20XX
Y. Chen, D. Cuccato, M. Bruschetta, F. Maran, A. Beghi.
An Automatic Sensitivity Updating Scheme for NMPC based on a Curvature Nonlinearity Measure. [submitted], 20XX
Abstract:
In fast nonlinear model predictive control the sensitivitycomputation is one of the key aspects to reducecomputational burden, in fact specific automated andefficient procedures for that have been developed. Howeverthe number of sensitivity computations required toadequately approximate the nonlinear dynamics is typicallyhigh and fixed a priori. In this paper, we developa sensitivity updating scheme capable of reducing thenumber of sensitivity computations exploiting an onlinecurvature-based measure of nonlinearity of the system.The proposed strategy is applied to the sequentialquadratic programming framework with specific attentionto the Real-Time Iteration implementation. Simulationson the inverted pendulum benchmark show asignificant reduction of the number of the sensitivity updates,hence a reduction of the overall computationaltime.
[ abstract ] [
BibTeX]
2023
D. Dalle Pezze, D. Deronjic, C. Masiero, D. Tosato, A. Beghi, G.A. Susto.
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring. Engineering Applications of Artificial Intelligence, vol. 124, 2023 [
BibTeX]
A. Beghi, N. Dall'Ora, D. Dalle Pezze, F. Fummi, C. Masiero, S. Spellini, G.A. Susto, F. Tosoni.
VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing. 26th Forum on specification and Design Languages, 2023 [
BibTeX]
2022
M. Maggipinto, A. Beghi, G.A. Susto.
A Deep Convolutional Autoencoder-based Approach for Anomaly Detection with Industrial, Non-images, 2-Dimensional data: a Semiconductor Manufacturing case study. IEEE Transactions on Automation Science and Engineering, vol. 19(3), pp. 1477-1490, 2022 [
url] [
BibTeX]
D. Dalle Pezze, D. Deronjic, C. Masiero, D. Tosato, A. Beghi, G.A. Susto.
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), 2022 [
url] [
BibTeX]
E. Marcelli, T. Barbariol, V. Savarino, A. Beghi, G.A. Susto.
A Revised Isolation Forest procedure for Anomaly Detection with High Number of Data Points. 23rd IEEE Latin-American Test Symposium (LATS2022), 2022 [
BibTeX]
F. Simmini, M. Rampazzo, F. Peterle, G.A. Susto, A. Beghi.
A Self-Tuning KPCA-based Approach to Fault Detection in Chiller Systems. IEEE Transactions on Control Systems Technology, vol. 30(4), 2022 [
BibTeX]
E. Anello, M. Chiara, F. Ferro, F. Ferrari, B. Mukaj, A. Beghi, G.A. Susto.
Anomaly Detection for the Industrial Internet of Things: an Unsupervised Approach for Fast Root Cause Analysis. IEEE Conference on Control Technology and Applications (CCTA), 2022 [
BibTeX]
D. Dalle Pezze, C. Masiero, D. Tosato, A. Beghi, G.A. Susto.
FORMULA: A Deep Learning Approach for Rare Alarms Predictions in Industrial Equipment. IEEE Transactions on Automation Science and Engineering, vol. 19(3), pp. 1491--1502, 2022 [
url] [
BibTeX]
A. Beghi, G.A. Susto, G. Zambonin, F. Altinier, T. Girotto, M. Rampazzo.
LAUNDRY DRYING MACHINE AND CONTROL METHOD THEREOF. Patent, 2022 [
BibTeX]
D. Tosato, E. Convento, C. Masiero, G.A. Susto, A. Beghi.
Packaging Industry Anomaly DEtection (PIADE). 2022 [
url] [
BibTeX]
K.S.S. Alamin, Y. Chen, S. Gaiardelli, S. Spellini, A. Calimera, A. Beghi, G.A. Susto, F. Fummi, S. Vinco.
SMARTIC: Smart Monitoring and Production Optimization for Zero-waste Semiconductor Manufacturing. 23rd IEEE Latin-American Test Symposium (LATS2022), 2022 [
BibTeX]
2021
N. Bargellesi, A. Beghi, M. Rampazzo, G.A. Susto.
AutoSS: A Deep Learning-Based Soft Sensor for Handling Time-Series Input Data. IEEE Robotics and Automation Letters, vol. 6(3), pp. 6100--6107, 2021
Abstract:
Soft Sensors are data-driven technologies that allow to have estimations of quantities that are impossible or costly to be measured. Unfortunately, the design of effective soft sensors is heavily impacted by time-consuming feature engineering steps that may lead to sub-optimal information, especially when dealing with time-series input data. While domain knowledge may come into help when handling feature extraction in soft sensing applications, the feature extraction typically limits the adoption of such technologies: in this work, we propose AutoSS, a Deep-Learning based approach that allows to overcome such issue. By exploiting autoencoders, dilated convolutions and an ad-hoc defined architecture, AutoSS allows to develop effective soft sensing modules even with time-series input data. The effectiveness of AutoSS is demonstrated on a real-world case study related to Internet of Things equipment.
[ abstract ] [
url] [
BibTeX]
S. Arena, Y. Budrov, M. Carletti, N. Gentner, M. Maggipinto, Y. Yang, A. Beghi, A. Kyek, G.A. Susto.
Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of SEM Images. IEEE Transactions on Semiconductor Manufacturing, 2021
Abstract:
Deep Learning approaches have revolutionized in the past decade the field of Computer Vision and, as a consequence, they are having a major impact in Industry 4.0 applications like automatic defect classification. Nevertheless, additional data, beside the image/video itself, is typically never exploited in a defect classification module: this aspect, given the abundance of data in data-intensive manufacturing environments (like semiconductor manufacturing) represents a missed opportunity. In this work we present a use case related to Scanning Electron Microscope (SEM) images where we exploit a Bayesian approach to improve defect classification. We validate our approach on a real-world case study and by employing modern Deep Learning architectures for classification.
[ abstract ] [
BibTeX]
2020
D. Tosato, D. Dalle Pezze, C. Masiero, G.A. Susto, A. Beghi.
Alarm Logs in Packaging Industry (ALPI). IEEEDataPort, 2020
Abstract:
The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks:
- Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.
- Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.
- Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.
- Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly.
All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).
[ abstract ] [
url] [
BibTeX]
M. Carletti, N. Gentner, Y. Yang, A. Kyek, M. Maggipinto, A. Beghi, G.A. Susto.
Interpretable Anomaly Detection for Knowledge Discovery in Semiconductor Manufacturing. Winter Simulation Conference, 2020
Abstract:
Machine Learning-based Anomaly Detection (AD) approaches are efficient tools to monitor complexprocesses. One of the advantages of such approaches is that they provide a unique anomaly indicator,a quantitative index that captures the degree of ’outlierness’ of the process at hand considering possiblyhundreds or more variables at the same time, the typical scenario in semiconductor manufacturing. Oneof the drawback of such approaches is that Root Cause Analysis is not guided by the system itself. Inthis work, we show the effectiveness of a method, called DIFFI, to equip Isolation Forest, one of themost popular AD algorithms, with interpretability traits that can help corrective actions and knowledgeunderstanding. Such approach is validated on real world semiconductor manufacturing data related to aChemical Vapor Deposition process.
[ abstract ] [
BibTeX]
2019
M. Carletti, C. Masiero, A. Beghi, G.A. Susto.
A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study. Procedia Manufacturing, vol. 38, pp. 233-240, 2019
Abstract:
We propose a Deep Learning (DL)-based approach for production performance forecasting in fresh products packaging. On the one hand, this is a very demanding scenario where high throughput is mandatory; on the other, due to strict hygiene requirements, unexpected downtime caused by packaging machines can lead to huge product waste. Thus, our aim is predicting future values of key performance indexes such as Machine Mechanical Efficiency (MME) and Overall Equipment Effectiveness (OEE). We address this problem by leveraging DL-based approaches and historical production performance data related to measurements, warnings and alarms. Different architectures and prediction horizons are analyzed and compared to identify the most robust and effective solutions. We provide experimental results on a real industrial case, showing advantages with respect to current policies implemented by the industrial partner both in terms of forecasting accuracy and maintenance costs. The proposed architecture is shown to be effective on a real case study and it enables the development of predictive services in the area of Predictive Maintenance and Quality Monitoring for packaging equipment providers.
[ abstract ] [
url] [
BibTeX]
M. Maggipinto, A. Beghi, G.A. Susto.
A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing. International Conference on Industrial Informatics (INDIN), pp. 187 -- 191, 2019
Abstract:
In modern manufacturing scenarios, detecting
anomalies in production systems is pivotal to keep high-quality
standards and reduce costs. Even in the Industry 4.0 context, realworld monitoring systems are often simple and based on the use
of multiple univariate control charts. Data-driven technologies
offer a whole range of tools to perform multivariate data analysis
that allow to implement more effective monitoring procedures.
However, when dealing with complex data, common data-driven
methods cannot be directly used, and a feature extraction
phase must be employed. Feature extraction is a particularly
critical operation, especially in anomaly detection tasks, and it is
generally associated with information loss and low scalability. In
this paper we consider the task of Anomaly Detection with twodimensional, image-like input data, by adopting a Deep Learningbased monitoring procedure, that makes use of convolutional
autoencoders. The procedure is tested on real Optical Emission
Spectroscopy data, typical of semiconductor manufacturing. The
results show that the proposed approach outperforms classical
feature extraction procedures.
