Forced Oscillations in the Western Interconnection with the Pacific DC Intertie Wide Area Damping Controller.
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In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.
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Abstract not provided.
The power take-off (PTO) is an integral part of wave energy conversion, and the design process is nontrivial. Better PTOs, and better processes for selecting and designing PTO architectures for various applications, would benefit devices that assist in powering the blue economy by decreasing time and money spent on PTO design and increasing the overall energy capture performance of these devices. This paper chronicles the selection process of a PTO for a small-scale surge-type wave energy converter (WEC) for the purpose of informing future PTO selection processes. Three PTO architectures are evaluated in WEC-Sim: a hydraulic check valve PTO, a hydraulic active valving PTO, and a directly electrified PTO. Simple models of each PTO are constructed. Because a model for the small-scale device was initially unavailable, the PTOs are simulated on a large-scale device. The results are scaled down using Froude scaling and compared to results from directly simulating a small-scale model. Strong assumptions are made because this work is early in the design stages, and a coarse look at PTO options was desired. Specifically, the effectiveness of controls is investigated, along with the efficiency of energy conversion. However, energy capture is only part of the consideration; there are also logistic concerns to be considered when selecting a PTO. For example, components for large-scale WECs are so large and expensive that it may make sense to custom-build PTO components, but small-scale WECs would benefit from off-the-shelf availability because the cost of customization would be a significant portion of the total capital cost of deployment at a small scale. Submersible, off-the-shelf components are much easier to source for hydraulic PTOs. Because of highly effective controls, efficient energy conversion, and availability of marine-grade components, an active valving hydraulic PTO is selected for this small-scale surge-type WEC.
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We demonstrate a mid-infrared detector using a phononic crystal resonator fabricated on 128° Y-cut bulk lithium niobate substrate, featuring an absorbed noise equivalent power of 30 pW√ Hz and a response time constant of 0.3 ms.
This erratum corrects a typographic error that appears in Table 1 of our earlier paper [ Optica 8 , 539 ( 2021 ) OPTIC8 2334-2536 10.1364/OPTICA.418984 ].