Infrared Physics & Technology最新文献_第9页

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

Naima Farman, Muhammad Mumtaz, M. Ahsan Mahmood, A.H. Dogar, Sadia Tahir, Kashif Raza, Izhar Ahmad

{"title":"Temperature-dependent optical and dielectric properties of polyvinyl chloride and polystyrene blends in terahertz regime","authors":"Naima Farman, Muhammad Mumtaz, M. Ahsan Mahmood, A.H. Dogar, Sadia Tahir, Kashif Raza, Izhar Ahmad","doi":"10.1016/j.infrared.2024.105546","DOIUrl":"10.1016/j.infrared.2024.105546","url":null,"abstract":"<div><p>Terahertz time-domain spectroscopy (THz-TDS) has been used to study the temperature-dependent refractive index, absorption coefficient and dielectric constant of polyvinyl chloride/polystyrene (PVC/PS) blends in frequency range of 0.2–1.8 THz. Moreover, the Sellmeier and thermo optic coefficients of PVC/PS blends with different weight ratios have been explored in the temperature range of 25–80 °C. These parameters are used to evaluate the dispersion properties of these blends in the observed frequency range. A clear indication of temperature dependence on the values of refractive index and the real dielectric constant of these blends have been observed. Their values decrease linearly with increasing temperature up to 80 °C. Whereas, no noticeable change has been observed in the imaginary dielectric constant and the absorption coefficient. These results provide a database of temperature-dependent optical and dielectric parameters of PVC/PS polymer blends for their efficient utilization for device fabrication in THz technology.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105546"},"PeriodicalIF":3.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130108","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

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

Indranath Mukhopadhyay , B.E. Billinghurst

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

Zhenglei Li, Weigang Pan, Shuxin Wang, Xiangmeng Tang, Hao Hu

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

Xianhua Yin, Hao Tian, Fuqiang Zhang, Chuanpei Xu, Linkai Tang, Yongbing Wei

{"title":"Identification of millet origin using terahertz spectroscopy combined with ensemble learning","authors":"Xianhua Yin, Hao Tian, Fuqiang Zhang, Chuanpei Xu, Linkai Tang, Yongbing Wei","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":"142 ","pages":"Article 105547"},"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

YOLO-SGF: Lightweight network for object detection in complex infrared images based on improved YOLOv8 YOLO-SGF：基于改进型 YOLOv8 的用于复杂红外图像中物体检测的轻量级网络

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-31 DOI: 10.1016/j.infrared.2024.105539

Cong Guo, Kan Ren, Qian Chen

{"title":"YOLO-SGF: Lightweight network for object detection in complex infrared images based on improved YOLOv8","authors":"Cong Guo, Kan Ren, Qian Chen","doi":"10.1016/j.infrared.2024.105539","DOIUrl":"10.1016/j.infrared.2024.105539","url":null,"abstract":"<div><p>The current mainstream object detection networks perform well in RGB visible images, but they require high computational resource and degrade in performance when applied to low-resolution infrared images. To address above issues, we propose a lightweight algorithm YOLO-SGF based on you-only-look-once version8 (YOLOv8). Firstly, the lightweight cross-scale feature map fusion network GCFVoV designed as neck to solve poor detection accuracy and maintain low complexity in lightweight networks. And a lightweight GCVF module in GCFVoV neck uses GSConv and Conv to process deep and shallow features respectively, which maximally preserves implicit connections between each channel and integrates multi-scale features. Secondly, we utilize ShuffleNetV2-block1 in combination with C2f for feature extraction, making the algorithm more lightweight and effectively. Finally, we propose the FIMPDIoU loss function, which focuses on overlooked objects in complex backgrounds and adjusts the prediction boxes using ratios specific to different sizes of objects. Compared with YOLOv8 in our infrared dataset, YOLO-SGF reduces the computational space complexity by 50 % and time complexity by 42 %, increases FPS<sub>32</sub> by 36.3 % and improves [email protected] ∼ 0.95 by 1.1 % in object detection. Our algorithm enhances the capability of object detection in infrared images especially in nighttime, low light, and occluded conditions. YOLO-SGF enables deployment on embedded edge devices with limited computing power, and provides a new idea for lightweight networks.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105539"},"PeriodicalIF":3.1,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162940","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

Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum 基于中红外光谱一次衍射的煤矸石分类和煤炭类型鉴定

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-31 DOI: 10.1016/j.infrared.2024.105537

Zekun Li , Leiying Xie , Ruonan Ji , Yuanping Chen , Shaowei Wang

{"title":"Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum","authors":"Zekun Li , Leiying Xie , Ruonan Ji , Yuanping Chen , Shaowei Wang","doi":"10.1016/j.infrared.2024.105537","DOIUrl":"10.1016/j.infrared.2024.105537","url":null,"abstract":"<div><p>Efficiently sorting coal gangue and identifying coal types are vital operations in coal preparation, yet they are traditionally resource-consuming, labor-intensive, and potentially hazardous. This work puts forward an straightforward method employing mid-infrared spectroscopy with first derivative spectrum to address these issues. The proposed technique focuses on the delineation and enhancement of characteristic spectra to detect subtle differences among samples. The method utilizes just a few characteristic spectra of 3740–3700 cm<sup>−1</sup>, 1790–1750 cm<sup>−1</sup>, 1615–1583 cm<sup>−1</sup>, 1580–1540 cm<sup>−1</sup>, 1550–1440 cm<sup>−1</sup>, 1270–1210 cm<sup>−1</sup> and 867–854 cm<sup>−1</sup> to achieve 100 % high-accuracy classification of coal gangue and identification of coal types with total 250 spectra, such as bituminite, anthracite, lignite, roof sandstone and gangue, without the need for secondary sample processing or the assistance of machine learning algorithms, simplifying the process considerably. Such a strategy not only significantly improves the efficiency of coal sorting but also endorses real-time on-site detection. It offers a theoretical foundation for advanced coal separation technology and its implementation in real-world mining operations.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105537"},"PeriodicalIF":3.1,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168850","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

