Pub Date : 2024-10-20DOI: 10.1016/j.infrared.2024.105596
Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.
{"title":"Learning infrared degradations for coherent visible image fusion in the undecimated dual-tree complex wavelet domain","authors":"","doi":"10.1016/j.infrared.2024.105596","DOIUrl":"10.1016/j.infrared.2024.105596","url":null,"abstract":"<div><div>Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532484","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}
Pub Date : 2024-10-19DOI: 10.1016/j.infrared.2024.105598
Two-dimensional (2D) NbSe2 is a new material with a variety of excellent properties. In this article, 2D NbSe2 nanosheets are prepared using liquid phase exfoliation (LPE) and spin coating methods. At the same time, the properties of NbSe2 were calculated by using density functional theory (DFT), exploring the changes in the electronic band structure of NbSe2 with the number of layers, and studying the optical properties of NbSe2. The nonlinear optical properties caused by the Pauli blocking effect and the absorption spectra are studied through typical nonlinear testing techniques and an ultraviolet–visible-near-infrared (UV–VIS-IR) spectrophotometer. In addition, 2 µm solid-state pulse lasers have important applications in a variety of fields. For the first time, 2D NbSe2 nanosheets are prepared as saturable absorbers (SA) and applied them to solid-state lasers as nonlinear optical modulation devices, successfully achieving the generation of ultra-short pulse lasers with a pulse duration of 445.4 ps in 2 µm band. Our research results prove that 2D NbSe2 nanosheets is a promising nanomaterial, can be prepared into nonlinear optical modulation devices with excellent performance, and show great application potential as ultrafast photonic devices. It is beneficial to the miniaturization of solid-state pulse lasers in subsequent applications.
{"title":"Nonlinear optical research on 2D NbSe2 nanosheets and their ultrafast photonics applications","authors":"","doi":"10.1016/j.infrared.2024.105598","DOIUrl":"10.1016/j.infrared.2024.105598","url":null,"abstract":"<div><div>Two-dimensional (2D) NbSe<sub>2</sub> is a new material with a variety of excellent properties. In this article, 2D NbSe<sub>2</sub> nanosheets are prepared using liquid phase exfoliation (LPE) and spin coating methods. At the same time, the properties of NbSe<sub>2</sub> were calculated by using density functional theory (DFT), exploring the changes in the electronic band structure of NbSe<sub>2</sub> with the number of layers, and studying the optical properties of NbSe<sub>2</sub>. The nonlinear optical properties caused by the Pauli blocking effect and the absorption spectra are studied through typical nonlinear testing techniques and an ultraviolet–visible-near-infrared (UV–VIS-IR) spectrophotometer. In addition, 2 µm solid-state pulse lasers have important applications in a variety of fields. For the first time, 2D NbSe<sub>2</sub> nanosheets are prepared as saturable absorbers (SA) and applied them to solid-state lasers as nonlinear optical modulation devices, successfully achieving the generation of ultra-short pulse lasers with a pulse duration of 445.4 ps in 2 µm band. Our research results prove that 2D NbSe<sub>2</sub> nanosheets is a promising nanomaterial, can be prepared into nonlinear optical modulation devices with excellent performance, and show great application potential as ultrafast photonic devices. It is beneficial to the miniaturization of solid-state pulse lasers in subsequent applications.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531967","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}
Pub Date : 2024-10-18DOI: 10.1016/j.infrared.2024.105593
Infrared spectral data often exhibit band overlap and random noise when it is applied to recognize the unknown chemical materials. To address these issues, a novel regularization-based spectral deconvolution method for unknown chemical material detection (DWTSD) was proposed in this paper. The discrete wedgelet transform is introduced to analyze the difference between the latent infrared spectrum and the noisy infrared spectrum. The instrument response function is also needed to estimate simultaneously with the latent infrared spectrum. Therefore, the improved total variation regularization is introduced to constrain the smoothness of the spectral lines. Then the split Bregman iteration algorithm is also introduced to optimize the cost function. The proposed DWTSD method is simple and offers good performance with low computational load. Experimental results on simulated and real infrared spectrums show that the proposed DWTSD method has good performance in noise reduction and spectral detail generation. With the proposed methodology, the problem of instrument aging can be largely eliminated, making the reconstruction of infrared spectra a more convenient tool for the extraction of features of an unknown material and their interpretation. The applicability of the method transcends infrared spectroscopy, offering utility in a spectrum of spectroscopic analyses.