[ abstract ] [
BibTeX]
G.A. Susto, L. Vettore, G. Zambonin, F. Altinier, D. Beninato, T. Girotto, M. Rampazzo, A. Beghi.
A Machine Learning-based Soft Sensor for Laundry Load Fabric Typology Estimation in Household Washer-Dryers. 5th IFAC International Conference on Intelligent Control and Automation Sciences, 2019 [
BibTeX]
G. Zambonin, F. Altinier, A. Beghi, L.D.S. Coelho, T. Girotto, M. Rampazzo, G. Reynoso-Meza, G.A. Susto.
Data-Driven Models for the Determination of Laundry Moisture Content in a Household Laundry Treatment Dryer Appliance. Lecture Notes in Control and Information Sciences – Proceedings, 2019
Abstract:
Two methods based on Regression are presented to determine the moisture content of items, e.g. clothes and the like, which are introduced in a household laundry dryer appliance. The aim of this work is to develop Soft Sensors (SS) for a household Heat Pump Washer-Dryer (WD-HP) to provide an estimation of the desired signal (the laundry
moisture during drying) avoiding the use of additional physical sensors with the goal of improving the current performance in terms of precision and energy consumption of the automatic drying cycle end and using the machine equipment already available. On an algorithmic point of view, the SS developed in this work exploits regularization methods and Genetic Programming for Symbolic Regression
in order to find suitable models for the purpose at hand. Proposed approaches have been tested on real data provided by an industrial partner.
[ abstract ] [
pdf] [
BibTeX]
L. Brunelli, C. Masiero, D. Tosato, A. Beghi, G.A. Susto.
Deep Learning-based Production Forecasting in Manufacturing: a Packaging Equipment Case Study. Procedia Manufacturing, vol. 38, pp. 248-255, 2019
Abstract:
We propose a Deep Learning (DL)-based approach for production performance forecasting in fresh products packaging. On the one hand, this is a very demanding scenario where high throughput is mandatory; on the other, due to strict hygiene requirements, unexpected downtime caused by packaging machines can lead to huge product waste. Thus, our aim is predicting future values of key performance indexes such as Machine Mechanical Efficiency (MME) and Overall Equipment Effectiveness (OEE). We address this problem by leveraging DL-based approaches and historical production performance data related to measurements, warnings and alarms. Different architectures and prediction horizons are analyzed and compared to identify the most robust and effective solutions. We provide experimental results on a real industrial case, showing advantages with respect to current policies implemented by the industrial partner both in terms of forecasting accuracy and maintenance costs. The proposed architecture is shown to be effective on a real case study and it enables the development of predictive services in the area of Predictive Maintenance and Quality Monitoring for packaging equipment providers.
[ abstract ] [
BibTeX]
M. Maggipinto, A. Beghi, S. McLoone, G.A. Susto.
DeepVM: A Deep Learning-based Approach with Automatic Feature Extraction for 2D Input Data Virtual Metrology. Journal of Process Control, vol. 84, pp. 24-34, 2019
Abstract:
Industry 4.0 encapsulates methods, technologies, and procedures that transform data into informed decisions and added value inan industrial context. In this regard, technologies such as Virtual Metrology or Soft Sensing have gained much interest in thelast two decades due to their ability to provide valuable knowledge for production purposes at limited added expense. However,these technologies have struggled to achieve wide-scale industrial adoption, largely due to the challenges associated with handlingcomplex data structures and the feature extraction phase of model building. This phase is generally hand-engineered and basedon specific domain knowledge, making it time consuming, difficult to automate, and prone to loss of information, thus ultimatelylimiting portability. Moreover, in the presence of complex data structures, such as 2-dimensional input data, there are no establishedprocedures for feature extraction. In this paper, we present a Deep Learning approach for Virtual Metrology, called DeepVM,that exploits semi-supervised feature extraction based on Convolutional Autoencoders. The proposed approach is demonstratedusing a real world semiconductor manufacturing dataset where the Virtual Metrology input data is 2-dimensional Optical EmissionSpectrometry data. The feature extraction method is tested with different types of state-of-the-art autoencoder.
[ abstract ] [
url] [
BibTeX]
M. Carletti, C. Masiero, A. Beghi, G.A. Susto.
Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis. 2019 IEEE International Conference on Systems, Man, and Cybernetics, 2019
Abstract:
In the past recent years, Machine Learning methodologies have been applied in countless application areas. In particular, they play a key role in enabling Industry4.0. However, one of the main obstacles to the diffusion of Machine Learning-based applications is related to the lack of interpretability of most of these methods. In this work, we propose an approach for defining a ‘feature importance’ in Anomaly Detection problems. Anomaly Detection is an important Machine Learning task that has an enormous applicability in industrial scenarios. Indeed, it is extremely relevant for the purpose of quality monitoring. Moreover, it is often the first step towards the design of a Machine Learning based smart monitoring solutions because Anomaly Detection can be implemented without the need of labelled data. The proposed feature importance evaluation approach is designed for Isolation Forest, one of the most commonly used algorithm for Anomaly Detection. The efficacy of the proposed method is tested on synthetic and real industrial datasets.
[ abstract ] [
url] [
BibTeX]
M. Maggipinto, E. Pesavento, F. Altinier, G. Zambonin, A. Beghi, G.A. Susto.
Laundry Fabric Classification in Vertical AxisWashing Machines using Data-driven Soft Sensors. Energies, vol. 12(21), 2019
Abstract:
Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM.
[ abstract ] [
url] [
BibTeX]
G. Zambonin, F. Altinier, A. Beghi, L.D.S. Coelho, N. Fiorella, T. Girotto, M. Rampazzo, G. Reynoso-Meza, G.A. Susto.
Machine Learning-based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances. Energies, vol. 20(12), pp. 1 -- 24, 2019
Abstract:
The aim is to develop soft sensors (SSs) to provide an estimation of the laundry moisture of clothes introduced in a household Heat Pump Washer–Dryer (WD-HP) appliance. The developed SS represents a cost-effective alternative to physical sensors, and it aims at improving the WD-HP performance in terms of drying process efficiency of the automatic drying cycle. To this end, we make use of appropriate Machine Learning models, which are derived by means of Regularization and Symbolic Regression methods. These methods connect easy-to-measure variables with the laundry moisture content, which is a difficult and costly to measure variable. Thanks to the use of SSs, the laundry moisture estimation during the drying process is effectively available. The proposed models have been tested by exploiting real data through an experimental test campaign on household drying machines.
[ abstract ] [
url] [
BibTeX]
2018
M. Maggipinto, M. Terzi, C. Masiero, A. Beghi, G.A. Susto.
A Computer Vision-inspired Deep Learning Architecture for Virtual Metrology modeling with 2-Dimensional Data. IEEE Transactions on Semiconductor Manufacturing, vol. 31(3), pp. 376 - 384, 2018
Abstract:
The rise of Industry 4.0 and data-intensive manufacturing makes Advanced Process Control (APC) applications more relevant than ever for process/production optimization, related costs reduction, and increased efficiency. One of the most important APC technologies is Virtual Metrology (VM). VM aims at exploiting information already available in the process/system under exam, to estimate quantities that are costly or impossible to measure. Machine Learning approaches are the foremost choice to design VM solutions. A serious drawback of traditional Machine Learning methodologies is that they require a features extraction phase that generally limits the scalability and performance of VM solutions. Particularly, in presence of multi-dimensional data, the feature extraction process is based on heuristic approaches that may capture features with poor predictive power. In this work, we exploit modern Deep Learning-based technologies that are able to automatically extract highly informative features from the data, providing more accurate and scalable VM solutions. In particular, we exploit Deep Learning architectures developed in the realm of Computer Vision to model data that have both spatial and time evolution. The proposed methodology is tested on a real industrial dataset related to Etching, one of the most important Semiconductor Manufacturing processes. The dataset at hand contains Optical Emission Spectroscopy data and it is paradigmatic of the feature extraction problem in VM under examination.
[ abstract ] [
url] [
BibTeX]
M. Maggipinto, C. Masiero, A. Beghi, G.A. Susto.
A Convolutional Autoencoder Approach for Feature Extraction in Virtual Metrology. Procedia Manufacturing, 28th International Conference on Flexible Automation and Intelligent Manufacturing, vol. 17, pp. 126-133, 2018
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, A. Schirru, S. Pampuri, A. Beghi, G. De nicolao.
A Hidden-Gamma Model-Based Filtering and Prediction Approach for Monotonic Health Factors in Manufacturing. Control Engineering Practice, vol. 74, pp. 84-94, 2018
Abstract:
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.
[ abstract ] [
url] [
pdf] [
BibTeX]
G.A. Susto, G. Zambonin, F. Altinier, E. Pesavento, A. Beghi.
