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Yazhou Yuan

Publications and source records attributed to Yazhou Yuan.

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MetaBlue: A Metasurface-Assisted Acoustic Underwater Localization System

Underwater localization is essential for marine exploration and autonomous underwater operations, yet existing radio frequency and optical approaches are limited by rapid attenuation or limited visibility. Acoustic sensing remains the most practical choice, but conventional acoustic systems typically rely on large arrays or multiple synchronized anchors, resulting in high hardware costs and complex deployment. This paper introduces a novel low-cost passive acoustic metasurface, MetaBlue , explicitly designed for underwater localization, which, when attached to an ordinary ultrasonic transmitter, transforms it into a directional "super-transmitter." The metasurface embeds direction-dependent spectral patterns into the transmitted waveform, enabling accurate angle-of-arrival (AoA) estimation using only a single hydrophone. For ranging, we present a new EM-acoustic mixed time-of-arrival (ToA) method that leverages the acoustic transducer's inherent low-frequency EM leakage as a timing reference, enabling precise ranging without shared clocks. This allows complete 3D localization with a single low-cost anchor. We evaluate the system across diverse real-world underwater settings, including pools, tanks, and outdoor environments. Experiments show that our design achieves an average AoA error of 8.7 degree and 3D localization error of 0.37 m at distances over 10 m. Even with a single anchor, the system maintains 0.73 m precision.

cs.NI

Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices

Green buildings (GBs) with renewable energy and building energy management systems (BEMS) enable efficient energy use and support sustainable development. Electric vehicles (EVs), as flexible storage resources, enhance system flexibility when integrated with stationary energy storage systems (ESS) for real-time scheduling. However, differing degradation and operational characteristics of ESS and EVs complicate scheduling strategies. This paper proposes a model-free deep reinforcement learning (DRL) method for joint real-time scheduling based on a combined battery system (CBS) integrating ESS and EVs. We develop accurate degradation models and cost estimates, prioritize EV travel demands, and enable collaborative ESS-EV operation under varying conditions. A prediction model optimizes energy interaction between CBS and BEMS. To address heterogeneous states, action coupling, and learning efficiency, the DRL algorithm incorporates double networks, a dueling mechanism, and prioritized experience replay. Experiments show a 37.94 percent to 40.01 percent reduction in operating costs compared to a mixed-integer linear programming (MILP) approach.

eess.SY