126,511 research outputs found
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
Criterion-referenced measurement: Its main applications, problems and findings
The need for criterion-referenced measurements has mainly arisen from the introduction of instructional programs organized according to modern principles from educational technology. Some of these programs are discussed, and it is indicated for what purposes criterion-referenced measurements are used. Three main problems of criterion-referenced measurement are distinguished: The problem of criterion-referenced scoring and score interpretation, the problem of criterion-referenced item and test analysis, and the problem of mastery testing. For each of these problems a variety of solutions of the paper to provide an overview of these and to introduce the reader to the original literature
A new adaptive response surface method for reliability analysis
Response surface method is a convenient tool to assess reliability for a wide range of structural mechanical problems. More specifically, adaptive schemes which consist in iteratively refine the experimental design close to the limit state have received much attention. However, it is generally difficult to take into account a lot of variables and to well handle approximation error. The method, proposed in this paper, addresses these points using sparse response surface and a relevant criterion for results accuracy. For this purpose, a response surface is built from an initial Latin Hypercube Sampling (LHS) where the most significant terms are chosen from statistical criteria and cross-validation method. At each step, LHS is refined in a region of interest defined with respect to an importance level on probability density in the design point. Two convergence criteria are used in the procedure: The first one concerns localization of the region and the second one the response surface quality. Finally, a bootstrap method is used to determine the influence of the response error on the estimated probability of failure. This method is applied to several examples and results are discussed
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Inverse Uncertainty Quantification (UQ) is a process to quantify the
uncertainties in random input parameters while achieving consistency between
code simulations and physical observations. In this paper, we performed inverse
UQ using an improved modular Bayesian approach based on Gaussian Process (GP)
for TRACE physical model parameters using the BWR Full-size Fine-Mesh Bundle
Tests (BFBT) benchmark steady-state void fraction data. The model discrepancy
is described with a GP emulator. Numerical tests have demonstrated that such
treatment of model discrepancy can avoid over-fitting. Furthermore, we
constructed a fast-running and accurate GP emulator to replace TRACE full model
during Markov Chain Monte Carlo (MCMC) sampling. The computational cost was
demonstrated to be reduced by several orders of magnitude.
A sequential approach was also developed for efficient test source allocation
(TSA) for inverse UQ and validation. This sequential TSA methodology first
selects experimental tests for validation that has a full coverage of the test
domain to avoid extrapolation of model discrepancy term when evaluated at input
setting of tests for inverse UQ. Then it selects tests that tend to reside in
the unfilled zones of the test domain for inverse UQ, so that one can extract
the most information for posterior probability distributions of calibration
parameters using only a relatively small number of tests. This research
addresses the "lack of input uncertainty information" issue for TRACE physical
input parameters, which was usually ignored or described using expert opinion
or user self-assessment in previous work. The resulting posterior probability
distributions of TRACE parameters can be used in future uncertainty,
sensitivity and validation studies of TRACE code for nuclear reactor system
design and safety analysis
Statistical extraction of process zones and representative subspaces in fracture of random composite
We propose to identify process zones in heterogeneous materials by tailored
statistical tools. The process zone is redefined as the part of the structure
where the random process cannot be correctly approximated in a low-dimensional
deterministic space. Such a low-dimensional space is obtained by a spectral
analysis performed on pre-computed solution samples. A greedy algorithm is
proposed to identify both process zone and low-dimensional representative
subspace for the solution in the complementary region. In addition to the
novelty of the tools proposed in this paper for the analysis of localised
phenomena, we show that the reduced space generated by the method is a valid
basis for the construction of a reduced order model.Comment: Submitted for publication in International Journal for Multiscale
Computational Engineerin
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
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