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Exploiting the huge amount of data collected by industries is definitely one of the main challenges of the so-called Big Data era. In this sense, Machine Learning has gained growing attention in the scientific community, as it allows to extract valuable information by means of statistical predictive models trained on historical process data. In Semiconductor Manufacturing, one of the most extensively employed data-driven applications is Virtual Metrology, where a costly or unmeasurable variable is estimated by means of cheap and easy to obtain measures that are already available in the system. Often, these measures are multi-dimensional, so traditional Machine Learning algorithms cannot handle them directly. Instead, they require feature extraction, that is a preliminary step where relevant information is extracted from raw data and converted into a design matrix. Features are often hand-engineered and based on specific domain knowledge. Moreover, they may be difficult to scale and prone to information loss, affecting the effectiveness and maintainability of machine learning procedures. In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit a complex bi-dimensional time and wavelength evolution, are used as input.

In semiconductor manufacturing, metrology is generally a high cost, non-value added operation that impacts significantly on cycle time. As such, reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives. Data-driven spatial dynamic sampling methodologies are here compared. Such strategies aim at minimizing the number of sites that need to be measured across a wafer surface while maintaining an acceptable level of wafer profile reconstruction accuracy. The Spatial Dynamic Sampling approaches are based on analyzing historical metrology data to determine, from a set of candidate wafer sites, the minimum set of sites that need to be monitored in order to reconstruct the full wafer profile using statistical regression techniques. Spatial Dynamic sampling is then implemented in various strategies that guarantee coverage of all the possible sites in a given set of process iteration. In this way, the risk of not detecting previously unseen process behavior is mitigated. In this work, we demonstrate the efficacy of spatial dynamic sampling methodologies using both simulation studies and metrology data from a semiconductor manufacturing process.

On-line and phone banking frauds are responsible for millions of dollars loss every year. In this work, we propose a Machine Learning-based Decision Support System to automatically associate a risk factor to each transaction performed through an on-line/mobile banking system. The proposed approach has a hierarchical architecture: First, an unsupervised Machine Learning module is used to detect abnormal patterns or wrongly labeled transactions; then, a supervised module provides a risk factor for the transactions that were not marked as anomalies in the previous step. Our solution exploits personal and historical information about the user, statistics that describe online traffic generated on the online/mobile banking system, and features extracted from motives of the transactions. The proposed approach deals with dataset unbalancing effectively. Moreover, it has been validated on a large database of transactions and on-line traffic provided by an industrial partner.

In the context of Smart Monitoring and Fault Detection and Isolation in industrial systems, the aim of Predictive Maintenance technologies is to predict the happening of process or equipment faults. In order for a Predictive Maintenance technology to be effective, its predictions have to be both accurate and timely for taking strategic decisions on maintenance scheduling, in a cost-minimization perspective. A number of Predictive Maintenance technologies are based on the use of “health factors” quantitative indicators associated with the equipment wear that exhibit a monotone evolution. In real industrial environment, such indicators are usually affected by measurement noise and non-uniform sampling time. In this work we present a methodology, formulated as a stochastic filtering problem, to optimally predict the evolution of the aforementioned health factors based on noisy and irregularly sampled observations. In particular, a hidden Gamma process model is proposed to capture the nonnegativity and nonnegativity of the derivative of the health factor. As such filtering problem is not amenable to a closed form solution, a numerical Monte Carlo approach based on particle filtering is here employed. An adaptive parameter identification procedure is proposed to achieve the best trade-off between promptness and low noise sensitivity. Furthermore, a methodology to identify the risk function associated to the observed equipment based on previous maintenance data is proposed. The present study is motivated and tested on a real industrial Predictive Maintenance problem in semiconductor manufacturing, with reference to a dry etching equipment.

