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ABSTRACT In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians’ accurate locations is crucial to traffic and pedestrian safety. Current systems adopt cameras and wireless sensors to estimate people’s locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from the camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a localization solution based on a Generative Adversarial Network (GAN) architecture. During training, it learns the underlying linkage between pedestrians’ camera-phone data correspondences. During inference, it generates refined position estimations based only on pedestrians’ phone data that consists of GPS, IMU, and FTM. Results show that our GAN produces 3D coordinates at 1 to 2 meters localization error across 5 different outdoor scenes. We further show that the proposed model supports self-learning. The generated coordinates can be associated with pedestrians’ bounding box coordinates to obtain additional camera-phone data correspondences. This allows automatic data collection during inference. Results show that after fine-tuning the GAN model on the expanded
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Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks
Traditional smart meters, which measure energy usage every 15 minutes or more and report it at least a few hours later, lack the granularity needed for real-time decision-making. To address this practical problem, we introduce a new method using generative adversarial networks (GAN) that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using convex optimization. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated historical energy data obtained from smart meters. The results demonstrate that the model can successfully create minute-by-minute temporally correlated profiles of power usage from 15-minute interval average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring and subsequently controlling such systems.
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- Award ID(s):
- 2145063
- PAR ID:
- 10565110
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-2103-5
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- El Paso, TX, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NAPS61145.2024.10741806
