arXiv subjects

Juncheng Guo

Publications and source records attributed to Juncheng Guo.

8 recordsLinked to original sources

arXiv2026

Gravity-Induced Thermal Rectification in Gaseous Systems

Thermal rectification (TR) typically relies on structural asymmetry or material heterogeneity. Here, we show that gravity alone can induce and modulate TR in gaseous systems. Using a minimal model of a single gas particle confined in a two-dimensional channel between heat baths at different temperatures, we analytically demonstrate that gravitational fields generate TR, enabling perfect unidirectional heat conduction across broad gravitational parameter ranges. This effect exhibits an intrinsic trade-off between rectification efficiency and heat power. Extending beyond the single-particle limit, numerical simulations confirm that gravitationally induced TR persists in interacting many-particle systems. Notably, in interacting gas mixtures, the rectification direction can be reversed. Gravity-mediated control of heat currents thus provides new fundamental insights into thermal transport and suggests design principles for gaseous thermal diodes.

cond-mat.stat-mech↗

arXiv2026

Heat Conduction in Momentum-Conserving Fluids: From quasi-2D to 3D systems

Using nonequilibrium and equilibrium molecular dynamics simulations, we investigate heat conduction in a momentum-conserving mesoscopic fluid modeled by multiparticle collision dynamics. Across quasi-two-dimensional (q-2D) to three-dimensional (3D) systems, we identify three distinct transport regimes: (i) a \emph{ballistic regime}, where thermal conductivity scales linearly with system size ($\kappa \sim L$) and the total heat current autocorrelation function $C(t)$ remains constant; (ii)~a \emph{kinetic regime}, characterized by size-independent $\kappa$ and exponentially decaying $C(t)$, demonstrating that normal heat conduction dominated by kinetic effects is far more ubiquitous than previously observed in 1D systems; and (iii)~a \emph{hydrodynamic regime}, where the q-2D system exhibits logarithmically divergent conductivity ($ \kappa \sim \ln L $ ) with $ C(t) \sim t^{-1} $ , while the 3D system displays finite $ \kappa $ and $ C(t) \sim t^{-3/2} $. Our results, observed in the hydrodynamic regime, quantitatively validate the scaling predictions for heat transport and reveal a clear dimensional crossover -- from 2D-like anomalous transport to 3D Fourier behavior. These results lay a foundation for understanding thermal transport in q-2D to 3D systems and have practical implications for the design of micro- and nanoscale thermal devices.

cond-mat.stat-mech↗

arXiv2026

SortScrews: A Dataset and Baseline for Real-time Screw Classification

Automatic identification of screw types is important for industrial automation, robotics, and inventory management. However, publicly available datasets for screw classification are scarce, particularly for controlled single-object scenarios commonly encountered in automated sorting systems. In this work, we introduce $\textbf{SortScrews}$, a dataset for casewise visual classification of screws. The dataset contains 560 RGB images at $512\times512$ resolution covering six screw types and a background class. Images are captured using a standardized acquisition setup and include mild variations in lighting and camera perspective across four capture settings. To facilitate reproducible research and dataset expansion, we also provide a reusable data collection script that allows users to easily construct similar datasets for custom hardware components using inexpensive camera setups. We establish baseline results using transfer learning with EfficientNet-B0 and ResNet-18 classifiers pretrained on ImageNet. In addition, we conduct a well-explored failure analysis. Despite the limited dataset size, these lightweight models achieve strong classification accuracy, demonstrating that controlled acquisition conditions enable effective learning even with relatively small datasets. The dataset, collection pipeline, and baseline training code are publicly available at https://github.com/ATATC/SortScrews.

cs.CV↗

arXiv2025

Gravity-Induced Modulation of Negative Differential Thermal Resistance in Fluids

We investigate how gravity influences negative differential thermal resistance (NDTR) in fluids modeled by multiparticle collision dynamics. In the integrable case, we derive the heat flux formula for the system exhibiting the NDTR effect, and show that by introducing a gravity along the direction of the thermodynamic force, the temperature difference required for the occurrence of NDTR can be greatly reduced. Meanwhile, we also demonstrate that the heat-bath-induced NDTR mechanism -- originally found to be applicable only to weakly interacting systems -- can now operate in systems with stronger interactions due to the presence of gravity, and further remains robust even in mixed fluids. These results provide new insights into heat transport and establish a theoretical foundation for designing fluidic thermal devices that harness the NDTR effect under gravity.

cond-mat.stat-mech↗

arXiv2023

Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.

cs.CR↗

arXiv2022

Negative differential thermal resistance of fluids induced by heat baths

It has recently been shown that in one-dimensional hard-point gases, there is a mechanism that induces negative differential thermal resistance (NDTR) between heat baths. We examine this mechanism in more general higher dimensional fluids described by multiparticle collision dynamics. We consider fluids in a finite cuboid region of three-dimensional space with each end in contact with a heat bath. Based on analytical results and numerical models, we find that the mechanism underlying NDTR also works for high-dimensional fluidic systems with weak interactions and is very robust to mixed fluids. Our results significantly advance knowledge of NDTR induced by heat bath and illuminate new directions to explore in fabricating fluid thermal transistors in micro- and nanosystems.

cond-mat.stat-mech↗

arXiv2020

Designing Few-layer Graphene Schottky Contact Solar Cell: Theoretical Efficiency Limits and Parametric Optimization

We theoretically study the efficiency limits and performance characteristics of few-layer graphene-semiconductor solar cells (FGSCs) based on a Schottky contact device structure. We model and compare the energy conversion efficiency of various configurations by explicitly considering the non-Richardson thermionic emission across few-layer graphene/semiconductor Schottky heterostructures. The calculations reveal that ABA-stacked trilayer graphene-silicon solar cell exhibits a maximal conversion efficiency exceeding 28\% due to a lower reversed saturation current when compared to that of the ABC-stacking configuration. The thermal coefficients of PCE for ABA and ABC stacking FGSCs are -0.064\%/K and -0.049\%/K, respectively. Our work offers insights for optimal designs of graphene-based solar cells, thus paving a route towards the design of high-performance FGSC for future nanoscale energy converters.

physics.app-ph↗

arXiv2020

Gridless Super-Resolution Sparse Recovery for Non-sidelooking STAP using Reweighted Atomic Norm Minimization

Sparse recovery Space-time Adaptive Processing (STAP) can reduce the requirements of clutter samples, and suppress clutter effectively using limited training samples for airborne radar. The whole angle-Doppler plane is discretized into small grid points uniformly in presently available sparse recovery STAP methods, however, the clutter ridge is not located exactly on the pre-discretized grid points in non-sidelooking STAP radar. The off-grid effect degrades the performance of STAP significantly. In this paper, a gridless sparse recovery STAP method is proposed based on reweighted atomic norm minimization, in which the clutter spectrum is precisely estimated in continuous angle-Doppler plane without resolution limit. Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries and STAP method utilizing atomic norm minimization.

eess.SP↗