Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noti...Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.展开更多
针对多型传感器采样频率不统一,现有机器学习算法难以有效处理混频数据输入,无法充分挖掘混频信号中的设备故障特征的问题,首先提出一种混频数据输入下的长短时记忆网络(multi-frequency long and short term memory network,MF-LSTM)架...针对多型传感器采样频率不统一,现有机器学习算法难以有效处理混频数据输入,无法充分挖掘混频信号中的设备故障特征的问题,首先提出一种混频数据输入下的长短时记忆网络(multi-frequency long and short term memory network,MF-LSTM)架构;然后,对不同采样频率的状态数据分别进行特征提取并进行特征融合,实现混频数据输入下的电气设备的故障诊断任务;最后,利用凯斯西储大学轴承数据集对所提模型进行了算例验证,结果表明:相比于单频信号输入,混频输入平均提高故障诊断精度1.72%。该实验结果证明了所提出的基于MF-LSTM的故障诊断框架的有效性和混频数据输入的必要性。展开更多
Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination me...Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.展开更多
Closed circuit television(CCTV)systems are widely used to inspect sewer pipe conditions.During the diagnosis process,the manual diagnosis of defects is time consuming,labor intensive and error prone.To assist inspecto...Closed circuit television(CCTV)systems are widely used to inspect sewer pipe conditions.During the diagnosis process,the manual diagnosis of defects is time consuming,labor intensive and error prone.To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images,this paper presents an image recognition algorithm that applies features extraction and machine learning approaches.An algorithm of image recognition techniques,including Hu invariant moment,texture features,lateral Fourier transform and Daubechies(DBn)wavelet transform,was used to describe the features of defects,and support vector machines were used to classify sewer pipe defects.According to the inspection results,seven defects were defined;the diagnostic system was applied to a sewer pipe system in a southern city of China,and 28,760 m of sewer pipes were inspected.The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%.The overall accuracy reached 84.1%.The diagnosing accuracy depended on the number of the training samples,and four fitting curves were applied to fit the data.According to this paper,the logarithmic fitting curve presents the highest coefficient of determination of 0.882,and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.展开更多
In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and back...In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and backpropagation neural network(BPNN).A total of 382 images of weeds competing with asparagus growth were collected,including 135 of Cirsium arvense(L.)Scop.,138 of Conyza sumatrensis(Retz.)E.Walker,and 109 of Calystegia hederacea Wall.The grayscale images were extracted from the RGB images of weeds using the 2G-R-B factor.Threshold segmentation of the grayscale image of weeds was applied using Otsu method.Then the internal holes of the leaves were filled through the expansion and corrosion morphological operations,and other interference targets were removed to obtain the binary image.The foreground image was obtained by masking the binary image and the RGB image.Then,the color moment algorithm was used to extract weeds color feature,the gray level co-occurrence matrix and the Local Binary Pattern(LBP)algorithm was used to extract weeds texture features,and seven Hu invariant moment features and the roundness and slenderness ratio of weeds were extracted as their shape features.According to the shape,color,texture,and fusion features of the test samples,a weed identification model was built.The test results showed that the recognition rate of Cirsium arvense(L.)Scop.,Calystegia hederacea Wall.and Conyza sumatrensis(Retz.)E.Walker were 82.72%(color feature),72.41%(shape feature),86.73%(texture feature)and 93.51%(fusion feature),respectively.Therefore,this method can provide a reference for the study of weeds identification in the asparagus field.展开更多
Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can ...Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.展开更多
In order to improve the accuracy and stability of fruit and vegetable image recognition by single feature, this project proposed multi-feature fusion algorithms and SVM classification algorithms. This project not only...In order to improve the accuracy and stability of fruit and vegetable image recognition by single feature, this project proposed multi-feature fusion algorithms and SVM classification algorithms. This project not only introduces the Reproducing Kernel Hilbert space to improve the multi-feature compatibility and improve multi-feature fusion algorithm, but also introduces TPS transformation model in SVM classifier to improve the classification accuracy, real-time and robustness of integration feature. By using multi-feature fusion algorithms and SVM classification algorithms, experimental results show that we can recognize the common fruit and vegetable images efficiently and accurately.展开更多
基金The paper is supported by the Research Foundation for OutstandingYoung Teachers , China University of Geosciences ( Wuhan) ( No .CUGQNL0616) Research Foundationfor State Key Laboratory of Geo-logical Processes and Mineral Resources ( No . MGMR2002-02)Hubei Provincial Depart ment of Education (B) .
文摘Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.