SeACPFusion: An Adaptive Fusion Network for Infrared and Visible Images based on brightness perception SeACPFusion：基于亮度感知的红外和可见光图像自适应融合网络

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-30 DOI: 10.1016/j.infrared.2024.105541

Wangjie Li , Xiaoyi Lv , Yaoyong Zhou , Yunling Wang , Min Li

{"title":"SeACPFusion: An Adaptive Fusion Network for Infrared and Visible Images based on brightness perception","authors":"Wangjie Li , Xiaoyi Lv , Yaoyong Zhou , Yunling Wang , Min Li","doi":"10.1016/j.infrared.2024.105541","DOIUrl":"10.1016/j.infrared.2024.105541","url":null,"abstract":"<div><p>Generating a single fused image that highlights important targets and preserves textural details is the aim of fusing visible and infrared images. The majority of deep learning-based fusion algorithms now in use can produce decent fusion outcomes; however, the modeling process still lacks consideration of the different amounts of information in different scenes or regions. Thus, we propose in this research SeACPFusion, a luminance-aware adaptive fusion network for infrared and visible images, which adaptively preserves the intensity information of the noticeable targets of the source images with the texture information of the background in an optimal ratio. Specifically, we design pixel-level luminance loss (PBL) to direct the fusion model’s training in real-time, and PBL retains the optimal intensity information according to the pixel luminance ratio of different source images. In addition, we designed the Channel Transformer (CTF) to consider the relationship between different attributes from the point of view of the feature channel and to focus on the key information by using the self-focusing mechanism to achieve the goal of adaptive fusion. Our extensive tests on the MSRS, RoadScene, and TNO datasets demonstrate that SeACPFusion surpasses nine representative deep learning methods on six objective metrics and achieves the best visual results in scenes such as overexposure or underexposure. In addition, the relatively efficient operation and fewer model parameters make our algorithm promising as a preprocessing module for downstream complicated vision tasks.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105541"},"PeriodicalIF":3.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122125","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

Lab-based scale measurements of internal storage of crude oil tank based on non-contact infrared thermography technique 基于非接触式红外热成像技术的实验室原油储罐内部存储测量

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-30 DOI: 10.1016/j.infrared.2024.105543

Hong-Wei Chen, Yu-Jun Guo, Yang Li, Yao-Yu Wei

引用次数: 0

Study of the n− region formation process in n-on-p HgCdTe devices n 对 p 碲化镉汞器件中 n 区形成过程的研究

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-30 DOI: 10.1016/j.infrared.2024.105544

Deming Kong , Yu Zhao , Yuan Dong , Yang Qiu , Shaonan Zheng , Qize Zhong , Quanzhi Sun , Liqi Zhu , Zhikai Gan , Xingyan Zhao , Ting Hu

引用次数: 0

SMALNet: Segment Anything Model Aided Lightweight Network for Infrared Image Segmentation SMALNet：用于红外图像分段的分段任何模型辅助轻量级网络

IF 3.1 3区物理与天体物理

Infrared Physics & Technology Pub Date : 2024-08-30 DOI: 10.1016/j.infrared.2024.105540

Kun Ding , Shiming Xiang , Chunhong Pan

{"title":"SMALNet: Segment Anything Model Aided Lightweight Network for Infrared Image Segmentation","authors":"Kun Ding , Shiming Xiang , Chunhong Pan","doi":"10.1016/j.infrared.2024.105540","DOIUrl":"10.1016/j.infrared.2024.105540","url":null,"abstract":"<div><p>Infrared based visual perception is important for night vision of autonomous vehicles, unmanned aerial vehicles (UAVs), etc. Semantic segmentation based on deep learning is one of the key techniques for infrared vision-based perception systems. Currently, most of the advanced methods are based on Transformers, which can achieve favorable segmentation accuracy. However, the high complexity of Transformers prevents them from meeting the real-time requirement of inference speed in resource constrained applications. In view of this, we suggest several lightweight designs that significantly reduce existing computational complexity. In order to maintain the segmentation accuracy, we further introduce the recent vision big model — Segment Anything Model (SAM) to supply auxiliary supervisory signals while training models. Based on these designs, we propose a lightweight segmentation network termed SMALNet (<u>S</u>egment Anything <u>M</u>odel <u>A</u>ided <u>L</u>ightweight <u>N</u>etwork). Compared to existing state-of-the-art method, SegFormer, it reduces 64% FLOPs while maintaining the accuracy to a large extent on two commonly-used benchmarks. The proposed SMALNet can be used in various infrared based vision perception systems with limited hardware resources.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105540"},"PeriodicalIF":3.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097688","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