In this paper, a new systematic methed of quantitative DFA is presented based on the function analysis.The reduction of the number of components forming product is realized by incorporating some parts as the features ...In this paper, a new systematic methed of quantitative DFA is presented based on the function analysis.The reduction of the number of components forming product is realized by incorporating some parts as the features of others. In order to evaluate assemblability of a product objectively, accurately and completely, the factors affecting assembability have been identified in terms of the production mode used to assemble product, and neural network and fuzzy set theory are adopted to quantify the effect of factors on assemblability. A case study is given, and the results demonstrate the effectiveness and validity of the method.展开更多
Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normali...Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, emotion recognition. Features based on prosody then derivate a Modified QDF （MQDF） to speech and voice quality are extracted and Principal Component Analysis Neural Network （PCANN） is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.展开更多
In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous p...In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.展开更多
The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain par...The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.展开更多
When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year...When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MFDFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data.展开更多
Objective: To delineate reliable morphological characteristics for identifying and separating female Phlebotomus caucasicus and Phlebotomus mongolensis which exist sympatrically in the main foci of zoonotic cutaneous ...Objective: To delineate reliable morphological characteristics for identifying and separating female Phlebotomus caucasicus and Phlebotomus mongolensis which exist sympatrically in the main foci of zoonotic cutaneous leishmaniasis in Iran.Methods: Sand flies were collected using sticky trap papers from active colonies of rodent burrows installed from 16 catching sites. Morphometric measurements were analyzed of 87 Phlebotomus caucasicus and 156 Phlebotomus mongolensis. Univariate and multivariate analysis were carried out to determine significant morphometric variables for discrimination of the two species. Finally, seven morphological characteristics of 65 female Phlebotomus caucasicus and 124 female Phlebotomus mongolensis were described.Results: Univariate and multivariate analyses of 10 morphometric variables via Discriminant Function Analysis(DFA) and Principal Component Analysis(PCA) showed that five morphometric variables had an accuracy of 100% for discriminating female Phlebotomus caucasicus and Phlebotomus mongolensis. Moreover, PCA revealed that the five morphometric variables with the highest loadings separated these two species. Morphological studies on antennal flagellum(and its associated structures) and mouth-parts of female specimens demonstrated significant differences in several structures.Conclusions: The results show that morphological and morphometrical features can be used to discriminate two female isomorphic species, Phlebotomus caucasicus and Phlebotomus mongolensis accurately.展开更多
Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects ...Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.展开更多
In this study, we analyze brain activity data describing functional magnetic resonance imaging (MRI) imaging of 820 subjects with each subject being scanned at 4 different times. This multiple scanning gives us an opp...In this study, we analyze brain activity data describing functional magnetic resonance imaging (MRI) imaging of 820 subjects with each subject being scanned at 4 different times. This multiple scanning gives us an opportunity to observe the consistency of imaging characteristics within the subjects as compared to the variability across the subjects. The most consistent characteristics are then used for the purpose of predicting subjects’ traits. We concentrate on four predictive methods (Regression, Logistic Regression, Linear Discriminant Analysis and Random Forest) in order to predict subjects’ traits such as gender and age based on the brain activities observed between brain regions. Those predictions are done based on the adjusted communication activity among the brain regions, as assessed from 4 scans of each subject. Due to a large number of such communications among the 116 brain regions, we performed a preliminary selection of the most