Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as...Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.展开更多
A phase-derived velocity measurement method is proposed in a wideband coherent system,based on a precise echo model considering the inner pulse Doppler effect caused by fast moving targets.The Cramer-Rao low band of v...A phase-derived velocity measurement method is proposed in a wideband coherent system,based on a precise echo model considering the inner pulse Doppler effect caused by fast moving targets.The Cramer-Rao low band of velocity measurement precision is deduced,demonstrating the high precision of the proposed method.Simulations and out-field experiments further validate the effectiveness of the proposed method in high-precision measurement and micro-motion extraction for targets with weak reflection intensity.Compared with the long-time integration approaches for velocity measurement,the phase-derived method is easy to implement and meets the requirement for high data rate,which makes it suitable for micro-motion feature extraction in wideband systems.展开更多
[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored...[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.展开更多
To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pur...To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pure volume prediction model was developed using Support Vector Regression(SVR)and Random Forest(RF),with hyperparameters fine-tuned via the Genetic Algorithm(GA).Building upon this,an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network,followed by field validation experiments.Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean absolute error,root mean square error,and mean absolute percentage error.In the extraction process of bedding boreholes,the influence of negative pressure on gas extraction concentration diminishes over time,yet it remains a critical factor in determining the extracted pure volume.In contrast,throughout the entire extraction period of cross-layer boreholes,both extracted pure volume and concentration exhibit pronounced sensitivity to fluctuations in extraction negative pressure.Field experiments demonstrated that the adaptive controlmodel enhanced the average extracted gas volume by 5.08% in the experimental borehole group compared to the control group during the later extraction stage,with a more pronounced increase of 7.15% in the first 15 days.The research findings offer essential technical support for the efficient utilization and long-term sustainable development of mine gas resources.The research findings offer essential technical support for gas disaster mitigation and the sustained,efficient utilization of mine gas.展开更多
In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction pr...In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirically analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi- mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracfing root-stock plant medicines.展开更多
A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.T...A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.展开更多
Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed vi...Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Distant supervision has the ability to generate a huge amount training data.Recently,the multi-instance multi-label learning is imported to distant supervision to combat noisy data and improve the performance of relat...Distant supervision has the ability to generate a huge amount training data.Recently,the multi-instance multi-label learning is imported to distant supervision to combat noisy data and improve the performance of relation extraction.But multi-instance multi-label learning only uses hidden variables when inference relation between entities,which could not make full use of training data.Besides,traditional lexical and syntactic features are defective reflecting domain knowledge and global information of sentence,which limits the system’s performance.This paper presents a novel approach for multi-instance multilabel learning,which takes the idea of fuzzy classification.We use cluster center as train-data and in this way we can adequately utilize sentencelevel features.Meanwhile,we extend feature set by paragraph vector,which carries semantic information of sentences.We conduct an extensive empirical study to verify our contributions.The result shows our method is superior to the state-of-the-art distant supervised baseline.展开更多
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying ...Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.展开更多
Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model ...Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.展开更多
Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. ...Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. The proposed approach combines spectral and spatial information based on the fusion of features extracted from panchromatic( PAN) and multispectral( MS) images using sparse autoencoder and its deep version. There are three steps in the proposed method,the first one is to extract spatial information of PAN image,and the second one is to describe spectral information of MS image. Finally,in the third step,the features obtained from PAN and MS images are concatenated directly as a simple fusion feature. The classification is performed using the support vector machine( SVM) and the experiments carried out on two datasets with very high spatial resolution. MS and PAN images from WorldView-2 satellite indicate that the classifier provides an efficient solution and demonstrate that the fusion of the features extracted by deep learning techniques from PAN and MS images performs better than that when these techniques are used separately. In addition,this framework shows that deep learning models can extract and fuse spatial and spectral information greatly,and have huge potential to achieve higher accuracy for classification of multispectral and panchromatic images.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intellig...The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intelligent ships.Mel scale frequency cepstral coefficient(MFCC)feature parameters are improved and optimized to form NewMFCC by introducing second-order difference and wavelet packet decomposition transformation methods in this paper.Transforming sound signals into a feature vector that fully describes the dynamic characteristics of ship sound signals and the high-and low-frequency information solves the problem of the inability to transport ordinary sound signals directly as signals for training in machine learning models.Radial basis function kernels are used to conduct support vector machine classifier simulation experiments.Five types of sound signals,namely,one type of ship sound signals and four types of interference sound signals,are categorized and identified as classification targets to verify the feasibility of the classification of ship sound signals and interference signals.The proposed method improves classification accuracy by approximately 15%.展开更多