A Soft Sensing approach for Clothes Load Estimation in Consumer Washing Machines. 2nd IEEE Conference on Control Technology and Applications (CCTA), 2018
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
M. Rampazzo, A. Beghi.
Designing and Teaching of an Effective Engineering Continuing Education Course: Modeling and Simulation of HVAC Systems. Computer Applications in Engineering Education, 2018 [
BibTeX]
G.A. Susto, M. Maggipinto, G. Zannon, F. Altinier, E. Pesavento, A. Beghi.
Machine Learning-based Laundry Weight Estimation for Vertical Axis Washing Machines. European Control Conference (ECC2018), pp. 3179 - 3184, 2018
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
D. Tognin, M. Rampazzo, M. Pagan, L. Carniello, A. Beghi.
Modelling and Simulation of an Artificial Tide Lagoon Generation System. IAMES 2018 - 1st IFAC Workshop on Integrated Assessment Modelling for Environmental Systems, 2018 [
BibTeX]
G. Zambonin, F. Altinier, L. Corso, A. Beghi, G.A. Susto.
Soft Sensors for Estimating Laundry Weight in Household Heat Pump Tumble Dryers. Conference on Automation Science and Engineering (CASE), 2018
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
2017
G.A. Susto, M. Terzi, A. Beghi.
Anomaly Detection Approaches for Semiconductor Manufacturing. Procedia Manufacturing,
27th International Conference on Flexible Automation and Intelligent Manufacturing, vol. 11, pp. 2018-2024, 2017
Abstract:
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
[ abstract ] [
url] [
BibTeX]
G.A. Susto, A. Beghi, S. McLoone.
Anomaly Detection through on-line Isolation Forest: an Application to Plasma Etching. IEEE/SEMI Advanced Semiconductor Manufacturing Conference, 2017
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
M. Terzi, C. Masiero, A. Beghi, M. Maggipinto, G.A. Susto.
Deep Learning for Virtual Metrology: Modeling with Optical Emission Spectroscopy Data. IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, M. Lionello, M. Rampazzo.
Energy-Efficient Management of a Wood Industry Facility. CCTA 2017, 1st IEEE Conference on Control Technology and Applications, 2017 [
BibTeX]
A. Beghi, G. Dalla Mana, M. Lionello, M. Rampazzo, E. Sisti.
Energy-Efficient Operation of an Indirect Adiabatic Cooling System for Data Centers. The 2017 American Control Conference, 2017 [
BibTeX]
F. Altinier, E. Pesavento, A. Beghi, G.A. Susto, G. Zambonin, G. Zannon.
Method for the Determination of a Laundry Weight in a Laundry Treatment Appliance. (Pub. No.: WO/2017/144085 International Application No.: PCT/EP2016/053788), 2017
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, G. Dalla Mana, M. Lionello, M. Rampazzo, E. Sisti.
Modelling and Control of a Free Cooling System for Data Centers. AICARR International Conference - Beyond NZEB Buildings, 2017 [
BibTeX]
A. Beghi, L. Cecchinato, G. Dalla Mana, M. Lionello, M. Rampazzo, E. Sisti.
Modelling and control of a free cooling system for Data Centers. Energy Procedia, 2017 [
BibTeX]
A. Beghi, P. Franceschetti, M. Rampazzo, E. Sisti, M. Lionello.
Modelling and Simulation of a Convective Low Temperature Sludge Dryer with Multilayer Belt. IEEE RTSI 2017 International Forum on Research and Technologies for Society and Industry, 2017 [
BibTeX]
A. Beghi, M. Rampazzo.
Modelling and Simulation of a Sludge Drying Process. The 33rd international CAE conference and exhibition. Simulation: the soul of industry 4.0, 2017 [
BibTeX]
M. Lissandrin, M. Rampazzo, L. Cecchinato, A. Beghi.
Optimal operational efficiency of chillers using oil-free centrifugal compressors. International Journal of Refrigeration, 2017 [
BibTeX]
A. Beghi, M. Rampazzo.
Reinforcement Learning Control of Transcritical Carbon Dioxide Supermarket Refrigeration Systems. IFAC 2017 World Congress, 2017 [
BibTeX]
M. Rampazzo, A. Cervato, A. Beghi.
Remote Refrigeration System Experiments for Control Engineering Education. Computer Applications in Engineering Education - Wiley, 2017 [
BibTeX]
2016
A. Beghi, F. Marcuzzi, M. Rampazzo.
A Virtual Laboratory for the Prototyping of Cyber-Physical Systems. 11th IFAC Symposium on Advances in Control Education, 2016 [
BibTeX]
A. Beghi, L. Cecchinato, G. Menegazzo, M. Rampazzo, F. Simmini.
Data-driven Fault Detection and Diagnosis for HVAC water chillers. Control Engineering Practice, vol. 53,, 2016 [
BibTeX]
G.A. Susto, A. Beghi.
Dealing with Time-Series Data in Predictive Maintenance Problems. Emerging Technologies and Factory Automation, 2016
Abstract:
In this paper an approach to deal with Predictive Maintenance (PdM) problems with time-series data is discussed. PdM is a important approach to tackle maintenance and it is gaining an increasing attention in advanced manufacturing to minimize scrap materials, downtime, and associated costs. PdM approaches are generally based on Machine Learning tools that require the availability of historical process and maintenance data. Given the exponential growth in data logging in modern equipment, time series dataset are increasingly available in PdM applications. To exploit time series data for PdM, a functional learning methodology, namely Supervised Aggregative Feature Extraction (SAFE), is here employed on a semiconductor manufacturing maintenance problem.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, F. Peterle, M. Rampazzo, F. Simmini.
Model-Based Fault Detection and Diagnosis for Centrifugal Chillers. SysTol’16, 3rd International Conference on Control and Fault-Tolerant Systems, 2016 [
BibTeX]
2015
A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo.
A Data-Driven Approach for Fault Diagnosis in HVAC Chiller Systems. The 2015 IEEE Multi-Conference on Systems and Control (MSC), 2015 [
BibTeX]
A. Beghi, A. Cervato, M. Rampazzo.
A Remote Refrigeration Laboratory for Control Engineering Education. IFAC IBCE 2015 Workshop on Internet Based Control Education, 2015 [
BibTeX]
G.A. Susto, A. Schirru, S. Pampuri, S. McLoone, A. Beghi.
Machine Learning for Predictive Maintenance: a Multiple Classifiers Approach. IEEE Transactions on Industrial Informatics, vol. 11(3), pp. 812 - 820, 2015
Abstract:
In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensionaland censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, S. Pampuri, A. Schirru, A. Beghi, G. De nicolao.
Multi-Step Virtual Metrology for Semiconductor Manufacturing: a Multilevel and Regularization Methods-based Approach. Computers & Operations Research, vol. 53, pp. 328–337, 2015
Abstract:
In semiconductor manufacturing, wafer quality control strongly relies on product monitoring and physical metrology. However, the involved metrology operations, generally performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, in common practice a small subset of only a productive lot is measured at the metrology stations and it is devoted to represent the entire lot. Virtual Metrology (VM) methodologies are able to obtain reliable predictions of metrology results at process time, without actually performing physical measurements; this goal is usually achieved by means of statistical models and by linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a given wafer (such as layer thickness and critical dimensions) depend on the whole processing and not on the last step before measurement only. In this paper, we investigate the possibilities to enhance VM prediction accuracy by exploiting the knowledge collected from previous process steps. We present two different schemes of multi-step VM, along with dataset preparation indications; special consideration will be reserved to regression techniques capable of handling high-dimensional input spaces. The proposed multi-step approaches are tested on production data provided by a partner semiconductor manufacturing industry.
[ abstract ] [
url] [
BibTeX]
2014
M. Bruschetta, F. Maran, A. Beghi.
A non-linear MPC based motion cueing imple- mentation for a 9 DOFs dynamic simulator platform. Proceedings of the 53rd IEEE Conference on Decision and Control, CDC 2014, pp. 2517--2522, 2014 [
BibTeX]
A. Beghi, L. Cecchinato, C. Corazzol, M. Rampazzo, F. Simmini, G.A. Susto.
A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems. 19th World Congress of the International Federation of Automatic Control, pp. 1953-1958, 2014
Abstract:
Faulty operations of Heating, Ventilation and Air
Conditioning (HVAC) chiller systems can lead to discomfort
for the occupants, energy wastage, unreliability and
shorter equipment life. Such faults need to be detected
early to prevent further escalation and energy losses.
Commonly, data regarding unforeseen phenomena and
abnormalities are rare or are not available at the moment
of HVAC systems installation: for this reason in this paper
an unsupervised One-Class SVM classifier employed as a
novelty detection system to identify unknown status and
possible faults is presented. The approach, that exploits
Principal Component Analysis to accent novelties w.r.t.
normal operations variability, has been tested on a HVAC
literature dataset.
[ abstract ] [
url] [
BibTeX]
A. Di Virgilio, M. Allegrini, A. Beghi, J. Belfi, N. Beverini, F. Bosi, B. Bouhadef, M. Calamai, G. Carelli, D. Cuccato, E. Maccioni, A. Ortolan, G. Passeggio, A. Porzio, M. Ruggiero, R. Santagata, S. Solimeno, A. Tartaglia.