{"title":"Discrete wedgelet transform regularization-based spectral deconvolution for infrared spectroscopy","authors":"","doi":"10.1016/j.infrared.2024.105593","DOIUrl":"10.1016/j.infrared.2024.105593","url":null,"abstract":"<div><div>Infrared spectral data often exhibit band overlap and random noise when it is applied to recognize the unknown chemical materials. To address these issues, a novel regularization-based spectral deconvolution method for unknown chemical material detection (DWTSD) was proposed in this paper. The discrete wedgelet transform is introduced to analyze the difference between the latent infrared spectrum and the noisy infrared spectrum. The instrument response function is also needed to estimate simultaneously with the latent infrared spectrum. Therefore, the improved total variation regularization is introduced to constrain the smoothness of the spectral lines. Then the split Bregman iteration algorithm is also introduced to optimize the cost function. The proposed DWTSD method is simple and offers good performance with low computational load. Experimental results on simulated and real infrared spectrums show that the proposed DWTSD method has good performance in noise reduction and spectral detail generation. With the proposed methodology, the problem of instrument aging can be largely eliminated, making the reconstruction of infrared spectra a more convenient tool for the extraction of features of an unknown material and their interpretation. The applicability of the method transcends infrared spectroscopy, offering utility in a spectrum of spectroscopic analyses.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532482","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}
Pub Date : 2024-10-18DOI: 10.1016/j.infrared.2024.105591
In practical application scenarios, maritime infrared videos often contain different types of sea state scenes, weather conditions, shooting time, shooting distance, etc. In these different types of maritime videos, ship targets have great differences in size, grayscale distribution and contrast, which brings difficulties to ship target detection. At the same time, the diversity of fluctuation, grayscale distribution and reflected light of the sea surface will bring unpredictable interference noise to ship target detection. How to accurately detect ship targets in complex and changeable maritime infrared videos is a challenging task and research focus. The key to achieving accurate ship detection is to extract robust target features that can effectively distinguish targets from all the background noises. In this paper, a novel infrared video ship target detection algorithm based on spatiotemporal slice target trajectory features extraction is proposed. The algorithm is very sensitive to real targets and has excellent anti-noise ability. The main innovation of the algorithm is to extract the target trajectory feature from the spatiotemporal slice of the sequence image and generate the trajectory feature map. We use the trajectory texture formed by the ship target in the spatiotemporal slice to extract the target feature, which can greatly suppress the background noise. The adaptive dilation linear model algorithm can effectively detect the target trajectory line in the spatiotemporal slice. In addition, we also make full use of the gradient of the target trajectory line to distinguish different target trajectory pixels, and propose an adaptive iterative dilation target region localization algorithm combined with gradient consistency. For object segmentation, we calculate the segmentation double-threshold using the adjacent surrounding background pixels of the target, so as to achieve target segmentation of multiple grayscale distribution types. Finally, in the comparison experiment, our algorithm shows superior target detection performance, especially when detecting ships from a large number of highlighted sea clutter background, the robust anti-noise ability of the algorithm can be highlighted.
