Search Constraints
Filtering by:
Discipline
Science
Remove constraint Discipline: Science
Discipline
Engineering
Remove constraint Discipline: Engineering
« Previous |
1 - 10 of 30
|
Next »
Number of results to display per page
View results as:
Search Results
-
- Creator:
- Agnit Mukhopadhyay
- Description:
- Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
- Citation to related publication:
- Mukhopadhyay, A., et al. (2020). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Forthcoming.
- Discipline:
- Engineering and Science
- Title:
- Dataset Containing Global Modeling Results Comparing Magnetopause Distances and CPCP
-
- Creator:
- Attari, Ali
- Description:
- Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
- Keyword:
- Digital Manometry
- Citation to related publication:
- Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
- Discipline:
- Engineering, Science, and Health Sciences
- Title:
- Data for "Comparison of Anorectal Function Measured using Wearable Digital Manometry and a High Resolution Manometry System." article (PLOS ONE) PONE-D-20-01826R1
-
- Creator:
- Jiao, Zhenbang, Chen, Yang, and Manchester, Ward
- Description:
- GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time. SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
- Keyword:
- Solar Flare Prediction and Machine Learning
- Citation to related publication:
- Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., Chen, Y., Solar Flare Intensity Prediction with Machine Learning Models, 2020, Space Weather Journal, in press
- Discipline:
- Science and Engineering
- Title:
- Data for Solar Flare Intensity Prediction with Machine Learning Models
-
- Creator:
- Chen, Yang and Manchester, Ward IV
- Description:
- GOES_flare_list: contains a list of more than 10,000 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time, GOES_B_flare_list: contains time series data of SDO/HMI SHARP parameters for B class solar flares , GOES_MX_flare_list: contains time series data of SDO/HMI SHARP parameters for M and X class solar flares, SHARP_B_flare_data_300.hdf5 and SHARP_MX_flare_data_300.hdf5 files contain time series more than 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model., and B_HARPs_CNNencoded_part_xxx.hdf5 and M_X HARPs_CNNencoded_part_xxx.hdf5 include neural network encoded features derived from vector magnetogram images derived from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI). These data files typically contains one or two sequences of magnetograms covering an active region for a period of 24h with a 1 hour cadence. We encode each magnetogram with frames of a fixed size of 8x16 with 512 channels.
- Keyword:
- machine learning, data science, and solar flare prediction
- Citation to related publication:
- Chen, Y., Manchester, W., Hero, A., Toth, G., DuFumier, B. Zhou, T., Wang, X., Zhu, H., Sun, Zeyu, Gombosi, T., Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters, Space Weather, 17, 1404–1426. https://doi.org/10.1029/2019SW002214
- Discipline:
- Science and Engineering
- Title:
- Data and Data products for machine learning applied to solar flares
-
- Creator:
- Malik, Hafiz and Khan, Muhammad Khurran, King Saud University
- Description:
- Details of the microphone used for data collection, acoustic environment in which data was collected, and naming convention used are provided here. 1 - Microphones Used: The microphones used to collect this dataset belong to 7 different trademarks. Table (1) illustrates the number of used Mics of different trademarks and models. Table 1: Trademarks and models of Mics Mic Trademark Mic Model # of Mics Shure SM-58 3 Electro-Voice RE-20 2 Sennheiser MD-421 3 AKG C 451 2 AKG C 3000 B 2 Neumann KM184 2 Coles 4038 2 The t.bone MB88U 6 Total 22 2- Environment Description: A brief description of the 6 environments in which the dataset was collected is presented here: (i) Soundproof room: a small room (nearly 1.5m × 1.5m × 2m), which is closed and completely isolated. With an exception of a small window in the front side of the room which is made of glass, all the walls of the room are made of wood and covered by a layer of sponge from the inner side, and the floor is covered by carpet. (ii) Class room: standard class room (6m × 5m × 3m). (iii) Lab: small lab (4m × 4m × 3m). All the walls are made of glasses and the floor is covered by carpet. The lab contains 9 computers. (iv) Stairs: is in the second floor. The place of recording is 3m × 5m (v) Parking: is the college parking. (vi) Garden: is an open space outside the buildings. 3- Naming Convention: This set of rules were followed as a naming convention to give each file in the dataset a unique name: (i) The file name is 19 characters long, and consists of 5 sections separated by underscores. (ii) The first section is of 3 characters indicates the Microphone trademark. (iii) The second section of 4 characters indicates the microphone model as in table (2). (iv) The third section of 2 characters indicates a specific microphone within a set of microphones of the same trademark and model, since we have more than one microphone of the same trademark and model. (v) The fourth section of 2 characters indicates the environment, where Soundproof room --> 01 Class room --> 02 Lab --> 03 Stairs --> 04 Parking --> 05 Garden --> 06 (vi) The fifth section of 2 characters indicates the language, where Arabic --> 01 English --> 02 Chinese --> 03 Indonesian --> 04 (vii) The sixth section of 2 characters indicates the speaker. Table 2: Microphones Naming Criteria Original Mic Trademark and model --> Naming Convenient Shure SM-58 --> SHU_0058 Electro-Voice RE-20 --> ELE_0020 Sennheiser MD-421 --> SEN_0421 AKG C 451 --> AKG_0451 AKG C 3000 B --> AKG_3000 Neumann KM184 --> NEU_0184 Coles 4038 --> COL_4038 The t.bone MB88U --> TBO_0088 For example: SEN_0421_02_01_02_03 is an English file recorded by speaker number 3 in the soundproof room using microphone number 2 of Sennheiser MD-421
- Keyword:
- audio forensic, multimedia forensics, microphone identification, tamper detection, splicing detection, and codec identification
- Citation to related publication:
- Muhammad Khurram Khan, Mohammed Zakariah, Hafiz Malik & Kim-Kwang Raymond Choo (2018). A novel audio forensic data-set for digital multimedia forensics, Australian Journal of Forensic Sciences, 50:5, 525-542, http://dx.doi.org/10.1080/00450618.2017.1296186
- Discipline:
- Government, Politics and Law, Science, and Engineering
- Title:
- The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms
-
- Creator:
- Mathews, Elizabeth and Verhoff, Frank
- Description:
- Each pdf is an electronic version of the paper output for each experiment. Each text file is the electronic version of the data on the computer cards for each experiment. These text files are directly readable by Excel. Once in Excel, the data can be manipulated as desired. Additional information is in the theses.
