4,447 research outputs found
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
A unified framework for online trip destination prediction
Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines
Design methods for modern electric and hybrid electric vehicles: concept design and context analysis
The IceCube Neutrino Observatory: Instrumentation and Online Systems
The IceCube Neutrino Observatory is a cubic-kilometer-scale high-energy
neutrino detector built into the ice at the South Pole. Construction of
IceCube, the largest neutrino detector built to date, was completed in 2011 and
enabled the discovery of high-energy astrophysical neutrinos. We describe here
the design, production, and calibration of the IceCube digital optical module
(DOM), the cable systems, computing hardware, and our methodology for drilling
and deployment. We also describe the online triggering and data filtering
systems that select candidate neutrino and cosmic ray events for analysis. Due
to a rigorous pre-deployment protocol, 98.4% of the DOMs in the deep ice are
operating and collecting data. IceCube routinely achieves a detector uptime of
99% by emphasizing software stability and monitoring. Detector operations have
been stable since construction was completed, and the detector is expected to
operate at least until the end of the next decade.Comment: 83 pages, 50 figures; updated with minor changes from journal review
and proofin
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