- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detection of Knot Defects on Coniferous Wood Surface Using Near Infrared Spectroscopy and Chemometrics
摘要： Lumber pieces usually contain defects such as knots, which strongly affect the strength and stiffness. To develop a model for rapid, accurate grading of lumbers based on knots, Douglas fir, spruce-pine-fir (SPF), Chinese hemlock, and Dragon spruce were used. The experiments explored the effects of modelling methods and spectral preprocess methods for knot detection, and investigated the feasibility of using a model built within one species to discriminate the samples from other species, using a novel variable selection method-random frog to select effective wavelengths. The results showed that least squares-support vector machines coupled with first derivative preprocessed spectra achieved best performance for both single and mixed models. Models built within Dragon spruce could be used to classify knot samples from SPF and Chinese hemlock but not Douglas fir, and vice versa. Eight effective wavelengths (1314 nm, 1358 nm, 1409 nm, 1340 nm, 1260 nm, 1586 nm, 1288 nm, and 1402 nm) were selected by RF to build effective wavelengths based models. The sensitivity, specificity, and accuracy in the validation set were 98.49%, 93.42%, and 96.30%, respectively. Good results could be obtained when using data at just eight wavelengths, as an alternative to evaluating the whole spectrum.
关键词： Coniferous wood,Knot detection,Near infrared spectroscopy (NIRS),Random frog algorithm,Least squares-support vector machines (LS-SVM)
更新于2025-09-23 15:23:52
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Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images
摘要： Quanti?cation of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quanti?cation of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identi?cation and machine learning based methods were implemented to quantify targets in the images recorded by a bright ?eld microscope. Images acquired by a 40× or a 20× objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40× objective and 79.7% for the 20× objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection.
关键词： Leukemia cells,Immunomagnetic beads,Support vector machines,Bright-?eld optical microscopy,Image-processing,Machine learning
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Towards Joint Land Cover and Crop Type Mapping with Numerous Classes
摘要： The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type maps using a highly analytic nomenclature of more than 40 classes. An intensive manual annotation procedure was carried out for the production of reference data. A class nomenclature based on CORINE land cover Level-3 was employed along with several additional crop-type classes. Multitemporal surface reflectance Landsat-8 data for the year of 2016 were used for all classification experiments with a linear SVM classifier. Quantitative and qualitative evaluation highlighted the efficiency of the proposed approach achieving high accuracy rates. Further analysis on individual classes’ performance highlighted the challenges in the proposed classification scheme as well as important outcomes regarding the spectral behavior of the considered categories.
关键词： support vector machines,CORINE Land Cover,Landsat-8,classification
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Recognition of Windmills in Remote Sensing Image By SVM and Morphological Attribute Filters
摘要： Windmills have the characteristics of small area and small quantity in remote sensing images, so the traditional methods of object classification and recognition are not suitable for the recognition of windmills. In this paper, we analyzed the spectral information and shape characteristics of windmill, and proposed a technique of recognition windmills in remote sensing images based on SVM (support vector machines) and morphological attribute filters. The main idea of technique can be parted into two steps: the remote sensing image are divided into windmill and windmill-like areas, using morphological attribute filters to filter out the windmill-like areas. In addition, we have recognized the distributed windmills group in the images of four regions, and verify the accuracy of the recognition technique.
关键词： morphological attribute filters,windmills,support vector machines,target recognition
更新于2025-09-23 15:22:29
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Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
摘要： Background: Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination. Results: A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination. Conclusions: The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.
关键词： Band selection,Soybean disease,Precision agriculture,Hyperspectral,Support vector machines,Genetic algorithm,Charcoal rot
更新于2025-09-23 15:21:01
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Geographical authenticity evaluation of Mentha haplocalyx by LIBS coupled with multivariate analyses
摘要： Mentha haplocalyx (mint) is a significant traditional Chinese medicine (TCM) listed in the Catalogue of “Medicinal and Food Homology”, therefore, its geographical origins (GOs) are critical to the medicinal and food value. Laser-induced breakdown spectroscopy (LIBS) is an advanced analytical technique for GOs certification, due to the fast multi-elemental analysis requiring minimal sample pretreatment. In this study, LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyses. The spectral data was analyzed by two chemometric algorithms, i.e. principal component analysis (PCA) and least squares support vector machines (LS-SVM). Specifically, the performance of LS-SVM with linear kernel and radial basis function (RBF) kernel was explored in sensitivity and robustness tests. Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance (a little inferior) in robustness test. Generally, LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel. The result indicated the potential for future applications in herbs and food, especially for in situ GOs applications of TCM authenticity rapidly.