- Keyword:
- Two Liquid Phase Processes, Droplet Size and Concentration, Population Balances, and Dispersed Phase Mixing
- Citation to related publication:
- Ross, S. L. (1971). Measurements and models of the dispersed phase mixing process (Doctoral dissertation). Retrieved from http://hdl.handle.net/2027.42/136886 and Verhoff, F. H. (1969). A study of the bivariate analysis of dispersed phase mixing (Doctoral dissertation). Retrieved from http://hdl.handle.net/2027.42/137651
- Discipline:
- Science and Engineering
- Title:
- Computer Data from Ross Thesis
-
- Creator:
- Crisp, Dakota N., Cheung, Warwick, Gliske, Stephen V., Lai, Alan, Freestone, Dean R., Grayden, David B., Cook, Mark J., and Stacey, William C.
- Description:
- The data and the scripts are to show that seizure onset dynamics and evoked responses change over the progression of epileptogenesis defined in this intrahippocampal tetanus toxin rat model. All tests explored in this study can be repeated with the data and scripts included in this repository. and Dataset citation: Crisp, D.N., Cheung, W., Gliske, S.V., Lai, A., Freestone, D.R., Grayden, D.B., Cook, MJ., Stacey, W.C. (2019). Epileptogenesis modulates spontaneous and responsive brain state dynamics [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/r6vg-9658
- Keyword:
- evoked response, stimulation, bifurcation, epilepsy, seizure, divergence, and dynamics
- Discipline:
- Health Sciences, Science, and Engineering
- Title:
- Epileptogenesis modulates spontaneous and responsive brain state dynamics - Code & Data
-
- Creator:
- Crisp, Dakota N., Saggio, Maria L., Scott, Jared, Stacey, William C., Nakatani, Mitsuyoshi, Gliske, Stephen V., and Lin, Jack
- Description:
- This data and scripts are meant to test and show seizure differentiation based on bifurcation theory. A zip file is included which contains real and simulated seizure waveforms, Matlab scripts, and metadata. The matlab scripts allow for visual review validation and objective feature analysis. The file “README.txt” provides more detail about each individual file within the zip file. and Data citation: Crisp, D.N., Saggio, M.L., Scott, J., Stacey, W.C., Nakatani, M., Gliske, S.F., Lin, J. (2019). Epidynamics: Navigating the map of seizure dynamics - Code & Data [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/ejhy-5h41
- Keyword:
- Bifurcation, Epilepsy, Seizure, and Divergence
- Citation to related publication:
- Saggio, M.L., Crisp, D., Scott, J., Karoly, P.J., Kuhlmann, L., Nakatani, M., Murai, T., Dümpelmann, M., Schulze-Bonhage, A., Ikeda, A., Cook, M., Gliske, S.V., Lin, J., Bernard, C., Jirsa, V., Stacey, W., 2020. In pre-print. Epidynamics characterize and navigate the map of seizure dynamics. bioRxiv 2020.02.08.940072. https://doi.org/10.1101/2020.02.08.940072
- Discipline:
- Health Sciences, Science, and Engineering
- Title:
- A taxonomy of seizure dynamotypes - Code & Data
-
- Creator:
- Hegedus, Alexander M
- Description:
- This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891 , The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information. , This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements: , and CASA 4.7.1 (or greater?) built on python 2.7 Example link for Red Hat 7 https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz Users may follow this guide to download and install the correct version of CASA for their system https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing CASA executables should be fairly straightforward to extract from the untarred files. gcc 4.8.5 or above (or below?) GCC installation instructions can be found here: https://gcc.gnu.org/install/ SPICE (I use cspice here) https://naif.jpl.nasa.gov/naif/toolkit_C.html As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc' '/home/alexhege/SPICE/LunarEph/moon_080317.tf' '/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf' '/home/alexhege/SPICE/LunarEph/pck00010.tpc' '/home/alexhege/SPICE/LunarEph/naif0008.tls' '/home/alexhege/SPICE/LunarEph/de430.bsp' ) All of which can be found at https://naif.jpl.nasa.gov/pub/naif/generic_kernels/ SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
- Citation to related publication:
- Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
- Discipline:
- Science and Engineering
- Title:
- Data and Code Set for "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array"
-
- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M. et al. 2020. Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Science doi:10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
- Title:
- Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information
- « Previous
- Next »
- 1
- 2
- 3