Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in te...Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.展开更多
Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in...Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.展开更多
In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical featu...In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical features and the elements of 3D solid. Various modes based on different datum geometrical elements, such as vertex, curve, surface, and so on, are then designed for generating local refined mesh. With the guidance of the defmed criteria, different modes are automatically selected to apply on the appropriate datum objects to program the element size in the local special areas. As a result, the control information of element size is successfully programmed covering the entire domain based on the geometrical features of 3D solid. A new algorithm based on Delatmay triangulation is then developed for generating 3D adaptive finite element mesh, in which the element size is dynamically specified to catch the geometrical features and suitable tetrahedron facets are selected to locate interior nodes continuously. As a result, adaptive mesh with good-quality elements is generated. Examples show that the proposed method can be successfully applied to adaptive finite element mesh automatic generation based on the geometrical features of 3D solid.展开更多
In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the ...ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.展开更多
This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online ide...This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.展开更多
A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the develo...A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.展开更多
In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures str...In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures strikes are different compared with those in the research region. In other words, the geophysical advantageous directions from the gravity and magnetic anomalies are not the same as those caused by the surface structures. The local horizontal gradient results from the gravity and magnetic anomalies show that the majority of earthquakes occur along an intense fault zone, which is a zone of abrupt gravity and negative magnetic change, where the shapes match very well. From the distribution of earthquakes in this area, we find that it has experienced more than 11 earthquake events with magnitude larger than Ms7.0. In addition, water development sites such as Jinshajiang, Lancangjiang, and the Red River and Pearl River watersheds have been hit ten times by earthquakes of this magnitude. It is observed that strong earthquakes occur frequently in the Holocene active fault zone.展开更多
Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning ...Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.Methods：Using self-developed CM clinical scales to collect cases,inquiry information,complexity,tongue manifestation and pulse manifestation were assessed.The number of cases collected was 2,218.Firstly,each case was differentiated by two CM specialists according to the same diagnostic criteria.The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed.Secondly,take the same diagnosis syndromes of two specialists as the results of the cases.According to injury information in the CM scale ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂,and according to the syndrome type in each case ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂.CM information data on cardiovascular disease cases were established.We studied CM syndrome classification and identification based on the relevant feature for each label（REAL） leaming method,and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5,10,15,20,30,50,70,and 100,respectively.Results：The syndromes with good diagnostic consistency were Heart（Xin）-qi deficiency,Heart-yang deficiency,Heart-yin deficiency,phlegm,stagnation of blood and stagnation of qi.Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver（Gan）.The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency.A different number of features,such as 5,10,15,20,30,40,50,70,and 100,respectively,were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy.The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.Conclnsions：CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency.The REAL method fully considers the relationship between syndrome types and injury symptoms,and is suitable for the establishment of models for CM syndrome classification and identification.This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.展开更多
For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed...For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.展开更多
OBJECTIVE: To study the sonographic features and patterns of cystic renal carcinomas. METHODS: Thirteen cases of cystic renal carcinoma confirmed by operation and pathology were examined by ultrasonography, and the cy...OBJECTIVE: To study the sonographic features and patterns of cystic renal carcinomas. METHODS: Thirteen cases of cystic renal carcinoma confirmed by operation and pathology were examined by ultrasonography, and the cystic walls, septa and solid mural nodules were studied. RESULTS: Solid mural nodules of some cases and irregular thickening of the cystic walls and septa were characteristic findings for the ultrasonic diagnosis of cystic renal carcinomas. According to their pathologic mechanisms and sonographic features, cystic renal carcinomas were classified into 3 patterns: unilocular cystic mass, multiloculated cystic mass and cystic-solid mass. CONCLUSIONS: Typical cystic renal carcinomas can be well diagnosed, while atypical cases may be misdiagnosed as benign renal cysts by ultrasonography. Color Doppler ultrasonography and needle aspiration guided by ultrasonography are helpful in the diagnosis of these atypical cases.展开更多
In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and rec...In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various delormations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.展开更多
A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this st...A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.展开更多
Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which wou...Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which would provide a reliable path for visual navigation.The mean image of pixel value in five channels(R,G,B,S and V)were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm,for achieving precise location of the feature points and ensuring the uniformity and density of the feature points.The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45ºin eight directions was then treated as the feature vector,and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points.In order to achieve the depth information of farmland road images,the energy method of feature points was used for eliminating the mismatched points.Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48%and 5.6 s,respectively,with the accuracy for depth recovery of-7.17%-2.97%in a certain sight distance.The mean uniformity,time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%,5.0-6.5 s,and higher than 88%,respectively,indicating that performance for achieving the feature points(e.g.,uniformity,matching accuracy,and algorithm real-time)of the improved SIFT algorithm were superior to that of conventional SIFT algorithm.This study provides an important reference for navigation technology of agricultural equipment based on machine vision.展开更多
基金supported by the NSFC Major Research Plan--Interpretable and Generalpurpose Next-generation Artificial Intelligence(No.92370205).
文摘Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.
基金This research was funded by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia under the Project Number(77/442).
文摘Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.
基金This project is supported by Provincial Project Foundation of Science and Technology of Guangdong, China(No.2002104040101).
