scholarly journals A new method to estimate planktonic oxygen metabolism using high‐frequency sensor measurements in mesocosm experiments and considering daytime and nighttime respirations

Author(s):  
Tanguy Soulié ◽  
Sébastien Mas ◽  
David Parin ◽  
Francesca Vidussi ◽  
Behzad Mostajir
Author(s):  
P. Urrutia‐Cordero ◽  
O. Langvall ◽  
P. Blomkvist ◽  
D.G. Angeler ◽  
S. Bertilsson ◽  
...  

2021 ◽  
Vol 9 (5) ◽  
pp. 465
Author(s):  
Angelos Ikonomakis ◽  
Ulrik Dam Nielsen ◽  
Klaus Kähler Holst ◽  
Jesper Dietz ◽  
Roberto Galeazzi

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.


2018 ◽  
Vol 20 (1) ◽  
pp. 651-661
Author(s):  
Gintare Linkeviciute ◽  
Renaldas Raisutis ◽  
Kristina Sakalauskiene ◽  
Jurgita Makstiene ◽  
Jonas Guzaitis ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Ilaria Lucrezia Amerise ◽  
Agostino Tarsitano

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.


2002 ◽  
Vol 95 (1) ◽  
pp. 29-32 ◽  
Author(s):  
Yiming Chen ◽  
Haiyan Zhang ◽  
Yanjuan Zhu ◽  
Ding Yu ◽  
Zhenfang Tang ◽  
...  

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