{"title":"Moving ships detection via the trajectory feature extraction from spatiotemporal slices of infrared maritime videos","authors":"","doi":"10.1016/j.infrared.2024.105591","DOIUrl":"10.1016/j.infrared.2024.105591","url":null,"abstract":"<div><div>In practical application scenarios, maritime infrared videos<!--> <!-->often contain different types of sea state scenes, weather conditions, shooting time, shooting distance, etc. In these different types of maritime videos, ship targets have great differences in size, grayscale<!--> <!-->distribution and contrast, which brings difficulties to ship target detection.<!--> <!-->At the same time, the diversity of fluctuation, grayscale distribution and reflected light of the sea surface will bring unpredictable interference noise to ship target detection.<!--> <!-->How to accurately detect ship targets in complex and changeable maritime infrared videos is a challenging task and research focus.<!--> <!-->The key to achieving accurate ship detection is to extract robust target features that can effectively distinguish targets from all the<!--> <!-->background noises.<!--> <!-->In this paper, a novel infrared video ship target detection algorithm based on spatiotemporal slice target trajectory features extraction is proposed. The algorithm is very sensitive to real targets and has excellent<!--> <!-->anti-noise ability.<!--> <!-->The main innovation of the algorithm is to extract the target trajectory feature from the spatiotemporal slice of the sequence image and generate the trajectory feature map.<!--> <!-->We use the trajectory texture formed by the ship target in the spatiotemporal slice to extract the target feature, which can greatly suppress the background noise.<!--> <!-->The adaptive dilation linear model algorithm can effectively detect the target trajectory line in the spatiotemporal slice. In addition, we also make full use of the gradient of the target trajectory line to distinguish different target trajectory pixels, and propose an adaptive iterative dilation target region localization algorithm combined with gradient consistency.<!--> <!-->For object segmentation, we calculate the segmentation double-threshold using the adjacent surrounding background pixels of the target, so as to achieve<!--> <!-->target segmentation<!--> <!-->of multiple grayscale<!--> <!-->distribution types. Finally, in the comparison experiment, our algorithm shows superior target detection performance, especially when detecting ships from a large number of highlighted sea clutter background, the robust anti-noise ability of the algorithm can be highlighted.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531966","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}
Pub Date : 2024-10-18DOI: 10.1016/j.infrared.2024.105594
Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.
{"title":"Study on bionics-based swarm intelligence optimization algorithms for wavelength selection in near-infrared spectroscopy","authors":"","doi":"10.1016/j.infrared.2024.105594","DOIUrl":"10.1016/j.infrared.2024.105594","url":null,"abstract":"<div><div>Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531965","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}
Pub Date : 2024-10-17DOI: 10.1016/j.infrared.2024.105582
In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model. The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.
在这项工作中，我们继续开发和研究热冲击响应谱（TSRS）方法，将其作为红外热成像（IRT）的替代数据处理方法。我们重点改进了当前的 TSRS 算法，并基于广泛应用的随机抽样方法，提出了寻找最佳热 Q 因子和特征频率对的优化方法。我们展示了通过选择特定的一维热 N 层模型计算得出的确定的最佳特征频率与参考模型和缺陷模型之间扩散长度的相应最大差异之间的定性关系。研究是在碳纤维增强聚合物（CFRP）制成的非均质板上进行的，该板在不同深度上存在人工方形缺陷。此外，还使用了两种不同的热源：氙闪灯和激光。这些热源不仅在基本物理特性上存在差异，而且产生的脉冲形状也各不相同。为了定量估计缺陷和非缺陷区域之间的对比度，并将这些结果与常用的红外热成像（IRT）数据后处理方法（如脉冲相位热成像（PPT）和热成像信号重建（TSR））进行比较，我们使用了谷本标准（TC）和信噪比（SNR）。
{"title":"Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model","authors":"","doi":"10.1016/j.infrared.2024.105582","DOIUrl":"10.1016/j.infrared.2024.105582","url":null,"abstract":"<div><div>In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model. The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.infrared.2024.105590
We propose a gate-controlled device structure for mesa-type infrared photon detectors with the means of improving surface conditions. Additional terminal added to the pn-junction allows surface charges to be manipulated by applying a constant E-field through a metal–oxide–semiconductor (MOS) structure. Short-wave infrared (SWIR) Indium Gallium Arsenide (InGaAs) sample with a cut-off wavelength of 1.69 m is characterized. A theoretical framework is provided to gating mechanism. Experimental results show that, the shunt component of the dark current improved as high as 63% by interrupting the channel formation on the surface. Further improvements in the generation–recombination (GR) current is noted at more than 90% at 300 K. The effective GR lifetime of 20 s is obtained under 50 V/m surface-gate bias.
