arXiv subjects

Mengfan Zhang

Publications and source records attributed to Mengfan Zhang.

9 recordsLinked to original sources

arXiv2026

Converter-Grid Interaction Stability Guaranteed Safe Deep Reinforcement Learning for Energy Storage Systems in Grid Frequency Support

The growing integration of converter interfaced renewable energy resources (RESs) intensifies stability challenges. Energy storage system (ESS) can provide fast and flexible frequency support to mitigate frequency deviations. However, the interface converter of ESS may encounter converter-grid interaction stability issues. This paper proposes a converter-grid interaction stability guaranteed safe DRL (CIS-DRL) method for ESS integrated power systems to achieve frequency regulation. We first obtain a double DNN-based stability region to identify the guaranteed converter-grid interaction stability. Next, a novel converter-grid interaction stability Safe-TD3 (CIS-STD3) algorithm is designed that integrates a stability feasibility projection layer to map unsafe actions into stable action set before execution, enforcing converter-grid interaction stability as a hard constraint throughout learning process. The proposed approach enables ESS for grid frequency support with 100% converter-grid interaction stability without violations. Experimental results show that the proposed CIS-DRL method achieves improved frequency regulation performance while preventing unstable operating points, demonstrating its practical applicability for real time ESS frequency support.

eess.SY↗

arXiv2026

Adaptive Memory Decay for Log-Linear Attention

Sequence models face a fundamental tradeoff between memory capacity and computational efficiency. Transformers achieve expressive context modeling at quadratic cost, while linear attention and state-space models run in linear time by compressing context into a fixed-size hidden state, inherently limiting recall. Log-linear attention navigates this tradeoff by organizing memory across a Fenwick tree hierarchy, growing its hidden state logarithmically with sequence length at log-linear compute cost. However, its memory decay parameter λ is fixed and independent of the input, assigning uniform weights across all hierarchy levels regardless of the content, which introduces unnecessary rigidity. We propose learning λ directly from the input via a lightweight two-layer MLP, producing per-token, per-level decay that adapts to content rather than position. A softplus activation lets each Fenwick tree level scale independently, avoiding the inter-level competition that softmax introduces. This modification preserves log-linear complexity exactly and adds negligible parameter overhead. We evaluate on associative recall, selective copying, and language modeling, finding that input-dependent decay consistently outperforms the baseline, with the largest gains in long-range memory settings where baseline λ degrades or collapses entirely.

cs.LG↗

arXiv2026

A Foldable and Agile Soft Electromagnetic Robot for Multimodal Navigation in Confined and Unstructured Environments

Multimodal locomotion is crucial for an animal's adaptability in unstructured wild environments. Similarly, in the human gastrointestinal tract, characterized by viscoelastic mucus, complex rugae, and narrow sphincters like the cardia, multimodal locomotion is also essential for a small-scale soft robot to conduct tasks. Here, we introduce a small-scale compact, foldable, and robust soft electromagnetic robot (M-SEMR) with more than nine locomotion modes designed for such a scenario. Featuring a six-spoke elastomer body embedded with liquid metal channels and driven by Laplace forces under a static magnetic field, the M-SEMR is capable of rapid transitions (< 0.35 s) among different locomotion modes. It achieves exceptional agility, including high-speed rolling (818 mm/s, 26 BL/s), omnidirectional crawling, jumping, and swimming. Notably, the robot can fold to reduce its volume by 79%, enabling it to traverse confined spaces. We further validate its navigation capabilities on complex terrains, including discrete obstacles, viscoelastic gelatin surfaces, viscous fluids, and simulated biological tissues. This system offers a versatile strategy for developing high-mobility soft robots for future biomedical applications.

cs.RO↗

arXiv2026

SpiderNets: Vision Models Predict Human Fear From Aversive Images

Phobias are common and impairing, and exposure therapy, which involves confronting patients with fear-provoking visual stimuli, is the most effective treatment. Scalable computerized exposure therapy requires automated prediction of fear directly from image content to adapt stimulus selection and treatment intensity. Whether such predictions can be made reliably and generalize across individuals and stimuli, however, remains unknown. Here we show that pretrained convolutional and transformer vision models, adapted via transfer learning, accurately predict group-level perceived fear for spider-related images, even when evaluated on new people and new images, achieving a mean absolute error (MAE) below 10 units on the 0-100 fear scale. Visual explanation analyses indicate that predictions are driven by spider-specific regions in the images. Learning-curve analyses show that transformer models are data efficient and approach performance saturation with the available data (~300 images). Prediction errors increase for very low and very high fear levels and within specific categories of images. These results establish transparent, data-driven fear estimation from images, laying the groundwork for adaptive digital mental health tools.

cs.CV↗

arXiv2025

SN 2021hpr: A Normal Type Ia Supernova Showing Excess Emission in the Early Rising Phase

