7,793 research outputs found
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
Data Challenges and Data Analytics Solutions for Power Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Statistical Methods for Semiconductor Manufacturing
In this thesis techniques for non-parametric modeling, machine learning, filtering and prediction and run-to-run control for semiconductor manufacturing are described.
In particular, algorithms have been developed for two major applications area:
- Virtual Metrology (VM) systems;
- Predictive Maintenance (PdM) systems.
Both technologies have proliferated in the past recent years in the semiconductor industries, called fabs, in order to increment productivity and decrease costs.
VM systems aim of predicting quantities on the wafer, the main and basic product of the semiconductor industry, that may be physically measurable or not. These quantities are usually ’costly’ to be measured in economic or temporal terms: the prediction is based on process variables and/or logistic information on the production that, instead,
are always available and that can be used for modeling without further costs.
PdM systems, on the other hand, aim at predicting when a maintenance action has to be performed. This approach to maintenance management, based like VM on statistical
methods and on the availability of process/logistic data, is in contrast with other classical approaches:
- Run-to-Failure (R2F), where there are no interventions performed on the machine/process until a new breaking or specification violation happens in the production;
- Preventive Maintenance (PvM), where the maintenances are scheduled in advance based on temporal intervals or on production iterations.
Both aforementioned approaches are not optimal, because they do not assure that breakings and wasting of wafers will not happen and, in the case of PvM, they may lead to unnecessary maintenances without completely exploiting the lifetime of the machine or of the process.
The main goal of this thesis is to prove through several applications and feasibility studies that the use of statistical modeling algorithms and control systems can improve the efficiency, yield and profits of a manufacturing environment like the semiconductor
one, where lots of data are recorded and can be employed to build mathematical models.
We present several original contributions, both in the form of applications and methods.
The introduction of this thesis will be an overview on the semiconductor fabrication process: the most common practices on Advanced Process Control (APC) systems
and the major issues for engineers and statisticians working in this area will be presented.
Furthermore we will illustrate the methods and mathematical models used in the applications.
We will then discuss in details the following applications:
- A VM system for the estimation of the thickness deposited on the wafer by the Chemical Vapor Deposition (CVD) process, that exploits Fault Detection and Classification (FDC) data is presented. In this tool a new clustering algorithm based on Information Theory (IT) elements have been proposed. In addition, the Least Angle Regression (LARS) algorithm has been applied for the first time to VM problems.
- A new VM module for multi-step (CVD, Etching and Litography) line is proposed, where Multi-Task Learning techniques have been employed.
- A new Machine Learning algorithm based on Kernel Methods for the estimation of scalar outputs from time series inputs is illustrated.
- Run-to-Run control algorithms that employ both the presence of physical measures and statistical ones (coming from a VM system) is shown; this tool is based on IT elements.
- A PdM module based on filtering and prediction techniques (Kalman Filter, Monte Carlo methods) is developed for the prediction of maintenance interventions in the Epitaxy process.
- A PdM system based on Elastic Nets for the maintenance predictions in Ion Implantation tool is described.
Several of the aforementioned works have been developed in collaborations with major European semiconductor companies in the framework of the European project UE FP7 IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance); such collaborations will be specified during the thesis, underlying the practical aspects of the implementation of the proposed technologies in a real industrial environment
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
METODY POMIARU PRĘDKOŚCI PRZEPŁYWU Z ZASTOSOWANIEM TOMOGRAFII POJEMNOŚCIOWEJ
The current methods of calculating the velocity field based on a twin-plane electrical capacitance system for multiphase flows are presented. The described methods allow to calculate the velocity profile of the multiphase flow in cross-section of industrial installation. The theoretical assumptions of the considered methods are also noticed. The main advantages and disadvantages of the authors’ flow velocity measurement methods are discussed.Artykuł przedstawia przegląd metod wyznaczania prędkości przepływu w oparciu o dwupłaszczowy system pojemnościowej tomografii procesowej. Zaprezentowane metody pozwalają na obliczenie profilu prędkości wielofazowych przepływów w przekroju poprzecznym instalacji przemysłowej. Przeprowadzono także dyskusję nad teoretycznymi założeniami omawianych metod oraz opisano główne zalety i wady autorskich metod pomiaru prędkości przepływów wielofazowych
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