promising pairs of brain regions. Logistic Regression performed best in classifying the subject gender based on communication activity among the brain regions. The accuracy rate was 85.6 percent for an AIC step-wise selected Logistic Regression model. On the other hand, the Logistic Regression model maintaining the entire set of ranked predictor was capable of getting an 87.7 percent accuracy rate. It is interesting to point out that the model with the AIC selected features was better classifying males, whereas the complete ranked model was better classifying females. The Random Forest technique performed best for prediction of age (grouped within five categories as provided by the original data) with 48.8 percent accuracy rate. Any set of predictors between 200 and 1600 was presenting similar rates of accuracy.展开更多
Until recently,Limosa limosa melanuroides was thought to be the only subspecies of Black-tailed Godwit in the East Asian-Australasian Flyway.For this reason,all previous occurrences and counts of Black-tailed Godwits ...Until recently,Limosa limosa melanuroides was thought to be the only subspecies of Black-tailed Godwit in the East Asian-Australasian Flyway.For this reason,all previous occurrences and counts of Black-tailed Godwits in the flyway have been assigned to melanuroides.However,a larger-bodied subspecies,bohaii,has recently been discovered in the flyway.As a result,the occurrence of Black-tailed Godwits in the flyway needs to be reconsidered such that the specific distribution of each subspecies becomes known.To this end,we developed a simple discriminant function to assign individuals to subspecies based on their bill and wing length.Cross-validation with individuals known to be bohaii or melanuroides,based on molecular analysis,showed the developed function to be 97.7%accurate.When applied to measurements of godwits captured at 22 sites across 9 countries in East-Southeast Asia and Australia,we found that bohaii and melanuroides occurred at most sites and overlapped in their distribution from Kamchatka to Australia.We examined photos from all along the flyway to verify this surprising result,confirming that both subspecies co-occur in most locations.Based on these results,we hypothesise that bohaii and melanuroides from the west of their breeding ranges mostly migrate over Chinese mainland.Birds of both subspecies from the east of their ranges are expected to migrate along the Pacific Ocean.We encourage ringing groups in East-Southeast Asia and Australia to use this simple method to keep adding knowledge about Black-tailed Godwits in the East Asian-Australasian Flyway.展开更多
文摘In this paper, a new systematic methed of quantitative DFA is presented based on the function analysis.The reduction of the number of components forming product is realized by incorporating some parts as the features of others. In order to evaluate assemblability of a product objectively, accurately and completely, the factors affecting assembability have been identified in terms of the production mode used to assemble product, and neural network and fuzzy set theory are adopted to quantify the effect of factors on assemblability. A case study is given, and the results demonstrate the effectiveness and validity of the method.
基金the Ministry of Education Fund (No: 20050286001)Ministry of Education "New Century Tal-ents Support Plan" (No:NCET-04-0483)Doctoral Foundation of Ministry of Education (No:20050286001).
文摘Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, emotion recognition. Features based on prosody then derivate a Modified QDF （MQDF） to speech and voice quality are extracted and Principal Component Analysis Neural Network （PCANN） is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.
文摘In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.
文摘The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.
基金Supported by the NSFC-Shandong Joint Fund “Study on the DisasterCausing Mechanism and Disaster Prevention Countermeasures of MultiSource Storm Surges”(No.U1706226)the National Natural Science Foundation of China “Coastal Engineering and Risk Assessment Based on a Four-Layer Nested Multi-Objective Probability Model”(No.51379195)+1 种基金the Natural Science Foundation of Shandong Province “Three-Layer Nested Multi-Objective Probability Prediction and Risk Assessment in Coastal Engineering”(No.ZR2013EEM034)the Program of Promotion Plan for Postgraduates’ Educational Quality “Paying More Attention to the Study on the Cultivation Mode of Mathematical Modeling for Engineering Postgraduates”(No.861801232417)
文摘When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MFDFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data.