文摘In order to provide a guidance to specify the element size dynamically during adaptive finite element mesh generation, adaptive criteria are firstly defined according to the relationships between the geometrical features and the elements of 3D solid. Various modes based on different datum geometrical elements, such as vertex, curve, surface, and so on, are then designed for generating local refined mesh. With the guidance of the defmed criteria, different modes are automatically selected to apply on the appropriate datum objects to program the element size in the local special areas. As a result, the control information of element size is successfully programmed covering the entire domain based on the geometrical features of 3D solid. A new algorithm based on Delatmay triangulation is then developed for generating 3D adaptive finite element mesh, in which the element size is dynamically specified to catch the geometrical features and suitable tetrahedron facets are selected to locate interior nodes continuously. As a result, adaptive mesh with good-quality elements is generated. Examples show that the proposed method can be successfully applied to adaptive finite element mesh automatic generation based on the geometrical features of 3D solid.
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
文摘ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.
基金supported by the State Grid Science&Technology Project(5100-202114296A-0-0-00).
文摘This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.
基金support from the Ministry of Education(MOE) Singapore Tier 1 (RG8/20)。
文摘A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.
基金supported by the Chinese Earthquake Administration,Institute of Seismology Foundation(IS201326126)Chinese earthquake scientific array exploration northern section of North South Seismic Belt gravity profile Foundation(201308011)
文摘In this study, we analyzed the gravity and, magnetic characteristics, and the occurrence of a fault zone and discussed the relationships between the two locations. The results reveal that the subsurface structures strikes are different compared with those in the research region. In other words, the geophysical advantageous directions from the gravity and magnetic anomalies are not the same as those caused by the surface structures. The local horizontal gradient results from the gravity and magnetic anomalies show that the majority of earthquakes occur along an intense fault zone, which is a zone of abrupt gravity and negative magnetic change, where the shapes match very well. From the distribution of earthquakes in this area, we find that it has experienced more than 11 earthquake events with magnitude larger than Ms7.0. In addition, water development sites such as Jinshajiang, Lancangjiang, and the Red River and Pearl River watersheds have been hit ten times by earthquakes of this magnitude. It is observed that strong earthquakes occur frequently in the Holocene active fault zone.
基金Supported by the National Natural Science Foundation of China(No.81173199)
文摘Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.Methods：Using self-developed CM clinical scales to collect cases,inquiry information,complexity,tongue manifestation and pulse manifestation were assessed.The number of cases collected was 2,218.Firstly,each case was differentiated by two CM specialists according to the same diagnostic criteria.The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed.Secondly,take the same diagnosis syndromes of two specialists as the results of the cases.According to injury information in the CM scale ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂,and according to the syndrome type in each case ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂.CM information data on cardiovascular disease cases were established.We studied CM syndrome classification and identification based on the relevant feature for each label（REAL） leaming method,and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5,10,15,20,30,50,70,and 100,respectively.Results：The syndromes with good diagnostic consistency were Heart（Xin）-qi deficiency,Heart-yang deficiency,Heart-yin deficiency,phlegm,stagnation of blood and stagnation of qi.Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver（Gan）.The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency.A different number of features,such as 5,10,15,20,30,40,50,70,and 100,respectively,were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy.The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.Conclnsions：CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency.The REAL method fully considers the relationship between syndrome types and injury symptoms,and is suitable for the establishment of models for CM syndrome classification and identification.This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.
文摘For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.
文摘OBJECTIVE: To study the sonographic features and patterns of cystic renal carcinomas. METHODS: Thirteen cases of cystic renal carcinoma confirmed by operation and pathology were examined by ultrasonography, and the cystic walls, septa and solid mural nodules were studied. RESULTS: Solid mural nodules of some cases and irregular thickening of the cystic walls and septa were characteristic findings for the ultrasonic diagnosis of cystic renal carcinomas. According to their pathologic mechanisms and sonographic features, cystic renal carcinomas were classified into 3 patterns: unilocular cystic mass, multiloculated cystic mass and cystic-solid mass. CONCLUSIONS: Typical cystic renal carcinomas can be well diagnosed, while atypical cases may be misdiagnosed as benign renal cysts by ultrasonography. Color Doppler ultrasonography and needle aspiration guided by ultrasonography are helpful in the diagnosis of these atypical cases.
文摘In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various delormations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.
文摘A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.
基金This work was financially supported by the Zhejiang Science and Technology Department Basic Public Welfare Research Project(LGN18F030001)the Major Project of Zhejiang Science and Technology Department(2016C02G2100540).
文摘Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which would provide a reliable path for visual navigation.The mean image of pixel value in five channels(R,G,B,S and V)were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm,for achieving precise location of the feature points and ensuring the uniformity and density of the feature points.The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45ºin eight directions was then treated as the feature vector,and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points.In order to achieve the depth information of farmland road images,the energy method of feature points was used for eliminating the mismatched points.Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48%and 5.6 s,respectively,with the accuracy for depth recovery of-7.17%-2.97%in a certain sight distance.The mean uniformity,time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%,5.0-6.5 s,and higher than 88%,respectively,indicating that performance for achieving the feature points(e.g.,uniformity,matching accuracy,and algorithm real-time)of the improved SIFT algorithm were superior to that of conventional SIFT algorithm.This study provides an important reference for navigation technology of agricultural equipment based on machine vision.
