A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm

• URL: http://arxiv.org/abs/2512.22792v2
• Date: Mon, 05 Jan 2026 13:46:47 GMT
• Title: A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm
• Authors: Shuai Chen, Yang Song, Chen Wang, Ziran Wang,
• Abstract summary: This study proposes the Spherical Normalization coupled Mahalanobis (SNM) module, a universal post-processing module for open-set gas recognition.<n>Experiments on the Vergara dataset demonstrate that the Transformer+SNM configuration achieves near-theoretical-limit performance in discriminating among multiple target gases.
• Score: 16.05394499283827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure induced by unknown gas interference. Existing methods predominantly rely on Euclidean distance or conventional classifiers, failing to account for anisotropic feature distributions and dynamic signal intensity variations. To address these issues, this study proposes the Spherical Normalization coupled Mahalanobis (SNM) module, a universal post-processing module for open-set gas recognition. First, it achieves geometric decoupling through cascaded batch and L2 normalization, projecting features onto a unit hypersphere to eliminate signal intensity fluctuations. Second, it utilizes Mahalanobis distance to construct adaptive ellipsoidal decision boundaries that conform to the anisotropic feature geometry. The architecture-agnostic SNM-Module seamlessly integrates with mainstream backbones including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer. Experiments on the public Vergara dataset demonstrate that the Transformer+SNM configuration achieves near-theoretical-limit performance in discriminating among multiple target gases, with an AUROC of 0.9977 and an unknown gas detection rate of 99.57% at 5% false positive rate, significantly outperforming state-of-the-art methods with a 3.0% AUROC improvement and 91.0% standard deviation reduction compared to Class Anchor Clustering (CAC). The module maintains exceptional robustness across five sensor positions, with standard deviations below 0.0028. This work effectively addresses the critical challenge of simultaneously achieving high accuracy and high stability in open-set gas recognition, providing solid support for industrial E-nose deployment.

Related papers

• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection [7.227431306238601]
  We propose the Cascaded Early Rejection (CER) framework, which realizes hierarchical filtering for anomaly detection via a coarse-to-fine logic.<n> Experimental results demonstrate that CER not only reduces computational overhead by 32% but also achieves a significant performance leap on the CIFAR-100 benchmark.
  arXiv  Detail & Related papers  (2026-02-06T02:55:35Z)
• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification [47.20483076887704]
  Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference.<n>We propose a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD)<n>We show that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification.
  arXiv  Detail & Related papers  (2026-01-19T07:42:45Z)
• HCFT: Hierarchical Convolutional Fusion Transformer for EEG Decoding [9.572621097681646]
  We propose a lightweight decoding framework named Hierarchical Conencephaloal Fusion Transformer (HCFT)<n>HCFT combines dual-branchal encoders and hierarchical Transformer blocks for multi-scale representation.<n>Results show that HCFT achieves 80.83% average accuracy and a Cohen's kappa of 0.6165 on BCI IV-2b, as well as 99.10% sensitivity, 0.0236 false positives per hour, and 98.82% specificity on CHB-MIT.
  arXiv  Detail & Related papers  (2026-01-18T06:36:30Z)
• ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM [5.5217350574838875]
  We present ICD-Net, a novel framework that enhances visual-inertial SLAM performance.<n>Our approach significantly improved trajectory estimation accuracy compared to standard VINS-Fusion.<n>Our method shows that neural network enhancement can effectively address multiple sources of SLAM degradation.
  arXiv  Detail & Related papers  (2025-11-13T11:25:02Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• Foggy Crowd Counting: Combining Physical Priors and KAN-Graph [19.690003059392893]
  This paper proposes a crowd-counting method with a physical a priori of atmospheric scattering.<n>It achieves a 12.2%-27.5% reduction in MAE metrics compared to mainstream algorithms in dense fog scenarios.
  arXiv  Detail & Related papers  (2025-09-29T09:59:36Z)
• Fluid Antenna System-assisted Physical Layer Secret Key Generation [64.92952968689636]
  This paper investigates physical-layer generation (PLKG) in multiant base station systems by leveraging a fluid antenna system (FAS) to dynamically radio environments.<n>We propose an assisted PLKG model that integrates transmit beamforming and port selection under independent and spatially correlated environments.<n>It is shown that the sliding window-based port selection method introduced in this paper achieves higher KGR with fewer chains through dynamic port selection.
  arXiv  Detail & Related papers  (2025-09-19T03:01:29Z)
• Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization [4.674584508653125]
  Jamming devices disrupt signals from the global navigation satellite system (GNSS)<n>Classical Angle of Arrival (AoA) methods exhibit reduced accuracy in multipath environments.<n>We propose a novel approach for detecting and classifying interference while estimating the distance, azimuth, and elevation of jamming sources.
  arXiv  Detail & Related papers  (2025-07-09T06:49:11Z)
• Differentiable NMS via Sinkhorn Matching for End-to-End Fabric Defect Detection [2.2901095820275317]
  NMS framework for fabric defect detection achieves superior localization precision through end-to-end optimization.<n>We reformulate NMS as a differentiable bipartite matching problem solved through the Sinkhorn-Knopp algorithm.<n>Experiments on the Tianchi fabric defect dataset demonstrate significant performance improvements over existing methods.
  arXiv  Detail & Related papers  (2025-05-11T16:22:58Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation [17.289252835606533]
  Uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed.<n> UDHF2-Net is a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP)<n>Mask-and-geo-knowledge-based uncertainty diffusion module (MUDM) is a self-supervised learning strategy.<n>A frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection.
  arXiv  Detail & Related papers  (2024-06-23T15:03:35Z)
• Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations [0.22499166814992438]
  We propose a method to inherently enhance the robustness and inference accuracy of BayNNs deployed in in-memory computing architectures. Empirical results show a graceful degradation in inference accuracy, with an improvement of up to $58.11%$.
  arXiv  Detail & Related papers  (2024-01-23T00:27:31Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior [54.629850694790036]
  spectral-normalized identity priors (SNIP) is a structured pruning approach that penalizes an entire residual module in a Transformer model toward an identity mapping. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance.
  arXiv  Detail & Related papers  (2020-10-05T05:40:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.