This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique...This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis（DDA） and advanced Monte Carlo（MC） technique to（1） enable more efficient MC by using a response surface model,（2） calibrate parameters with an iterative test-analysis correlation based upon DDA, and（3） utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed stochastic method makes it possible to obtain a precise model updating outcome with acceptable calculation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influences the iterative calibration process and thus, the calibration outcome, indicating that an integration of different metrics might yield improved results.展开更多
A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra a...A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra are decomposed by WT for data com-pression and a forward feature selection is further employed to extract the relevant informationfrom the wavelet coefficients,reducing both classification errors and model complexity.Adiscriminant-CAIMAN(D-CAIMAN)method is utilized to build the classification model inwavelet domain on the basis of reduced wavelet coefficients of spectral variables.NIR spectradata set of 265 salviae miltiorrhizae radia samples from 9 different geographical origins is usedas an example to test the classification performance of the algorithm.For a comparison,k-nearest neighbor(KNN),linear discriminant analysis(LDA)and quadratic discriminant analysis(QDA)methods are also employed.D-CAIMAN with wavelet-based feature selection(WD-CAIMAN)method shows the best performance,achieving the total classification rate of ioo%in both cross-validation set and prediction set.It is worth noting that the WD-CAIMANclassifier also shows improved sensitivity,selectivity and model interpretability in thecla.ssifications.展开更多
The discrete excitation-emission-matrix fluorescence spectra (EEMS) at 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, and 590 nm) and emission wavelengths ranging from 600-750 nm wer...The discrete excitation-emission-matrix fluorescence spectra (EEMS) at 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, and 590 nm) and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species. A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed. For laboratory simulatively mixed samples, the samples mixed from 43 algal species (the algae of one division accounted for 25%, 50%, 75%, 85%, and 100% of the gross biomass, respectively), the average discrimination rates at the level of division were 65.0%, 87.5%, 98.6%, 99.0%, and 99.1%, with average relative contents of 18.9%, 44.5%, 68.9%, 73.4%, and 82.9%, respectively; the samples mixed from 32 red tide algal species (the dominant species accounted for 60%, 70%, 80%, 90%, and 100% of the gross biomass, respectively), the average correct discrimination rates of the dominant species at the level of genus were 63.3%, 74.2%, 78.8%, 83.4%, and 79.4%, respectively. For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass (chlorophyll), the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus, respectively. For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007, the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level; for the 12 samples obtained from Jiaozhou Bay in August 2007, the dominant species of all the 12 samples were recognized at the division level. The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for phytoplankton population.展开更多
OBJECTIVE:To screen the target population with symptomatic bradyarrhythmia for pharmacotherapy.METHODS:This study use database from clinical trial that use Yuanjiang decoction(元姜方),a traditional Chinese medicinal p...OBJECTIVE:To screen the target population with symptomatic bradyarrhythmia for pharmacotherapy.METHODS:This study use database from clinical trial that use Yuanjiang decoction(元姜方),a traditional Chinese medicinal prescription.Eligible participants were recruited and treated with Yuanjiang decoction(composed of 6 Chinese herbal medicines),200 m L twice daily.Cross-contingency analysis,logistic regression analysis,interaction analysis,discriminant analysis and 10-fold cross validation methods were used to establish discriminant model.RESULTS:This study concluded that the clinical treatment of bradyarrhythmia has a clear effect.Low minimum heart rate,high maximum heart rate are risk factors that affect the efficacy.Patients with only one comorbid disease did not significantly affect the efficacy,but patient with two or more diseases of coronary heart disease,hypertension,paroxysmal atrial fibrillation,premature ventricular contraction and premature atrial contraction at the same time did not have a good effect.Using the discriminant analysis method to establish a efficacy prediction model,y=0.07X1+0.16X2-0.65X3-1.12X4-0.71X5-0.75X6-0.91X7(X1=24 h mean heart rate,X2=minimum heart rate,X3=coronary heart disease,X4=paroxysmal atrial fibrillation,X5=premature ventricular contraction,X6=sinus block,X7=atrioventricular block).CONCLUSION:Our model based on the clinical features of patients with bradyarrhythmia.Should be useful aid for predicting pharmacotherapy response and could screen the optimal pharmacotherapy target.展开更多
The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic mod...A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samp...Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.展开更多
Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake predi...Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.展开更多
