- 标题
- 摘要
- 关键词
- 实验方案
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Predicting Apple Firmness and Soluble Solids Content Based on Hyperspectral Scattering Imaging Using Fourier Series Expansion
摘要： This article reports on using a Fourier series expansion method to extract features from hyperspectral scattering profiles for apple fruit firmness and soluble solids content (SSC) prediction. Hyperspectral scattering images of ‘Golden Delicious’ (GD), ‘Jonagold’ (JG), and ‘Delicious’ (RD) apples, harvested in 2009 and 2010, were acquired using an online hyperspectral imaging system over the wavelength region of 500 to 1000 nm. The moment method and Fourier series expansion method were used to analyze the scattering profiles of apples. The zeroth-first order moment (Z-FOM) spectra and Fourier coefficients were extracted from each apple, which were then used for developing fruit firmness and SSC prediction models using partial least squares (PLS) and least squares support vector machine (LSSVM). The PLS models based on the Fourier coefficients improved the standard errors of prediction (SEP) by 4.8% to 19.9% for firmness and by 2.4% to 13.5% for SSC, compared with the PLS models using the Z-FOM spectra. The LSSVM models for the prediction set of Fourier coefficients achieved better SEP results, with improvements of 4.4% to 11.3% for firmness and 2.8% to 16.5% for SSC over the LSSVM models for the Z-FOM spectra data and 3.7% to 12.6% for firmness and 5.4% to 8.6% for SSC over the PLS models for the Fourier coefficients. Experiments showed that Fourier series expansion provides a simple, fast, and effective means for improving hyperspectral scattering prediction of fruit internal quality when used with either PLS or LSSVM.
关键词： Partial least squares,Soluble solids content,Apples,Least squares support vector machine,Fourier series expansion,Hyperspectral scattering imaging,Firmness
更新于2025-09-23 15:22:29
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Remote Vital Sign Recognition through Machine Learning augmented UWB
摘要： This paper describes an experimental demonstration of machine learning (ML) techniques supplementing radar to distinguish and detect vital signs of users in a domestic environment. This work augments an intelligent location awareness system previously proposed by the authors. That research employed Ultra-Wide Band (UWB) radar complemented by supervised machine learning techniques to remotely identify a person’s room location via ?oor plan training and time stamp correlations. Here, the remote breathing and heartbeat signals are analyzed through Short Term Fourier Transformation (STFT) to determine the Micro-Doppler signature of those vital signs in different room locations. Then, Multi-Class Support Vector Machine (MC-SVM) is implemented to train the system to intelligently distinguish between vital signs during different activities. Statistical analysis of the experimental results supports the proposed algorithm. This work could be used to further understand, for example, how active older people are by engaging in typical domestic activities.
关键词： Short Term Fourier Transform (STFT),Breathing,Ultra-Wide Band (UWB),Multi-Class Support Vector Machine (MC-SVM),Heartbeat,Indoor Positioning System (IPS)
更新于2025-09-23 15:22:29
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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
摘要： Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine?SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.
关键词： hyperspectral imaging technology,standard germination tests,support vector machine model,accelerated aging,principal component analysis,maize kernel
更新于2025-09-23 15:22:29
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Automatic Detection of Driver Impairment Based on Pupillary Light Reflex
摘要： The main objective of this paper is to determine the feasibility of designing a driver drunkenness detection system based on the dynamic analysis of a subject’s pupillary light reflex (PLR). This involuntary reaction is widely utilized in the medical field to diagnose a variety of diseases, and in this paper, the effectiveness of such a method to reveal an impairment condition due to alcohol abuse is evaluated. The test method consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes; for extracting the pupil size profiles from the video sequences, a two-step methodology is described, where in the first phase, the iris/pupil search within the image is performed, and in the second stage, the image is cropped to perform pupil detection on a smaller image to improve time efficiency. The undesired pupil dynamics arising in the PLR are defined and evaluated; a spontaneous oscillation of the pupil diameter is observed in the range [0, 2] Hz and the accommodation reflex causes pupil constriction of about 10% of the iris diameter. A database of pupillary light responses is acquired on different subjects in baseline condition and after alcohol consumption, and for each one, a first-order model is identified. A set of features is introduced to compare the two populations of responses and is used to design a support vector machine classifier to discriminate between “Sober” and “Drunk” states.
关键词： pupil dynamics,video processing,system identification,ADAS,support vector machine,classification
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automated Analysis of Remotely Sensed Images Using the Unicore Workflow Management System
摘要： The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel methods running in High-Performance Computing (HPC) environments are essential. Managing an HPC processing pipeline is non-trivial for a scientist, especially when the computing environment is heterogeneous and the set of tasks has complex dependencies. This paper proposes an end-to-end scientific workflow approach based on the UNICORE workflow management system for automating the full chain of Support Vector Machine (SVM)-based classification of remotely sensed images. The high-level nature of UNICORE workflows allows to deal with heterogeneity of HPC computing environments and offers powerful workflow operations such as needed for parameter sweeps. As a result, the remote sensing workflow of SVM-based classification becomes re-usable across different computing environments, thus increasing usability and reducing efforts for a scientist.
关键词： High-Performance Computing (HPC),Remote Sensing,Scientific Workflows,UNICORE,Support Vector Machine (SVM)
更新于2025-09-23 15:21:21
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A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
摘要： Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For classification, we use support vector machine (SVM). The experiments are performed on three well-known hyperspectral benchmark datasets. The proposed algorithm is compared with the state of the art methods. The obtained results of this fusion show that our method outperforms the other approaches by increasing the classification accuracy in a good timing. This method may be improved for more performance.
关键词： hyperspectral images,spectral and spatial features,classification,SVM,mutual information,GLCM,grey level cooccurrence matrix,support vector machine
更新于2025-09-23 15:21:21
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Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging
摘要： Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.
关键词： object-wise,pixel-wise,support vector machine,near-infrared hyperspectral imaging,raisins
更新于2025-09-23 15:21:21
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Evaluation of electrical efficiency of photovoltaic thermal solar collector
摘要： In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
关键词： hybrid machine learning model,Renewable energy,photovoltaic-thermal (PV/T),least square support vector machine (LSSVM),adaptive neuro-fuzzy inference system (ANFIS),neural networks (NNs)
更新于2025-09-23 15:21:01
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Identification of Gravesa?? ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method
摘要： Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.
关键词： support vector machine (SVM),linear discriminant analysis (LDA),Graves’ ophthalmology,laser-induced breakdown spectroscopy (LIBS),k-nearest neighbor (kNN),generalized regression neural network (GRNN)
更新于2025-09-23 15:21:01
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[IEEE 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) - Fukuoka, Japan (2020.2.19-2020.2.21)] 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) - Photovoltaic Cell Defect Detection Model based-on Extracted Electroluminescence Images using SVM Classifier
摘要： Electroluminescence (EL) imaging is used to analyze the characteristics of solar cells. This technique provides various details about solar panel modules such as solar cell characteristics, materials used, health status, defects, etc. The derived features from solar panel images provide a significant source of information for photovoltaic applications such as fault detection assessment. In this work, a method for classifying between the normal and a defective solar cell was implemented using EL imaging with selected digital image processing techniques through the Support Vector Machine (SVM) classifier. The EL images are processed using feature extraction procedures. The system was observed to provide an accuracy of 95%. The algorithm presented was implemented in MATLAB R2019b programming environment.
关键词： photovoltaic module,solar panel,and support vector machine.,digital processing,image electroluminescence imaging
更新于2025-09-23 15:21:01