Using Seismic Data to Build a Deep Learning Model for Detection and Characterization of Vehicles
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Chai, Chengping↗
Engineering topics
Kerekes, Ryan
Publications and source records attributed to Kerekes, Ryan.
Comparison of Power System Current Sensors via Playback of Electrical Disturbances
The need to accurately measure high-frequency content in power system voltage and current phenomena is increasingly becoming more of a priority. As the amount of distributed energy resources (DER) and nonlinear loads penetrating the grid increases, so do challenges associated with traditional measurement and metering applications. In this paper, three commercially-available medium-voltage-level current sensors are characterized in terms of their harmonic amplitude and phase performance against reference signals that are “played back” through the sensors through the use of an arbitrary waveform generator. It is shown that none of the three sensors studied are able to faithfully replicate all of the input signals completely, though there are advantages and disadvantages to each in terms of noise, resonance, and induced phase drift. Additionally, the Goodness-of-Fit metric, typically used for PMU model validation, is used to generate side-by-side comparisons of sensor accuracy over a small window around the events under study.
Wilson, Aaron↗
Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)
This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).
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On the Performance of Tree-Based Antennas for SLF-VLF Signal Reception
A full-wave electromagnetic simulation and experimental study is carried out to explore the effects of tree-induced dielectric loading on the receiving properties of an electrically small, earth-grounded wire monopole. It is demonstrated that by connecting the upper end of the wire to the tree trunk, the input impedance of the monopole can be significantly transformed, leading to an enhancement in the received signal strength that becomes more noticeable as the frequency is decreased; for example, at the lowest frequency considered, it is seen that the tree-loaded antenna can increase the received load voltage by as much as ~40 dB, when compared to the case without tree loading. An application of the tree-based antenna for low-frequency power line signal monitoring is presented. Overall, simulation results for the input impedance and received signal response are in good agreement with measurement data.
Liao, DaHan↗
Flaming Moe
Current clamp measurements collected on various small electronic devices. Details on the data set can be found in J. M. Vann, T. P. Karnowski, R. Kerekes, C. D. Cooke and A. L. Anderson, A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions, in IEEE Transactions on Electromagnetic Compatibility, vol. 60, no. 1, pp. 122-131, Feb. 2018, doi: 10.1109/TEMC.2017.2692962.
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