This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburba...This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburban landscape.Indeed,the introduction of traffic restrictions in towns with low emission zones encourages a detailed study,on a fine spatial scale,of the differences in accessibility between different modes of transport,for different services and for different journey times.Using spatial analysis techniques,we examine accessibility patterns in relation to services such as shops,healthcare,education,and tourism,highlighting significant disparities between transport modes.The findings reveal that car travel provides the highest accessibility across all service categories,particularly for healthcare and recreational services,while bicycle and public transit accessibility is more limited,especially in peripheral areas.A Principal Component Analysis(PCA)synthesizes the multimodal accessibility data,and hierarchical clustering identifies distinct patterns of accessibility using different transport modes across the city.The study further explores temporal trends in accessibility,showing how different modes perform over varying travel times.Based on these findings,we propose targeted policy interventions aimed at improving public transit,enhancing cycling infrastructure,decentralizing essential services,and promoting mixed-use urban development.Future research directions include examining socio-economic disparities,the impact of emerging mobility technologies,and the environmental implications of accessibility patterns.This research provides valuable insights for urban planners seeking to improve mobility equity and sustainability in urban areas.展开更多
Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC...Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.展开更多
The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cut...The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its v...In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its variability and evaluate the validation of reanalyzed precipitation. The results showed that northward movement of the summer rain belt was a wavelike propagation, which was always accompanied by rainfall breaks and could be treated as one event under time scale of about 1 month only. The first 4 EOFs accounted for 28% and 35% of total variance from observation and reanalysis, respectively, and were roughly consistent with each other. The first and third EOFs for observation mainly represented interweekly, interseasonal and interannual variations and contained some summer intraseasonal fluctuations also. The second and fourth ones mainly represented some rather strong summer intraseasonal fluctuations for a paticular year and contained interweekly, interseasonal and interannual variations also. Although there is still room for improvement, the ECMWF reanalysis is the best available dataset with global coverage and daily variability.展开更多
The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but stil...The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.展开更多
The aim of this study is to compare the Discrete wavelet decomposition and the modified Principal Analysis Component (PCA) decomposition to analyze the stabilogram for the purpose to provide a new insight about human ...The aim of this study is to compare the Discrete wavelet decomposition and the modified Principal Analysis Component (PCA) decomposition to analyze the stabilogram for the purpose to provide a new insight about human postural stability. Discrete wavelet analysis is used to decompose the stabilogram into several timescale components (i.e. detail wavelet coefficients and approximation wavelet coefficients). Whereas, the modified PCA decomposition is applied to decompose the stabilogram into three components, namely: trend, rambling and trembling. Based on the modified PCA analysis, the trace of analytic trembling and rambling in the complex plan highlights a unique rotation center. The same property is found when considering the detail wavelet coefficients. Based on this property, the area of the circle in which 95% of the trace’s data points are located, is extracted to provide important information about the postural equilibrium status of healthy subjects (average age 31 ± 11 years). Based on experimental results, this parameter seems to be a valuable parameter in order to highlight the effect of visual entries, stabilogram direction, gender and age on the postural stability. Obtained results show also that wavelets and the modified PCA decomposition can discriminate the subjects by gender which is particularly interesting in biometric applications and human stability simulation. Moreover, both techniques highlight the fact that male are less stable than female and the fact that there is no correlation between human stability and his age (under 60).展开更多
针对传统方法在运动鞋用户评论的感性因子提取中存在的效率低下、维度冗余问题,提出一种结合大语言模型(large language model,LLM)与主成分分析(principal component analysis,PCA)的自动化提取方法。以亚马逊电商平台的8680条运动鞋...针对传统方法在运动鞋用户评论的感性因子提取中存在的效率低下、维度冗余问题,提出一种结合大语言模型(large language model,LLM)与主成分分析(principal component analysis,PCA)的自动化提取方法。以亚马逊电商平台的8680条运动鞋用户评论为研究对象,采用GLM-4-9B-Chat模型自动生成感性词汇对,经数据清理后获得7619条有效数据;通过TF-IDF向量化处理后,设计k=10、15、20、25四组K-means聚类实验,对冗余维度进行合并优化,最终收敛得到6个核心感性因子。该方法通过整合LLM自动化提取、多聚类去冗余与PCA分析,为运动鞋感性工学的自动分析提供了一条技术路径,也为纺织服装领域的感性因子自动化提取研究提供了有益参考。展开更多
Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project...Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.展开更多