A ring lasers array for fundamental physics. Comptes Rendus Physique,, vol. 15(10), pp. 868--874, 2014 [
BibTeX]
M. Bruschetta, F. Maran, A. Beghi.
An MPC approach to the design of motion cueing algorithms for a high performance 9 DOFs driving simulator. Proceedings of the 2014 Driving Simulation Conference, 2014 [
BibTeX]
D. Cuccato, A. Beghi, J. Belfi, N. Beverini, A. Ortolan, A. Di Virgilio.
Controlling the nonlinear inter cavity dynamics of large he-Ne laser gyroscopes. Metrologia, vol. 51, pp. 97--107, 2014 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Efficient algorithms for the reconstruction and prediction of atmospheric turbulence in AO systems. Proceedings of the European Control Conference (ECC14), pp. 2430 - 2435, 2014
Abstract:
Technological advances and the ever-growing human quest for improving
the resolution of telescope observations are motivating the design of
larger and larger ground telescopes: indeed, the larger is the telescope
lens diameter, the better is the diffraction limited resolution of the
telescope. Unfortunately, the terrestrial atmospheric turbulence, if not
properly compensated, negatively affects the telescope observations,
limiting its real resolution. Adaptive Optics (AO) systems are used in
large ground telescopes in order to compensate the effect of the
atmosphere, and hence to make the real telescope resolution be
determined by the diffraction properties of the lens.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Efficient algorithms for the reconstruction and prediction of atmospheric turbulence in AO systems. Proc. of the European Control Conference (ECC), pp. 2430--2435, 2014
Abstract:
Technological advances and the ever-growing human quest for improving the resolution of telescope observations are motivating the design of larger and larger ground telescopes: indeed, the larger is the telescope lens diameter, the better is the diffraction limited resolution of the telescope. Unfortunately, the terrestrial atmospheric turbulence, if not properly compensated, negatively affects the telescope observations, limiting its real resolution. Adaptive Optics (AO) systems are used in large ground telescopes in order to compensate the effect of the atmosphere, and hence to make the real telescope resolution be determined by the diffraction properties of the lens. AO systems exploit the measurements of wavefront sensors to estimate the current values of the atmospheric turbulence, and compensate its effect by properly adapting the shape of a set of deformable mirrors. As the size of the telescope lenses is increasing, then the size of the AO system (e.g. the number of deformable mirror actuators and the size of the wavefront sensor) is increasing as well. This causes the increase of the computational burden needed to compute a proper compensation of the effect of the atmosphere. Consequently, as the potential telescope resolution increases, the task of the AO systems becomes more challenging. Motivated by the need of providing AO solutions useful for the next generations of ground telescopes, then a number of efficient algorithms have been recently considered in the literature to solve the problems related to the AO system. This paper considers the combination of a recently proposed very efficient phase reconstruction method, namely the CuRe, with a properly defined Kalman filter in order to obtain a dynamic compensation of the atmospheric turbulence. The performance of the proposed approach is investigated in some simulations.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo, F. Simmini.
Energy efficient control of HVAC systems with ice cold thermal energy storage. Journal of Process Control, vol. 24(6), pp. 773–781, 2014 [
BibTeX]
A. Beghi, F. Marcuzzi, M. Rampazzo, M. Virgulin.
Enhancing the simulation-centric design of Cyber-Physical and Multi-Physics Systems through co-simulation. 17th Euromicro Conference on Digital System Design (DSD 2014), 2014 [
BibTeX]
N. Beverini, M. Allegrini, A. Beghi, J. Belfi, B. Bouhadef, M. Calamai, G. Carelli, D. Cuccato, A. Di Virgilio, E. Maccioni, A. Ortolan, A. Porzio, R. Santagata, A. Tartaglia.
Measuring general relativity effects in a terrestrial lab by means of laser gyroscopes. Laser Physics, vol. 24(7), pp. 074005, 2014 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Nonstationary multiscale turbulence simulation based on local PCA. ISA Transactions, 2014
Abstract:
Turbulence simulation methods are of fundamental importance for
evaluating the performance of control strategies for Adaptive Optics
(AO) systems. In order to obtain a reliable evaluation of the
performance a statistically accurate turbulence simulation method has to
be used. This work generalizes a previously proposed method for
turbulence simulation based on the use of a multiscale stochastic model.
The main contributions of this work are: first, a multiresolution local
PCA representation is considered. In typical operating conditions, the
computational load for turbulence simulation is reduced approximately by
a factor of 4, with respect to the previously proposed method, by means
of this PCA representation. Second, thanks to a different low
resolution method, based on a moving average model, the wind velocity
can be in any direction (not necessarily that of the spatial axes).
Finally, this paper extends the simulation procedure to generate, if
needed, turbulence samples by using a more general model than that of
the frozen flow hypothesis.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, M. Lissandrin, M. Rampazzo.
Oil-Free Centrifugal Chiller Optimal Operation. The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014), 2014 [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
Virtual rider design: Optimal manoeuvre definition and tracking. Modelling, Simulation and Control of Two-Wheeled Vehicles, pp. 83--115, 2014 [
BibTeX]
2013
G.A. Susto, A. Schirru, S. Pampuri, D. Pagano, S. McLoone, A. Beghi.
A Predictive Maintenance System for Integral Type Faults based on Support Vector Machines: an Application to Ion Implantation. Automation Science and Engineering (CASE), 2013 IEEE International Conference on, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, A. Beghi.
A virtual metrology system based on least angle regression and statistical clustering. Applied Stochastic Models in Business and Industry, vol. 29(4), pp. 362-376, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
A virtual rider for motorcycles: Maneuver regulation of a multibody vehicle model. IEEE Transactions on Control Systems Technology, vol. 21(2), pp. 332--346, 2013 [
BibTeX]
P. Facco, A. Masiero, A. Beghi.
Advances on Multivariate Image Analysis for Product Quality Monitoring. Journal of Process Control, vol. 23, pp. 89--98, 2013 [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo, F. Simmini.
Modeling and Control of HVAC Systems with Ice Cold Thermal Energy Storage. Proceedings of the 52nd Conference on Decision and Control, 2013 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Multiscale modeling for the simulation of not completely frozen flow turbulence. 3rd Adaptive Optics for Extreme Large Telescopes conference (AO4ELT3), 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Multiscale phase screens synthesis based on local PCA. Proceedings of the IEEE International Conference on Control & Automation (ICCA 2013), 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Multiscale phase screens synthesis based on local PCA. Applied Optics, vol. 52(33), pp. 7987--8000, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
On the computation of Kalman gain in large adaptive optics systems. Proceedings of the 21st Mediterranean Conference on Control & Automation (MED13), pp. 1374-1379, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Cenedese, A. Beghi, A. Masiero.
On the estimation of atmospheric turbulence layers for AO systems. Proceedings of the ECC13 conference, pp. 4196-4201, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, S. McLoone, A. Schirru, S. Pampuri, D. Pagano, A. Beghi.
Prediction of Integral Type Failures in Semiconductor Manufacturing through Classification Methods. 18-th IEEE Conference on Emerging Technologies and Factory Automation, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, L. Corso, M. Rampazzo, F. Simmini.
Process History-Based Fault Detection and Diagnosis for VAVAC Systems. Proceedings of the 2013 IEEE Multi-Conference on Systems and Control (MSC 2013), 2013 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Turbulence modeling and Kalman prediction for the control of large AO systems. Proceedings of the 52nd IEEE International Conference on Decision and Control (CDC2013) - accepted, 2013
Abstract:
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.
[ abstract ] [
url] [
BibTeX]
2012
G.A. Susto, A. Schirru, S. Pampuri, A. Beghi.
A Predictive Maintenance System based on Regularization Methods for Ion-Implantation. 23rd IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 175-180, 2012
Abstract:
Ion Implantation is one of the most sensitiveprocesses in Semiconductor Manufacturing. It consists inimpacting accelerated ions with a material substrate and isperformed by an Implanter tool. The major maintenanceissue of such tool concerns the breaking of the tungstenfilament contained within the ion source of the tool. Thiskind of fault can happen on a weekly basis, and theassociated maintenance operations can last up to 3 hours.It is important to optimize the maintenance activities bysynchronizing the Filament change operations with otherminor maintenance interventions. In this paper, a PredictiveMaintenance (PdM) system is proposed to tackle such issue;the filament lifetime is estimated on a statistical basisexploiting the knowledge of physical variables acting onthe process. Given the high-dimensionality of the data,the statistical modeling has been based on RegularizationMethods: Lasso, Ridge Regression and Elastic Nets. Thepredictive performances of the aforementioned regularizationmethods and of the proposed PdM module have beentested on actual productive semiconductor data.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, A. Beghi, C. De luca.
A Predictive Maintenance System for Epitaxy Processes based on Filtering and Prediction Techniques. IEEE Transactions on Semiconductor Manufacturing, vol. 25, pp. 638 - 649, 2012
Abstract:
Silicon Epitaxial Deposition is a process strongly influenced by wafer temperature behaviour, that has to be constantly monitored to avoid the production of defective wafers. However, temperature measurements are not reliable and the sensors have to be appropriately calibrated with some dedicated procedure. A Predictive Maintenance (PdM) System is proposed here with the aim of predicting process behaviour and scheduling control actions on the sensors in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the Particle Filter with Gaussian Kernel Density Estimator. The accuracy of the PdM module has been tested on real industrial production datasets.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, G. Cosi, M. Rampazzo.
