Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather fo...Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is n...Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is no research devoted to the spectral detection of rice yellow mottle virus (RYMV) infection, especially in the asymptomatic or early stages. This work proposes the use of hyperspectral fluorescence and reflectance data at leaf level for the detection of this disease in asymptomatic stages. A greenhouse experiment was therefore conducted to collect hyperspectral fluorescence and reflectance data at different stages of infection. These data allowed to calculate nine vegetation indices: one from fluorescence spectra and eight from reflectance spectra. A t-test made it possible to identify, from the second day after infection, four relevant reflectance vegetation indices to discriminate healthy leaves from those infected: these are Photochemical Reflectance Index (PRI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Structure Intensive Pigment Index (SIPI) and Simple Ratio Pigment Index (SRPI). The fluorescence index was less sensitive in detecting infection. The four significant vegetation indices for the detection of RYMV were then used to build and evaluate models for discriminating plants according to their health status by the supervised classification of support vector machine (SVM) at different stages of infection. The maximum overall accuracy is 92.5% six days after inoculation (6 DAI). The sixth day after inoculation would be the adequate day to detect RYMV. This plants discrimination was validated by the mean reflectance spectra and by the histograms showing the differences between the average reflectance vegetation indices values of the two types of plants. Our results demonstrate the feasibility of differentiating RYMV-infected samples. They suggest that support vector machine learning models could be developed to diagnose RYMV-infected plants based on vegetation indices derived from spectral profiles at early stages of disease development.展开更多
Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy...Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.展开更多
文摘Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
文摘Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is no research devoted to the spectral detection of rice yellow mottle virus (RYMV) infection, especially in the asymptomatic or early stages. This work proposes the use of hyperspectral fluorescence and reflectance data at leaf level for the detection of this disease in asymptomatic stages. A greenhouse experiment was therefore conducted to collect hyperspectral fluorescence and reflectance data at different stages of infection. These data allowed to calculate nine vegetation indices: one from fluorescence spectra and eight from reflectance spectra. A t-test made it possible to identify, from the second day after infection, four relevant reflectance vegetation indices to discriminate healthy leaves from those infected: these are Photochemical Reflectance Index (PRI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Structure Intensive Pigment Index (SIPI) and Simple Ratio Pigment Index (SRPI). The fluorescence index was less sensitive in detecting infection. The four significant vegetation indices for the detection of RYMV were then used to build and evaluate models for discriminating plants according to their health status by the supervised classification of support vector machine (SVM) at different stages of infection. The maximum overall accuracy is 92.5% six days after inoculation (6 DAI). The sixth day after inoculation would be the adequate day to detect RYMV. This plants discrimination was validated by the mean reflectance spectra and by the histograms showing the differences between the average reflectance vegetation indices values of the two types of plants. Our results demonstrate the feasibility of differentiating RYMV-infected samples. They suggest that support vector machine learning models could be developed to diagnose RYMV-infected plants based on vegetation indices derived from spectral profiles at early stages of disease development.
基金supported by the National Key R&D Program of China“R&D of security technologies and equipment for national oil and gas reserves”(No.:2017YFC0805800)National Natural Science Foundation of China“Model study of hydrogen-damage induced failure and integration evaluation for X80 pipe steel”(No.:51874322).
文摘Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.