In order to prevent and control environmental mass incidents,by comprehensively using literature research,case analysis and logical reasoning method,27 factors influencing environmental mass incidents were selected. A...In order to prevent and control environmental mass incidents,by comprehensively using literature research,case analysis and logical reasoning method,27 factors influencing environmental mass incidents were selected. Among them,15 key influencing factors were screened by Delphi-PCA-frequency analysis composite method. The key influencing factors were analyzed,and corresponding countermeasures were put forward.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on princip...To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.展开更多
Radish(Raphanus sativus L.)is an important vegetable crop worldwide.High nutritional quality was critical in its genetic improvement and production.The nutritional quality of 42 Chinese radish cultivars was analyzed i...Radish(Raphanus sativus L.)is an important vegetable crop worldwide.High nutritional quality was critical in its genetic improvement and production.The nutritional quality of 42 Chinese radish cultivars was analyzed in this study.The contents of six nutritional facts,dry matter(DM),crude fiber(CF),total soluble sugar(TSS),vitamin C(Vc),protein,and nitrate,ranged from 29.7 to 88.2,4.507 to 18.546,2.233 to 15.457,0.1416 to 0.3341,0.34 to 1.15,and 1.81 to 5.89 g·kg^-1 fresh weight(FW),respectively.Significant differences among the 42 radish cultivars were detected in the contents of nutritional facts.The data were subjected to cross-correlation analysis and principal component analysis(PCA).It was found that DM content was positively correlated with the content of TSS(r=0.7104),Vc(r=0.4011)and protein(r=0.4120).Vitamin C(Vc)content of radish showed a positive correlation(r=0.3300)with the protein content.According to the principal component analysis,out of the 42 radish cultivars,Nau-17,Nau-28,Nau-6,Nau-11,Nau-10,Nau-27,and Nau-31 were detected with very high scores in comprehensive evaluation.It could be concluded that abundant diversity of nutritional fact content occurred in different radish genotypes,and PCA analysis was effective for selecting radish germplasm with high quality.The results could contribute useful knowledge of nutritional quality,and provide important germplasms for the elite cultivar development and the inheritance study of nutritional facts in radish.展开更多
文摘This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburban landscape.Indeed,the introduction of traffic restrictions in towns with low emission zones encourages a detailed study,on a fine spatial scale,of the differences in accessibility between different modes of transport,for different services and for different journey times.Using spatial analysis techniques,we examine accessibility patterns in relation to services such as shops,healthcare,education,and tourism,highlighting significant disparities between transport modes.The findings reveal that car travel provides the highest accessibility across all service categories,particularly for healthcare and recreational services,while bicycle and public transit accessibility is more limited,especially in peripheral areas.A Principal Component Analysis(PCA)synthesizes the multimodal accessibility data,and hierarchical clustering identifies distinct patterns of accessibility using different transport modes across the city.The study further explores temporal trends in accessibility,showing how different modes perform over varying travel times.Based on these findings,we propose targeted policy interventions aimed at improving public transit,enhancing cycling infrastructure,decentralizing essential services,and promoting mixed-use urban development.Future research directions include examining socio-economic disparities,the impact of emerging mobility technologies,and the environmental implications of accessibility patterns.This research provides valuable insights for urban planners seeking to improve mobility equity and sustainability in urban areas.
基金funding from the National Natural Science Foundation of China (Grant No.42277175)the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056)the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).
文摘Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.
基金National Natural Science Foundation of China(No.51805079)Shanghai Natural Science Foundation,China(No.17ZR1400600)Fundamental Research Funds for the Central Universities,China(No.16D110309)
文摘The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
文摘In this study, principal component analysis(PCA) and complex Morlet wavelet transform were used with daily rainfall in China for the period 1980-1993(1 May-31 Dec.) from observation and ECMWF reanalysis to study its variability and evaluate the validation of reanalyzed precipitation. The results showed that northward movement of the summer rain belt was a wavelike propagation, which was always accompanied by rainfall breaks and could be treated as one event under time scale of about 1 month only. The first 4 EOFs accounted for 28% and 35% of total variance from observation and reanalysis, respectively, and were roughly consistent with each other. The first and third EOFs for observation mainly represented interweekly, interseasonal and interannual variations and contained some summer intraseasonal fluctuations also. The second and fourth ones mainly represented some rather strong summer intraseasonal fluctuations for a paticular year and contained interweekly, interseasonal and interannual variations also. Although there is still room for improvement, the ECMWF reanalysis is the best available dataset with global coverage and daily variability.
