50,501 research outputs found
Methods of Technical Prognostics Applicable to Embedded Systems
HlavnĂ cĂlem dizertace je poskytnutĂ ucelenĂ©ho pohledu na problematiku technickĂ© prognostiky, kterĂĄ nachĂĄzĂ uplatnÄnĂ v tzv. prediktivnĂ ĂșdrĆŸbÄ zaloĆŸenĂ© na trvalĂ©m monitorovĂĄnĂ zaĆĂzenĂ a odhadu ĂșrovnÄ degradace systĂ©mu Äi jeho zbĂœvajĂcĂ ĆŸivotnosti a to zejmĂ©na v oblasti komplexnĂch zaĆĂzenĂ a strojĆŻ. V souÄasnosti je technickĂĄ diagnostika pomÄrnÄ dobĆe zmapovanĂĄ a reĂĄlnÄ nasazenĂĄ na rozdĂl od technickĂ© prognostiky, kterĂĄ je stĂĄle rozvĂjejĂcĂm se oborem, kterĂœ ovĆĄem postrĂĄdĂĄ vÄtĆĄĂ mnoĆŸstvĂ reĂĄlnĂœch aplikaci a navĂc ne vĆĄechny metody jsou dostateÄnÄ pĆesnĂ© a aplikovatelnĂ© pro embedded systĂ©my. DizertaÄnĂ prĂĄce pĆinĂĄĆĄĂ pĆehled zĂĄkladnĂch metod pouĆŸitelnĂœch pro ĂșÄely predikce zbĂœvajĂcĂ uĆŸitnĂ© ĆŸivotnosti, jsou zde popsĂĄny metriky pomocĂ, kterĂœch je moĆŸnĂ© jednotlivĂ© pĆĂstupy porovnĂĄvat aĆ„ uĆŸ z pohledu pĆesnosti, ale takĂ© i z pohledu vĂœpoÄetnĂ nĂĄroÄnosti. Jedno z dizertaÄnĂch jader tvoĆĂ doporuÄenĂ a postup pro vĂœbÄr vhodnĂ© prognostickĂ© metody s ohledem na prognostickĂĄ kritĂ©ria. DalĆĄĂm dizertaÄnĂm jĂĄdrem je pĆedstavenĂ tzv. ÄĂĄsticovĂ©ho filtrovanĂ (particle filtering) vhodnĂ© pro model-based prognostiku s ovÄĆenĂm jejich implementace a porovnĂĄnĂm. HlavnĂ dizertaÄnĂ jĂĄdro reprezentuje pĆĂpadovou studii pro velmi aktuĂĄlnĂ tĂ©ma prognostiky Li-Ion baterii s ohledem na trvalĂ© monitorovĂĄnĂ. PĆĂpadovĂĄ studie demonstruje proces prognostiky zaloĆŸenĂ© na modelu a srovnĂĄvĂĄ moĆŸnĂ© pĆĂstupy jednak pro odhad doby pĆed vybitĂm baterie, ale takĂ© sleduje moĆŸnĂ© vlivy na degradaci baterie. SouÄĂĄstĂ prĂĄce je zĂĄkladnĂ ovÄĆenĂ modelu Li-Ion baterie a nĂĄvrh prognostickĂ©ho procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of Californiaâs California Institute for Energy and the Environment, from 2003-2014
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkâthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
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