A PSO-based algorithm for optimal multiple chiller systems operation. Applied Thermal Engineering, vol. 32,, pp. 31-40, 2012 [
BibTeX]
A. Beghi, M. Bruschetta, F. Maran.
A real time implementation of MPC based motion cueing strategy for driving simulators. Proceedings of the 51st IEEE Conference on Decision and Control CDC 2012, pp. 6340--6345, 2012 [
BibTeX]
S. Longo, L. Cecchinato, M. Rampazzo, M. Bonaldi, A. Beghi, L. Conti.
A vibration-free, thermally controlled setup for mechanical thermal noise measurements. The European Physical Journal Applied Physics, vol. 57,, 2012 [
BibTeX]
A. Beghi, F. Maran, A. De simoi.
A virtual environment for the design of power management strategies for hybrid motorcycles. Latest trends in Circuits Automatic Control and Signal Processing Proceedings of the 3rd International Conference on Circuits Systems Control Signals (cscs ’12), pp. 198-203, 2012 [
BibTeX]
G.A. Susto, A. Beghi.
An Information Theory-based Approach to Data Clustering for Virtual Metrology and Soft Sensors. 3rd International conference on CIRCUITS, SYSTEMS, CONTROL, SIGNALS, pp. 198--203, 2012
Abstract:
Soft Sensors (SSs) are on-line estimators of “hardly to be measured” quantities of a process. The difficultyin measuring can be related to economic or temporal costs that cannot be afforded in a high-intensivemanufacturing production. In semiconductor manufacturing this technology goes with the name of Virtual Metrology(VM) systems. While a lot of efforts in research have been produced in the past years to identify the bestregression algorithms for these statistical modules, small amount of work has been done to develop algorithms fordata clustering of the entire production. This paper contains a new Information Theory-based approach to dataclustering for Virtual Metrology and Soft Sensors; the proposed algorithm allows to automatically split the datasetinto groups to be equally modeled. The proposed approach has been tested on real industrial dataset.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, A. Schirru, S. Pampuri, G. De nicolao, A. Beghi.
An Information-Theory and Virtual Metrology-based approach to Run-to-Run Semiconductor Manufacturing Control. Automation Science and Engineering (CASE), 2012 IEEE International Conference on, pp. 358 -363, 2012
Abstract:
Virtual Metrology (VM) module have become popular in the
past years and they are now widely adopted in the
semiconductor plants. However, nowadays, still few works
have been presented to deal with the interaction between VM
and Run-to-Run (R2R), the most common control approach in
the fabs. We present in this paper a new strategy to
integrate VM with R2R based on Information Theory measure.
The proposed control method penalizes statistical measure
based on their statistical distance from the physical
measure. This new approach also cope with the virtual loop
control, where the R2R runs for several process iterations
without in-situ measures, but based only on VM predictions.
The results are compared with the actual state-of-the-art.
[ abstract ] [
url] [
BibTeX]
A. Beghi, M. Bruschetta, F. Maran, D. Minen.
An MPC approach to the design of motion cueing algorithms for small size driving simulators. Proceedings of the Driving Simulation Conference 2012, pp. --, 2012 [
BibTeX]
G.A. Susto, S. Pampuri, A. Schirru, G. De nicolao, S. McLoone, A. Beghi.
Automatic Control and Machine Learning for Semiconductor Manufacturing: Review and Challenges. 10th European Workshop on Advanced Control and Diagnosis, 2012
Abstract:
Semiconductor manufacturing is one of the most technologically advanced industrial sectors. Process quality and control are critical for decreasing costs and increasing yield. The contribution of automatic control and statistical modeling in this area can drastically impact production performance. For this reason in the past decade major collaborative research projects have been undertaken between fab industries and academia in the areas of Virtual Metrology, Predictive Maintenance, Fault Detection, Run-to-Run control and modeling. In this paper we review some this research, discuss its impact on production and highlight current challenges.
[ abstract ] [
BibTeX]
A. Beghi, J. Belfi, N. Beverini, B. Bouhadef, D. Cuccato, A. Di Virgilio, A. Ortolan.
Compensation of the laser parameter fluctuations in large ring-laser gyros: a Kalman filter approach. applied optics, vol. 51, pp. 7518-7528, 2012 [
BibTeX]
G.A. Susto, A. Beghi.
Least Angle Regression for Semiconductor Manufacturing Modeling. Control Applications (CCA), 2012 IEEE International Conference on, pp. 658--663, 2012
Abstract:
In semiconductor manufacturing plants, monitoringphysical properties of all wafers is fundamental in order tomaintain good yield and high quality standards. However, suchan approach is too costly and in practice only few wafers in a lotare actually monitored. Virtual Metrology (VM) systems allowto partly overcome the lack of physical metrology. In a VMscheme, tool data are used to predict, for every wafer, metrologymeasurements. In this paper, we present a VM system for aChemical Vapor Deposition (CVD) process. On the basis ofthe available metrology results and of the knowledge, for everywafer, of equipment variables, it is possible to predict CVDthickness. In this work we propose a VM module based onLARS to overcome the problem of high dimensionality andmodel interpretability. The proposed VM models have beentested on industrial production data sets.
[ abstract ] [
url] [
BibTeX]
S. Pampuri, A. Schirru, G.A. Susto, G. De nicolao, A. Beghi, C. De luca.
Multistep Virtual Metrology Approaches for Semiconductor Manufacturing Processes. Automation Science and Engineering (CASE), 2012 IEEE International Conference on, pp. 91 -- 96, 2012
Abstract:
In semiconductor manufacturing, state of the art for wafer
quality control relies on product monitoring and feedback
control loops; the involved metrology operations, performed
by means of scanning electron microscopes, are particularly
cost-intensive and time-consuming. For this reason, it is
not possible to evaluate every wafer: in common practice, a
small subset of a productive lot is measured at the
metrology station and devoted to represent the whole lot.
Virtual Metrology (VM) methodologies are able to obtain
reliable predictions of metrology results at process time,
without actually performing physical measurements; this
goal is usually achieved by means of statistical models,
linking process data and context information to target
measurements. Since semiconductor manufacturing processes
involve a high number of sequential operations, it is
reasonable to assume that the quality features of a certain
wafer (such as layer thickness, critical dimensions,
electrical test results) depend on the whole processing and
not only on the last step before measurement. In this
paper, we investigate the possibilities to improve the
Virtual Metrology quality relying on knowledge collected
from previous process steps. We will present two different
scheme of multistep VM, along with dataset preparation
indications; special consideration will be reserved to
regression techniques capable of handling high dimensional
input spaces. The proposed multistep approaches will be
tested against actual data from semiconductor manufacturing
industry.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Nonstationary turbulence simulation with an efficient multiscale approach. Proc. of the IEEE Multi-Conference on Systems and Control (MSC12), 2012
Abstract:
This paper considers the problem of simulating the turbulence effect on ground telescope observations. The approach presented here is an evolution of a recently proposed approach [3]. The main contributions with respect to [3] are: First, the Haar transform at the basis of the multiscale model in [3] is shown to be equivalent to a local PCA representation. This equivalence allows to reduce the computational complexity of the simulation algorithm by neglecting the components in the signal with lower energy. Furthermore, the simulation of nonstationary turbulence is obtained by properly changing the values of the multiscale model: Such change is eased by the invariance of the PCA spatial basis with respect to the change of turbulence statistical characteristics. The proposed approach is validated by means of some simulations.
[ abstract ] [
url] [
BibTeX]
G.A. Susto, S. Pampuri, A. Schirru, A. Beghi.
Optimal Tuning of Epitaxy Pyrometers. 23rd IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 294-299, 2012
Abstract:
Epitaxy is a process strongly dependent onwafer temperature. Unfortunately, the performance ofthe pyrometers in charge of sensing wafer temperaturedeteriorate with the usage. This represents the majormaintenance issue for epitaxy process engineers who haveto frequently calibrate pyrometers emissivity coefficient. Atthe present state the change of the emissivity coefficient isheuristically based on fab tradition and process engineersexperience. We present a statistical tool to map therelationship between change in the temperature readingsand emissivity adjustments. The module has been testedon real industrial dataset.
[ abstract ] [
url] [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
Trajectory exploration of a rigid motorcycle model. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, vol. 20, pp. 424--437, 2012 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Turbulence Modeling and Estimation for AO systems. Proc. of the SPIE Conference on Astronomical Telescopes and Instrumentation, 2012
Abstract:
Nowadays, the adaptive optics (AO) system is of fundamental importance to reduce the effect of atmospheric turbulence on the images formed on large ground telescopes. In this paper the AO system takes advantage of the knowledge of the current turbulence characteristics, that are estimated by data, to properly control the deformable mirrors. The turbulence model considered in this paper is based on two assumptions: considering the turbulence as formed by a discrete set of layers moving over the telescope lens, and each layer is modeled as a Markov-Random-Field. The proposed Markov-Random-Field approach is exploited for estimating the layers’ characteristics. Then, a linear predictor of the turbulent phase, based on the computed information on the turbulence layers, is constructed. Since scalability and low computational complexity of the control algorithms are important requirements for real AO systems, the computational complexity properties of the proposed model are investigated. Interestingly, the proposed model shows a good scalability and an almost linear computational complexity thanks to its block diagonal structure. Performances of the proposed method are investigated by means of some simulations.
