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Title: Pruning Dynamic Event Trees Using Density Peaks with Dynamic Time Warping.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1375589
Report Number(s):
SAND2016-7809PE
646553
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the 6220/6230 Summer Student Mini-Symposium held August 16, 2016 in Albuquerque, NM.
Country of Publication:
United States
Language:
English

Citation Formats

Martin, Nevin, and Denman, Matthew R. Pruning Dynamic Event Trees Using Density Peaks with Dynamic Time Warping.. United States: N. p., 2016. Web.
Martin, Nevin, & Denman, Matthew R. Pruning Dynamic Event Trees Using Density Peaks with Dynamic Time Warping.. United States.
Martin, Nevin, and Denman, Matthew R. Mon . "Pruning Dynamic Event Trees Using Density Peaks with Dynamic Time Warping.". United States. doi:. https://www.osti.gov/servlets/purl/1375589.
@article{osti_1375589,
title = {Pruning Dynamic Event Trees Using Density Peaks with Dynamic Time Warping.},
author = {Martin, Nevin and Denman, Matthew R},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Aug 01 00:00:00 EDT 2016},
month = {Mon Aug 01 00:00:00 EDT 2016}
}

Conference:
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  • Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less