arXiv subjects

Long Gao

Publications and source records attributed to Long Gao.

16 recordsLinked to original sources

arXiv2026

A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems

Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-world conditions remains challenging. Spectral interference from inverter switching, hardware heterogeneity, operating-condition drift, and environmental noise collectively compromise conventional AFCI solutions. This paper proposes a lightweight, transferable, and self-adaptive learning-driven framework (LD-framework) for intelligent DC arc-fault detection. At the device level, LD-Spec learns compact spectral representations enabling efficient on-device inference and near-perfect arc discrimination. Across heterogeneous inverter platforms, LD-Align performs cross-hardware representation alignment to ensure robust detection despite hardware-induced distribution shifts. To address long-term evolution, LD-Adapt introduces a cloud-edge collaborative self-adaptive updating mechanism that detects unseen operating regimes and performs controlled model evolution. Extensive experiments involving over 53,000 labeled samples demonstrate near-perfect detection, achieving 0.9999 accuracy and 0.9996 F1-score. Across diverse nuisance-trip-prone conditions, including inverter start-up, grid transitions, load switching, and harmonic disturbances, the method achieves a 0% false-trip rate. Cross-hardware transfer shows reliable adaptation using only 0.5%-1% labeled target data while preserving source performance. Field adaptation experiments demonstrate recovery of detection precision from 21% to 95% under previously unseen conditions. These results indicate that the LD-framework enables a scalable, deployment-oriented AFCI solution maintaining highly reliable detection across heterogeneous devices and long-term operation.

eess.SP↗

arXiv2025

Hyperspectral Mamba for Hyperspectral Object Tracking

Hyperspectral object tracking holds great promise due to the rich spectral information and fine-grained material distinctions in hyperspectral images, which are beneficial in challenging scenarios. While existing hyperspectral trackers have made progress by either transforming hyperspectral data into false-color images or incorporating modality fusion strategies, they often fail to capture the intrinsic spectral information, temporal dependencies, and cross-depth interactions. To address these limitations, a new hyperspectral object tracking network equipped with Mamba (HyMamba), is proposed. It unifies spectral, cross-depth, and temporal modeling through state space modules (SSMs). The core of HyMamba lies in the Spectral State Integration (SSI) module, which enables progressive refinement and propagation of spectral features with cross-depth and temporal spectral information. Embedded within each SSI, the Hyperspectral Mamba (HSM) module is introduced to learn spatial and spectral information synchronously via three directional scanning SSMs. Based on SSI and HSM, HyMamba constructs joint features from false-color and hyperspectral inputs, and enhances them through interaction with original spectral features extracted from raw hyperspectral images. Extensive experiments conducted on seven benchmark datasets demonstrate that HyMamba achieves state-of-the-art performance. For instance, it achieves 73.0\% of the AUC score and 96.3\% of the DP@20 score on the HOTC2020 dataset. The code will be released at https://github.com/lgao001/HyMamba.

cs.CV↗

arXiv2025

Hyperspectral Adapter for Object Tracking based on Hyperspectral Video

Object tracking based on hyperspectral video attracts increasing attention to the rich material and motion information in the hyperspectral videos. The prevailing hyperspectral methods adapt pretrained RGB-based object tracking networks for hyperspectral tasks by fine-tuning the entire network on hyperspectral datasets, which achieves impressive results in challenging scenarios. However, the performance of hyperspectral trackers is limited by the loss of spectral information during the transformation, and fine-tuning the entire pretrained network is inefficient for practical applications. To address the issues, a new hyperspectral object tracking method, hyperspectral adapter for tracking (HyA-T), is proposed in this work. The hyperspectral adapter for the self-attention (HAS) and the hyperspectral adapter for the multilayer perceptron (HAM) are proposed to generate the adaption information and to transfer the multi-head self-attention (MSA) module and the multilayer perceptron (MLP) in pretrained network for the hyperspectral object tracking task by augmenting the adaption information into the calculation of the MSA and MLP. Additionally, the hyperspectral enhancement of input (HEI) is proposed to augment the original spectral information into the input of the tracking network. The proposed methods extract spectral information directly from the hyperspectral images, which prevent the loss of the spectral information. Moreover, only the parameters in the proposed methods are fine-tuned, which is more efficient than the existing methods. Extensive experiments were conducted on four datasets with various spectral bands, verifing the effectiveness of the proposed methods. The HyA-T achieves state-of-the-art performance on all the datasets.

