scholarly journals Identification and Disease Index Inversion of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data at Canopy Level

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hui Wang ◽  
Feng Qin ◽  
Qi Liu ◽  
Liu Ruan ◽  
Rui Wang ◽  
...  
Keyword(s):  
Leaf Rust ◽  
Stripe Rust ◽  
Wavelet Packet ◽  
Hyperspectral Data ◽  
Disease Index ◽  
Identification Accuracy ◽  
Support Vector ◽  
Wheat Diseases ◽  
Svm Model ◽  
Better Than

Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built usingK-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

Multivariate information fusion for identifying antifungal peptides with Hilbert-Schmidt Independence Criterion

2021 ◽  
Vol 16 ◽  
Author(s):  
Haohao Zhou ◽  
Hao Wang ◽  
Yijie Ding ◽  
Jijun Tang
Keyword(s):  
Fungal Infections ◽  
Practical Significance ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Sequence Information ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Antifungal Peptides ◽  
Independence Criterion ◽  
Better Than

Background: Antifungal peptides (AFP) have been found to be effective against many fungal infections. Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information). Method: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built. Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models. Conclusion: Our method will be a useful tool for identifying antifungal peptides.

Research of load identification based on multiple-input multiple-output SVM model selection

2011 ◽  
Vol 226 (5) ◽  
pp. 1395-1409 ◽  
Author(s):  
W Mao ◽  
M Tian ◽  
G Yan
Keyword(s):  
Model Selection ◽  
Multiple Input Multiple Output ◽  
Identification Accuracy ◽  
Support Vector ◽  
Load Identification ◽  
Multi Objective ◽  
Svm Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Leave One Out

In this article, the problem of multiple-input multiple-output (MIMO) load identification is addressed. First, load identification is proved in dynamic theory as non-linear MIMO black-box modelling process. Second, considering the effect of hyper-parameters in small-size sample problem, a new MIMO Support Vector Machine (SVM) model selection method based on multi-objective particle swarm optimization is proposed in order to improve the identification's performance. The proposed method treats the model selection of MIMO SVM as a multi-objective optimization problem, and leave-one-out generalization errors of all output models are minimized simultaneously. Once the Pareto-optimal solutions are found, the SVM model with the best generalization ability is determined. The proposed method is evaluated in the experiment of dynamic load identification on cylinder stochastic vibration system, demonstrating its benefits in comparison to the existing model selection methods in terms of identification accuracy and numerical stability, especially near the peaks.

scholarly journals Real-time flood forecast using the coupling support vector machine and data assimilation method

2013 ◽  
Vol 16 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Xiao-Li Li ◽  
Haishen Lü ◽  
Robert Horton ◽  
Tianqing An ◽  
Zhongbo Yu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Scale ◽  
Support Vector ◽  
Flood Forecast ◽  
Risk Minimization ◽  
Svm Model ◽  
The Impact ◽  
Assimilation Time ◽  
Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

scholarly journals Determining damages in ceramic plates by using discrete wavelet packet transform and support vector machine

2020 ◽  
Vol 10 (5) ◽  
pp. 4759
Author(s):  
Mehmet Yumurtaci ◽  
Gokhan Gokmen ◽  
Tahir Cetin Akinci
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Experimental Equipment ◽  
Analysis Method ◽  
Discrete Wavelet Packet Transform ◽  
Cracked Plates ◽  
Svm Model

In this study, an analysis was conducted by using discrete wavelet packet transform (DWPT) and support vector machine (SVM) methods to determine undamaged and cracked plates. The pendulum was used to land equal impacts on plates in this experimental study. Sounds, which emerge from plates as a result of the impacts applied to undamaged and cracked plates, are sound signals used in the analysis and DWPT of these sound signals were obtained with 128 decompositions for feature extraction. The first four components, reflecting the characteristics of undamaged and cracked plates within these 128 components, were selected for enhancing the performance of the classifier and energy values were used as feature vectors. In the study, the SVM model was created by selecting appropriate C and γ parameters for the classifier. Undamaged and cracked plates were seen to be successfully identified by an analysis of the training and testing phases. Undamaged and cracked statuses of the plates that are undamaged and have the analysis had identified different cracks. The biggest advantage of this analysis method used is that it is high-precision, is relatively low in cost regarding experimental equipment and requires hardware.

Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine

2015 ◽  
Vol 727-728 ◽  
pp. 872-875
Author(s):  
Wen Bo Na ◽  
Qing Feng Jiang ◽  
Zhi Wei Su
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Rbf Neural Networks ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Better Than

In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.

scholarly journals Introgression of leaf rust and stripe rust resistance from Sharon goatgrass (Aegilops sharonensis Eig) into bread wheat (Triticum aestivum L.)

