- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Development of a radiative transfer model for the determination of toxic gases by Fourier transform–infrared spectroscopy with a support vector machine algorithm
摘要： This report describes a radiative transfer model for Fourier transform-infrared (FT-IR) spectroscopy to create close-to-reality toxic gas spectra by reflecting the unique spectral responses of detectors and using the atmospheric radiative transfer code, MODTRAN. This system can be highly useful in overcoming the limitations for measuring toxic gases in open environments. The emulated gas spectra can be used to train support vector machine (SVM) for chemical gas detection. Its detection performance is evaluated with nerve agents (tabun, sarin, soman, and cyclosarin) and a simulant gas (sulfur hexafluoride) for indoor and outdoor experiments by using two off-the-shelf FT-IR gas detectors. The experimental results show that the proposed SVM algorithm successfully detected and classified targeted gases while reducing false negative and false positive detection rates.
关键词： support vector machine gas detection,Fourier transform infrared remote sensing,support vector machine,hyperspectral imaging,Fourier transform – infrared spectroscopy,stand-off detection
更新于2025-09-04 15:30:14
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Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine
摘要： A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.
关键词： pipeline leakage localization,method of characteristics (MOC),FBG hoop strain sensor,support vector regression (SVR),particle swarm optimization (PSO) algorithm,support vector machine (SVM)
更新于2025-09-04 15:30:14
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Efficient Shaped-Beam Reflectarray Design Using Machine Learning Techniques
摘要： This papers introduces the use of machine learning techniques for an ef?cient design of shaped-beam re?ectarrays considerably accelerating the overall process while providing accurate results. The technique is based on the use of Support Vector Machines (SVMs) for the characterization of the re?ection coef?cient matrix, which provides an ef?cient way for deriving the scattering parameters associated with the unit cell dimensions. In this way, the SVMs are used within the design process to obtain a re?ectarray layout instead of a Full-Wave analysis tool based on Local Periodicity (FW-LP). The accuracy of the SVMs is assessed and the in?uence of the discretization of the angle of incidence is studied. Finally, a considerable acceleration is achieved with regard to the FW-LP and other works in the literature employing Arti?cial Neural Networks.
关键词： Machine Learning,Support Vector Machine (SVM),re?ectarray
更新于2025-09-04 15:30:14
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking
摘要： Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LM-SCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.
关键词： thermal infrared object tracking,deep motion features,deep appearance features,large margin structured convolution operator,structured output support vector machine,discriminative correlation filter
更新于2025-09-04 15:30:14
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Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images
摘要： Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.
关键词： Genetic Algorithm,Principal Component Analysis,Support Vector Machine,Drusen,Gabor Filters
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - JeJu, Korea (South) (2018.6.24-2018.6.26)] 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Real-Time Traffic Sign Recognition
摘要： Traffic sign recognition is a technology by which a vehicle is able to recognize the traffic signs on the road. In this paper, we propose a novel traffic sign recognition that can operate robustly and accurately for real scenes of Korean roads. The proposed method first detects a potential traffic sign and then recognizes the content of the potential traffic sign. With this approach, the proposed method can robustly recognize small traffic signs from long distances and reduce false alarm significantly. We employ Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) in a two-step model. Compared with the original HOG and SVM method using three Hyundai data sequences with ground truth, our proposed method outperforms significantly and operates robustly in different conditions. The source code and datasets are available online at https://github.com/comvisdinh/realtimetrafficsignrecognition.
关键词： support vector machine,histogram of oriented gradients,Traffic sign recognition
更新于2025-09-04 15:30:14
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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 - Remote Sensing Inversion of Water Quality Parameters in Longquan Lake Based on PSO-SVR Algorithm
摘要： The paper uses the PSO-SVR algorithm to inverse the water quality parameters based on GF-1 remote sensing image in Longquan lake where is located in Chengdu, Sichuan Province. Longquan Lake is a key drinking water source in Chengdu, so its water quality is very critical. Particle swarm optimization (PSO) optimizes the parameters of the support vector regression (SVR) inversion model to establish the new PSO-SVR inversion model, and PSO can effectively improve the efficiency and the accuracy of the SVR inversion model. At the same, the empirical inversion model was established by using the measured hyperspectral data and concentration of water quality parameters. Comparing with SVR inversion model, PSO-SVR inversion model achieves a better result in the application of suspended solids and Chlorophyll concentration inversion.
关键词： GA-SVM,Support vector machine,Water quality inversion,Genetic algorithm,Suspended solids
更新于2025-09-04 15:30:14
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Glaucoma diagnosis using discrete wavelet transform and histogram features from fundus images
摘要： Glaucoma is one of the main eye diseases; it cause progressive deterioration of optic nerve fibers due to increased fluid pressure. The existing methods of glaucoma diagnosis are time consuming, expensive and require practiced clinicians to understand the eye problems. Hence fast, cheap and more accurate glaucoma diagnosis methods are needed. This paper presents an innovative idea for diagnosis of glaucoma using third level two dimensional discrete wavelet transform (2D DWT) and histogram features from fundus images. The 2D DWT is used to decompose the glaucoma and healthy images and histogram features are extracted from 2D DWT decomposed sub band images. The least square support vector machine (LS-SVM) is used as a classifier which classifies the glaucoma and healthy images using the extracted features. The proposed method yielded classification accuracy of 88.33%, 87.50%, and 86.67% for ten, eight and five-fold cross validation respectively. The obtained classification accuracy, sensitivity and specificity are 88.33%, 90.00%, and 85.00% for tenfold cross validation respectively. Obtained results prove that the performance of the proposed method is better compared to the existing methods. It may considerably increases the diagnosis speed of ophthalmologists.
关键词： Discrete Wavelet Transform,Feature Extraction,Glaucoma,Support Vector Machine,Pre-Processing
更新于2025-09-04 15:30:14
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Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology
摘要： Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.
关键词： automated classification,cell selection,SVM,Support Vector Machine,PWS,buccal smear,Partial wave spectroscopy,nanocytology
更新于2025-09-04 15:30:14