In semiconductor manufacturing, metrology is generally a high cost nonvalue-added operation that significantly impacts on cycle time. As such, reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives. A novel data-driven spatial dynamic sampling methodology is presented that minimizes the number of sites that need to be measured across a wafer surface while maintaining an acceptable level of wafer profile reconstruction accuracy. The methodology is based on analyzing historical metrology data using forward selection component analysis (FSCA) to determine, from a set of candidate wafer sites, the minimum set of sites that need to be monitored in order to reconstruct the full wafer profile using statistical regression techniques. Dynamic sampling is then implemented by clustering unmeasured sites in accordance with their similarity to the FSCA selected sites and temporally selecting a different sample from each cluster. In this way, the risk of not detecting previously unseen process behavior is mitigated. We demonstrate the efficacy of the proposed methodology using both simulation studies and metrology data from a semiconductor manufacturing process.

In this paper we propose a distributed implementation
of the relaxed Alternating Direction Method of Multipliers algorithm
(R-ADMM) for optimization of a separable convex cost
function, whose terms are stored by a set of interacting agents,
one for each agent. Specifically  the local cost stored by each node is in
general a function of both the state of the node and the states of its
neighbors, a framework that we refer to as `partition-based' optimization.
This framework presents a great flexibility and can be adapted to a large
number of different applications.
By recasting the problem into an operator theoretical framework, the proposed
algorithm is shown to be provably robust against random packet losses that
might occur in the communication between
neighboring nodes. Finally, the effectiveness of the proposed algorithm is
confirmed by  a set of compelling numerical simulations run over random
geometric graphs subject to i.i.d. random packet losses.

Fabric care home appliances are pervasive inhouses worldwide and manufactures are constantly working for improving product performance, efficiency, and usability. From a manufacturing perspective, increase of performancehas to be attained while minimizing the increase of production costs. In this context, a Soft Sensor for estimating the clothes load weight in a horizontal axis household washing machines ishere presented. The proposed Soft Sensor is based on Machine Learning approaches. Several methodologies, both time-seriesand feature-based, are employed and compared. The approach has been tested on real world data on commercial household washing machines.

In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Building upon operator theory, first, we derive an ADMM-like algorithm, referred to as relaxed ADMM (R-ADMM) via a generalized Peaceman-Rachford Splitting operator on the Lagrange dual formulation of the original optimization problem. This algorithm depends on two parameters, namely the averaging coefficient $\alpha$ and the augmented Lagrangian coefficient $\rho$ and we show that by setting $\alpha=1/2$ we recover the standard ADMM algorithm as a special case. Moreover, first, we reformulate our R-ADMM algorithm into an implementation that presents reduced complexity in terms of memory, communication and computational requirements. Second, we propose a further reformulation which let us provide the first ADMM-like algorithm with guaranteed convergence properties even in the presence of lossy communication. Finally, this work is complemented with a set of compelling numerical simulations of the proposed algorithms over random geometric graphs subject to i.i.d. random packet losses.

Subjects affected by Type I Diabetes (T1D) are constantly confronted with the complicated problem of administering themselves an adequate amount of insulin, so as to keep their blood-glucose concentration in a nearly physiological range. Recently, powerful technological tools have been developed to better face this challenge, in particular the so-called Artificial Pancreas (AP). Unluckily, the AP actuator, an insulin pump, is subject to faults, with potential serious consequences for subjects' safety. This calls for the development of advanced fault detection (FD) methods, leveraging the unprecedented data availability in this application. In this paper we tackle the problem of detecting insulin pump malfunctioning using a model-free approach, so that the complex sub-task of identifying a model of patients physiology is avoided. Moreover, we employed unsupervised methods since labeled data are hardly available in practice. The adopted data-driven Anomaly Detection (AD) methods are Local Outlier Factor and Connectivity-based Outlier Factor. The methods are applied on a feature set able to account for the physiological dynamics of T1D patients. The proposed algorithms are tested on a synthetic dataset, generated using the “UVA/Padova Type 1 Diabetic Simulator”, an accurate nonlinear computer simulator of the T1D subject physiology. Both methods show precision ~75% and recall ~60%• The described approach is suitable both for embedding in medical devices, such as the AP, and implementation in cloud-based remote monitoring systems.

