39 research outputs found
How the OH reactivity affects the ozone production efficiency: case studies in Beijing and Heshan, China
Full text linkTotal OH reactivity measurements were conducted on the Peking
University campus (Beijing) in August 2013 and in Heshan (Guangdong province)
from October to November 2014. The daily median OH reactivity was 20 ± 11 s−1 in Beijing and 31 ± 20 s−1 in Heshan, respectively.
The data in Beijing showed a distinct diurnal pattern with the maxima over
27 s−1 in the early morning and minima below 16 s−1 in the afternoon. The
diurnal pattern in Heshan was not as evident as in Beijing. Missing
reactivity, defined as the difference between measured and calculated OH
reactivity, was observed at both sites, with 21 % missing reactivity in Beijing and
32 % missing reactivity in Heshan. Unmeasured primary species, such as
branched alkenes, could contribute to missing reactivity in Beijing,
especially during morning rush hours. An observation-based model with the RACM2
(Regional Atmospheric Chemical Mechanism version 2) was used to understand
the daytime missing reactivity in Beijing by adding unmeasured oxygenated
volatile organic compounds and simulated intermediates of the degradation
from primary volatile organic compounds (VOCs). However, the model could not find a convincing
explanation for the missing reactivity in Heshan, where the ambient air was found to be
more aged, and the missing reactivity was presumably attributed to oxidized
species, such as unmeasured aldehydes, acids and dicarbonyls. The ozone
production efficiency was 21 % higher in Beijing and 30 % higher in
Heshan when the model was constrained by the measured reactivity, compared to
the calculations with measured and modeled species included, indicating the
importance of quantifying the OH reactivity for better understanding ozone
chemistry
An artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San AntonioIncludes bibliographical referencesAn unsupervised artificial neural network (ANN) based on the ART2-A algorithm is compared to a rule-based method for fault classification on an automated assembly machine. Machine data is collected using three greyscale sensors and two redundant limit switches for 11 different operating conditions. Descriptive features are extracted from the raw data and two data sets, each containing 180 feature vectors, are created for testing both methods. The first data set contains 'real' feature vectors obtained from the original sensor signals, and the second data set contains 'simulated' feature vectors obtained by scaling the 'real' feature vectors. The second data set is used to test the performance of each system when variations are present in the input space. During testing, the rule-based system correctly classified 98.3% of all feature vectors, but its classification thresholds needed to be manually adjusted to accommodate the 'simulated' data set. The ART2-A network perfectly classified the 'real' data set into 13 clusters, and then correctly classified the 'simulated' data into the same 13 clusters without any modification to the algorithm's tuning parameter, vigilance
Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014
No full textPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; An unsupervised artificial neural network (ANN) based on the ART2-A algorithm is compared to a rule-based method for fault classification on an automated assembly machine. Machine data is collected using three greyscale sensors and two redundant limit switches for 11 different operating conditions. Descriptive features are extracted from the raw data and two data sets, each containing 180 feature vectors, are created for testing both methods. The first data set contains ‘real’ feature vectors obtained from the original sensor signals, and the second data set contains ‘simulated’ feature vectors obtained by scaling the ‘real’ feature vectors. The second data set is used to test the performance of each system when variations are present in the input space. During testing, the rule-based system correctly classified 98.3% of all feature vectors, but its classification thresholds needed to be manually adjusted to accommodate the ‘simulated’ data set. The ART2-A network perfectly classified the 'real' data set into 13 clusters, and then correctly classified the 'simulated' data into the same 13 clusters without any modification to the algorithm's tuning parameter, vigilanc
Matching disparate geospatial datasets and validating matches using spatial logic
Full text linkIn recent years, the emergence and development of crowd-sourced geospatial data has provided challenges and opportunities to national mapping agencies as well as commercial mapping organisations. Crowd-sourced data involves non-specialists in data collection, sharing and maintenance. Compared to authoritative geospatial data, which is collected by surveyors or other geodata professionals, crowd-sourced data is less accurate and less structured, but often provides richer user-based information and reflects real world changes more quickly at a much lower cost.
In order to maximize the synergistic use of authoritative and crowd-sourced geospatial data, this research investigates the problem of how to establish and validate correspondences (matches) between spatial features from disparate geospatial datasets. To reason about and validate matches between spatial features, a series of new qualitative spatial logics was developed. Their soundness, completeness, decidability and complexity theorems were proved for models based on a metric space. A software tool `MatchMaps' was developed, which generates matches using location and lexical information, and verifies consistency of matches using reasoning in description logic and qualitative spatial logic. MatchMaps was evaluated by the author and experts from Ordnance Survey, the national mapping agency of Great Britain. In experiments, it achieved high precision and recall, as well as reduced human effort. The methodology developed and implemented in MatchMaps has a wider application than matching authoritative and crowd-sourced data and could be applied wherever it is necessary to match two geospatial datasets of vector data
Matching disparate geospatial datasets and validating matches using spatial logic
No full textIn recent years, the emergence and development of crowd-sourced geospatial data has provided challenges and opportunities to national mapping agencies as well as commercial mapping organisations. Crowd-sourced data involves non-specialists in data collection, sharing and maintenance. Compared to authoritative geospatial data, which is collected by surveyors or other geodata professionals, crowd-sourced data is less accurate and less structured, but often provides richer user-based information and reflects real world changes more quickly at a much lower cost.
In order to maximize the synergistic use of authoritative and crowd-sourced geospatial data, this research investigates the problem of how to establish and validate correspondences (matches) between spatial features from disparate geospatial datasets. To reason about and validate matches between spatial features, a series of new qualitative spatial logics was developed. Their soundness, completeness, decidability and complexity theorems were proved for models based on a metric space. A software tool `MatchMaps' was developed, which generates matches using location and lexical information, and verifies consistency of matches using reasoning in description logic and qualitative spatial logic. MatchMaps was evaluated by the author and experts from Ordnance Survey, the national mapping agency of Great Britain. In experiments, it achieved high precision and recall, as well as reduced human effort. The methodology developed and implemented in MatchMaps has a wider application than matching authoritative and crowd-sourced data and could be applied wherever it is necessary to match two geospatial datasets of vector data
Energy and nutrient intakes of Sri Lankan patients with type 2 diabetes mellitus: a cross-sectional survey
No full text