In this paper, firstly, we propose a new method for choosing regularization parameter λ for lasso regression, which differs from traditional method such as multifold cross-validation, our new method gives the maximum...In this paper, firstly, we propose a new method for choosing regularization parameter λ for lasso regression, which differs from traditional method such as multifold cross-validation, our new method gives the maximum value of parameter λ directly. Secondly, by considering another prior form over model space in the Bayes approach, we propose a new extended Bayes information criterion family, and under some mild condition, our new EBIC (NEBIC) is shown to be consistent. Then we apply our new method to choose parameter for sequential lasso regression which selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized in the subsequent steps. Then sequential lasso uses NEBIC as the stopping rule. Finally, we apply our algorithm to identify the nonzero entries of precision matrix for high-dimensional linear discrimination analysis. Simulation results demonstrate that our algorithm has a lower misclassification rate and less computation time than its competing methods under considerations.展开更多
Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In ord...Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.展开更多
A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directl...A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.展开更多
[Objective] The aim of this study was to establish mathematical models for judging the aroma types of middle and upper flue-cured tobacco leaves according to the contents and proportions of aroma compositions. [Method...[Objective] The aim of this study was to establish mathematical models for judging the aroma types of middle and upper flue-cured tobacco leaves according to the contents and proportions of aroma compositions. [Method] The aroma types of tobacco leaves were judged based on stepwise discriminant analysis, using 63 C3F and 65 B2F tobacco leaf samples from 13 tobacco producing regions in 11 provinces of China （Huili in Sichuan, Baokang in Hubei, Wulong in Chongqing, Lu- oyang in Henan, Zhucheng in Shandong, Wuyi Mountain in Fujian, Malong in Yun- nan, Chuxiong in Yunnan, Bijie in Guizhou, Liuyang in Hunan, Suiyang in Guizhou, Kaiyuan in Liaoning, Nanxiong in Guangdong） as calibration samples, and 67 aroma components as indices. And the Fisher discriminant functions were verified using 21 C3F and 19 B2F tobacco leaf samples. [Result] Variation coefficients of the propor- tions were lower than that of contents of most aroma components in middle and upper leaves of the samples, indicating that the proportions were more stable than contents of aroma components. The proportions of benzyl alcohol, solanone, β-dam- ascone, neophytadiene, farnesylacetone A, palmitic acid, thunbergol, methyl linole- nate and cembratriene-diol were all over 1% in both middle and upper leaves, al- though the dominant aroma components of the same aroma type varied between middle and upper leaves. Moreover, 11, 18, 7 and 11 aroma components were re- spectively introduced into the Fisher discriminant functions established based on the contents and proportions of middle and upper flue-cured tobacco leaves, which ex- hibited accuracy rates of 91.7%, 100%, 91.7% and 91.7% in the judgments of other tobacco leaf samples. The results revealed that the components those determined aroma types in middle leaves were obviously more than in upper leaves. In middle leaves, the accuracy rates of aroma type judgment could be improved by using the proportions rather than the contents of aroma components as indices. However, the functions based on the proportions and the contents of aroma components in upper leaves gave close accuracy rates. [Conclusion] The results of the study will provide references for identifying aroma types of flue-cured tobacco leaves in future work.展开更多
Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that re...Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate 3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.展开更多
Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In or...Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online fault diagnosis, sufficiently utilize the finite information of faults, and enhance the diagnostic performance, an improved multi-model Fisher discriminant analysis is represented. The trait of the proposed method is that the training data sets are made of the current measured information and the past major discriminant information, and not only the current information or the whole batch data. An industrial typical multi-stage streptomycin fermentation process is used to test the performance of fault diagnosis of the proposed method.展开更多
Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., in...Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.展开更多
A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the p...A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.展开更多
Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and n...Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.展开更多
A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,t...A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.展开更多
基金supported by the National Natural Science Foundation of China (No. 10972019)the Innovation Foundation of BUAA for Ph.D. Graduates of China, and the China Scholarship Council
文摘This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis（DDA） and advanced Monte Carlo（MC） technique to（1） enable more efficient MC by using a response surface model,（2） calibrate parameters with an iterative test-analysis correlation based upon DDA, and（3） utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed stochastic method makes it possible to obtain a precise model updating outcome with acceptable calculation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influences the iterative calibration process and thus, the calibration outcome, indicating that an integration of different metrics might yield improved results.
基金Financial support from China Postdoctoral Science Foundation Special Funded Project(2013T60604)Zhejang Provincial Public Welfare Application Project of China(2012C21102)are gratefully acknowledged.
