333,738 research outputs found
A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition
Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain expertsâ opinion about the data description. The proposed approach is committed to modelling of a
compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its
practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed
A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing
We propose a compressed sampling and dictionary learning framework for
fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is
generated from a model for the reflected sensor signal. Imperfect prior
knowledge is considered in terms of uncertain local and global parameters. To
estimate a sparse representation and the dictionary parameters, we present an
alternating minimization algorithm that is equipped with a pre-processing
routine to handle dictionary coherence. The support of the obtained sparse
signal indicates the reflection delays, which can be used to measure
impairments along the sensing fiber. The performance is evaluated by
simulations and experimental data for a fiber sensor system with common core
architecture.Comment: Accepted for publication in Journal of the Optical Society of America
A [ \copyright\ 2017 Optical Society of America.]. One print or electronic
copy may be made for personal use only. Systematic reproduction and
distribution, duplication of any material in this paper for a fee or for
commercial purposes, or modifications of the content of this paper are
prohibite
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
Metacognitive Development and Conceptual Change in Children
There has been little investigation to date of the way metacognition is involved in conceptual change. It has been recognised that analytic metacognition is important to the way older children acquire more sophisticated scientific and mathematical concepts at school. But there has been barely any examination of the role of metacognition in earlier stages of concept acquisition, at the ages that have been the major focus of the developmental psychology of concepts. The growing evidence that even young children have a capacity for procedural metacognition raises the question of whether and how these abilities are involved in conceptual development. More specifically, are there developmental changes in metacognitive abilities that have a wholescale effect on the way children acquire new concepts and replace existing concepts? We show that there is already evidence of at least one plausible example of such a link and argue that these connections deserve to be investigated systematically
Expanding sensor networks to automate knowledge acquisition
The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment
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