38,488 research outputs found
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System
Simultaneous Localization and Mapping using RGB-D cameras has been a fertile
research topic in the latest decade, due to the suitability of such sensors for
indoor robotics. In this paper we propose a direct RGB-D SLAM algorithm with
state-of-the-art accuracy and robustness at a los cost. Our experiments in the
RGB-D TUM dataset [34] effectively show a better accuracy and robustness in CPU
real time than direct RGB-D SLAM systems that make use of the GPU. The key
ingredients of our approach are mainly two. Firstly, the combination of a
semi-dense photometric and dense geometric error for the pose tracking (see
Figure 1), which we demonstrate to be the most accurate alternative. And
secondly, a model of the multi-view constraints and their errors in the mapping
and tracking threads, which adds extra information over other approaches. We
release the open-source implementation of our approach 1 . The reader is
referred to a video with our results 2 for a more illustrative visualization of
its performance
Ensemble Joint Sparse Low Rank Matrix Decomposition for Thermography Diagnosis System
Composite is widely used in the aircraft industry and it is essential for manufacturers to monitor its health and quality. The most commonly found defects of composite are debonds and delamination. Different inner defects with complex irregular shape is difficult to be diagnosed by using conventional thermal imaging methods. In this paper, an ensemble joint sparse low rank matrix decomposition (EJSLRMD) algorithm is proposed by applying the optical pulse thermography (OPT) diagnosis system. The proposed algorithm jointly models the low rank and sparse pattern by using concatenated feature space. In particular, the weak defects information can be separated from strong noise and the resolution contrast of the defects has significantly been improved. Ensemble iterative sparse modelling are conducted to further enhance the weak information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted to detect the inner debond on multiple carbon fiber reinforced polymer (CFRP) composites. A comparative analysis is presented with general OPT algorithms. Not withstand above, the proposed model has been evaluated on synthetic data and compared with other low rank and sparse matrix decomposition algorithms
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Cognate facilitation effects in bilingual children of varying language dominance
A widely accepted theory is that bilinguals activate both of their languages regardless of which is in use. Though there is abundant research on this phenomenon in bilingual adults, less research has focused on bilingual children. Cognates (i.e., words that share meaning and sound across languages) have frequently been used to explore language co-activation. The present study investigates cognate facilitation effects in child bilinguals of varying language dominance. Spanish-English bilingual children between 6 and 10 years old performed a picture-naming task that included pictures of cognates and non-cognates. Children who were more English-dominant experienced larger cognate facilitation effects when producing words in their non-dominant language but not in their dominant language. In contrast, children with more balanced dominance did not experience cognate facilitation effects in either language. The findings from this study may have implications for the development of the bilingual lexicon.Psycholog
Extracting 3D parametric curves from 2D images of Helical objects
Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively
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