57,558 research outputs found
Fast computation of the performance evaluation of biometric systems: application to multibiometric
The performance evaluation of biometric systems is a crucial step when
designing and evaluating such systems. The evaluation process uses the Equal
Error Rate (EER) metric proposed by the International Organization for
Standardization (ISO/IEC). The EER metric is a powerful metric which allows
easily comparing and evaluating biometric systems. However, the computation
time of the EER is, most of the time, very intensive. In this paper, we propose
a fast method which computes an approximated value of the EER. We illustrate
the benefit of the proposed method on two applications: the computing of non
parametric confidence intervals and the use of genetic algorithms to compute
the parameters of fusion functions. Experimental results show the superiority
of the proposed EER approximation method in term of computing time, and the
interest of its use to reduce the learning of parameters with genetic
algorithms. The proposed method opens new perspectives for the development of
secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012
Methods of Technical Prognostics Applicable to Embedded Systems
Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického 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.
Model Cards for Model Reporting
Trained machine learning models are increasingly used to perform high-impact
tasks in areas such as law enforcement, medicine, education, and employment. In
order to clarify the intended use cases of machine learning models and minimize
their usage in contexts for which they are not well suited, we recommend that
released models be accompanied by documentation detailing their performance
characteristics. In this paper, we propose a framework that we call model
cards, to encourage such transparent model reporting. Model cards are short
documents accompanying trained machine learning models that provide benchmarked
evaluation in a variety of conditions, such as across different cultural,
demographic, or phenotypic groups (e.g., race, geographic location, sex,
Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex
and Fitzpatrick skin type) that are relevant to the intended application
domains. Model cards also disclose the context in which models are intended to
be used, details of the performance evaluation procedures, and other relevant
information. While we focus primarily on human-centered machine learning models
in the application fields of computer vision and natural language processing,
this framework can be used to document any trained machine learning model. To
solidify the concept, we provide cards for two supervised models: One trained
to detect smiling faces in images, and one trained to detect toxic comments in
text. We propose model cards as a step towards the responsible democratization
of machine learning and related AI technology, increasing transparency into how
well AI technology works. We hope this work encourages those releasing trained
machine learning models to accompany model releases with similar detailed
evaluation numbers and other relevant documentation
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