292,470 research outputs found
Testing non-nested structural equation models
In this paper, we apply Vuong's (1989) likelihood ratio tests of non-nested
models to the comparison of non-nested structural equation models. Similar
tests have been previously applied in SEM contexts (especially to mixture
models), though the non-standard output required to conduct the tests has
limited their previous use and study. We review the theory underlying the tests
and show how they can be used to construct interval estimates for differences
in non-nested information criteria. Through both simulation and application, we
then study the tests' performance in non-mixture SEMs and describe their
general implementation via free R packages. The tests offer researchers a
useful tool for non-nested SEM comparison, with barriers to test implementation
now removed.Comment: 24 pages, 6 figure
European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0
The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag
Analysis of Testing-Based Forward Model Selection
This paper introduces and analyzes a procedure called Testing-based forward
model selection (TBFMS) in linear regression problems. This procedure
inductively selects covariates that add predictive power into a working
statistical model before estimating a final regression. The criterion for
deciding which covariate to include next and when to stop including covariates
is derived from a profile of traditional statistical hypothesis tests. This
paper proves probabilistic bounds, which depend on the quality of the tests,
for prediction error and the number of selected covariates. As an example, the
bounds are then specialized to a case with heteroskedastic data, with tests
constructed with the help of Huber-Eicker-White standard errors. Under the
assumed regularity conditions, these tests lead to estimation convergence rates
matching other common high-dimensional estimators including Lasso
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
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