{"title":"Active Surface Passivation for mesa type short-wave infrared InGaAs Photodetectors","authors":"","doi":"10.1016/j.infrared.2024.105590","DOIUrl":"10.1016/j.infrared.2024.105590","url":null,"abstract":"<div><div>We propose a gate-controlled device structure for mesa-type infrared photon detectors with the means of improving surface conditions. Additional terminal added to the pn-junction allows surface charges to be manipulated by applying a constant E-field through a metal–oxide–semiconductor (MOS) structure. Short-wave infrared (SWIR) Indium Gallium Arsenide (InGaAs) sample with a cut-off wavelength of 1.69 <span><math><mi>μ</mi></math></span>m is characterized. A theoretical framework is provided to gating mechanism. Experimental results show that, the shunt component of the dark current improved as high as 63% by interrupting the channel formation on the surface. Further improvements in the generation–recombination (GR) current is noted at more than 90% at 300 K. The effective GR lifetime of 20 <span><math><mi>μ</mi></math></span>s is obtained under 50 V/<span><math><mi>μ</mi></math></span>m surface-gate bias.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531963","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}
Pub Date : 2024-10-10DOI: 10.1016/j.infrared.2024.105584
Objective
Angelica sinensis is one of the commonly used Chinese herbal medicine in traditional Chinese medicine clinic, exhibits different pharmacological characteristics due to variations in the content of active ingredients in its head, body, and tail. Therefore, research on the identification methods of different medicinal parts of Angelica sinensis is of great practical significance. Terahertz Time-Domain Spectroscopy (THz-TDS) technology is widely used in the field of nondestructive testing because of its unique electromagnetic wave characteristics. This study explores the feasibility of combining THz-TDS with chemometrics to identify different medicinal parts of Angelica sinensis.
Methods
By comparing the spectral response characteristics of different parts of Angelica sinensis to various optical parameters, the absorption coefficient spectrum in the 0.6–3.0 THz range was selected, and three types of feature extraction algorithms, namely, joint Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA), were used to establish the classification models of Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM) in turn, and optimize the models by using the crown porcupine algorithm (Crested Porcupine Optimizer (CPO) to optimize the model.
Results
The research results indicate that the CPO optimizer significantly improved the classification accuracy of the models, with the accuracy of the ELM, RF, and SVM models increasing by 4.36%, 1.11%, and 12.22%, respectively. The SPA-CPO-SVM model exhibited the best overall performance, achieving accuracies of 96.11% and 97.96% on the prediction and training sets, respectively, while the number of input features was only 5% of the total feature set.
Conclusion
The results show that the fully joint feature extraction strategy and optimization algorithm can play a powerful synergistic effect in model construction, confirming the feasibility of THz-TDS technology to correctly identify different medicinal parts of Angelica sinensis, and providing an important reference for the application of terahertz technology in the identification of Chinese herbal medicines.
{"title":"Application of terahertz time-domain spectroscopy and chemometrics-based crested porcupine algorithm in identification of different medicinal parts of Angelica sinensis","authors":"","doi":"10.1016/j.infrared.2024.105584","DOIUrl":"10.1016/j.infrared.2024.105584","url":null,"abstract":"<div><h3>Objective</h3><div>Angelica sinensis is one of the commonly used Chinese herbal medicine in traditional Chinese medicine clinic, exhibits different pharmacological characteristics due to variations in the content of active ingredients in its head, body, and tail. Therefore, research on the identification methods of different medicinal parts of Angelica sinensis is of great practical significance. Terahertz Time-Domain Spectroscopy (THz-TDS) technology is widely used in the field of nondestructive testing because of its unique electromagnetic wave characteristics. This study explores the feasibility of combining THz-TDS with chemometrics to identify different medicinal parts of Angelica sinensis.</div></div><div><h3>Methods</h3><div>By comparing the spectral response characteristics of different parts of Angelica sinensis to various optical parameters, the absorption coefficient spectrum in the 0.6–3.0 THz range was selected, and three types of feature extraction algorithms, namely, joint Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA), were used to establish the classification models of Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM) in turn, and optimize the models by using the crown porcupine algorithm (Crested Porcupine Optimizer (CPO) to optimize the model.