文摘针对多型传感器采样频率不统一,现有机器学习算法难以有效处理混频数据输入,无法充分挖掘混频信号中的设备故障特征的问题,首先提出一种混频数据输入下的长短时记忆网络(multi-frequency long and short term memory network,MF-LSTM)架构;然后,对不同采样频率的状态数据分别进行特征提取并进行特征融合,实现混频数据输入下的电气设备的故障诊断任务;最后,利用凯斯西储大学轴承数据集对所提模型进行了算例验证,结果表明:相比于单频信号输入,混频输入平均提高故障诊断精度1.72%。该实验结果证明了所提出的基于MF-LSTM的故障诊断框架的有效性和混频数据输入的必要性。
文摘Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.
文摘Closed circuit television(CCTV)systems are widely used to inspect sewer pipe conditions.During the diagnosis process,the manual diagnosis of defects is time consuming,labor intensive and error prone.To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images,this paper presents an image recognition algorithm that applies features extraction and machine learning approaches.An algorithm of image recognition techniques,including Hu invariant moment,texture features,lateral Fourier transform and Daubechies(DBn)wavelet transform,was used to describe the features of defects,and support vector machines were used to classify sewer pipe defects.According to the inspection results,seven defects were defined;the diagnostic system was applied to a sewer pipe system in a southern city of China,and 28,760 m of sewer pipes were inspected.The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%.The overall accuracy reached 84.1%.The diagnosing accuracy depended on the number of the training samples,and four fitting curves were applied to fit the data.According to this paper,the logarithmic fitting curve presents the highest coefficient of determination of 0.882,and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.
基金This work was partially supported by the National Natural Science Foundation of China(Grant No.32071905No.61771224)+3 种基金the National Key Research and Development Plan of China(Grant No.2018YFF0213601)the National Natural Science Foundation of China(Grant No.61771224)the Jiangsu Demonstration Project of Modern Agricultural Machinery Equipment and Technology(Grant No.NJ2019-19)the China Agriculture Research System(CARS-23-C03).
文摘In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and backpropagation neural network(BPNN).A total of 382 images of weeds competing with asparagus growth were collected,including 135 of Cirsium arvense(L.)Scop.,138 of Conyza sumatrensis(Retz.)E.Walker,and 109 of Calystegia hederacea Wall.The grayscale images were extracted from the RGB images of weeds using the 2G-R-B factor.Threshold segmentation of the grayscale image of weeds was applied using Otsu method.Then the internal holes of the leaves were filled through the expansion and corrosion morphological operations,and other interference targets were removed to obtain the binary image.The foreground image was obtained by masking the binary image and the RGB image.Then,the color moment algorithm was used to extract weeds color feature,the gray level co-occurrence matrix and the Local Binary Pattern(LBP)algorithm was used to extract weeds texture features,and seven Hu invariant moment features and the roundness and slenderness ratio of weeds were extracted as their shape features.According to the shape,color,texture,and fusion features of the test samples,a weed identification model was built.The test results showed that the recognition rate of Cirsium arvense(L.)Scop.,Calystegia hederacea Wall.and Conyza sumatrensis(Retz.)E.Walker were 82.72%(color feature),72.41%(shape feature),86.73%(texture feature)and 93.51%(fusion feature),respectively.Therefore,this method can provide a reference for the study of weeds identification in the asparagus field.
基金The authors are grateful to Taif University Researchers Supporting Project Number(TURSP-2020/215)Taif University,Taif,Saudi Arabia.This work is also supported by the Faculty of Computer Science and Information Technology,University of Malaya,under Postgraduate Research Grant(PG035-2016A).
文摘Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December 2019.It was tagged as a pandemic soon by the WHO being a serious public medical conditionworldwide.In spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)images.In this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features.Impressive features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of virus.The optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning rate.This task is accomplished by exceptional mating conduct of Black Widow spiders.In this strategy,cannibalism is incorporated.During this phase,fitness outcomes are rejected and are not satisfied by the proposed model.The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces.VGG16 model identifies the imagewhich has a place with which it is dependent on the distinctions in images.The impact of the distinctions on labels during training stage is studied and predicted for test images.The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.
基金This paper has been supported by the National Natural Science Foundation of China (Grant No. 61371040).
文摘In order to improve the accuracy and stability of fruit and vegetable image recognition by single feature, this project proposed multi-feature fusion algorithms and SVM classification algorithms. This project not only introduces the Reproducing Kernel Hilbert space to improve the multi-feature compatibility and improve multi-feature fusion algorithm, but also introduces TPS transformation model in SVM classifier to improve the classification accuracy, real-time and robustness of integration feature. By using multi-feature fusion algorithms and SVM classification algorithms, experimental results show that we can recognize the common fruit and vegetable images efficiently and accurately.
