15 skills found
usgs / Groundmotion ProcessingParsing and processing ground motion data
iris-edu / GmvThe Python code for the IRIS DMC's Ground Motion Visualization (GMV) data product. GMV is a video-based IRIS DMC data product that illustrates how seismic waves travel away from an earthquake location by animating the normalized recorded wave amplitudes at each seismometer location using colored markers.
alexisInSAR / EGMStoolkitEGMS toolkit is a set of python scripts to download and manage the InSAR data from European Ground Motion Service.
rizac / EGSIMA web service and Python library for selecting and testing ground shaking intensity models (GSIM)
shilpakancharla / Event Based Velocity Prediction SnnNeuromorphic computing uses very-large-scale integration (VLSI) systems with the goal of replicating neurobiological structures and signal conductance mechanisms. Neuromorphic processors can run spiking neural networks (SNNs) that mimic how biological neurons function, particularly by emulating the emission of electrical spikes. A key benefit of using SNNs and neuromorphic technology is the ability to optimize the size, weight, and power consumed in a system. SNNs can be trained and employed in various robotic and computer vision applications; we attempt to use event-based to create a novel approach in order to the predict velocity of objects moving in frame. Data generated in this work is recorded and simulated as event camera data using ESIM. Vicon motion tracking data provides the ground truth position and time values, from which the velocity is calculated. The SNNs developed in this work regress the velocity vector, consisting of the x, y, and z-components, while using the event data, or the list of events associated with each velocity measurement, as the input features. With the use of the novel dataset created, three SNN models were trained and then the model that minimized the loss function the most was further validated by omitting a subset of data used in the original training. The average loss, in terms of RMSE, on the test set after using the trained model on the omitted subset of data was 0.000386. Through this work, it is shown that it is possible to train an SNN on event data in order to predict the velocity of an object in view. (Spring 2022 MS Computer Science Thesis - North Carolina State University)
Yizhuo-Mu / Deep End To End Motion Planning ROSOur reproduction of paper "Pfeiffer M et al. From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots"
bakerjw / NGAW2 CorrelationsCompute IM correlations from NGA-West2 ground motion data
DOI-USGS / Ghsc Esi Groundmotion ProcessingParsing and processing ground motion data
GeorgePapazafeiropoulos / OpenSeismoMatlabOpen source software for strong ground motion data processing
LSDtopotools / LsdfailtoolsA collection of tools for combining satellite-derived ground motion data with slope stability models.
GeorgePapazafeiropoulos / PEER Ground Motion Data Base ReaderRead and resample earthquake record time histories from the Pacific Earthquake Engineering Research Center (PEER) Ground Motion Data Base
somu15 / Bayesian Ground Motion SelectionThese are a set of codes for simulating the Conditional Spectrum using a Bayesian Analysis. Simulated ground motions can be conveniently combined with real ground motion data through these codes. For more information, please refer to "A Bayesian Treatment of the Conditional Spectrum Approach for Ground Motion Selection". Report by Somayajulu Dhulipala and Madeleine Flint.
African-Robotics-Unit / Acinoset ViewerA repository for a 3D ground truth data creation tool for the AcinoNet cheetah motion tracking paper
wq1989 / Benchmark Dataset For Adaptive Stride Length EstimationThe lack of benchmarking datasets for pedestrian stride length estimation makes it hard to pinpoint differences of published methods. Existing datasets either lack the ground-truth of each stride or are limited to small spaces with single scene or motion pattern. To fully evaluate the performance of proposed ASLE algorithm, we conducted benchmark dataset for natural pedestrian dead reckoning using smartphone sensors and FM-INS module. we leveraged the FM-INS module to provide the ground-truth of each stride with motion distance errors in 0.3% of the entire travel distance. The datasets were obtained from a group of healthy adults with natural motion patterns (fast walking, normal walking, slow walking, running, jumping). The datasets contained more than 22 km, 10000 strides of gait measurements. The datasets cover both indoor and outdoor cases, including: stairs, escalators, elevators, office environments, shopping mall, streets and metro station. To maximize compatibility, all data is published in open and simple file formats. The sensor is sampled at 100 Hz. Throughout the datasets, the users hold the phone in their hand in front of their chest. The samples hold nine degree-of-freedom sensor data and the corresponding stride number, stride length and total walking distance.
CivilKen / Seismic Wave DirectivityBased on previous research, ground motion can be amplified in certain direction and show with significant anisotropy. The causes still remain unclear, and different researchers have attributed this phenomenon to several factors, including topographic effect, local geological heterogeneities, wave polarization, wave trapped in fault zone and etc. This phenomenon might have severe impacts on buildings that cause damages, especially in the near-fault area. However, the current seismic design code focus on the perpendicular direction of fault strike only, which is not suitable enough for real situation. The objective of this study will focus on seismic wave directivity in near-fault zone. A total of 104 earthquake events with basic geological data were collected. Causative factors were selected based on previous research. There are three main causes considered of free field stations, included wave polarization, anisotropic stiffness and forward directivity. Data of influence factors were collected accordingly, and Arias Intensity is used to describe the directivity of seismic wave. The deep learning technique was applied to predict Arias Intensity distribution with the given parameters. This research used TensorFlow as the main deep learning tool.
