arXiv subjects

Jingchun Wang

Publications and source records attributed to Jingchun Wang.

7 recordsLinked to original sources

arXiv2026

Scalable Optimization for Mobility-Aware Coordinated Electric Vehicle Charging in Distribution Power Networks

Rapid growth in electric-vehicle (EV) charging demand is placing increasing stress on power distribution networks (PDNs), whose hosting capacity is often limited and spatially uneven. Beyond demonstrating that coordination can help, this paper answers an open question that is central for planners: What is the maximal achievable benefit of EV charging demand flexibility from spatial and temporal shifting in reducing overload-driven distribution upgrades at a regional scale? We introduce MAC (Mobility-Aware Coordinated EV charging) to establish a credible upper bound, which entails rethinking charging flexibility around individual mobility, fusing travel itineraries with feeder-level hosting-capacity data, and solving population-scale optimization with spatio-temporal coupling to certified near-optimality. (i) MAC expands feasible scheduling by coupling charging decisions over the full mobility horizon. Instead of enforcing per-session energy recovery, it only requires the EV state-of-charge (SOC) to remain sufficient for upcoming trips. (ii) MAC is computationally scalable via an iterated price response (IPR) scheme. Each iteration posts a locational-temporal price, collects the fleet's best responses in parallel, and updates the price from the observed capacity shortage. Custom batched subproblem solvers remove the per-iteration bottleneck of solving millions of best responses. In a future-oriented 30% EV adoption scenario for the San Francisco Bay Area, MAC almost eliminates overload-driven upgrade needs relative to unmanaged charging. Comparing across the baseline spectrum, mobility-aware flexibility alone removes most of the overload, outperforming even fully coordinated session-based charging, and coordination on top suppresses most of the remainder. Both levers are thus essential, and the resulting best-case benchmarks provide references for PDN planning and operations.

eess.SY↗

arXiv2026

Towards Quantitative Reaction Dynamics of O3

The reaction dynamics of O(3P) + O2(3Sigma_g-) collisions in the O3(1A') electronic ground state is characterized on a high-level MRCI+Q/aug-cc-pVQZ potential energy surface represented as a reproducing kernel. For the atom exchange reactions involving the ^{16}O and ^{18}O isotopes as the atomic collision partner, associated with rates k6(T) and k8(T), respectively, a negative temperature-dependence of k(T), consistent with experiments was found. The absolute rates typically underestimate measured rates by 50 percent, depending on the experiment considered. For the ratio R(T) = k8(T)/k6(T), the measured T-dependence was found, including a cusp at lower temperatures. The differences between experiments and computations are primarily due to neglect of quantum effects, primarily zero-point effects. For the atomization reaction, leading to 3O(3P), the rates is lower by approximately one order of magnitude compared with experiments, which is a clear improvement over simulations using previous potential energy surfaces computed with smaller basis sets. Non-adiabatic effects are deemed minor for the atom exchange reactions.

physics.chem-ph↗

arXiv2025

Explainable Deep Learning Based Adversarial Defense for Automatic Modulation Classification

Deep learning (DL) has been widely applied to enhance automatic modulation classification (AMC). However, the elaborate AMC neural networks are susceptible to various adversarial attacks, which are challenging to handle due to the generalization capability and computational cost. In this article, an explainable DL based defense scheme, called SHapley Additive exPlanation enhanced Adversarial Fine-Tuning (SHAP-AFT), is developed in the perspective of disclosing the attacking impact on the AMC network. By introducing the concept of cognitive negative information, the motivation of using SHAP for defense is theoretically analyzed first. The proposed scheme includes three stages, i.e., the attack detection, the information importance evaluation, and the AFT. The first stage indicates the existence of the attack. The second stage evaluates contributions of the received data and removes those data positions using negative Shapley values corresponding to the dominating negative information caused by the attack. Then the AMC network is fine-tuned based on adversarial adaptation samples using the refined received data pattern. Simulation results show the effectiveness of the Shapley value as the key indicator as well as the superior defense performance of the proposed SHAP-AFT scheme in face of different attack types and intensities.

eess.SP↗

arXiv2025

Reaction Dynamics for the [NNO] System from State-Resolved and Coarse-Grained Models

The dynamics for the NO($X^2 Π$) + N($^4$S) $\leftrightarrow$ N$_{2}(X^{1}Σ_{g}^{+}$) + O($^{3}$P) reaction was followed in the $^3$A' electronic state using state-to-state (STS) and Arrhenius-based rates from two different high-level potential energy surfaces represented as a reproducing kernel (RKHS) and permutationally invariant polynomials (PIPs). Despite the different number of bound states supported by the RKHS- and PIP-PESs the ignition points from STS and Arrhenius rates are at $\sim 10^{-6}$ s whether or not reverse rates are from assuming microreversibility or explicitly given. Conversion from NO to N$_2$ is incomplete if Arrhenius-rates are used but complete turnover is observed if STS-information is used. This is due to non-equilibrium energy flow and state dynamics which requires a state-based description. Including full dissociation leads asymptotically to the correct 2:1 [N]:[O] concentration with little differences for the species' dynamics depending on the PES used for the STS-information. In conclusion, concentration profiles from coarse-grained simulations are consistent over 14 orders of magnitude in time using STS-information based on two different high-level PESs.

physics.chem-ph↗

arXiv2025

Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions

Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems.

physics.chem-ph↗

arXiv2024

Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach

Deep learning (DL) has significantly improved automatic modulation classification (AMC) by leveraging neural networks as the feature extractor.However, as the DL-based AMC becomes increasingly widespread, it is faced with the severe secure issue from various adversarial attacks. Existing defense methods often suffer from the high computational cost, intractable parameter tuning, and insufficient robustness.This paper proposes an eXplainable artificial intelligence (XAI) defense approach, which uncovers the negative information caused by the adversarial attack through measuring the importance of input features based on the SHapley Additive exPlanations (SHAP).By properly removing the negative information in adversarial samples and then fine-tuning(FT) the model, the impact of the attacks on the classification result can be mitigated.Experimental results demonstrate that the proposed SHAP-FT improves the classification performance of the model by 15%-20% under different attack levels,which not only enhances model robustness against various attack levels but also reduces the resource consumption, validating its effectiveness in safeguarding communication networks.

eess.SP↗

arXiv2018

Multi-epoch analysis of the X-ray spectrum of the active galactic nucleus in NGC 5506

We present a multi-epoch X-ray spectroscopy analysis of the nearby narrow-line Seyfert I galaxy NGC 5506. For the first time, spectra taken by Chandra, XMM-Newton, Suzaku, and NuSTAR - covering the 2000-2014 time span - are analyzed simultaneously, using state-of-the-art models to describe reprocessing of the primary continuum by optical thick matter in the AGN environment. The main goal of our study is determining the spin of the supermassive black hole (SMBH). The nuclear X-ray spectrum is photoelectrically absorbed by matter with column density $\simeq 3 \times 10^{22}$ cm$^{-2}$. A soft excess is present at energies lower than the photoelectric cut-off. Both photo-ionized and collisionally ionized components are required to fit it. This component is constant over the time-scales probed by our data. The spectrum at energies higher than 2 keV is variable. We propose that its evolution could be driven by flux-dependent changes in the geometry of the innermost regions of the accretion disk. The black hole spin in NGC 5506 is constrained to be 0.93$\pm _{ 0.04 }^{0.04}$ at 90% confidence level for one interesting parameter.

astro-ph.HE↗