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Name: learn.py Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Software required: python2 software stack, including numpy, optparse, pandas, keras, sklearn and matplotlib Language: python Program size: 17kB Name: out.txt Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Description: Output from learn.py when operating on cnn_data.txt Name: cnn_data.csv Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 ASCII plaintext, comma-separated values, with comment-lines indicated by a ``#''. Header precisely define the file format Size: 266MB
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Macroinvertebrate kick-sampling results on the River Beas in November 2017. Additional details are available in: https://doi.org/10.3390/w10091247 This research was funded by the UK Natural Environment Research Council, grant number NE/N016394/1.
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Dataset contains: GISAXS maps od the investigated films, optical , XRD, I-V and quantum efficinecy data
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Here we provide data and code used in the paper "Mitochondrial fission and fusion dynamics generate efficient, robust, and evenly-distributed network topologies in budding yeast cells".
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Stimulation Artifact Source Separation
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Raw data and scripts to support the article: Johannisson, W., Harnden, R., Zenkert, D., Lindbergh, G., Shape-Morphing Carbon Fiber Composite using Electrochemical Actuation.
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The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. The test data, or similar data, has been used for some publications, including: Vidal, C., Naguib, M., Gross, O., Malysz, P., Kollmeyer P. and Emadi, A. (2020). Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks. [online] Sae.org. Available at: https://www.sae.org/publications/technical-papers/content/2020-01-1181/ [Accessed 28 Jan. 2020]. C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
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The data are based from mouse tracking experiments at https://md.hicc.cs.kumamoto-u.ac.jp. 1. Filenames with "P2P" contains network packet captured of one click event submitted from the client to the server. The filenames also have the number of variables included such as name, email, date, current URL, etc, refer to "Summary_of_Data.ods". 2. Filename with "Local" contains resource cost and mouse tracking data of five users attempting a quiz session on the server via the Internet on 25 December 2018. 3. Filename with "Implementation" contains resource cost of a mouse tracking implementation of two quiz session attempted by twenty-two students on each session from School of Engineering and Applied Sciences, National University of Mongolia to the server at Human Interface and Cyber Communication laboratory, Kumamoto University on 03 January 2019. Unfortunately, the mouse tracking data is not open for a limited amount of time.
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INstrumen SKala perilaku agresi siber
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SatStress, Matlab, and GMT4 codes used to investigate Coulomb failure on Ganymede.
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