[ abstract ] [
url] [
BibTeX]
2011
A. Beghi, L. Cecchinato, M. Rampazzo.
A multi-phase genetic algorithm for the efficient management of multi-chiller systems. ENERGY CONVERSION AND MANAGEMENT, vol. 52, pp. 1650--1661, 2011 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
A multiscale stochastic approach for phase screens synthesis. APPLIED OPTICS, vol. 50, pp. 4124--4133, 2011
Abstract:
Simulating
the turbulence effect on ground telescope observations is of
fundamental importance for the design and test of suitable control
algorithms for adaptive optics systems. In this paper we propose a
multiscale approach for efficiently synthesizing turbulent phases at
very high resolution. First, the turbulence is simulated at low
resolution, taking advantage of a previously developed method for
generating phase screens. Then, high-resolution phase screens are
obtained as the output of a multiscale linear stochastic system. The
multiscale approach significantly improves the computational efficiency
of turbulence simulation with respect to recently developed methods.
Furthermore, the proposed procedure ensures good accuracy in reproducing
the statistical characteristics of the turbulent phase.
[ abstract ] [
url] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
A multiscale stochastic approach for phase screens synthesis. Proceedings of the 2011 American Control Conference ACC 2011, pp. 3084--3089, 2011
Abstract:
Simulating the turbulence effect on ground tele-
scope observations is of fundamental importance for the design
and test of suitable control algorithms for adaptive optics
systems. In this paper we propose a multiscale approach for
efficiently synthesizing turbulent phases at very high reso-
lutions: First, the turbulence is simulated at low resolution
taking advantage of a previously developed method for gen-
erating phase screens, [2]. Then, high resolution phase screens
are obtained as the output of a multiscale linear stochastic
system. The multiscale approach significantly improves the
computational efficiency of turbulence simulation with respect
to recently developed methods [1],[2],[8]. Furthermore, the
proposed procedure ensures good accuracy in reproducing the
statistical characteristics of the turbulent phase.
[ abstract ] [
BibTeX]
G.A. Susto, A. Beghi, C. De luca.
A Predictive Maintenance System for Silicon Epitaxial Deposition. Proceeding of 7th IEEE International Conference on Automation Science and Engineering, pp. 262-267, 2011
Abstract:
Silicon Epitaxial Deposition is a process strongly influenced by wafer temperature behavior, that has to be constantly monitored to avoid the production of defective wafers. A Predictive Maintenance (PdM) System is here proposed with the aim of predicting process behavior and scheduling control actions in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the Particle Filter with Gaussian Kernel Density Estimator. The accuracy of the PdM module has been tested on real fab data. The proposed approach is flexible and can handle the presence of different recipes on the same equipment.
[ abstract ] [
url] [
BibTeX]
A. Beghi, L. Cecchinato, G. Cosi, M. Rampazzo.
A PSO Algorithm for Optimal Multiple Chiller Operation. Proceedings of 23rd IIR International Congress of Refrigeration, 2011 [
BibTeX]
G.A. Susto, A. Beghi, C. De luca.
A Virtual Metrology System for Predicting CVD Thickness with Equipment Variables and Qualitative Clustering. Proceeding of 16th IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1-4, 2011
Abstract:
In semiconductor manufacturing plants, monitoring of all wafers is fundamental in order to maintain good yield and high quality standards. However, this is a costly approach and in practice only few wafers in a lot are actually monitored. With a Virtual Metrology (VM) system it is possible 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. Various data mining techniques are proposed. Due to the huge fragmentation of data derived from CVD's mixed production, several kind of data clustering have been adopted. The proposed models have been tested on real productive industrial data sets.
[ abstract ] [
url] [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
A virtual rider for motorcycles: Maneuver regulation of a multibody vehicle model. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011 [
BibTeX]
P. Facco, A. Masiero, F. Bezzo, A. Beghi, M. Barolo.
An improved multivariate image analysis method for quality control of nanober membranes. Proceeding of the 18th IFAC World Congress, pp. 12066--12071, 2011 [
BibTeX]
P. Facco, A. Masiero, F. Bezzo, M. Barolo, A. Beghi.
Improved multivariate image analysis for product quality monitoring. Chemometrics and Intelligent Laboratory Systems, vol. 109(1), pp. 42--50, 2011 [
BibTeX]
A. Beghi, L. Cecchinato.
Modeling and Adaptive Control of Low Capacity Chillers for HVAC Applications. APPLIED THERMAL ENGINEERING, 2011 [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo.
On-Line Auto-Tuning Control of Electronic Expansion Valves. INTERNATIONAL JOURNAL OF REFRIGERATION, 2011 [
BibTeX]
M. Baseggio, A. Beghi, M. Bruschetta, F. Maran, M. Pozzi, D. Minen.
Study on the Next Generation Motion Cueing for Driving Simulators. Proceedings of the 21st JSAE Annual Congress, 2011 [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo.
Thermal and comfort control for Radiant Heating/Cooling Systems. Proceedings of the IEEE MSC 2011, pp. 258--263, 2011 [
pdf] [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
Trajectory Exploration of a Rigid Motorcycle Model. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011 [
BibTeX]
A. Beghi, L. Cecchinato, F. Paggiaro, M. Rampazzo.
VAVAC Systems Modeling and Simulation for FDD Applications. Proceedings of the International Conference on Control Applications ICCA 2011, pp. 800--805, 2011 [
BibTeX]
2010
A. Saccon, J. Hauser, A. Beghi.
A dynamic inversion approach to motorcycle trajectory exploration. Proceedings of the Bicycle and Motorcycle Dynamics Symposium BMD2010, 2010 [
BibTeX]
A. Beghi, M. Bertinato, L. Cecchinato, M. Rampazzo.
A multi-phase genetic algorithm for the efficient management of multi-chiller systems. Proceedings of 10th REHVA World Congress CLIMA 2010, 2010 [
BibTeX]
S. Bolognani, F. Gambato, M. Rampazzo, A. Beghi.
Efficient Conditioning of Energy in AFE-Based Distributed GenerationUnits. Proceedings of the 19th IEEE International Conference on Control Applications Part of 2010 IEEE Multi-conference on Systems and Control, pp. 1910--1915, 2010 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Estimating turbulent phase characteristics in MCAO systems. Proceedings of the 49th IEEE Conference on Decision and Control, 2010 [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo, F. Simmini.
Load forecasting for the efficient energy management of HVAC systems. Proceedings of the 2nd IEEE International Conference on Sustainable Energy Technologies ICSET'10, 2010 [
BibTeX]
A. Beghi, L. Cecchinato, G. Cosi, M. Rampazzo.
Two-Layer Control of Multi-Chiller Systems. Proceedings of the 19th IEEE International Conference on Control Applications Part of 2010 IEEE Multi-conference on Systems and Control, pp. 1892--1897, 2010 [
BibTeX]
2009
A. Beghi, M. Bertinato, L. Cecchinato, M. Rampazzo.
A multi-phase genetic algorithm for the efficient management of multi-chiller systems. Proceedings of the 7th Asian Control Conference, pp. 1685-1690, 2009 [
BibTeX]
A. Beghi, L. Cecchinato.
A simulation environment for dry-expansion evaporators with application to the design of autotuning control algorithms for electronic expansion valves. international journal Of refrigeration, vol. 32, pp. 1765-1775, 2009 [
BibTeX]
M. Albieri, A. Beghi, C. Bodo, L. Cecchinato.
Advanced control systems for single compressor chiller units. international journal Of refrigeration, vol. 32, pp. 1068-1076, 2009 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Algorithms for turbulence compensation in large adaptive optics systems. Proceedings of the Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 835-840, 2009 [
pdf] [
BibTeX]
C. De luca, E. Maran, J. Baumgartl, A. Beghi.
Application of a Run-to-Run Controller to a Vapor Phase Epitaxy Process. Proceedings of the 20th Annual ieee/semi Advanced Semiconductor Manufacturing Conference - asmc 2009, pp. 211-216, 2009 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
On the estimation of atmospheric turbulence statistical characterics. Proceedings of the 18th IEEE International Conference on Control Applications Part of 2009 IEEE Multi-conference on Systems and Control, pp. 625-630, 2009 [
pdf] [
BibTeX]
A. Beghi, L. Cecchinato, M. Rampazzo.
On-line auto-tuning regulation of Electronic Expansion Valve for evaporator control. Proceedings of the 2009 IEEE International Conference on Control and Automation, pp. 569-574, 2009 [
BibTeX]
A. Zen, L. Cecchinato, A. Beghi, C. Bodo, A. Scodellaro, M. Albieri.
Patent number European patent nr. ep2012068 (a1): Method for regulating the delivery temperature of a service fluid in output from a refrigerating machine. 2009 [
BibTeX]