cs.CV↗

arXiv2025

Transport Efficiency for Networks Obtained by Vertex Merging Operation

Transport is an important function of networks. Studying transport efficiency sheds light on the dynamic processes occurring within various underlying structures and offers a wide range of applications. To construct networks with different transport efficiencies, we focus on the networks obtained by vertex merging operation, which involves connecting multiple graphs through a single node. In this paper, we examine unbiased random walks on these networks and analyze their first-passage properties, including the mean first-passage time (MFPT), the mean trapping time (MTT), and the global-mean first-passage time (GFPT), which characterizes the transport (search) efficiency within the networks. We rigorously derive close-form solutions for these quantities. Results show that all these quantities are governed by the first-passage properties of the constituent components. Additionally, we propose a general method for optimizing the transport (search) efficiency by selecting a suitable node and adjusting the growth of the number of nodes in the subgraphs. We validate our findings using lollipop and barbell graphs. Our results indicate that for an arbitrary GFPT scaling exponent $\alpha \in [1, 3]$, we can construct a network with GFPT scales with the network size $N$ as $\text{GFPT} \sim N^{\alpha}$ through vertex merging operation. These conclusions provide valuable insights for designing and optimizing network structures.

nlin.CD↗

arXiv2025

JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework

Change detection (CD) in remote sensing images plays a vital role in Earth observation. However, the scarcity of high-resolution, comprehensive open-source datasets and the difficulty in achieving robust performance across varying change types remain major challenges. To address these issues, we introduce JL1-CD, a large-scale, sub-meter CD dataset consisting of 5,000 image pairs. We further propose a novel Origin-Partition (O-P) strategy and integrate it into a Multi-Teacher Knowledge Distillation (MTKD) framework to enhance CD performance. The O-P strategy partitions the training set by Change Area Ratio (CAR) and trains specialized teacher models on each subset. The MTKD framework then distills complementary knowledge from these teachers into a single student model, enabling improved detection results across diverse CAR scenarios without additional inference cost. Our MTKD approach demonstrated strong performance in the 2024 ``Jilin-1'' Cup challenge, ranking first in the preliminary and second in the final rounds. Extensive experiments on the JL1-CD and SYSU-CD datasets show that the MTKD framework consistently improves the performance of CD models with various network architectures and parameter sizes, establishing new state-of-the-art results. Code and dataset are available at https://github.com/circleLZY/MTKD-CD.

cs.CV↗

arXiv2024

Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences

Emotion recognition is an important part of affective computing. Extracting emotional cues from human gaits yields benefits such as natural interaction, a nonintrusive nature, and remote detection. Recently, the introduction of self-supervised learning techniques offers a practical solution to the issues arising from the scarcity of labeled data in the field of gait-based emotion recognition. However, due to the limited diversity of gaits and the incompleteness of feature representations for skeletons, the existing contrastive learning methods are usually inefficient for the acquisition of gait emotions. In this paper, we propose a contrastive learning framework utilizing selective strong augmentation (SSA) for self-supervised gait-based emotion representation, which aims to derive effective representations from limited labeled gait data. First, we propose an SSA method for the gait emotion recognition task, which includes upper body jitter and random spatiotemporal mask. The goal of SSA is to generate more diverse and targeted positive samples and prompt the model to learn more distinctive and robust feature representations. Then, we design a complementary feature fusion network (CFFN) that facilitates the integration of cross-domain information to acquire topological structural and global adaptive features. Finally, we implement the distributional divergence minimization loss to supervise the representation learning of the generally and strongly augmented queries. Our approach is validated on the Emotion-Gait (E-Gait) and Emilya datasets and outperforms the state-of-the-art methods under different evaluation protocols.

cs.CV↗

arXiv2021

Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images

Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguishing the samples of the minority class from the majority class. Previous methods generally aim to either learn a new feature space to map training samples together or to fit training samples by autoencoder-like models. These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized. In this paper, we propose a novel deep learning-based method to learn compact features by adding constraints on the bottleneck features, and to preserve descriptive features by training an autoencoder at the same time. Through jointly optimizing the constraining loss and the autoencoder's reconstruction loss, our method can learn more relevant features associated with the given class, making the majority and minority samples more distinguishable. Experimental results on three clinical datasets (including the MRI breast images, FFDM breast images and chest X-ray images) obtains state-of-art performance compared to previous methods.

eess.IV↗

arXiv2021

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional middle classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. All codes of this work will be publicly available upon acceptance of the paper.

eess.IV↗

arXiv2020

Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level Aligning

Recently, Unsupervised Domain Adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepany in the remote sensing images. To address this problem, this paper designs a novel end-to-end semantic segmentation network, namely Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images which usually involve various resolution images. The proposed SRDA-Net includes three parts: a Super-Resolution and Segmentation (SRS) model which focuses on recovering high-resolution image and predicting segmentation map, a Pixel-level Domain Classifier (PDC) for determining which domain the pixel belongs to, and an Output-space Domain Classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminates the resolution difference between source and target domains, but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net.

cs.CV↗

arXiv2020

A Fast Radio Burst discovered in FAST drift scan survey

We report the discovery of a highly dispersed fast radio burst, FRB~181123, from an analysis of $\sim$1500~hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0--1.5~GHz observing band. We measure the peak flux density to be $>0.065$~Jy and the corresponding fluence $>0.2$~Jy~ms. Based on the observed dispersion measure of 1812~cm$^{-3}$~pc, we infer a redshift of $\sim 1.9$. From this, we estimate the peak luminosity and isotropic energy to be $\lesssim 2\times10^{43}$~erg~s$^{-1}$ and $\lesssim 2\times10^{40}$~erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95\% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences $>0.025$~Jy~ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs.