In this paper,we present an open python procedure with Jupyter notebook,for data extraction and vectorization of geophysical explo ration profile.Constrained by observation routes and traffic conditions,geophysical ex...In this paper,we present an open python procedure with Jupyter notebook,for data extraction and vectorization of geophysical explo ration profile.Constrained by observation routes and traffic conditions,geophysical exploration profiles tend to bend curved roads for easy observation,however,it must be projected onto a straight line when data processing and analyzing.After projection,we don’t know the true position of the obtained crustal structure.Nonetheless,when the results used as an initial constraint condition for other geophysical inversion,such as gravity inversion,we need to know the true position of the data rather than the distance to the starting point.We solved this problem by profile vectorization and reprojection.The method can be used for extraction data of various geophysical exploration profiles,such as seismic reflection profiles,gravity profiles.展开更多
This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion...This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion Vector (MV) field and the macro block types are extracted from the H.264/AVC compressed video, and then merge MVs with the same characteristic. Secondly, an improved ACA is used to classify the MV field into different motion homogenous regions. At the same time, use macro block types to determine the location of objects. Finally, using the complementarities of macro block template and MVs clustering template to obtain final objects. Experimental results for several video sequences demonstrate that in the case of ensuring accuracy, the proposed approach can extract moving object faster.展开更多
The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The m...The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.展开更多
In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）....In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）. Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM＇s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.展开更多
Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature e...Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature extraction is particularly important. Linear Discriminant Analysis (LDA) is an effective feature extraction method. However, the traditional LDA cannot solve the nonlinear problem and small sample problem existing in high dimensional space. In this paper, the method of the Support Vector-based Direct Discriminant Analysis (SVDDA) is proposed. It incorporates SVM algorithm into LDA, extends SVM to nonlinear eigenspace, and optimizes eigenvalue to improve performance. Moreover, this paper combines SVDDA with the social computing theory. The experiments were tested on different face datasets. Compared with other existing methods, SVDDA has higher robustness and optimal performance.展开更多
Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy,a fault diagnosis method based on Xgboost algorithm feature extraction ...Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy,a fault diagnosis method based on Xgboost algorithm feature extraction is proposed.When the Xgboost algorithm classifies features,it generates an order of importance of the input features.The time domain features were extracted from the vibration signal of the rolling bearing,the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition.Firstly,the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy.Then,Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis.Finally,important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy.The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.展开更多
基金funded by the Chinese State Grid Jiangsu Electric Power Co.,Ltd.Science and Technology Project Funding,Grant Number J2023031.
文摘Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.
基金Supported by the National Natural Science Foundation of China(61301189)111 Project of China(B14010)
文摘A phase-derived velocity measurement method is proposed in a wideband coherent system,based on a precise echo model considering the inner pulse Doppler effect caused by fast moving targets.The Cramer-Rao low band of velocity measurement precision is deduced,demonstrating the high precision of the proposed method.Simulations and out-field experiments further validate the effectiveness of the proposed method in high-precision measurement and micro-motion extraction for targets with weak reflection intensity.Compared with the long-time integration approaches for velocity measurement,the phase-derived method is easy to implement and meets the requirement for high data rate,which makes it suitable for micro-motion feature extraction in wideband systems.
基金Supported by the National Natural Science Foundation of China(31101085)the Program for Young Core Teachers of Colleges in Henan(2011GGJS-094)the Scientific Research Project for the High Level Talents,North China University of Water Conservancy and Hydroelectric Power~~
文摘[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.
基金funded by the National Key Research and Development Program of China,grant number:2023YFF0615404.
文摘To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pure volume prediction model was developed using Support Vector Regression(SVR)and Random Forest(RF),with hyperparameters fine-tuned via the Genetic Algorithm(GA).Building upon this,an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network,followed by field validation experiments.Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean absolute error,root mean square error,and mean absolute percentage error.In the extraction process of bedding boreholes,the influence of negative pressure on gas extraction concentration diminishes over time,yet it remains a critical factor in determining the extracted pure volume.In contrast,throughout the entire extraction period of cross-layer boreholes,both extracted pure volume and concentration exhibit pronounced sensitivity to fluctuations in extraction negative pressure.Field experiments demonstrated that the adaptive controlmodel enhanced the average extracted gas volume by 5.08% in the experimental borehole group compared to the control group during the later extraction stage,with a more pronounced increase of 7.15% in the first 15 days.The research findings offer essential technical support for the efficient utilization and long-term sustainable development of mine gas resources.The research findings offer essential technical support for gas disaster mitigation and the sustained,efficient utilization of mine gas.
基金Supported by the National Natural Science Foundation of China(21146009,21376014)
文摘In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirically analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi- mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracfing root-stock plant medicines.
文摘A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.
文摘Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
文摘Distant supervision has the ability to generate a huge amount training data.Recently,the multi-instance multi-label learning is imported to distant supervision to combat noisy data and improve the performance of relation extraction.But multi-instance multi-label learning only uses hidden variables when inference relation between entities,which could not make full use of training data.Besides,traditional lexical and syntactic features are defective reflecting domain knowledge and global information of sentence,which limits the system’s performance.This paper presents a novel approach for multi-instance multilabel learning,which takes the idea of fuzzy classification.We use cluster center as train-data and in this way we can adequately utilize sentencelevel features.Meanwhile,we extend feature set by paragraph vector,which carries semantic information of sentences.We conduct an extensive empirical study to verify our contributions.The result shows our method is superior to the state-of-the-art distant supervised baseline.
