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- 摘要
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- 实验方案
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Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France
摘要： Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in di?erent contexts. However, an assessment of important features and classi?cation results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classi?cations using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the Sélune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3–9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discrimi- nant. The selected features were used as variables for classi?cation using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classi?cations were conducted using all features computed, without selection. The best classi?ca- tion performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classi?cation. The latter also achieved the best classi?cation performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.
关键词： Machine Learning,Riparian forests,tree genera identification,Support Vector Machine (SVM),Airborne Laser Scanner (ALS),Random Forest (RF)
更新于2025-09-19 17:13:59
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[IEEE 2019 28th Wireless and Optical Communications Conference (WOCC) - Beijing, China (2019.5.9-2019.5.10)] 2019 28th Wireless and Optical Communications Conference (WOCC) - A Modified DAG-SVM Algorithm for the Fault Diagnosis in Satellite Communication System
摘要： With the continuous development of satellite industry, online monitoring and fault diagnosis for satellite communication system becomes more important. Due to the difficulty in obtaining sufficient features of communication system, conventional multi-classification algorithm Directed Acyclic Graph Support Vector Machine (DAG-SVM) has low diagnostic efficiency and poor coupling diagnosis performance. On the other hand, it has been proved that extending the feature space can effectively improve the classification performance. Therefore, this paper proposed a modified multi-classification algorithm called Feature-Extended Directed Acyclic Graph Least Square Twin Support Vector Machine (FEDAG-LSTSVM). The new algorithm combined all features and their random combinations to establish coupling and redundancy for every feature, and then constructed the Separable Metric (SM) as classification measurement to arrange the structure sequencing of DAG-LSTSVM. To verify the utility of the algorithm, the satellite communication system were taken as experimental data. Preliminary simulation results demonstrate that the proposed algorithm improves the fault diagnosis accuracy to 89.69% but with 54.20% less computational time in 10-fold cross-validation compared with DAG-SVM, which means it can be well applied to diagnose fault for satellites communication system.
关键词： Directed Acyclic Graph Support Vector Machine,Fault diagnosis,Feature extension,Multi-Classification
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Hydrogenation of polycrystalline silicon films for passivating contacts solar cells
摘要： The strong temporal backscatter signature of rice growing above the water’s surface allows for the use of synthetic aperture radar (SAR) for paddy rice crop mapping in Southern Vietnam (Mekong Delta). In Northern Vietnam (Red River Delta), rice mapping using SAR is a challenge and is rarely performed because of the complex land-use/land-cover. Nevertheless, information about rice fields is needed for hydrological simulations in river basins such as the Cau River basin. The objective of this research is to investigate the potential of RADARSAT-2 band-C in identifying rice fields over a large and fragmented land-use area. Two methods are proposed, one for each data type, adapted to the land-use/land-cover of the study area. The thresholding technique, with a statistical analysis of the temporal variation of rice backscattering, was applied to the HH like-polarized ratio of dual-pol data. The support vector machine (SVM) algorithm was applied to the full quad-pol and a single HH-polarization calculated from polarimetric data. This study demonstrates that RADARSAT-2 dual- and quad-pol data can be successfully used to identify cultivated rice fields. However, the dual-pol data seems less efficient than the quad-pol data and the SVM classification is more flexible than the thresholding technique. Between the full quad-pol and a single polarization, the overall classification accuracy shows that the results derived from the single HH polarization are 3 to 10% less accurate than those derived from the classification of full quad-pol data. The results show the usefulness of polarimetric C-band data for the identification of rice fields in Northern Vietnam.
关键词： RADARSAT-2,support vector machine (SVM),thresholding,Cau river basin,rice identification
更新于2025-09-19 17:13:59
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Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques
摘要： Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2 c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2 v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2 v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.
