arXiv subjects

Xiaobing Zhang

Publications and source records attributed to Xiaobing Zhang.

14 recordsLinked to original sources

arXiv2026

Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management

Integrating electric mobility, including electric vehicles (EVs), electric trucks (ETs), and renewable energy sources (RES) with the power grid is paramount for decarbonization, efficiency, and stability. A critical gap remains, however: existing smart-grid and e-mobility cost-benefit analysis (CBA) approaches do not yet provide a unified framework for appraising AI-driven operational digital platforms (ODPs) that jointly coordinate EV/ET charging, renewable generation, and grid operations across sectoral and national boundaries. This paper develops a seven-step CBA framework tailored to this class of platform. The framework maps each layer of a multi-layered AI architecture to traceable, monetizable benefit streams-panning economic efficiency, grid reliability, and environmental externalities--while explicitly accounting for AI-specific capital and operational expenditures that conventional appraisals omit. Applied to a ten-year, three-country deployment across Austria, Hungary, and Slovenia, the analysis indicates a robust positive investment case under the modeled assumptions, confirmed through scenario sensitivity analysis, one-way parameter ranking, and probabilistic simulation. Benefit composition and country-level drivers differ systematically across national contexts, yet the economic rationale is preserved in each, reflecting the framework's adaptability to heterogeneous electrification trajectories. The findings indicate the economic viability of AI-driven digital platforms for cross-sectoral energy--mobility integration and highlight the critical role of ODPs in advancing decarbonization in the mobility--power nexus. To that end, they have direct implications for the design and appraisal of digital infrastructure investments under the EU's Fit for 55 and REPowerEU programmes.

econ.GN↗

arXiv2025

Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs

Global cooperation is posited as a pivotal solution to address climate change, yet significant barriers, like free-riding, hinder its realization. This paper develops a dynamic game-theoretic model to analyze the stability of coalitions under multiple stochastic climate tippings, and a technology-sharing mechanism is designed in the model to combat free-ridings. Our results reveal that coalitions tend to shrink over time as temperatures rise, owing to potential free-ridings, despite a large size of initial coalition. The threat of climate tipping reduces the size of stable coalitions compared to the case where tipping is ignored. However, at post-tipping period, coalitions temporarily expand as regions respond to the shock, though this cooperation is short-lived and followed by further shrink. Notably, technology-sharing generates greater collective benefits than sanctions, suggesting that the proposed dynamic technology-sharing pathway bolsters coalition resilience against free-riding while limiting the global warming. This framework highlights the critical role of technology-sharing in fostering long-term climate cooperation under climate tipping uncertainties.

econ.GN↗

arXiv2024

Mapping Hydrogen Evolution Activity Trends of V-based A15 Superconducting Alloys

Exploring high-efficiency and low-cost electrocatalysts is valuable for water-splitting technologies. Recently, Si-group compounds have attracted increasing attention in electrocatalysis, considering the abundant Si-group elements on Earth. However, Si-group compounds for HER electrocatalysis have not been systematically studied. In this study, we unveil the activity trends of non-noble metal catalyst A15-type V3M (i.e., V3Si, V3Ge, and V3Sn) superconductors and show that V3Si is the most efficient HER catalyst because of the high electronic conductivity and suitable d-band center. Among them, the V3Si only requires 33.4 mV to reach 10 mA cm-2, and only 57.6 mV and 114.6 mV are required to attain a high current density of 100 mA cm-2 and 500 mA cm-2, respectively. These low overpotentials are close to the 34.3 mV at 10 mA cm-2 of state-of-art Pt/C (20 %) but superior to 168.5 mV of Pt/C (20 %) at 100 mA cm-2. Furthermore, the V3Si illustrates exceptional durability with no obvious decay in the 120 h at the different current densities (i.e., 10 - 250 mA cm-2). The excellent HER activity of V3Si alloy can be ascribed to the synergies of superior electronic conductivity and suitable d-band center. Moreover, DFT calculations reveal that the absolute hydrogen adsorption Gibbs free energy is decreased after introducing the V to Si. Beyond offering a stable and high-performance electrocatalyst in an acidic medium, this work inspires the rational design of desirable silicide electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2024