文摘A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra are decomposed by WT for data com-pression and a forward feature selection is further employed to extract the relevant informationfrom the wavelet coefficients,reducing both classification errors and model complexity.Adiscriminant-CAIMAN(D-CAIMAN)method is utilized to build the classification model inwavelet domain on the basis of reduced wavelet coefficients of spectral variables.NIR spectradata set of 265 salviae miltiorrhizae radia samples from 9 different geographical origins is usedas an example to test the classification performance of the algorithm.For a comparison,k-nearest neighbor(KNN),linear discriminant analysis(LDA)and quadratic discriminant analysis(QDA)methods are also employed.D-CAIMAN with wavelet-based feature selection(WD-CAIMAN)method shows the best performance,achieving the total classification rate of ioo%in both cross-validation set and prediction set.It is worth noting that the WD-CAIMANclassifier also shows improved sensitivity,selectivity and model interpretability in thecla.ssifications.
基金supported by National High-Tech Research and Development Program of China (863 Program)(No.2009AA063005)Natural Science Foundation of Shandong Province (No.ZR2009EM001)
文摘The discrete excitation-emission-matrix fluorescence spectra (EEMS) at 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, and 590 nm) and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species. A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed. For laboratory simulatively mixed samples, the samples mixed from 43 algal species (the algae of one division accounted for 25%, 50%, 75%, 85%, and 100% of the gross biomass, respectively), the average discrimination rates at the level of division were 65.0%, 87.5%, 98.6%, 99.0%, and 99.1%, with average relative contents of 18.9%, 44.5%, 68.9%, 73.4%, and 82.9%, respectively; the samples mixed from 32 red tide algal species (the dominant species accounted for 60%, 70%, 80%, 90%, and 100% of the gross biomass, respectively), the average correct discrimination rates of the dominant species at the level of genus were 63.3%, 74.2%, 78.8%, 83.4%, and 79.4%, respectively. For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass (chlorophyll), the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus, respectively. For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007, the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level; for the 12 samples obtained from Jiaozhou Bay in August 2007, the dominant species of all the 12 samples were recognized at the division level. The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for phytoplankton population.
基金National Natural Science Foundation of China(No.82004352)National Medicine Master's Inheritance Studio Construction Project of the State Administration of Traditional Chinese Medicine(Weng Weiliang Academic Succession Studio)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ14-YQ-006)。
文摘OBJECTIVE:To screen the target population with symptomatic bradyarrhythmia for pharmacotherapy.METHODS:This study use database from clinical trial that use Yuanjiang decoction(元姜方),a traditional Chinese medicinal prescription.Eligible participants were recruited and treated with Yuanjiang decoction(composed of 6 Chinese herbal medicines),200 m L twice daily.Cross-contingency analysis,logistic regression analysis,interaction analysis,discriminant analysis and 10-fold cross validation methods were used to establish discriminant model.RESULTS:This study concluded that the clinical treatment of bradyarrhythmia has a clear effect.Low minimum heart rate,high maximum heart rate are risk factors that affect the efficacy.Patients with only one comorbid disease did not significantly affect the efficacy,but patient with two or more diseases of coronary heart disease,hypertension,paroxysmal atrial fibrillation,premature ventricular contraction and premature atrial contraction at the same time did not have a good effect.Using the discriminant analysis method to establish a efficacy prediction model,y=0.07X1+0.16X2-0.65X3-1.12X4-0.71X5-0.75X6-0.91X7(X1=24 h mean heart rate,X2=minimum heart rate,X3=coronary heart disease,X4=paroxysmal atrial fibrillation,X5=premature ventricular contraction,X6=sinus block,X7=atrioventricular block).CONCLUSION:Our model based on the clinical features of patients with bradyarrhythmia.Should be useful aid for predicting pharmacotherapy response and could screen the optimal pharmacotherapy target.
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
基金Project (2010CB732004) supported by the National Basic Research Program of China
文摘A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
基金The National Natural Science Foundation of China(No.61231002,61273266)the Ph.D.Programs Foundation of Ministry of Education of China(No.20110092130004)
文摘Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.
文摘Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.
文摘In this paper, firstly, we propose a new method for choosing regularization parameter λ for lasso regression, which differs from traditional method such as multifold cross-validation, our new method gives the maximum value of parameter λ directly. Secondly, by considering another prior form over model space in the Bayes approach, we propose a new extended Bayes information criterion family, and under some mild condition, our new EBIC (NEBIC) is shown to be consistent. Then we apply our new method to choose parameter for sequential lasso regression which selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized in the subsequent steps. Then sequential lasso uses NEBIC as the stopping rule. Finally, we apply our algorithm to identify the nonzero entries of precision matrix for high-dimensional linear discrimination analysis. Simulation results demonstrate that our algorithm has a lower misclassification rate and less computation time than its competing methods under considerations.