astro-ph.HE↗

arXiv2019

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.

astro-ph.HE↗

arXiv2014

Simulation of background reduction and Compton depression in low-background HPGe spectrometer at a surface laboratory

High-purity germanium detectors are well suited to analysis the radioactivity of samples. In order to reduce the environmental background, low-activity lead and oxygen free copper are installed outside of the probe to shield gammas, outmost is a plastic scintillator to veto the cosmic rays, and an anti-Compton detector can improve the Peak-to-Compton ratio. Using the GEANT4 tools and taking into account a detailed description of the detector, we optimize the sizes of the detectors to reach the design indexes. A group of experimental data from a HPGe spectrometer in using were used to compare with the simulation. As to new HPGe Detector simulation, considering the different thickness of BGO crystals and anti-coincidence efficiency, the simulation results show that the optimal thickness is 5.5cm, and the Peak-to-Compton ratio of 40K is raised to 1000 when the anti-coincidence efficiency is 0.85. As the background simulation, 15 cm oxygen-free copper plus 10 cm lead can reduce the environmental gamma rays to 0.0024 cps/100 cm3 Ge (50keV~2.8MeV), which is about 10-5 of environmental background.

physics.ins-det↗

arXiv2011

Throughput and Delay Scaling in Supportive Two-Tier Networks

Consider a wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density $m=n^β$ with $β\geq 2$, which can only access the spectrum opportunistically to limit the interference to the primary tier. Based on the assumption that the secondary tier is allowed to route the packets for the primary tier, we investigate the throughput and delay scaling laws of the two tiers in the following two scenarios: i) the primary and secondary nodes are all static; ii) the primary nodes are static while the secondary nodes are mobile. With the proposed protocols for the two tiers, we show that the primary tier can achieve a per-node throughput scaling of $λ_p(n)=Θ(1/\log n)$ in the above two scenarios. In the associated delay analysis for the first scenario, we show that the primary tier can achieve a delay scaling of $D_p(n)=Θ(\sqrt{n^β\log n}λ_p(n))$ with $λ_p(n)=O(1/\log n)$. In the second scenario, with two mobility models considered for the secondary nodes: an i.i.d. mobility model and a random walk model, we show that the primary tier can achieve delay scaling laws of $Θ(1)$ and $Θ(1/S)$, respectively, where $S$ is the random walk step size. The throughput and delay scaling laws for the secondary tier are also established, which are the same as those for a stand-alone network.

cs.IT↗

arXiv2009

Transmission Capacities for Overlaid Wireless Ad Hoc Networks with Outage Constraints

We study the transmission capacities of two coexisting wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We define transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability (1 minus the outage probability). The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, where the SR network limits its interference to the PR network by carefully controlling the density of its transmitters. Assuming that the nodes are distributed according to Poisson point processes and the two networks use different transmission ranges, we quantify the transmission capacities for both of these two networks and discuss their tradeoff based on asymptotic analyses. Our results show that if the PR network permits a small increase of its outage probability, the sum transmission capacity of the two networks (i.e., the overall spectrum efficiency per unit area) will be boosted significantly over that of a single network.

cs.IT↗

arXiv2008

Delay-Throughput Tradeoff for Supportive Two-Tier Networks

Consider a static wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density $m=n^β$ with $β\geq 2$, which can only access the spectrum opportunistically to limit the interference to the primary tier. By allowing the secondary tier to route the packets for the primary tier, we show that the primary tier can achieve a throughput scaling of $λ_p(n)=Θ(1/\log n)$ per node and a delay-throughput tradeoff of $D_p(n)=Θ(\sqrt{n^β\log n}λ_p(n))$ for $λ_p(n)=O(1/\log n)$, while the secondary tier still achieves the same optimal delay-throughput tradeoff as a stand-alone network.

cs.IT↗

arXiv2008

Scaling Laws for Overlaid Wireless Networks: A Cognitive Radio Network vs. a Primary Network

We study the scaling laws for the throughputs and delays of two coexisting wireless networks that operate in the same geographic region. The primary network consists of Poisson distributed legacy users of density n, and the secondary network consists of Poisson distributed cognitive users of density m, with m>n. The primary users have a higher priority to access the spectrum without particular considerations for the secondary users, while the secondary users have to act conservatively in order to limit the interference to the primary users. With a practical assumption that the secondary users only know the locations of the primary transmitters (not the primary receivers), we first show that both networks can achieve the same throughput scaling law as what Gupta and Kumar [1] established for a stand-alone wireless network if proper transmission schemes are deployed, where a certain throughput is achievable for each individual secondary user (i.e., zero outage) with high probability. By using a fluid model, we also show that both networks can achieve the same delay-throughput tradeoff as the optimal one established by El Gamal et al. [2] for a stand-alone wireless network.

cs.IT↗