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Seoho Jung

Publications and source records attributed to Seoho Jung.

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Tunable nonlinear electromechanics at the zero-point motion scale

Nonlinearity at the scale of zero-point motion opens new possibilities for the control and readout of nanomechanical systems, but achieving this remains a formidable challenge. Here we demonstrate that ultrastrong coupling (USC) between a nanotube mechanical oscillator and a double-quantum-dot electronic two-level system enables a mechanical Kerr (Duffing) nonlinearity at the zero-point motion scale. In the dispersive regime, this large coupling yields a mechanical anharmonicity of $\alpha = 1.4\%$ - three orders of magnitude larger than in previous work - while preserving the predominantly mechanical nature of the lowest energy states. We further demonstrate a purely quadratic cavity-based continuous readout of the mechanical motion. This continuous nonlinear optomechanical readout is enforced by a double-quantum dot symmetry, which can be broken by gate tuning to introduce a large linear transduction. These results establish a tunable USC platform that enables strong mechanical anharmonicity and nonlinear continuous readout at the zero-point motion scale.

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Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehicles

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.

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