The data is collected from database WIND for equipment manufacuturing industries in China. The sample period is from 2008 to 2014.
We obtained 6 million instances to be used as an analysis for modelling CO2 behavior. The Data Logging and sensors nodes acquisition are every 1 second.
In an infectious disease outbreak the identification of pathogen genome sequence variants provides epidemiologists with high-resolution transmission diagnostics that can help cluster patients; identify cohorts of individuals who need testing; and identify new variants that may compromise existing vaccines, therapeutics, and low-resolution detection diagnostics. The Oxford Nanopore MinION™ is a uniquely portable nucleic acid sequencing device that has been used in limited-resource settings for this purpose, e.g., during the 2014-2016 outbreak of Ebolavirus (EBOV) disease in Africa. We desc
Multiple README files are found within the compressed archives in this dataset. Most files are self-explanatory for biomedical research scientists who are familiar with the analysis of variants in nucleotide sequence data.
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Please see the Information Hiding Criteria Ver. 6 document.
This dataset includes the IHC standard movies, which are high quality raw movies. The types of size are 2K and 4K.
The original movies are sampled by 16-bit-depth.
- 4K-size 16-bit raw movies:
- 2K-size 16-bit raw movies:
- 2K-size 8-bit raw movies: [5.3GB each]
A 16-bit raw image file is quantized to 8-bit-depth uncompressed AVI files.
[ Acknowledgments ]
The 2K raw video clips were taken with a Canon Cinema EOS C500 system with support from Canon Inc. The IHC Committee would like to thank this company for its valuable contributions.
The data are used to identify the kinematic parameters deviation of Cartesian robot, train Gaussian Process Regression (GPR) model, record the compensation result of four calibration methods under different loading conditions.
Compensation results file: It expresses the compensation results in 8 test points when using four calibration methods under different loading conditions. We can see Figure16 in this paper.
HCT+BD+GPR_training file: These data record 320 groups of position points of the end effector after using HCT+BD model to compensate. We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model. 10-fold cross validation results of GPR model about x and z error are obtained by using these data. They are shown in Figure14 and Figure15 in this paper.
HCT+GPR_training file: These data record 320 groups of position points of the end effector after using HCT model to compensate. We can get 320 groups of residual error data by simply calculating the difference between these data and these designated positions. And they are used to train GPR model.
Identify_kinematic_parameter_deviation file: Using nonlinear least squares method to minimize the difference between the amended position and actual position. We can get the deviation of kinematic parameters. The procedure to identify the deviation of kinematic parameters is shown in Figure 4. And we can see the result of deviation in Table 2 in this paper.
The water consumption from different house holds recorded for a period of one year
Water consumption in different time instances