基金Supported by National Science and Technology Support Program of China(Grant No.2015BAF07B04)
文摘Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.
基金supported by the National Natural Science Foundation of China (Nos.50875161 and 50821003)the Natural Science Foundation of Jiangxi Province,China (No.0450017)
文摘Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.
基金Supported by the National Natural Science Foundation of China(No.61472103,61772158,U.1711265)
文摘Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. The proposed approach combines spectral and spatial information based on the fusion of features extracted from panchromatic( PAN) and multispectral( MS) images using sparse autoencoder and its deep version. There are three steps in the proposed method,the first one is to extract spatial information of PAN image,and the second one is to describe spectral information of MS image. Finally,in the third step,the features obtained from PAN and MS images are concatenated directly as a simple fusion feature. The classification is performed using the support vector machine( SVM) and the experiments carried out on two datasets with very high spatial resolution. MS and PAN images from WorldView-2 satellite indicate that the classifier provides an efficient solution and demonstrate that the fusion of the features extracted by deep learning techniques from PAN and MS images performs better than that when these techniques are used separately. In addition,this framework shows that deep learning models can extract and fuse spatial and spectral information greatly,and have huge potential to achieve higher accuracy for classification of multispectral and panchromatic images.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
文摘The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intelligent ships.Mel scale frequency cepstral coefficient(MFCC)feature parameters are improved and optimized to form NewMFCC by introducing second-order difference and wavelet packet decomposition transformation methods in this paper.Transforming sound signals into a feature vector that fully describes the dynamic characteristics of ship sound signals and the high-and low-frequency information solves the problem of the inability to transport ordinary sound signals directly as signals for training in machine learning models.Radial basis function kernels are used to conduct support vector machine classifier simulation experiments.Five types of sound signals,namely,one type of ship sound signals and four types of interference sound signals,are categorized and identified as classification targets to verify the feasibility of the classification of ship sound signals and interference signals.The proposed method improves classification accuracy by approximately 15%.
基金the support from Science for Earthquake Resilience of the China Earthquake Administration(XH17022)the National Natural Science Foundation of China(Grant No.U1939204,No.41204014)+1 种基金the Scientific Research Fund of Institute of Seismology and Institute of Crustal Dynamics,China Earthquake Administration(Grant No.IS20146141)National Key Research and Development Plan(Grant No.2017YFC1500204).
文摘In this paper,we present an open python procedure with Jupyter notebook,for data extraction and vectorization of geophysical explo ration profile.Constrained by observation routes and traffic conditions,geophysical exploration profiles tend to bend curved roads for easy observation,however,it must be projected onto a straight line when data processing and analyzing.After projection,we don’t know the true position of the obtained crustal structure.Nonetheless,when the results used as an initial constraint condition for other geophysical inversion,such as gravity inversion,we need to know the true position of the data rather than the distance to the starting point.We solved this problem by profile vectorization and reprojection.The method can be used for extraction data of various geophysical exploration profiles,such as seismic reflection profiles,gravity profiles.
基金Supported by the Shanghai Normal University Funded Project(No.SK 201127)
文摘This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion Vector (MV) field and the macro block types are extracted from the H.264/AVC compressed video, and then merge MVs with the same characteristic. Secondly, an improved ACA is used to classify the MV field into different motion homogenous regions. At the same time, use macro block types to determine the location of objects. Finally, using the complementarities of macro block template and MVs clustering template to obtain final objects. Experimental results for several video sequences demonstrate that in the case of ensuring accuracy, the proposed approach can extract moving object faster.
基金Supported by the Project of National Key Laboratory Fund
文摘The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.
基金Supported by National Natural Science Foundation of China(NSFC)(61232014,61421062,61472010)National Key Technology R&D Program of China(2015BAK01B06)
文摘In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）. Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM＇s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.
文摘Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature extraction is particularly important. Linear Discriminant Analysis (LDA) is an effective feature extraction method. However, the traditional LDA cannot solve the nonlinear problem and small sample problem existing in high dimensional space. In this paper, the method of the Support Vector-based Direct Discriminant Analysis (SVDDA) is proposed. It incorporates SVM algorithm into LDA, extends SVM to nonlinear eigenspace, and optimizes eigenvalue to improve performance. Moreover, this paper combines SVDDA with the social computing theory. The experiments were tested on different face datasets. Compared with other existing methods, SVDDA has higher robustness and optimal performance.
基金This work was supported by The National Natural Science Foundation of China(No.51475086)Fundamental Research Funds for the Central Universities of China(No.N162312001)CAST-BISEE Foundation(No.CAST-BISEE2019-019).
文摘Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy,a fault diagnosis method based on Xgboost algorithm feature extraction is proposed.When the Xgboost algorithm classifies features,it generates an order of importance of the input features.The time domain features were extracted from the vibration signal of the rolling bearing,the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition.Firstly,the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy.Then,Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis.Finally,important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy.The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.