In this paper, we shed light on two fundamental actuation capabilities of multirotors. The first is the degree of coupling between the total force and total moment generated by the propellers. The second is the ability to robustly fly completely still in place after the loss of one or more propellers, in the case of mono-directional propellers. These are formalized through the definition of some algebraic conditions on the control allocation matrices. The theory is valid for any multirotor, with arbitrary number, position, and orientation of the propellers. As a show case for the general theory, we demonstrate that standard star-shaped hexarotors with collinear propellers are not able to robustly fly completely still at a constant spot using only five of their six propellers. To deeply understand this counterintuitive result, it is enough to apply our theory, which clarifies the role of the tilt angles and locations of the propellers. The theory is also able to explain why, on the contrary, both the tilted star-shaped and the Y-shaped hexarotors can fly with only five out of six propellers. The analysis is validated with both simulations and extensive experimental results showing recovery control after rotor losses.

Over the past few years, a huge number of distributed camera calibration strategies have been proposed for video surveillance and monitoring systems involving mobile terminals. Many of the proposed solutions rely on consensus-based algorithms, which aim at estimating the configuration of the network via a message passing protocol. In this paper we propose an improved consensus-based distributed camera calibration strategy that exploits a robust initialization, together with a pruning protocol to remove faulty links which could propagate excessively-noisy information through the network reducing the convergence time. The proposed solution seems to improve the state-of-the-art strategies in terms of accuracy, convergence speed, and computational complexity.

Over the last decades, small satellites have become very appealing among the space community for their low complexity and high flexibility. Many proposed mission concepts foresee the employment of miniature spacecraft for a variety of applications, many of which are economically unfeasible with traditional vehicles. This is due to the fact that the development of miniaturized and standardized space vehicles may considerably reduce the design, manufacturing and lunch costs involved. Furthermore, the reduced unitary mass of small satellites allows launches of multiple vehicles equipped with independent functionalities, thus achieving increased flexibility and redundancy. In the future, one additional opportunity could be given by the capability to assemble spacecraft in orbit to form reconfigurable structures. This would further boost the number of possibilities in terms of applications and operations. Nevertheless, the novelty of such concept and the intrinsic complexity of its practical realization still require a considerable research effort. In fact, only few navigation and docking technologies for nano- and micro satellites have been designed and proved in relevant environment. In this framework, the authors focus on the development and validation of critical technologies for close navigation, rendezvous and docking suitable for nanosatellites.

This paper presents a laboratory experiment for the validation on a complete rendezvous, navigation and docking package compatible with the common CubeSat standard. The experiment is conducted on a low friction table, with one free moving vehicle (chaser) that approaches and docks to a fixed target interface. The test facility allows three degrees of freedom to the nanosat mock-up. The vehicle is equipped with an autonomous package that features (1) a camera-based vision system for relative navigation, (2) a set of independent electro-magnetic coils for final alignment and soft docking, (3) a single-actuator hard docking system for structural connection between the chaser and the target, (4) a dedicated electronics package for motion control and system status monitoring. The mobile platform is also equipped with a set of flat air bearing with a dedicated high-pressure pneumatic circuit for frictionless in-plane motion.

This paper describes the docking package, the CubeSat mock-up and the test facility in details, with reference to the main design considerations. Numerical simulations have been conducted to foresee the dynamical behaviour of the system and to select the appropriate control algorithm. An intensive experimental campaign aims at the validation of numerical results and at the functional verification and performance estimation for each subsystem and for the system as a whole. The numerical and experimental results are presented and compared, allowing to draw useful conclusions for the future development steps. 

In laundry treatment appliances, the weight ofthe laundry loaded by the user inside the drum dramaticallyaffects the operating behavior. Therefore, it is important toobtain a good estimate of the said quantity in order tocorrectly configure the machine before the washing/dryingstarts. In Vertical Axis Washing Machinesthe laundry weightis computed by exploiting the quantity of water absorbed bythe clothes. However, such approach does not grant accurateresults because the water absorption depends on the clothesfabric. For this reason, we propose a Soft Sensing approachfor weight estimation that exploits the information obtainedfrom physical sensors available on board without added costs.Data-driven Soft Sensors are developed, where, using MachineLearning techniques, a statistical model of the phenomenon ofinterest is created from a set of sample data.

