction problem under examination.

Wireless sensor networks (WSNs) can provide numerous benefits in industrial automation. By removing the cable infrastructure, the wireless architecture enables the possibility for nodes in a network to dynamically and autonomously group into clusters according to the communication features and the data they collect. This capability allows to leverage the flexibility and robustness of industrial WSNs in supervisory intelligent systems for high-level tasks, such as, for example, environmental sensing, condition monitoring, and process automation. In this paper, a clustering strategy is studied that partitions a sensor network into a nonfixed number of nonoverlapping clusters according to the communication network topology and measurements distribution: To this aim, both a centralized and a distributed algorithm are designed that do not require a cluster-head structure or other network assumptions. As a validation, these strategies are tested on a real dataset coming from a structured environment and the effectiveness of the clustering procedure is also investigated to perform anomalies detection in an industrial production process.

The invention relates to a method for the determination of a laundry weight in a laundry treatment appliance comprising: Selecting a laundry program in the laundry treatment appliance; Starting the selected laundry program; Sensing a plurality of parameters indicating operating conditions of the laundry treatment appliance during the laundry program; and Predicting a weight of the laundry present within the laundry treatment appliance based on said plurality of parameters by means of a data-driven soft sensor.

This paper presents a robust technique, based on Krylov-subspace method, for the reduction of large-scale state-space models arising in many electromagnetic applications in fusion machines. The proposed approach, built on the Arnoldi algorithm, aims at reducing the number of states of the system and lowering the computational effort, with a negligible loss of accuracy in the numerical solution. A detailed performance study is presented on an ITER-like machine, addressing both 2-D and 3-D problems.

The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60?h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H??=??1 at ? N ~ 1.8 and n/n GW ~ 0.6) have been sustained at 2 MA during 5?s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed.

The control and optimization of oxygen content in wine matrices is becoming more and more important in wine production in order to guarantee their best quality. It is well known that in a first phase O2 is necessary to facilitate the development and activity of yeasts, to favor the combination of anthocyanins and color stabilization, and to help reducing the astringency of red wines. In a second phase, however, during the maturation, O2 can severely deteriorate the organoleptic characteristics of the wine. The main oenological practices commonly performed in wine cellars causes remarkable amounts of oxygen to dissolve in wine. For this reason, an instrumentation able to be used in cellars is needed to detect the amount of dissolved oxygen in the wine; as well, a plant technology is needed which is capable of eliminating excess oxygen. In this work, a new economic prototype of optical sensor for oxygen measurements in oenological matrices is developed. It is based on the sampling of the light emission of a polysulfone polymer membrane containing 5,10,15,20-Tetraphenyl-21H,23H-porphyrin platinum (II) (PtTPP). The experimental parameter used for calibration of the sensor is the life time of the PtTPP, obtained from the fitting of the emission decay profile produced by stimulation of the membrane with short pulses emitted by a 390 nm excitation LED. The membrane guarantees a linear behavior of the Stern-Volmer equation and long-lasting signal stability [1,2]. Studies on the behavior of the sensor have been performed in different environments such as air, water, synthetic wine, and also in real red and white wine samples at different temperatures from 5 ° to 20 ° C. The sensor has been suitably designed to work in food matrices and to optimize the noise signal ratio, while keeping the price of the components as low as possible. The sensor was tested for a month within a barrel of 10 m containing wine in the first fermentation phase. In particular, two equal sensors were placed at two levels of depth compared to the wine infeed level: at 0.5 and 2.5 m, respectively. The oxygen content measured during this period was always constant and equal to 0.2%. We thank Smart Future S.r.l. and the project "WOW: DEPLOYMENT OF WSAN TECHNOLOGY FOR MONITORING OXYGEN IN WINE PRODUCTS" financed by the Veneto Region ex LR 5/2001 - ex LR 9/2007.

The diffusion in Power Systems of distributed renewable energy resources, electric vehicles and controllable loads has made advanced monitoring systems fundamental to cope with the consequent disturbances in power flows; advanced monitoring systems can be employed for Anomaly Detection, Root Cause Analysis and Control purposes.

Several Machine Learning-based approaches have been developed in the past recent years to detect if a power system is running under anomalous conditions and, eventually, to classify such situation with respect to known problems.

One of the aspects that makes Power Systems challenging to be tackled, is that the monitoring has to be performed on streams of data that have a time series evolution; this issue is generally tackled by performing a features extraction procedure before the classication phase. The features extraction phase consists of translating the informative content of time series data into scalar quantities: such procedure may be a time-consuming step that requires the involvement of process experts to avoid loss of information in the making; moreover, extracted features designed to capture certain behaviors of the system, may not be informative under unseen conditions leading to poor monitoring performances.

A different type of data-driven approaches, that will be reviewed in this chapter, allow to perform classication directly on the raw time series data, avoiding the features extraction phase: among these approaches, Dynamic Time Warping and Symbolic-based methodologies have been widely applied in many application areas.

In the following, pros and cons of each approach will be discussed and practical implementation guidelines will be provided.