关键词： laser-induced breakdown spectroscopy (LIBS),geographical origin,herb authenticity,least squares support vector machines,Mentha haplocalyx
更新于2025-09-23 15:19:57
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[IEEE 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - Novosibirsk, Russia (2019.10.21-2019.10.27)] 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - The Use of Spread Spectrum Signals to Increase the Noise Immunity of Optical Communication Systems Based on the Effect of LED Reversibility
摘要： Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this paper, four different classi?cation techniques—decision tree algorithms, support vector machines, arti?cial neural networks, and discriminant analysis—are used to build classi?er models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and speci?city performance metrics. Although all the classi?er models show comparably high classi?cation performances, C5.0 classi?er is the best with respect to accuracy, precision, and speci?city. In addition, an analysis of the variable importance for each classi?er model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The ?ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ?ndings may be used to improve the measurement instruments.
关键词： linear discriminant analysis,Arti?cial neural networks,support vector machines,decision trees,classi?cation algorithms,performance evaluation
更新于2025-09-23 15:19:57
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Document Verification: A Cloud-Based Computing Pattern Recognition Approach to Chipless RFID
摘要： In this paper, we propose a novel means of verifying document originality using chipless RFID systems. The document sender prints a chipless RFID tag into the paper and does a frequency scanning in the 57–64 GHz spectrum of the document. The results of scattering parameters in individual step frequencies are stored in a cloud database, denoised and passed to pattern classi?ers, such as support vector machines or ensemble networks. These supervised learners train themselves based on these data on the remote/cloud computer. The document receiver veri?es this frequency ?ngerprint by using the same scanning method, sending the scattering parameters to the cloud server and getting the decoded data. Paper originality is veri?ed if the decoded data are as expected. The advantages of our cloud chipless RFID processing deployments are cost reduction and increased security and scalability.
关键词： chipless tag,classi?cation algorithms,Radio frequency identi?cation,support vector machines,pattern recognition,cloud computing,ensemble networks
更新于2025-09-19 17:15:36
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[IEEE 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) - Dhaka, Bangladesh (2019.9.26-2019.9.28)] 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) - Analysis of Photovoltaic Characteristics of Carbon Nanotube Incorporated Perovskite Solar Cell with CNT Chirality Variation
摘要： In the advanced manufacturing industry, planar switched reluctance motors (PSRMs) have proved to be a promising candidate due to their advantages of high precision, low cost, low heat loss, and ease of manufacture. However, their inverse force function, which provides vital phase current command for precise motion, is highly nonlinear and hard to be accurately modeled. This paper proposes a novel inverse force function using sparse least squares support vector machines (LS-SVMs) to achieve nonlinear modeling for precise motion of a PSRM. The required training and testing sets of sparse LS-SVMs are ?rst obtained from experimental measurement. A sparse LS-SVMs regression is further developed using training set to accurately model the inverse force function. Accordingly, the function is tested via the testing set to assess its feasibility. Finally, the proposed approach is applied to the PSRM system with dSPACE controller for trajectory tracking, and its effectiveness and superior performance are veri?ed through experimental results.
关键词： Inverse force function,least squares support vector machines (LS-SVMs),phase current estimation,nonlinear modeling,planar switched reluctance motors (PSRMs)
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Topological and Geometrical Effects in the Bulk Hall Response of Driven-Dissipative Photonic Lattices
摘要： In the advanced manufacturing industry, planar switched reluctance motors (PSRMs) have proved to be a promising candidate due to their advantages of high precision, low cost, low heat loss, and ease of manufacture. However, their inverse force function, which provides vital phase current command for precise motion, is highly nonlinear and hard to be accurately modeled. This paper proposes a novel inverse force function using sparse least squares support vector machines (LS-SVMs) to achieve nonlinear modeling for precise motion of a PSRM. The required training and testing sets of sparse LS-SVMs are ?rst obtained from experimental measurement. A sparse LS-SVMs regression is further developed using training set to accurately model the inverse force function. Accordingly, the function is tested via the testing set to assess its feasibility. Finally, the proposed approach is applied to the PSRM system with dSPACE controller for trajectory tracking, and its effectiveness and superior performance are veri?ed through experimental results.
关键词： Inverse force function,least squares support vector machines (LS-SVMs),phase current estimation,nonlinear modeling,planar switched reluctance motors (PSRMs)
更新于2025-09-19 17:13:59