Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques

2006 ◽  
Vol 83 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Sandy Owega ◽  
Badi-Uz-Zaman Khan ◽  
Greg J. Evans ◽  
Robert E. Jervis ◽  
Mike Fila
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Long Range ◽  
Aerosol Transport ◽  
Back Trajectories ◽  
Transport Patterns ◽  
Via Classification

scholarly journals Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP<sup>2</sup>Ex aircraft campaign: chemical composition, size distributions, and the impact of convection

2021 ◽  
Vol 21 (5) ◽  
pp. 3777-3802
Author(s):  
Miguel Ricardo A. Hilario ◽  
Ewan Crosbie ◽  
Michael Shook ◽  
Jeffrey S. Reid ◽  
Maria Obiminda L. Cambaliza ◽  
...  
Keyword(s):  
Long Range ◽  
Southwest Monsoon ◽  
Long Range Transport ◽  
Northwest Pacific ◽  
Back Trajectories ◽  
Air Masses ◽  
China Sea ◽  
Range Transport ◽  
Transport Patterns ◽  
The Impact

Abstract. The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT.

scholarly journals Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Lizeth Vargas Palomino ◽  
Valder Steffen ◽  
Roberto Mendes Finzi Neto
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Probabilistic Neural Network ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Electromechanical Impedance ◽  
Analysis Methods

Impedance-based structural health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded on the surface of the structure (or embedded). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to quantify the damage. Distinguishing damage groups from a universe containing different types of damage is a major challenge in structural health monitoring. There are several types of failures that can occur in a given structure, such as cracks, fissures, loss of mechanical components (e.g., rivets), corrosion, and wear. It is important to characterize each type of damage from the impedance signals considered. In the present paper, probabilistic neural network and fuzzy cluster analysis methods are used for identification, localization, and classification of two types of damage, namely, cracks and rivet losses. The results show that probabilistic neural network and fuzzy cluster analysis methods are useful for identification, localization, and classification of these types of damage.

scholarly journals Comparison of the applicability of neural networks and cluster classification methods on the example company's financial situation

2010 ◽  
Vol 58 (6) ◽  
pp. 579-586 ◽  
Author(s):  
Oldřich Trenz ◽  
Vladimír Konečný
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Border Region ◽  
Financial Indicators ◽  
Financial Situation ◽  
Invaluable Tool ◽  
Self Learning ◽  
Classification Capability

The paper is focused on comparing the classification ability of the model with self-learning neutral network and methods from cluster analysis. The emphasis is particularly on the comparison of different approaches to a specific application example of the commitment, the classification of then financial situation. The aim is to critically evaluate different approaches at the level of application and deployment options.The verify the classification capability of the different approaches were used financial data from the database „Credit Info“, in particular data describing the financial situation of the two hundred eleven farms of homogeneous and uniform primary field.Input data were from the methods used, modified and evaluated by appropriate methodology. Found the final solution showed that the used approaches do not show significant differences, and they can say that they are equivalent. Based on this finding can formulate the conclusion that the approach of artificial intelligence (self-learning neural network) is as effective as a partial methods in the field of cluster analysis. In both cases, these approaches can be an invaluable tool in decision making.When the financial situation is evaluated by the expert, the calculation of liquidity, profitability and other financial indicators are making some simplification. In this respect, neural networks perform better, since these simplifications in them selves are not natively included. They can better assess and somewhat ambiguous cases, including businesses with undefined financial situation, the so-called data in the border region. In this respect, support and representation of the graphical layout of the resulting situation sorted out objects using software implemented neural network model.

scholarly journals Long-range transport patterns into the tropical northwest Pacific during the CAMP<sup>2</sup>Ex aircraft campaign: chemical composition, size distributions, and the impact of convection

2020 ◽  
Author(s):  
Miguel Ricardo A. Hilario ◽  
Ewan Crosbie ◽  
Michael Shook ◽  
Jeffrey S. Reid ◽  
Maria Obiminda L. Cambaliza ◽  
...  
Keyword(s):  
Long Range ◽  
Southwest Monsoon ◽  
Long Range Transport ◽  
Northwest Pacific ◽  
Back Trajectories ◽  
Air Masses ◽  
China Sea ◽  
Range Transport ◽  
Transport Patterns ◽  
The Impact

Abstract. The tropical Western North Pacific (TWNP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 Aug to 5 Oct 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TWNP from the Maritime Continent (MC), Peninsular Southeast Asia (PSEA), East Asia (EA), and West Pacific (WP). A mid-campaign monsoon shift on 20 Sep 2019 led to distinct transport patterns between the southwest monsoon (before 20 Sep) and monsoon transition (after 20 Sep). During the southwest monsoon, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low (high) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The monsoon transition saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT) as an indicator of convection. MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking on public health and climate.

Neurophysiologic classification of Wilson's disease by using cluster analysis

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Wilson’S Disease ◽  
Wilson's Disease

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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