We present extensive optical observations of a nearby Type Ia supernova (SN Ia), SN 2021hpr, located in the spiral galaxy NGC 3147 at a distance of $\sim$ 45 Mpc. Our observations cover a phase within $\sim 1-2$ days to $\sim 290$ days after the explosion. SN 2021hpr is found to be a spectroscopically normal SN Ia, with an absolute B-band peak magnitude of $M_{max}(B) \approx -19.16 \pm 0.14$ mag and a post-peak decline rate of $Δm_{15}(B)= 1.00 \pm 0.01 $ mag. Early-time light curves showed a $\sim 7.0 \%$ excess emission compared to a homogeneously expanding fireball model, likely due to SN ejecta interacting with a companion or immediate circumstellar matter. The optical spectra of SN 2021hpr are overall similar to those of normal SNe Ia, but characterized by prominent detached high-velocity features (HVFs) of Si {\sc ii} and Ca {\sc ii} in the early phase. After examining a small sample of well-observed normal SNe Ia, we find that the HVFs are likely common for the subgroup with early-excess emission. The association of early bump feature with the HVFs could be attributed to density or abundance enhancement at the outer layer of the exploding star, likely as a result of interactions with companion$/$CSM or experiencing more complete burning. Nevertheless, the redshifted Fe {\sc ii} and Ni {\sc ii} lines in the nebular-phase spectra of SN 2021hpr, contrary to the blueshift trend seen in other SNe Ia showing early bump features, indicate its peculiarity in the explosion that remains to be understood.

astro-ph.HE↗

arXiv2025

EDA-Q: Electronic Design Automation for Superconducting Quantum Chip

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.

cs.ET↗

arXiv2022

Binary open clusters in the Gaia data

Context. Observations indicate that the fraction of potential binary star clusters in the Milky Way is either the same or lower than that of the Magellanic Clouds. The unprecedented precision in the parallax measurements by Gaia has allowed for the discovery of a growing number of new binary open clusters (OCs). Aims. We aim to survey the candidates of truly binary open clusters that are formed simultaneously, using information from the Gaia database. Methods. Based on the most recent catalog of open clusters, we investigated the interactions of adjacent binary open clusters in our Galaxy within separations of 50 pc. We compared their coordinates, proper motions, parallaxes, and color-magnitude diagrams(CMDs) via binary plots for all candidate pairs. The candidates of truly binary open clusters are selected on the basis of their common proper motions and consistent behaviors in the CMDs of different clusters that are limited to a separation of 50 pc. Results. About ten pairs of the selected binary open clusters appear to be the same clusters, based on evidence that almost half ofthe cluster members are shared. Fourteen pairs are possibly true binaries, implying that they may come from the same clouds, among which five pairs are newly discovered. In addition, two clusters, UBC 46 and UBC 192, were found to be part of the stellar complex LISCA I. Our results confirm that OCs born in groups are usually composed of young open clusters.

astro-ph.SR↗

arXiv2021

SN 2017fgc: A Fast-Expanding Type Ia Supernova Exploded in Massive Shell Galaxy NGC 474

We present extensive optical photometric and spectroscopic observations of the high-velocity (HV) Type Ia supernova (SN Ia) 2017fgc, covering the phase from $\sim$ 12 d before to $\sim 389$ d after maximum brightness. SN 2017fgc is similar to normal SNe Ia, with an absolute peak magnitude of $M_{\rm max}^{B} \approx$ $-19.32 \pm 0.13$ mag and a post-peak decline of $Δm_{15}(B)$ = $1.05 \pm 0.07$ mag. Its peak bolometric luminosity is derived as $1.32 \pm 0.13) \times 10^{43} $erg s$^{-1}$, corresponding to a $^{56}$Ni mass of $0.51 \pm 0.03 M_{\odot}$. The light curves of SN 2017fgc are found to exhibit excess emission in the $UBV$ bands in the early nebular phase and pronounced secondary shoulder/maximum features in the $RrIi$ bands. Its spectral evolution is similar to that of HV SNe Ia, with a maximum-light Si II velocity of $15,000 \pm 150 $km s$^{-1}$ and a post-peak velocity gradient of $\sim$ $120 \pm 10 $km s$^{-1} $d$^{-1}$. The Fe II and Mg II lines blended near 4300 Å and the Fe II, Si II, and Fe III lines blended near 4800 Å are obviously stronger than those of normal SNe Ia. Inspecting a large sample reveals that the strength of the two blends in the spectra, and the secondary peak in the $i/r$-band light curves, are found to be positively correlated with the maximum-light Si II velocity. Such correlations indicate that HV SNe~Ia may experience more complete burning in the ejecta and/or that their progenitors have higher metallicity. Examining the birthplace environment of SN 2017fgc suggests that it likely arose from a stellar environment with young and high-metallicity populations.

astro-ph.HE↗

arXiv2020

Modeling of Individual HRTFs based on Spatial Principal Component Analysis

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

eess.AS↗