A. Zen, L. Cecchinato, A. Beghi, C. Bodo, A. Scodellaro, M. Albieri.
Patent number European patent nr. ep2012069 (a1): Method for regulating the delivery temperature of a service fluid in output from a refrigerating machine. 2009 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
System theoretic tools in Adaptive Optics. Proceedings of the 2009 IEEE International Conference on Control and Automation, pp. 1049-1054, 2009 [
pdf] [
BibTeX]
2008
A. Beghi, A. Cenedese, F. Maran, A. Masiero.
A comparison of Kalman filter based algorithms for turbulent phase control in an adaptive optics system. Proceedings of the47th IEEE Conference on Decision and Control, pp. 1839--1844, 2008 [
pdf] [
BibTeX]
A. Beghi, L. Cecchinato, M. De carli.
A dynamic model for the thermal-hygrometric simulation of buildings. Proceedings of the 2008 IFAC World Conference, pp. 13271-13276, 2008 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
A Markov-Random-Field-based approach to modeling and prediction of atmospheric turbulence. 16th Mediterranean Conference on Control and AutomationCongress Centre Ajaccio FranceJune 25-27 2008, pp. 1735--1740, 2008 [
pdf] [
BibTeX]
A. Beghi, U. Bianchini, C. Bodo, L. Cecchinato.
A simulation environment for dry-expansion evaporators with application to the design of autotuning control algorithms for electronic expansion valves. Proc. of IEEE Conference on Automation Science and Engineering case 2008, pp. 814-820, 2008 [
BibTeX]
M. Albieri, A. Beghi, C. Bodo, L. Cecchinato.
A virtual prototyping approach to the design of advanced chiller control systems. Proceedings of the 2008 IFAC World Conference, pp. 5768-5769, 2008 [
BibTeX]
A. Saccon, J. Hauser, A. Beghi.
A Virtual Rider for Motorcycles: An Approach Based on Optimal Control and Maneuver Regulation. Proc. of 2008 IEEE Symposium on Communications Control and Signal Processing, pp. 243-248, 2008 [
BibTeX]
A. Beghi, M. Cavinato, A. Cenedese.
Nonlinear dynamic modeling for control of fusion devices. Proceedings of the47th IEEE Conference on Decision and Control, pp. 3133--3138, 2008 [
pdf] [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
On the estimation of atmospheric turbulence layers. Proceedings of the 17th World CongressThe International Federation of Automatic Control, pp. 8984--8989, 2008 [
pdf] [
BibTeX]
G. Panella, A. Solfa, A. Beghi, M. Bisiacco.
Patent number ep1972210 (a1): Pasteurising system control methodology. 2008 [
BibTeX]
A. Zen, L. Cecchinato, A. Beghi, C. Bodo, A. Scodellaro, M. Albieri.
Patent number European patent nr. ep2000754 (a2): Method for estimation the thermal load of a circuit for a service fluid at outlet from a refrigerating machine. 2008 [
BibTeX]
A. Zen, L. Cecchinato, A. Beghi, C. Bodo, A. Scodellaro, M. Albieri.
Patent number European patent nr. ep2000758 (a1): Control device for a refrigerating machine. 2008 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Stochastic realization approach to the efficient simulation of phase screens. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A OPTICS IMAGE SCIENCE AND VISION, vol. 25 (2), pp. 515--525, 2008 [
pdf] [
BibTeX]
E. Toffoli, G. Baldan, G. Albertin, L. Schenato, A. Chiuso, A. Beghi.
Thermodynamic Identification of Buildings using Wireless Sensor Networks. IFAC World Congress on Automatic Control (IFAC 08), 2008 [
pdf] [
BibTeX]
2007
A. Beghi, A. Cenedese, A. Masiero.
A comparison between Zernike and PCA representation of atmospheric turbulence. Proc. of the 46th IEEE Conference on Decision and Control (CDC2007), pp. 572--577, 2007 [
pdf] [
BibTeX]
M. Albieri, A. Beghi, C. Bodo, L. Cecchinato.
A simulation environment for the design of advanced chiller control systems. Proc. of IEEE Conference on Automation Science and Engineering case 2007, pp. 962-967, 2007 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
A stochastic realization approach to the efficient simulation of phase screens. Proc. of the European Control Conference (MED2007), pp. 5079--5086, 2007 [
pdf] [
BibTeX]
A. Beghi.
Eigenstructure-based model reduction for generalized RL networks. A Tribute to Antonio Lepschy. pp. 1-17, 2007 [
BibTeX]
A. Beghi, A. Cenedese, A. Masiero.
Atmospheric turbulence prediction: a PCA approach. Proc. of the 46th IEEE Conference on Decision and Control (CDC2007), pp. 566--571, 2007 [
pdf] [
BibTeX]
A. Beghi, M. Liberati, S. Mezzalira, S. Peron.
Grey-box modeling of a motorcycle shock absorber for virtual prototyping applications. simulation modeling practice And theory, vol. 15, pp. 894-907, 2007 [
BibTeX]
2006
R. Frezza, A. Beghi.
A virtual motorcycle driver for closed-loop simulation. IEEE Control Systems Magazine, vol. 5, pp. 62-77, 2006 [
BibTeX]
A. Beghi, R. Frezza.
Advances in motorcycle design and control. IEEE control systems, vol. 5, pp. 32-33, 2006 [
BibTeX]
A. Beghi, M. Liberati, S. Peron, D. Sette.
Black box Modelling of a Two-Stroke Racing Motorcycle Engine for Virtual Prototyping Appplications. Proceedings of the IEEE Conference on Automation Science and Engineering IEEE case 2006, pp. 290-295, 2006 [
BibTeX]
A. Cenedese, A. Beghi.
How to Represent the Shape of a Deformable Object and Ease the Control of the Deformation?. Proc. of the 17th International Symposium on Mathematical Theory of Networks and Systems, pp. 1427--1431, 2006 [
BibTeX]
M. Cavinato, G. Marchiori, A. Soppelsa, A. Beghi, A. Cenedese.
MHD modes control in fusion devices. Proceedings of the 45th IEEE Conference on Decision & Control, pp. 2244--2249, 2006 [
BibTeX]
A. Beghi, L. Nardo, M. Stevanato.
Observer-based discrete-time sliding mode throttle control for drive-by-wire operation of a racing motorcycle engine. IEEE transactions On control systems technology, vol. 14, pp. 767-775, 2006 [
BibTeX]
A. Cenedese, A. Beghi.
Optimal Approach to Shape Parameter Control. Proc. of the 6th Asian Control Conference, pp. 556--563, 2006 [
BibTeX]
2005
A. Beghi, L. Nardo, M. Stevanato.
A sliding mode throttle controller for drive-by-wire operation of a racing motorcycle engine. pp. 557-562, 2005 [
BibTeX]
A. Beghi, A. Cenedese.
Advances in Real Time Plasma Boundary Reconstruction: from the Gap Description to a Deformable Model Approach. IEEE CONTROL SYSTEMS, vol. 25, pp. 44--64, 2005 [
pdf] [
BibTeX]
R. Frezza, A. Beghi, G. Notarstefano.
Almost kinematic reducibility of a car model with small lateral slip angle for control design. pp. 343-348, 2005 [
BibTeX]
A. Beghi, M. Cavinato, A. Cenedese, D. Ciscato, S. Simionato, A. Soppelsa.
An integral approach to plasma shape control. FUSION ENGINEERING AND DESIGN, vol. 74, pp. 579--586, 2005 [
pdf] [
BibTeX]
A. Cenedese, A. Beghi, S. Simionato.
Controlling Curves on the Plane: an Approach to Shape Control in Fusion Devices. 13th Mediterranean Conference on Control and Automation (MED2005), pp. 1178--1183, 2005 [
BibTeX]
C. Spagnol, R. Muradore, M. Assom, A. Beghi, R. Frezza.
Model based gps/ins integration for high accuracy land vehicle applications. pp. 400-405, 2005 [
BibTeX]
S. Zilli, R. Frezza, A. Beghi.
Model Based gps/ins Integration for High Accuracy Land Vehicle Applications: Calibration of a Swarm of mems Sensors. pp. 952-956, 2005 [
BibTeX]
2004
M. Liberati, A. Beghi, S. Mezzalira, S. Peron.
Grey box modelling of a motorcycle shock absorber. pp. 755-760, 2004 [
BibTeX]
R. Frezza, A. Saccon, A. Beghi.
Model predictive and hierarchical control for path following with motorcycles. pp. 767-772, 2004 [
BibTeX]
L. Gasbarro, A. Beghi, R. Frezza, F. Nori, C. Spagnol.
Motorcycle Trajectory Reconstruction by Integration of Vision and mems Accelerometers. pp. 779-783, 2004 [
BibTeX]
A. Beghi, M. Bortoletto, L. Cecchinato, R. Del bianco, L. Schibuola, R. Zecchin.
Tecniche di identificazione applicate alla stima della prestazione energetica degli edifici. 2004 [
BibTeX]
C. Spagnol, R. Muradore, M. Assom, A. Beghi, R. Frezza.
Trajectory reconstruction by integration of gps and a swarm of mems accelerometers: model and analysis of observability. pp. 64-69, 2004 [
BibTeX]
2003
A. Beghi, M. Bisiacco.
A note on the relationships between high gain state feedback and relay systems. pp. T1016-1T10166, 2003 [
BibTeX]