基金Supported by the National Basic Research Program of China （2013CB733600）, the National Natural Science Foundation of China （21176073）, the Doctoral Fund of Ministry of Education of China （20090074110005）, the Program for New Century Excellent Talents in University （NCET-09-0346）, Shu Guang Project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
基金The National Natural Science Foundation of China (No.61374194)
文摘A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.
基金Supported by the Fund from Hongyun Honghe Tobacco(Group)Co.Ltd.(HYHH2012YL01)~~
文摘[Objective] The aim of this study was to establish mathematical models for judging the aroma types of middle and upper flue-cured tobacco leaves according to the contents and proportions of aroma compositions. [Method] The aroma types of tobacco leaves were judged based on stepwise discriminant analysis, using 63 C3F and 65 B2F tobacco leaf samples from 13 tobacco producing regions in 11 provinces of China （Huili in Sichuan, Baokang in Hubei, Wulong in Chongqing, Lu- oyang in Henan, Zhucheng in Shandong, Wuyi Mountain in Fujian, Malong in Yun- nan, Chuxiong in Yunnan, Bijie in Guizhou, Liuyang in Hunan, Suiyang in Guizhou, Kaiyuan in Liaoning, Nanxiong in Guangdong） as calibration samples, and 67 aroma components as indices. And the Fisher discriminant functions were verified using 21 C3F and 19 B2F tobacco leaf samples. [Result] Variation coefficients of the propor- tions were lower than that of contents of most aroma components in middle and upper leaves of the samples, indicating that the proportions were more stable than contents of aroma components. The proportions of benzyl alcohol, solanone, β-dam- ascone, neophytadiene, farnesylacetone A, palmitic acid, thunbergol, methyl linole- nate and cembratriene-diol were all over 1% in both middle and upper leaves, al- though the dominant aroma components of the same aroma type varied between middle and upper leaves. Moreover, 11, 18, 7 and 11 aroma components were re- spectively introduced into the Fisher discriminant functions established based on the contents and proportions of middle and upper flue-cured tobacco leaves, which ex- hibited accuracy rates of 91.7%, 100%, 91.7% and 91.7% in the judgments of other tobacco leaf samples. The results revealed that the components those determined aroma types in middle leaves were obviously more than in upper leaves. In middle leaves, the accuracy rates of aroma type judgment could be improved by using the proportions rather than the contents of aroma components as indices. However, the functions based on the proportions and the contents of aroma components in upper leaves gave close accuracy rates. [Conclusion] The results of the study will provide references for identifying aroma types of flue-cured tobacco leaves in future work.
基金Project(50490274) supported by the National Natural Science Foundation of China
文摘Based on the principle of Mahalanobis distance discriminant analysis (DDA) theory, a stability classification model for mine-lane surrounding rock was established, including six indexes of discriminant factors that reflect the engineering quality of surrounding rock: lane depth below surface, span of lane, ratio of directly top layer thickness to coal thickness, uniaxial comprehensive strength of surrounding rock, development degree coefficient of surrounding rock joint and range of broken surrounding rock zone. A DDA model was obtained through training 15 practical measuring samples. The re-substitution method was introduced to verify the stability of DDA model and the ratio of mis-discrimination is zero. The DDA model was used to discriminate 3 new samples and the results are identical with actual rock kind. Compared with the artificial neural network method and support vector mechanic method, the results show that this model has high prediction accuracy and can be used in practical engineering.
基金Supported by the National Natural Science Foundation of China （No.60421002）.
文摘Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online fault diagnosis, sufficiently utilize the finite information of faults, and enhance the diagnostic performance, an improved multi-model Fisher discriminant analysis is represented. The trait of the proposed method is that the training data sets are made of the current measured information and the past major discriminant information, and not only the current information or the whole batch data. An industrial typical multi-stage streptomycin fermentation process is used to test the performance of fault diagnosis of the proposed method.
基金supported by the National Hi-tech Research and Development Program of China (No.2006BAK03B02-04) the New Century Excellent Talent Support Plan of Ministry of Education of China (No.NCET-06-0477)
文摘Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.
基金Supported by the National 11th Five-Year Science and Technology Supporting Plan of China(2006BAB02A02)Central South University Innovation funded projects (2009ssxt230, 2009ssxt234)
文摘A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.
文摘Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.
基金supported by National Natural Science Foundation of China(60802069,61273270)the Fundamental Research Funds for the Central Universities of China+1 种基金Natural Science Foundation of Guangdong Province(2014A030313173)Science and Technology Program of Guangzhou(2014Y2-00165,2014J4100114,2014J4100095)
基金Project(50490274)supported by the National Natural Science Foundation of China
文摘A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.