Wafer metrology is an expensive and time consuming activity in semiconductor manufacturing, but is essential to support advanced process control, predictive maintenance and other quality assurance functions. Keeping metrology to a minimum is therefore desirable. In the context of spatial sampling of wafers this has motivated the development of a number of data driven methodologies for optimizing wafer sampling plans. Two such methodologies are considered in this paper. The first combines Principal Component Analysis and Minimum Variance Estimation (PCA-MVE) to determine an optimum subset of sites from historical metrology data from a larger candidate set, while the second employs Forward Selection Component Analysis (FSCA), an unsupervised variable selection technique, to achieve the same result. We investigate the relationship between these two approaches and show that under specific conditions a regularized extension of FSCA, denoted FSCA-R, and PCA-MVE are equivalent. Numerical studies using simulated data verify the equivalence conditions. Results for simulated and industrial case studies show that the improvement in wafer profile reconstruction accuracy with regularization is not statistically significant for the case studies considered, and that when PCA-MVE is implemented with a denoising step as originally proposed, it is outperformed by FSCA. Therefore, FSCA is the preferred methodology.

In the last years, international space-related companies and agencies are manifesting great interest in on-orbit servicing. Innovative solutions to perform on-orbit operations such as refuelling, payload updating and maintenance, subsystems repairing and inspection are under study and all the new ideas and technologies under development are perceived as extremely functional and cost-effective, capable of increasing the operational lifetime of a satellite and decreasing the costs related to its complete replacement.

For these reasons, the development of an automatic, standard and reliable docking system would simplify the accomplishment of on-orbit servicing procedures. Presently, there has been an increasing interest in developing different technologies for proximity navigation and rendezvous manoeuvres but no competitive or commercial technologies are currently available to perform autonomous rendezvous and docking between small-satellites. One promising solution is represented by relative magnetic navigation, where the chaser relative position and attitude can be controlled thanks to magnetic interactions with the target vehicle.

This paper presents an overview of the PACMAN experiment: PACMAN is a technology demonstrator developed by a team of university and PhD students in the framework of ESA Education Fly Your Thesis! 2017 programme and supported by the University of Padova. The experiment has been selected for the 68^th ESA Parabolic Flight Campaign that took place in December 2017. The main goal of the project was to develop and validate in low-gravity conditions an integrated system for proximity navigation and soft-docking based on magnetic interactions, suitable for small-scale spacecraft. This has been accomplished by launching a miniature spacecraft mock-up (1U CubeSat) towards a free-floating target that generates a static magnetic field; a set of actively-controlled magnetic coils on-board the spacecraft mock-up, assisted by dedicated localization sensors, have been used to control its attitude and position relative to the target. This experimental setup allowed to study the behaviour of a miniature spacecraft subjected to controlled magnetic interactions in low-gravity conditions and to validate the theoretical/numerical models that describe such interactions.

The paper describes the experiment design, realization and execution, from the initial concept to the Parabolic Flight Campaign tests. The experiment working principle is illustrated with particular attention towards the navigation and soft-docking subsystems, and the analysis of retrieved scientific results is finally presented.

Presently, no competitive or commercial solution is currently available to perform autonomous rendezvous and docking between small-satellites. Therefore, in the last years there has been an increasing interest in developing different technologies for proximity navigation and rendezvous manoeuvres, addressing the main issues of fuel consumption and the strong impact of close range navigation subsystems on satellites mass budget and complexity. One promising solution is represented by relative magnetic navigation, where the chaser relative position and attitude can be controlled thanks to magnetic interactions with the target vehicle.

