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of 3 101
pro vyhledávání: '"Easwaran A"'
Autor:
Samaddar, Ankita, Easwaran, Arvind
Most vehicular applications in electric vehicles use IEEE 802.11p protocol for vehicular communications. Vehicle rebalancing application is one such application that has been used by many car rental service providers to overcome the disparity between
Externí odkaz:
http://arxiv.org/abs/2410.13031
Publikováno v:
Sustainable Energy, Grids and Networks, 38, 101334 (2024)
In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance and load demand using a real-world dataset from an island in Southeast A
Externí odkaz:
http://arxiv.org/abs/2409.19568
Autor:
Shivaraman, Nitin, Schuster, Patrick, Ramanathan, Saravanan, Easwaran, Arvind, Steinhorst, Sebastian
Time synchronization of devices in Internet-of-Things (IoT) networks is one of the challenging problems and a pre-requisite for the design of low-latency applications. Although many existing solutions have tried to address this problem, almost all so
Externí odkaz:
http://arxiv.org/abs/2409.14323
Autor:
Shivaraman, Nitin, Fittler, Jakob, Ramanathan, Saravanan, Easwaran, Arvind, Steinhorst, Sebastian
The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stati
Externí odkaz:
http://arxiv.org/abs/2409.14293
The utilization of Electric Vehicles (EVs) in car rental services is gaining momentum around the world and most commercial fleets are expected to fully adopt EVs by 2030. At the moment, the baseline solution that most fleet operators use is a Busines
Externí odkaz:
http://arxiv.org/abs/2409.12439
Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unreliable predictions. Existing works only extract the appearance features o
Externí odkaz:
http://arxiv.org/abs/2409.09953
Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model's training distribution to prevent potentially unsafe actions. However, OOD detectors are often
Externí odkaz:
http://arxiv.org/abs/2409.00880
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, especially for oblique DTs, and does take a significant training time. F
Externí odkaz:
http://arxiv.org/abs/2408.09135
The development of digital twins (DTs) for physical systems increasingly leverages artificial intelligence (AI), particularly for combining data from different sources or for creating computationally efficient, reduced-dimension models. Indeed, even
Externí odkaz:
http://arxiv.org/abs/2406.19670
This work studies fixed priority (FP) scheduling of real-time jobs with end-to-end deadlines in a distributed system. Specifically, given a multi-stage pipeline with multiple heterogeneous resources of the same type at each stage, the problem is to a
Externí odkaz:
http://arxiv.org/abs/2403.13411