scholarly journals Error analysis method for monocular vision pose measurement system

2019 ◽  
Vol 40 (1) ◽  
pp. 77-81
Author(s):  
HAO Renjie ◽  
WANG Zhongyu ◽  
LI Yaru
Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4478
Author(s):  
Jiangying Zhao ◽  
Yongbiao Hu ◽  
Mingrui Tian

Excavation is one of the broadest activities in the construction industry, often affected by safety and productivity. To address these problems, it is necessary for construction sites to automatically monitor the poses of excavator manipulators in real time. Based on computer vision (CV) technology, an approach, through a monocular camera and marker, was proposed to estimate the pose parameters (including orientation and position) of the excavator manipulator. To simulate the pose estimation process, a measurement system was established with a common camera and marker. Through comprehensive experiments and error analysis, this approach showed that the maximum detectable depth of the system is greater than 11 m, the orientation error is less than 8.5°, and the position error is less than 22 mm. A prototype of the system that proved the feasibility of the proposed method was tested. Furthermore, this study provides an alternative CV technology for monitoring construction machines.


1970 ◽  
Vol 26 (1) ◽  
pp. 175-180 ◽  
Author(s):  
Leon P. Hall ◽  
M. La Verne La Driere

80 emotionally disturbed and 80 neurologically impaired boys in a public school setting were matched for WISC Full Scale IQ and chronological age. An analysis was made of the Similarities subtest responses utilizing both the error-analysis method of Spence and the cognitive style procedure of Sigel for purposes of comparison. The Wilcoxon matched-pairs signed-ranks technique was used in analyzing the data. The results were as follows: (1) the error analysis approach provided the greater potential for differential diagnosis between the two groups under consideration; (2) use of the cognitive style scores permitted improved definition of the dynamic implications of the error-analysis categories, inadequate abstracting and narrative-descriptive responses; (3) diagnostic power was not increased when all responses as opposed to errors alone were considered.


Author(s):  
Wataru MOCHIZUKI ◽  
Mao KONOSU ◽  
Tsuyoshi IKEYA ◽  
Daisuke INAZU ◽  
Akio OKAYASU

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.


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