关键词： soybean,tepary bean,Gaussian processes,guar,pigeon pea,support vector machine,partial least square
更新于2025-09-19 17:13:59
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Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines
摘要： The detection and differentiation of tetracyclines (TCs) has received increasing attention due to the severe threat they pose to human health and the ecological balance. A dual-channel fluorescence sensor array based on two carbon quantum dots (CDs) was fabricated to distinguish between four TCs, including tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), and metacycline (MTC). A distinct fluorescence variation pattern (I/I0) was produced when CDs interacted with the four TCs. This pattern was analyzed by LDA and SVM. This was the first time that SVM was used for data processing of fluorescence sensor arrays. LDA and SVM showed that the array has the capacity for parallel and accurate determination of TCs at concentrations between 1.0 μM and 150 μM. In addition, the interference experiment using metal ions and antibiotics as possible coexisting interference substances proves that the sensor array has excellent selectivity and anti-interference ability. The array was also used for the accurate detection and identification of TCs in binary mixtures, and furthermore, the four TCs were successfully identified in river water and milk samples. Besides, the sensor array successfully identified the four TCs in 72 unknown samples with a 100% accuracy. The results proved that SVM can achieve the same accurate classification and prediction as LDA, and considering its additional advantages, it can be used as an optional supplementary method for data processing, thereby expanding the data processing field.
关键词： linear discriminant analysis,sensor array,support vector machine,tetracyclines,carbon quantum dots
更新于2025-09-19 17:13:59
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A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification
摘要： Diverse welding processes have been utilized in manufacturing industry for years. But up to date, welding quality still cannot be guaranteed, due to the lack of an efficient and on-line welding defects monitoring method, and this leads to increased manufacturing costs. In this paper, a method based on feature extraction and machine learning algorithm for on-line quality monitoring and defects classification was presented. Plasma radiation was captured by an optical fiber probe, and delivered by an optical fiber to the spectrometer. The captured spectral signal was processed by selecting sensitive emission lines and extracting features of spectral data’s evolution, which realized spectral data compression with low computational cost. After selecting the proper training data set, the designed ANN and SVM allows automatic detection and classification of welding defects. The validity of proposed method was successfully approved by test data set in welding experiments. Welding experiments on galvanized steel sheets showed the corresponding relationship between the output of classifiers and welding defects. Finally, the two classifiers were compared. Experiments indicated the performance of ANN is slightly better than that of SVM, while both of them have its own advantages.
关键词： Laser welding,Support vector machine,Plasma spectral analysis,Artificial neural network
更新于2025-09-19 17:13:59
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Machine learning algorithms for predicting the amplitude of chaotic laser pulses
摘要： Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular dynamical regime that can show ultrahigh intensity pulses, reminiscent of rogue waves. We compare the goodness of the forecast for several popular methods in machine learning, namely, deep learning, support vector machine, nearest neighbors, and reservoir computing. Finally, we analyze how their performance for predicting the height of the next optical pulse depends on the amount of noise and the length of the time series used for training.
关键词： chaotic systems,laser pulses,reservoir computing,deep learning,forecasting,support vector machine,machine learning,nearest neighbors
更新于2025-09-16 10:30:52
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[IEEE 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - Chengdu, China (2019.5.25-2019.5.28)] 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - ABC-SVM and PSO-RF Model for Photovoltaic Forecasting Based on Big Data
摘要： Prediction of photovoltaic output is of great significance to the stable operation of microgrid system. Firstly, the artificial bee colony based support mechine (ABC-SVM) method is used to train historical meteorological data and photovoltaic output data, which can divide the weather condition into four categories. Secondly, tens of thousands of data are selected under four types of meteorological conditions, and each group of data is trained by particle swarm optimization based random forest (PSO-RF) model. After training, the four different PSO-RF model with different parameters can be obtained for the photovoltaic forecasting individually. Finally, we collect weather information and photovoltaic data from a microgrid station in Yangjiang Guangdong province to test our combined model. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional RF methods.
关键词： random forest,support vector machine,particle swarm optimization,artificial bee colony
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Machine Learning Scheme for Geometrically-shaped Constellation Classification utilizing Support Vector Machine in Multi-access Internet of Vehicle Lighting
摘要： In this paper, we propose a multi-access Internet of Vehicles (IoV) scheme based on multi-band DFTS-OFDM VLC system. The experimental results show that with the bandwidth of 62.5MHz, the dynamic range was enhanced 1.6 dBm employing SVM in hexagonal constellation Geometrically-shaped (GS) 16QAM and the overall capacity is 250Mbps.
关键词： discrete Fourier transform spread (DFT-S),Geometrically-shaped,optical orthogonal frequency division multiplexing (OFDM),Visible light communication,Internet of Vehicles,Support vector machine
更新于2025-09-16 10:30:52
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Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm
摘要： In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.
关键词： chaotic search,least squares support vector machine,chicken swarm optimization algorithm,storage system,sustainable lithium-ion battery
更新于2025-09-16 10:30:52