Genome ◽  
2014 ◽  
Vol 57 (6) ◽  
pp. 309-316 ◽  
Author(s):  
E. Millet ◽  
J. Manisterski ◽  
P. Ben-Yehuda ◽  
A. Distelfeld ◽  
J. Deek ◽  
...  
Keyword(s):  
Leaf Rust ◽  
Stripe Rust ◽  
Rust Resistance ◽  
Wheat Chromosome ◽  
Triticum Aestivum L ◽  
The United States ◽  
Stripe Rust Resistance ◽  
Wheat Diseases ◽  
Aegilops Sharonensis ◽  
Resistant Varieties

Leaf rust and stripe rust are devastating wheat diseases, causing significant yield losses in many regions of the world. The use of resistant varieties is the most efficient way to protect wheat crops from these diseases. Sharon goatgrass (Aegilops sharonensis or AES), which is a diploid wild relative of wheat, exhibits a high frequency of leaf and stripe rust resistance. We used the resistant AES accession TH548 and induced homoeologous recombination by the ph1b allele to obtain resistant wheat recombinant lines carrying AES chromosome segments in the genetic background of the spring wheat cultivar Galil. The gametocidal effect from AES was overcome by using an “anti-gametocidal” wheat mutant. These recombinant lines were found resistant to highly virulent races of the leaf and stripe rust pathogens in Israel and the United States. Molecular DArT analysis of the different recombinant lines revealed different lengths of AES segments on wheat chromosome 6B, which indicates the location of both resistance genes.

Signal Detection of Welding Joint Flaw Based on Wavelet-Support Vector Machine

2015 ◽  
Vol 1120-1121 ◽  
pp. 1385-1389
Author(s):  
Xin Yin ◽  
Yuan Peng Liu ◽  
Xian Zhang Feng
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Structural Risk Minimization Principle ◽  
Signal Characteristics ◽  
Svm Model ◽  
Structural Risk ◽  
Multi Classification

The friction welded joints made by GH4169 heat metal alloys are detected by U1traPAC system of the ultrasonic wave explore instrument. Aimed at the blemish signal characteristics, this article introduce Support Vector Machine (SVM) theory, which is based on statistical theory and structural risk minimization principle, to carry out multi-classification study of the detection signal. We decompose de-noising signals with wavelet packet transform, and extract energy eigenvalues according to "energy- defects". In accordance with designed "1-to-v" SVMs scheme, we respectively input normalized eigenvector to the SVM model to obtain the Forecast data. It is verificated that the limited existing data and information is well used by SVM and the signal is accurately been classificated. All of these verify that SVM has a strong generalization ability.

scholarly journals An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 271 ◽  
Author(s):  
Yuntian Feng ◽  
Guoliang Wang ◽  
Zhipeng Liu ◽  
Runming Feng ◽  
Xiang Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Training Data ◽  
Identification Accuracy ◽  
Support Vector ◽  
Learning Method ◽  
Target Domain ◽  
Identification Method ◽  
Svm Model ◽  
Testing Data ◽  
Task Simulation

Aiming at the current problem that it is difficult to deal with an unknown radar emitter in the radar emitter identification process, we propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine (SVM) model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then, we design a semi-supervised co-training algorithm using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Simulation experiments show that the proposed method can effectively identify an unknown radar emitter and still maintain high identification accuracy within a certain measurement error range.

scholarly journals A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine

2020 ◽  
Vol 12 (4) ◽  
pp. 620 ◽  
Author(s):  
Jing Zhang ◽  
Haiqing Tian ◽  
Di Wang ◽  
Haijun Li ◽  
Abdul Mounem Mouazen
Keyword(s):  
Sugar Beet ◽  
Growth Stage ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Sugar Accumulation ◽  
Grey Wolf ◽  
Above Ground Biomass ◽  
Wavelength Selection ◽  
Grey Wolf Optimization ◽  
Svm Model

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE–GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE–GWO and MDE–GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE–GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.

Modeling River Stream Flow Using Support Vector Machine

2013 ◽  
Vol 315 ◽  
pp. 602-605 ◽  
Author(s):  
Ali Rafidah ◽  
Yacob Suhaila
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Stream Flow ◽  
Moving Average ◽  
Support Vector ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Svm Model ◽  
Record Data ◽  
Better Than

Support Vector Machine (SVM) is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in river stream flow forecasting. In this paper, SVM is proposed for river stream flow forecasting. To assess the effectiveness SVM, we used monthly mean river stream flow record data from Pahang River at Lubok Paku, Pahang. The performance of the SVM model is compared with the statistical Autoregressive Integrated Moving Average (ARIMA) and the result showed that the SVM model performs better than the ARIMA models to forecast river stream flow Pahang River.

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