A. Cenedese, A. Beghi, D. Ciscato, F. Sartori.
Active contours approach for plasma boundary reconstruction. FUSION ENGINEERING AND DESIGN, vol. 66-8, pp. 675--680, 2003 [
BibTeX]
A. Beghi, A. Cenedese.
BOUNDARY RECONSTRUCTION AND GEOMETRIC PARAMETERISATION FOR PLASMA SHAPE CONTROL. Proc. of the 42nd IEEE Conference on Decision and Control (CDC2003), pp. 4622--4627, 2003 [
BibTeX]
A. Beghi, M. Bisiacco, S. Daros, L. Nardo.
Sliding mode throttle control for drive by wire operation of a racing motorcycle engine. Computational methods in circuits and systems applications. pp. 140-145, 2003 [
BibTeX]
D. Ciscato, R. Oboe, A. Beghi, F. Marcassa, P. Capretta, R. Antonello, F. Soldavini.
Il servoposizionamento delle testine nei dischi rigidi- Una sfida per la meccatronica. automazione e strumentazione, vol. 3, pp. 130-136, 2003 [
BibTeX]
A. Beghi, L. Cecchinato, M. De carli, L. Meggiolaro.
Modelli multi-room e problematiche di controllo nella simulazione energetica del sistema edificio-impianto. pp. 1531-1542, 2003 [
BibTeX]
R. Frezza, A. Beghi.
Simulating a motorcycle driver. New Trends in Nonlinear Dynamics and Control and Their Applications. pp. 175-186, 2003 [
BibTeX]
A. Beghi, M. Bisiacco, S. Daros, L. Nardo.
Sliding mode throttle control for drive by wire operation of a racing motorcycle engine. pp. 257-12576, 2003 [
BibTeX]
2002
R. Oboe, P. Capretta, A. Beghi, F. Chrappan soldavini.
A Simulation and Control Design Environment for Single Stage and Dual Stage Hard Disk Drives. ieee/asme transactions On mechatronics, vol. 8, pp. 161-170, 2002 [
BibTeX]
A. Beghi, A. Ferrante, M. Pavon.
How to steer a quantum system over a Schroedinger bridge. Quantum Information Processing, vol. 1(3), pp. 183-206, 2002 [
BibTeX]
A. Beghi, A. Portone.
Model reductionn by substructuring. pp. 331-13316, 2002 [
BibTeX]
A. Beghi, W. Krajewski, A. Lepschy, U. Viaro.
Remarks on delay approximations based on feedback. pp. 412-419, 2002 [
BibTeX]
A. Beghi, A. Boaretto, L. Cecchinato, M. De carli.
Un modello dinamico per la simulazione termoigrometrica degli edifici in ambiente matlab. pp. IA43-IA50, 2002 [
BibTeX]
2001
A. Beghi, V. Coccorese, A. Portone.
Model reduction for electromechanical systems with application to tokamak control. Advances in Systems Science: Measurements Circuits and Control. pp. 382-387, 2001 [
BibTeX]
A. Beghi.
An application of Selective Modal Analysis to tokamak modeling and control. IEEE transactions On control systems technology, vol. 9, pp. 574-589, 2001 [
BibTeX]
A. Beghi, M. Cavinato, A. Cenedese, D. Ciscato, G. Marchiori, A. Portone.
ITER-FEAT reverse shear simulations with a non linear MHD equilibrium code. FUSION ENGINEERING AND DESIGN, vol. 56?57, pp. 777--782, 2001 [
BibTeX]
A. Beghi, R. Oboe, P. Capretta, F. Chrappan soldavini.
Loop shaping issues in hard disk drive servo system design. Proc. aim 2001, 2001 [
BibTeX]
A. Beghi, R. Oboe.
lqg / ltr control of a dual stage actuator hard disk drive with piezoelectric secondary actuator. Proceedings ECC 01, 2001 [
BibTeX]
A. Beghi, M. Cavinato, A. Cenedese, D. Ciscato, G. Marchiori.
Plasma vertical stabilization in ITER-FEAT. FUSION ENGINEERING AND DESIGN, vol. 56?57, pp. 783--788, 2001 [
BibTeX]
R. Oboe, A. Beghi, P. Capretta, F. Chrappan soldavini.
Simulator for Single Stage and Dual Stage Hard Disk Drives. Proceedings aim 2001, 2001 [
BibTeX]
2000
A. Beghi, D. Ciscato.
Aggregation-based model reduction for tokamak control. pp. 395-400, 2000 [
BibTeX]
A. Beghi, A. Ferrante, M. Pavon.
Steering a quantum system over a Schroedinger bridge. Proceedings of the 14th International Symposium on Mathematical Theory of Networks and Systems - MTNS 2000, pp. B268:1-B268:5, 2000 [
BibTeX]
A. Beghi.
Stochastic terminal control by reciprocal processes. pp. 269/1-269/7, 2000 [
BibTeX]
1999
R. Oboe, A. Beghi, B. Murari.
Modeling and control of a dual stage actuator hard disk drive with piezoelectric secondary actuator. Proceedings of ieee/asme International Conference on Advanced Intelligent Mechatronics (AIM?99), 1999 [
BibTeX]
A. Beghi.
On model reduction based on eigenstructure analysis for a class of electromechanical systems. vol. CX!, pp. 41-54, 1999 [
BibTeX]
1998
A. Beghi, D. D'alessandro.
Discrete-time optimal control with control-dependent noise and Generalized Riccati Difference Equations. automatica, vol. 34, pp. 1031-1034, 1998 [
BibTeX]
A. Beghi, D. Ciscato, M. Cavinato, G. Marchiori.
iter model reduction by selective modal analysis. vol. 1, pp. 507-510, 1998 [
BibTeX]
M. Cavinato, G. Marchiori, A. Beghi, D. Ciscato, A. Portone.
iter scenario simulations with a non-linear mhd equilibrium code. vol. 1, pp. 587-590, 1998 [
BibTeX]
A. Beghi.
On finite-horizon covariance control. Mathematical theory of Networks and Systems. pp. 69-72, 1998 [
BibTeX]
A. Beghi, L. Finesso, G. Picci.
Mathematical Theory of Networks and Systems. 1998 [
BibTeX]
P. Mondino, R. Albanese, G. Ambrosino, M. Ariola, A. Beghi, .. Et al.
Plasma current position and shape control for iter. vol. 1, pp. 595-598, 1998 [
BibTeX]
1997
A. Beghi, A. Lepschy, U. Viaro.
Approximating delay elements via feedback. IEEE transactions On circuits And systems i. fundamental theory And applications, vol. 44, pp. 824-828, 1997 [
BibTeX]
A. Beghi, M. Bertocco.
Combined GLR/Kalman filter techniques for fault detection in power systems. vol. 7, pp. 151-156, 1997 [
BibTeX]
A. Beghi.
Continuous-time Gauss-Markov processes with fixed reciprocal dynamics. journal Of mathematical systems estimation And control, vol. 7, pp. 343-367, 1997 [
BibTeX]
B. Levy, A. Beghi.
Discrete-time Gauss-Markov processes with fixed reciprocal dynamics. journal Of mathematical systems estimation And control, vol. 7, pp. 55-79, 1997 [
BibTeX]
A. Beghi, D. Ciscato, A. Portone.
Model reduction techniques in tokamak modelling. vol. 4, pp. 3691, 1997 [
BibTeX]
A. Beghi, D. D'alessandro.
Some remarks on fsn models and Generalized Riccati Equations. 1997 [
BibTeX]
1996
A. Beghi, M. Bertocco.
A robust fault detection algorithm for the improvement of otdr sensitivity. vol. 2, pp. 818-821, 1996 [
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A. Beghi, A. Lepschy, U. Viaro.
On the simplification of the mathematical model of a delay element. Advanced Manufacturing Systems and Technology. pp. 617-624, 1996 [
BibTeX]
A. Beghi.
On the relative entropy of discrete-time Markov processes with given end-point densities. IEEE transactions On information theory, vol. 42, pp. 1529-1535, 1996 [
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A. Beghi, A. Lepschy, U. Viaro.
The Hurwitz matrix and the computation of second-order information indices. Stability Theory Proceedings of Hurwitz Centenary Conference. pp. 1-10, 1996 [
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1995
A. Beghi.
Discrete-time lqg optimal control with actuator noise intensity related to actuator signal variance. vol. 4, pp. 3406-3407, 1995 [
BibTeX]
A. Beghi.
On the relative entropy of discrete-time Gauss-Markov processes with given end-point variances. vol. 3, pp. 1693-1698, 1995 [
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1994
A. Beghi, A. Lepschy, U. Viaro.
A property of the Routh table and its use. IEEE transactions On automatic control, vol. 39, pp. 2494-2496, 1994 [
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A. Beghi, P. Carbone, F. Zanin.
A self-tuning Kalman Filter for power system measurement applications. vol. 1, pp. 633-638, 1994 [
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1993
A. Beghi, A. Lepschy, U. Viaro.
Recursive evaluation of the squared l_2 norm of a rational function. vol. CLI, pp. 199-208, 1993 [
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1992
A. Beghi, R. Frezza.
On the connection between Stochastic Boundary Value Problems and the Riccati equation. vol. CIV, pp. 5-17, 1992 [
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