The first low-mass eclipsing binary within the fully convective zone from TMTS

We present a comprehensive photometric and spectroscopic analysis of the short-period ($\sim$5.32 hours) and low-mass eclipsing binary TMTSJ0803 discovered by Tsinghua-Ma Huateng Telescope for Survey (TMTS). By fitting the light curves and radial velocity data with the Wilson--Devinney code, we find that the binary is composed of two late spotted active M dwarfs below the fully convective boundary. This is supported by the discovery of a significant Balmer emission lines in the LAMOST spectrum and prominent coronal X-ray emission. In comparison with the typical luminosity of rapidly rotating fully convective stars, the much brighter X-ray luminosity ($L_{X}/L_{\rm{bol}} = 0.0159 \pm 0.0059$) suggests the stellar magnetic activity of fully convective stars could be enhanced in such a close binary system. Given the metallicity of [M/H] = $-$ 0.35 dex as inferred from the LAMOST spectrum, we measure the masses and radii of both stars to be $M_{1} = 0.169 \pm 0.010~M_{\odot}$, $M_{2} = 0.162 \pm 0.016~M_{\odot}$, $R_{1} = 0.170 \pm 0.006~R_{\odot}$, and $R_{2} = 0.156 \pm 0.006~R_{\odot}$, respectively. Based on the luminosity ratio from the light curve modeling, the effective temperatures of two components are also estimated. In comparison with the stellar evolution models, the radii and effective temperatures of two components are all below the isochrones. The radius deflation might be mainly biased by a small radial velocity (RV) data or (and) a simple correction on RVs, while the discrepancy in effective temperature might be due to the enhanced magnetic activity in this binary.

astro-ph.SR↗

arXiv2022

Event-triggered Finite-time Control Using Inverse-optimal Implicit Lyapunov Function

This work deals with the event-triggered finite-time control for high-order systems based on an implicit Lyapunov function (ILF). With the construction of an inverse optimal problem, a novel expression of ILF is obtained. By designing the event-triggering mechanism elaborately, it is guaranteed that the trivial solution of the closed-loop system is globally finite-time stable and there exists no Zeno phenomenon. Extensions to the scenario with a multi-agent system are studied where a finite-time tracking control drives all the agents to reach a consensus. The obtained theoretical results are supported by numerical simulations.

math.OC↗

arXiv2022

Balanced control between performance and saturation for constrained nonlinear systems

This paper addresses the balanced control between performance and saturation for a class of constrained nonlinear systems, including the branches: balanced command filtered backstepping (BCFB) and balanced performance control (BPC). To balance the interconnection and conflict between performance and saturation constraints, define a performance safety evaluation (PSE) function, which evaluates the system safety under the destabilizing effect variables (DEVs) like saturation quantity and filter errors, then the cumulative effects of DEVs are fully utilized and compensated for the performance recovery. Specifically, there exists some degree of tolerance for the DEVs in the safety region, and the compensation operation works when the evaluation of the system goes dangerous. The advantages of the proposed methodology are illustrated in the numerical simulation.

eess.SY↗

arXiv2021

Space-and-time-synchronized simultaneous vehicle tracking/formation using cascaded prescribed-time control

In this paper, we present a space-and-time-synchronized control method with application to the simultaneous tracking/formation. In the framework of polar coordinates, through correlating and decoupling the reference/actual kinematics between the self vehicle and target, time and space are separated, controlled independently. As such, the specified state can be achieved at the predetermined terminal time, meanwhile, the relative trajectory in space is independent of time. In addition, for the stabilization before the predesigned time, a cascaded prescribed-time control theorem is provided as the preliminary of vehicle tracking control. The obtained results can be directly extended to the simultaneous tracking/formation of multiple vehicles. Finally, numerical examples are provided to verify the effectiveness and superiority of the proposed scheme.

eess.SY↗

arXiv2021

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets.

