scholarly journals Dynamic Data Driven Approach for Modeling Human Error

2015 ◽  
Vol 51 ◽  
pp. 1643-1654 ◽  
Author(s):  
Wan-Lin Hu ◽  
Janette J. Meyer ◽  
Zhaosen Wang ◽  
Tahira Reid ◽  
Douglas E. Adams ◽  
...  
Keyword(s):  
Human Error ◽  
Data Driven ◽  
Dynamic Data ◽  
Data Driven Approach

scholarly journals A Dynamic Data-driven Approach for Operation Planning of Microgrids

2015 ◽  
Vol 51 ◽  
pp. 2543-2552 ◽  
Author(s):  
Xiaoran Shi ◽  
Haluk Damgacioglu ◽  
Nurcin Celik
Keyword(s):  
Data Driven ◽  
Operation Planning ◽  
Dynamic Data ◽  
Data Driven Approach

scholarly journals Using Shape Memory Alloys: A Dynamic Data Driven Approach

2013 ◽  
Vol 18 ◽  
pp. 1844-1850 ◽  
Author(s):  
Craig C. Douglas ◽  
Victor Calo ◽  
Derrick Cerwinsky ◽  
Li Deng ◽  
Yalchin Efendiev
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Data Driven ◽  
Dynamic Data ◽  
Data Driven Approach

scholarly journals An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 799
Author(s):  
Hyeong-Tak Lee ◽  
Jeong-Seok Lee ◽  
Hyun Yang ◽  
Ik-Soon Cho
Keyword(s):  
Human Error ◽  
Significant Contribution ◽  
Pattern Analysis ◽  
Spatial Clustering ◽  
Data Driven ◽  
Automatic Identification ◽  
Maritime Industry ◽  
Clustering Method ◽  
Dbscan Algorithm ◽  
Data Driven Approach

As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

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