Infrared Physics & Technology最新文献

Research on model transfer strategies based on the fusion of NIR-MIR spectral data 基于近红外-红外光谱数据融合的模型转移策略研究

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-12 DOI: 10.1016/j.infrared.2024.105545

{"title":"Research on model transfer strategies based on the fusion of NIR-MIR spectral data","authors":"","doi":"10.1016/j.infrared.2024.105545","DOIUrl":"10.1016/j.infrared.2024.105545","url":null,"abstract":"<div><p>This study investigated the impact of different data fusion strategies on the performance of soluble solids content (SSC) prediction models based on near-infrared and mid-infrared spectroscopic techniques. In the data-level fusion approach, we applied standard normal variate and multiplicative scatter correction for pre-processing the NIR and MIR data. For the feature-level fusion, we utilized successive projections algorithm and competitive adaptive reweighted sampling to select informative wavelengths, and then applied direct orthogonal projection (DOP) for model transfer. The study employed a dataset of 150 honey samples to evaluate the impact of different data fusion strategies on model performance. To effectively evaluate model performance, we utilized the coefficient of R<sup>2</sup> and RMSEP as evaluation metrics. By comparing the results of data-level fusion, feature-level fusion and single-spectrum model transfer, the results showed that spectral data fusion improved the model transfer performance compared to the single-spectrum approach, with feature-level fusion exhibiting the most significant advantages. The effective variable selection techniques in feature-level fusion successfully removed a substantial amount of interfering data and significantly reduced noise influence, thereby improving the model accuracy. Specifically, the use of feature-level fusion improved the predictive model’s R<sup>2</sup> from 0.319 to 0.878 and reduced the RMSEP from 1.974 to 0.613°Brix, demonstrating the significant advantages of this approach in enhancing model transfer performance. The research findings provide important reference and theoretical support for future studies in the field of food quality assessment and other near-infrared spectroscopic data applications. This not only validates the effectiveness of the feature-level fusion approach, but also lays the foundation for establishing efficient and reliable predictive models.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensing performance comparison for temperature and liquid level sensor based on uniform and step-Etched microfiber TFBG 基于均匀和阶跃蚀刻超细纤维 TFBG 的温度和液位传感器的传感性能比较

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-10 DOI: 10.1016/j.infrared.2024.105554

引用次数: 0

6-ns level passively Q-switched Nd:KNaY (WO4)2 laser at 1.07 µm 1.07 微米波长的 6-ns 级被动 Q 开关 Nd:KNaY (WO4)2 激光器

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-10 DOI: 10.1016/j.infrared.2024.105553

引用次数: 0

High repetition-Rate laser diode side-Pumped burst-mode Nd:YAG laser with enhanced output pulse uniformity 输出脉冲均匀性更强的高重复率激光二极管侧泵浦猝发模式 Nd:YAG 激光器

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-08 DOI: 10.1016/j.infrared.2024.105551

引用次数: 0

A fast deployable model for crack identification with laser thermography testing 利用激光热成像测试识别裂纹的快速部署模型

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-07 DOI: 10.1016/j.infrared.2024.105552

引用次数: 0

GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion GAN-HA：带有新型异构双判别器网络的生成式对抗网络和基于注意力的红外与可见光图像融合新策略

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-03 DOI: 10.1016/j.infrared.2024.105548

{"title":"GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion","authors":"","doi":"10.1016/j.infrared.2024.105548","DOIUrl":"10.1016/j.infrared.2024.105548","url":null,"abstract":"<div><p>Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images. Thermal radiation information is mainly expressed through image intensities, while texture details are typically expressed through image gradients. However, existing dual-discriminator generative adversarial networks (GANs) often rely on two structurally identical discriminators for learning, which do not fully account for the distinct learning needs of infrared and visible image information. To this end, this paper proposes a novel GAN with a heterogeneous dual-discriminator network and an attention-based fusion strategy (GAN-HA). Specifically, recognizing the intrinsic differences between infrared and visible images, we propose, for the first time, a novel heterogeneous dual-discriminator network to simultaneously capture thermal radiation information and texture details. The two discriminators in this network are structurally different, including a salient discriminator for infrared images and a detailed discriminator for visible images. They are able to learn rich image intensity information and image gradient information, respectively. In addition, a new attention-based fusion strategy is designed in the generator to appropriately emphasize the learned information from different source images, thereby improving the information representation ability of the fusion result. In this way, the fused images generated by GAN-HA can more effectively maintain both the salience of thermal targets and the sharpness of textures. Extensive experiments on various public datasets demonstrate the superiority of GAN-HA over other state-of-the-art (SOTA) algorithms while showcasing its higher potential for practical applications.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temperature-dependent optical and dielectric properties of polyvinyl chloride and polystyrene blends in terahertz regime 太赫兹机制下聚氯乙烯和聚苯乙烯混合物随温度变化的光学和介电特性

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-01 DOI: 10.1016/j.infrared.2024.105546

引用次数: 0

Addendum to “Perturbation mediated forbidden transitions in the in-plane rocking mode of O-18 substituted methanol: Very high-resolution Fourier transform spectroscopy using globar and synchrotron radiation sources in the 10-µm region” [Infrared Phys. Technol. 128 (2023) 104525] "O-18 取代的甲醇平面内摇摆模式中的扰动介导禁止跃迁：在 10 微米区域使用球杆和同步辐射源的极高分辨率傅立叶变换光谱" [Infrared Phys. Technol.

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-01 DOI: 10.1016/j.infrared.2024.105483

引用次数: 0

Corrigendum to “A point cloud denoising network based on manifold in an unknown noisy environment” [Infrared Phys. Technol. 132 (2023) 104735] 对 "未知噪声环境下基于流形的点云去噪网络 "的更正[红外物理技术. 132 (2023) 104735]

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-01 DOI: 10.1016/j.infrared.2023.105059

引用次数: 0

Identification of millet origin using terahertz spectroscopy combined with ensemble learning 利用太赫兹光谱与集合学习相结合识别小米产地

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-09-01 DOI: 10.1016/j.infrared.2024.105547

{"title":"Identification of millet origin using terahertz spectroscopy combined with ensemble learning","authors":"","doi":"10.1016/j.infrared.2024.105547","DOIUrl":"10.1016/j.infrared.2024.105547","url":null,"abstract":"<div><p>It’s crucial for both producers and consumers to accurately trace the origin of millet, given the significant differences in price and taste that exist between millets from various origins. The traditional method of identifying the origin of millet is time-consuming, laborious, complex, and destructive. In this study, a new method for fast and non-destructive differentiation of millet origins is developed by combining terahertz time domain spectroscopy with ensemble learning. Firstly, three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and kernel extreme learning machine (KELM), were used to build different discriminative models, and then the impact of six different preprocessing methods on the models’ classification performance was compared. It was observed that models employing Savitzky-Golay preprocessing exhibited pronounced superiority in accurately determining the millet’s geographical origins. Building upon these findings, the research introduces an innovative ensemble learning strategy, leveraging both topsis and stacking techniques, to harness the collective strengths of the three algorithms. The outcomes of this approach reveal its remarkable capacity to distinguish millets originating from five distinct locations without the necessity for any parameter fine-tuning. The accuracy, F1 score, and Kappa on the prediction set are all 100 %, which significantly outperforms the single model, traditional voting method, and stacking method. The culmination of this study suggests that the integration of terahertz time-domain spectroscopy and TOPSIS-Stacking ensemble learning emerges as a promising method for the swift and non-intrusive discrimination of millet geographical origins with remarkable precision.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0