PACMAN experiment is a technology demonstrator that has been developed by a team of university and PhDs students in the framework of ESA Education Fly Your Thesis! 2017 programme and supported by the University of Padova. The experiment has been selected to fly during the 68^th ESA Parabolic Flight Campaign, currently scheduled to take place this December. The main goal of the project is to develop and validate in low-gravity conditions an integrated system for proximity navigation and soft-docking based on magnetic interactions, suitable for small-scale spacecraft. This will be accomplished by launching a miniature spacecraft mock-up towards a free-floating target that generates a static magnetic field; a set of actively-controlled magnetic coils on-board the spacecraft mock-up, assisted by dedicated localization sensors, will be used to control its attitude and position relative to the target.

The realization of PACMAN experiment will allow to study the behaviour of a miniature spacecraft subjected to controlled magnetic interactions in low-gravity conditions and to validate the theoretical/numerical models that describe such interactions.

This paper presents an overview from the concept and design of the experiment to the Parabolic Flight Campaign tests. The experiment working principle will be illustrated, along with the design and assembly phases. Particular attention will be made towards the problem solving approach. Alternatives and backup solutions are introduced as part of the lessons learned during the entire programme. Finally, the analysis of retrieved scientific results will be showed.

The laundry weight of the loaded in the drum of a laundry treatment machine is an important piece of information; laundry weight can be used to set various washing/drying cycle parameters and to optimize performances and efficiency. Unfortunately, dedicated weight sensors cannot be included in consumer laundry equipment given the related costs. For this reason, we present in this work a soft sensor approach for estimating laundry weight based on sensors already in place in a laundry treatment equipment; in particular, we consider here a heat pump tumble dryer as case study. The proposed soft sensor is based on regularization, a popular approach in Machine Learning to provide models without overfitting the training data. Different studies are provided in this work, by considering different constrains on timing and complexity of the Soft Sensor solution. The developed Soft Sensors have been tested on laboratory data provided by an industrial partner.

The relationship between fMRI and MEG signals between different cortical regions (functional connectivity, FC) has been extensively analyzed in the resting state (De Pasquale et al 2010; Brookes et al 2011; Hipp et al 2012). Much less is known about FC modulations from rest to task states, and how they appear respectively in these two imaging modalities. Previously we have shown task-specific alterations of FC in fMRI during a visuospatial attention task (Spadone et al., PNAS, 2015). Specifically, decrements of resting correlation in visual areas were coupled with increments of correlation between visual and dorsal attention regions. Here, we compared fMRI with band-limited power (BLP) correlation obtained with MEG on the same group of subjects. Aim (i) is to measure frequency specific task-related FC modulations in MEG. Aim (ii) is to compare fMRI- and MEG-FC modulations (task-rest).

A purely geometric approach has been investigated to reconstruct the Demonstration Fusion Power Reactor (DEMO) plasma boundary for control purposes. The whole plasma boundary is reconstructed by using a deformable template method based on B-splines. The final curve shape is achieved by minimizing the distance between a limited number of estimated and measured (at present provided by an equilibrium code) plasma boundary points along the reflectometer lines of sight. The resulting unconstrained optimization problem is solved by a simulated annealing algorithm. The method is complemented by including the available plasma and poloidal field coil current measurements to refine the boundary reconstruction in the X-point region. The robustness with respect to random measurement random errors and to a reduction in the number of measurements is discussed. The main equilibrium and shape geometric quantities (such as plasma cross-sectional area, plasma center position, elongation, and triangularity) were computed and compared to the corresponding quantities of a DEMO reference equilibrium.

The goal of Aspect-Based Sentiment Analysis is to identify opinions regarding specific targets and the corresponding sentiment polarity in a document. The proposed approach is designed for real-world scenarios, where the amount of available information and annotated data is often too limited to train supervised models. We focus on the two core tasks of Aspect-Based Sentiment Analysis: aspect and sentiment polarity classification. The first task – which consists in the identification of the opinion targets in a document – is tackled by means of a weakly-supervised technique based on Non-negative Matrix Factorization. This strategy allows users to easily embed some a priori domain knowledge by means of short seed terms lists. Experimental results on publicly available data sets related to online reviews suggest that the proposed approach is very flexible and can be easily adapted to different languages and domains.