</div></div><div><h3>Results</h3><div>The research results indicate that the CPO optimizer significantly improved the classification accuracy of the models, with the accuracy of the ELM, RF, and SVM models increasing by 4.36%, 1.11%, and 12.22%, respectively. The SPA-CPO-SVM model exhibited the best overall performance, achieving accuracies of 96.11% and 97.96% on the prediction and training sets, respectively, while the number of input features was only 5% of the total feature set.</div></div><div><h3>Conclusion</h3><div>The results show that the fully joint feature extraction strategy and optimization algorithm can play a powerful synergistic effect in model construction, confirming the feasibility of THz-TDS technology to correctly identify different medicinal parts of Angelica sinensis, and providing an important reference for the application of terahertz technology in the identification of Chinese herbal medicines.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572616","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}
Pub Date : 2024-10-10DOI: 10.1016/j.infrared.2024.105580
Laser output power can be significantly enhanced by a double-cladding fiber, as it possesses a large area of inner-cladding for pumping. However, the coupling efficiency of the double-cladding fiber (DCF) is impacted by the shape of the inner cladding. In this study, the simulation results showed that, the hexagonal inner cladding fiber exhibits the highest absorption efficiency of 94.07 % among the fibers with various typical shapes using the three-dimensional ray tracing method at a length of 100 mm. For the experiments, a type of fluorotellurite (TBY, TeO2-BaF2-Y2O3) glass was chosen for its high capacity of rare-earth ions adoption. Subsequently, a finely structured erbium-doped hexagonal DCF was fabricated based on the hot-extrusion method for the first time, with a minimum loss of 1.25 dB/m at 1310 nm. Additionally, the coupling efficiency of the hexagonal DCF was recorded to be 39.47 % at a fiber length of 53 cm, based on the energy distribution experiment. The damage threshold of the hexagonal DCF at 980 nm could be increased to above 26.5 W, nearly doubling that of the single-cladding fiber. Furthermore, a wider fluorescence spectrum with a full width at half maximum (FWHM) of 30 nm was demonstrated by the hexagonal double-cladding fiber, which indicates its potential for high-power laser applications.
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Pub Date : 2024-10-09DOI: 10.1016/j.infrared.2024.105587
The Bayan Obo deposit is the world’s largest polymetallic associated minerals deposit of rare earths, iron and niobium, and the rarity of its physical properties restrict the knowledge and understanding of its laws. Taking the high-grade mixed rare earth concentrate of Bayan Obo as the research object, terahertz time-domain spectroscopy (THz-TDS) has been adopted for the systematic investigation of high-grade rare earth concentrate base on the traditional X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM) and thermogravimetric-differential thermal analysis (TG-DTA). The absorption coefficient and refractive index of high-grade rare earth ores and their associated minerals of fluorite and dolomite, are all investigated by terahertz time-domain spectroscopy. The terahertz spectral response is affected by the type of mineral and its content. The acquired rare earth terahertz spectral data are processed by correlation analysis. Three machine learning algorithms, Partial Least Squares Regression (PLSR), Random forest (RF) and Multilayer Perceptron (MLP), are used to achieve quantitative detection of their concentrations and components with the coefficient of determination R2 of the absorption coefficient of the optical parameter reaching up to 0.975, 0.992 and 0.984, respectively. This work promotes the growing understanding of terahertz transmission spectroscopy of rare earth-bearing minerals, which can be used to help guide the search for minerals, and to detect, identify as well as quantify them in geology. Terahertz time-domain spectroscopy supplies a new method for study of rare earth resources, and the comprehensive development and utilization of resources in the Bayan Obo deposit.
巴彦奥博矿床是世界上最大的稀土、铁和铌多金属伴生矿床，其物理性质的稀有性限制了人们对其规律的认识和理解。以巴彦奥博高品位混合稀土精矿为研究对象，在传统的 X 射线荧光（XRF）、X 射线衍射（XRD）、扫描电子显微镜（SEM）和热重-差热分析（TG-DTA）的基础上，采用太赫兹时域光谱（THz-TDS）对高品位稀土精矿进行了系统研究。太赫兹时域光谱法研究了高品位稀土矿及其伴生矿物萤石和白云石的吸收系数和折射率。太赫兹光谱响应受矿物类型及其含量的影响。获取的稀土太赫兹光谱数据经过相关分析处理。利用三种机器学习算法，即部分最小二乘法回归（PLSR）、随机森林（RF）和多层感知器（MLP），实现了对其浓度和成分的定量检测，光学参数吸收系数的判定系数 R2 分别达到 0.975、0.992 和 0.984。这项工作促进了人们对含稀土矿物太赫兹透射光谱学的进一步了解，可用于指导寻找矿物，以及在地质学中探测、识别和量化矿物。太赫兹时域光谱学为研究稀土资源和巴彦奥博矿床资源的综合开发利用提供了一种新方法。
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