cs.CV↗

arXiv2020

Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation

Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations. However, most existing works address the domain discrepancy by aligning the data distributions of two domains at a global image level whereas the local consistencies are largely neglected. This paper presents an innovative local contextual-relation consistent domain adaptation (CrCDA) technique that aims to achieve local-level consistencies during the global-level alignment. The idea is to take a closer look at region-wise feature representations and align them for local-level consistencies. Specifically, CrCDA learns and enforces the prototypical local contextual-relations explicitly in the feature space of a labelled source domain while transferring them to an unlabelled target domain via backpropagation-based adversarial learning. An adaptive entropy max-min adversarial learning scheme is designed to optimally align these hundreds of local contextual-relations across domain without requiring discriminator or extra computation overhead. The proposed CrCDA has been evaluated extensively over two challenging domain adaptive segmentation tasks (e.g., GTA5 to Cityscapes and SYNTHIA to Cityscapes), and experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.

cs.CV↗

arXiv2019

The finite density scaling laws of condensation phase transition in zero range processes on scale-free networks

The dynamics of zero-range processes on complex networks is expected to be influenced by the topological structure of underlying networks. A real space complete condensation phase transition in the stationary state may occur. We have studied the finite density effects of the condensation transition in both the stationary and dynamical zero-range process on scale-free networks. By means of grand canonical ensemble method, we predict analytically the scaling laws of the average occupation number with respect to the finite density for the steady state. We further explore the relaxation dynamics of the condensation phase transition. By applying the hierarchical evolution and scaling ansatz, a scaling law for the relaxation dynamics is predicted. Monte Carlo simulations are performed and the predicted density scaling laws are nicely validated.

cond-mat.stat-mech↗

arXiv2019

Txilm: Lossy Block Compression with Salted Short Hashing

Current blockchains are restricted by the low throughput. Aimed at this problem, we propose Txilm, a protocol that compresses the size of transaction presentation in each block to save the bandwidth of the network. In this protocol, a block carries short hashes of TXIDs instead of complete transactions. Combined with the sorted transactions based on TXIDs, Txilm realizes 80 times of data size reduction compared with the original blockchains. We also evaluate the probability of hash collisions, and provide methods of resolving such collisions. Finally, we design strategies to protect against potential attacks on Txilm.

cs.CR↗

arXiv2012

Condensation phase transition in nonlinear fitness networks

We analyze the condensation phase transitions in out-of-equilibrium complex networks in a unifying framework which includes the nonlinear model and the fitness model as its appropriate limits. We show a novel phase structure which depends on both the fitness parameter and the nonlinear exponent. The occurrence of the condensation phase transitions in the dynamical evolution of the network is demonstrated by using Bianconi-Barabasi method. We find that the nonlinear and the fitness preferential attachment mechanisms play important roles in formation of an interesting phase structure.

cond-mat.stat-mech↗

arXiv2011

Tsallis mapping in growing complex networks with fitness

We introduce Tsallis mapping in Bianconi-Barabási (B-B) fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics, which is characterized by a dimensionless nonextensivity parameter $q$. It is found that this new phenomenological parameter plays an important role in the evolution of networks: the underlying evolving networks may undergo a different phases depending on the $q$ exponents, comparing to the original B-B fitness model, and the corresponding critical transition "temperature" is identified.

cond-mat.stat-mech↗

arXiv2009

The condensation in non-growing complex networks under Boltzmann limit

We extend the Bianconi-Barabási (B-B) fitness model to the non-growing complex network with fixed number of nodes and links. It is found that the statistical physics of this model makes it an appropriate representation of the Boltzmann statistics in the context of complex networks. The phase transition of this extended model is illustrated with numerical simulation and the corresponding "critical temperature" $T_c$ is identified. We note that the "non-condensation phase" in $T > T_c$ regime is different with "fit-get-rich" (FGR) phase of B-B model and that the connectivity degree distribution P(k) deviates from power-law distribution at given temperatures.

cond-mat.dis-nn↗