基金supported by Tehran University of Medical Sciences(Project No.27252)
文摘Objective: To delineate reliable morphological characteristics for identifying and separating female Phlebotomus caucasicus and Phlebotomus mongolensis which exist sympatrically in the main foci of zoonotic cutaneous leishmaniasis in Iran.Methods: Sand flies were collected using sticky trap papers from active colonies of rodent burrows installed from 16 catching sites. Morphometric measurements were analyzed of 87 Phlebotomus caucasicus and 156 Phlebotomus mongolensis. Univariate and multivariate analysis were carried out to determine significant morphometric variables for discrimination of the two species. Finally, seven morphological characteristics of 65 female Phlebotomus caucasicus and 124 female Phlebotomus mongolensis were described.Results: Univariate and multivariate analyses of 10 morphometric variables via Discriminant Function Analysis(DFA) and Principal Component Analysis(PCA) showed that five morphometric variables had an accuracy of 100% for discriminating female Phlebotomus caucasicus and Phlebotomus mongolensis. Moreover, PCA revealed that the five morphometric variables with the highest loadings separated these two species. Morphological studies on antennal flagellum(and its associated structures) and mouth-parts of female specimens demonstrated significant differences in several structures.Conclusions: The results show that morphological and morphometrical features can be used to discriminate two female isomorphic species, Phlebotomus caucasicus and Phlebotomus mongolensis accurately.
基金supported by National Research Foundation(NRF)of Korea under the auspices of the Ministry of Science and ICT,Republic of Korea(No.NRF-2020R1A2B5B03096000,to KSH).
文摘Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.
文摘In this study, we analyze brain activity data describing functional magnetic resonance imaging (MRI) imaging of 820 subjects with each subject being scanned at 4 different times. This multiple scanning gives us an opportunity to observe the consistency of imaging characteristics within the subjects as compared to the variability across the subjects. The most consistent characteristics are then used for the purpose of predicting subjects’ traits. We concentrate on four predictive methods (Regression, Logistic Regression, Linear Discriminant Analysis and Random Forest) in order to predict subjects’ traits such as gender and age based on the brain activities observed between brain regions. Those predictions are done based on the adjusted communication activity among the brain regions, as assessed from 4 scans of each subject. Due to a large number of such communications among the 116 brain regions, we performed a preliminary selection of the most promising pairs of brain regions. Logistic Regression performed best in classifying the subject gender based on communication activity among the brain regions. The accuracy rate was 85.6 percent for an AIC step-wise selected Logistic Regression model. On the other hand, the Logistic Regression model maintaining the entire set of ranked predictor was capable of getting an 87.7 percent accuracy rate. It is interesting to point out that the model with the AIC selected features was better classifying males, whereas the complete ranked model was better classifying females. The Random Forest technique performed best for prediction of age (grouped within five categories as provided by the original data) with 48.8 percent accuracy rate. Any set of predictors between 200 and 1600 was presenting similar rates of accuracy.
基金funded by the National Natural Science Foundation of China[31830089,31801985,32270518]。
文摘Until recently,Limosa limosa melanuroides was thought to be the only subspecies of Black-tailed Godwit in the East Asian-Australasian Flyway.For this reason,all previous occurrences and counts of Black-tailed Godwits in the flyway have been assigned to melanuroides.However,a larger-bodied subspecies,bohaii,has recently been discovered in the flyway.As a result,the occurrence of Black-tailed Godwits in the flyway needs to be reconsidered such that the specific distribution of each subspecies becomes known.To this end,we developed a simple discriminant function to assign individuals to subspecies based on their bill and wing length.Cross-validation with individuals known to be bohaii or melanuroides,based on molecular analysis,showed the developed function to be 97.7%accurate.When applied to measurements of godwits captured at 22 sites across 9 countries in East-Southeast Asia and Australia,we found that bohaii and melanuroides occurred at most sites and overlapped in their distribution from Kamchatka to Australia.We examined photos from all along the flyway to verify this surprising result,confirming that both subspecies co-occur in most locations.Based on these results,we hypothesise that bohaii and melanuroides from the west of their breeding ranges mostly migrate over Chinese mainland.Birds of both subspecies from the east of their ranges are expected to migrate along the Pacific Ocean.We encourage ringing groups in East-Southeast Asia and Australia to use this simple method to keep adding knowledge about Black-tailed Godwits in the East Asian-Australasian Flyway.
