arXiv subjects

Junhyung Lee

Publications and source records attributed to Junhyung Lee.

5 recordsLinked to original sources

arXiv2026

Battery health reporting fails independent validation across manufacturers

Battery state-of-health (SOH) reported by on-board battery management systems (BMS) is the primary metric available to electric vehicle (EV) owners and regulators, yet no study has validated its reliability across manufacturers against independent measurements. Here we show, through an epidemiological study of 1,114 EVs spanning five manufacturers and 375 days, that battery health reporting is fundamentally unreliable: real capacity differences of 12-25% exist within every model, but BMS SOH fails to track them, with correlations ranging from \r{ho} = 0.10 (non-significant) to \r{ho} = 0.62 only under restrictive filtering, while 384 vehicles do not expose SOH at all. A manufacturer-independent electrochemical marker achieves 74-89% degradation classification accuracy across all platforms without requiring BMS data, and a controlled laboratory validation on cells identical to those in the fleet confirms that partial-voltage-window charge measurements track reference capacity with \r{ho} > 0.80 across all 60 voltage windows (p < 0.001). These findings reveal a structural information asymmetry with direct implications for the EU Battery Regulation's 2027 SOH transparency mandate, California's Advanced Clean Cars (ACC) II durability requirements, warranty enforcement, used-vehicle valuation, right-to-repair legislation, and second-life battery markets.

eess.SP↗

arXiv2026

Nonlinear Electro-Optic Visible Photonic Circuits for Solid-State Quantum Defects

Integrated visible photonic engines for solid-state quantum defects provide a foundation for scalable quantum networks. While miniaturization is advancing, active manipulation remains limited by the difficulty of achieving simultaneous milliwatt-scale visible light generation and high-contrast modulation. Despite extensive efforts, the concurrent chip-scale realization of nonlinear frequency conversion and fast temporal gating for high-fidelity quantum control has remained elusive. Here, we demonstrate a monolithic thin-film lithium niobate (TFLN) platform integrating periodically poled frequency conversion with GHz-bandwidth electro-optic (EO) switching. The device delivers off-chip green-light power exceeding 1 mW with an extinction ratio (ER) of 42.2 dB, enabling coherent spin control and time-resolved lifetime measurements of individual nitrogen-vacancy (NV) centers in diamond through nanosecond gating. System performance is validated through pulsed optically detected magnetic resonance (ODMR), Rabi oscillations, and Ramsey interference, supported by time-tagged photon counting with nanosecond resolution. By unifying sufficient nonlinear light generation with high-speed active manipulation, this platform establishes a scalable framework for the realization of high-rate quantum communication nodes.

physics.optics↗

arXiv2025

Integrated bright source of polarization-entangled photons using lithium niobate photonic chips

Quantum photonics has rapidly advanced as a key area for developing quantum technologies by harnessing photons' inherent quantum characteristics, particularly entanglement. Generation of entangled photon pairs, known as Bell states, is crucial for quantum communications, precision sensing, and quantum computing. While bulk quantum optical setups have provided foundational progress, integrated quantum photonic platforms now offer superior scalability, efficiency, and integrative potential. In this study, we demonstrate a compact and bright source of polarization-entangled Bell state utilizing continuous-wave pumping on thin film lithium niobate (TFLN) integrated photonics. Our periodically poled lithium niobate device achieves on-chip brightness of photon pair generation rate of 508.5 MHz/mW, surpassing other integrated platforms including silicon photonics. This demonstration marks the first realization of polarization entanglement on TFLN platforms. Experimentally measured metrics confirm high-quality entangled photon pairs with a purity of 0.901, a concurrence of 0.9, and a fidelity of 0.944. We expect our compact quantum devices to have great potential for advancing quantum communication systems and photonic quantum technologies.

physics.optics↗

arXiv2022

MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection

Most scanning LiDAR sensors generate a sequence of point clouds in real-time. While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets. In this paper, we propose a novel 3D object detection architecture, which can encode LiDAR point cloud sequences acquired by multiple successive scans. The encoding process of the point cloud sequence is performed on two different time scales. We first design a short-term motion-aware voxel encoding that captures the short-term temporal changes of point clouds driven by the motion of objects in each voxel. We also propose long-term motion-guided bird's eye view (BEV) feature enhancement that adaptively aligns and aggregates the BEV feature maps obtained by the short-term voxel encoding by utilizing the dynamic motion context inferred from the sequence of the feature maps. The experiments conducted on the public nuScenes benchmark demonstrate that the proposed 3D object detector offers significant improvements in performance compared to the baseline methods and that it sets a state-of-the-art performance for certain 3D object detection categories. Code is available at https://github.com/HYjhkoh/MGTANet.git

cs.CV↗

arXiv2022

D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence

LiDAR sensors are widely used for 3D object detection in various mobile robotics applications. LiDAR sensors continuously generate point cloud data in real-time. Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. However, recent studies have shown that the performance of object detection can be further enhanced by utilizing spatio-temporal information obtained from point cloud sequences. In this paper, we propose a new 3D object detector, named D-Align, which can effectively produce strong bird's-eye-view (BEV) features by aligning and aggregating the features obtained from a sequence of point sets. The proposed method includes a novel dual-query co-attention network that uses two types of queries, including target query set (T-QS) and support query set (S-QS), to update the features of target and support frames, respectively. D-Align aligns S-QS to T-QS based on the temporal context features extracted from the adjacent feature maps and then aggregates S-QS with T-QS using a gated attention mechanism. The dual queries are updated through multiple attention layers to progressively enhance the target frame features used to produce the detection results. Our experiments on the nuScenes dataset show that the proposed D-Align method greatly improved the performance of a single frame-based baseline method and significantly outperformed the latest 3D object detectors.

cs.CV↗