文摘The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.
文摘The aim of this study is to compare the Discrete wavelet decomposition and the modified Principal Analysis Component (PCA) decomposition to analyze the stabilogram for the purpose to provide a new insight about human postural stability. Discrete wavelet analysis is used to decompose the stabilogram into several timescale components (i.e. detail wavelet coefficients and approximation wavelet coefficients). Whereas, the modified PCA decomposition is applied to decompose the stabilogram into three components, namely: trend, rambling and trembling. Based on the modified PCA analysis, the trace of analytic trembling and rambling in the complex plan highlights a unique rotation center. The same property is found when considering the detail wavelet coefficients. Based on this property, the area of the circle in which 95% of the trace’s data points are located, is extracted to provide important information about the postural equilibrium status of healthy subjects (average age 31 ± 11 years). Based on experimental results, this parameter seems to be a valuable parameter in order to highlight the effect of visual entries, stabilogram direction, gender and age on the postural stability. Obtained results show also that wavelets and the modified PCA decomposition can discriminate the subjects by gender which is particularly interesting in biometric applications and human stability simulation. Moreover, both techniques highlight the fact that male are less stable than female and the fact that there is no correlation between human stability and his age (under 60).
文摘针对传统方法在运动鞋用户评论的感性因子提取中存在的效率低下、维度冗余问题,提出一种结合大语言模型(large language model,LLM)与主成分分析(principal component analysis,PCA)的自动化提取方法。以亚马逊电商平台的8680条运动鞋用户评论为研究对象,采用GLM-4-9B-Chat模型自动生成感性词汇对,经数据清理后获得7619条有效数据;通过TF-IDF向量化处理后,设计k=10、15、20、25四组K-means聚类实验,对冗余维度进行合并优化,最终收敛得到6个核心感性因子。该方法通过整合LLM自动化提取、多聚类去冗余与PCA分析,为运动鞋感性工学的自动分析提供了一条技术路径,也为纺织服装领域的感性因子自动化提取研究提供了有益参考。
基金State Key Laboratory of Hydroscience and Hydraulic Engineering of Tsinghua University,Grant/Award Number:2019-KY-03Key Technology of Intelligent Construction of Urban Underground Space of North China University of Technology,Grant/Award Number:110051360022XN108-19+3 种基金Research Start-up Fund Project of North China University of Technology,Grant/Award Number:110051360002Yujie Project of North China University of Technology,Grant/Award Number:216051360020XN199/006National Natural Science Foundation of China,Grant/Award Numbers:51522903,51774184National Key R&D Program of China,Grant/Award Numbers:2018YFC1504801,2018YFC1504902。
文摘Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.
基金Supported by the National Natural Science Foundation of China(41001338)Key Projects of Henan Colleges and Universities(15A610012)
文摘In order to prevent and control environmental mass incidents,by comprehensively using literature research,case analysis and logical reasoning method,27 factors influencing environmental mass incidents were selected. Among them,15 key influencing factors were screened by Delphi-PCA-frequency analysis composite method. The key influencing factors were analyzed,and corresponding countermeasures were put forward.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
基金supported by the National Natural Science Foundation of China(71401052)the Key Project of National Social Science Fund of China(12AZD108)+2 种基金the Doctoral Fund of Ministry of Education(20120094120024)the Philosophy and Social Science Fund of Jiangsu Province Universities(2013SJD630073)the Central University Basic Service Project Fee of Hohai University(2011B09914)
文摘To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.
基金grants from the National Natural Science Foundation of China(30571193)the 863 Program of China(2008AA10Z150)+3 种基金the National Key Technologies R&D Program of China(2008BA DB 1B03)the 111 Project from Ministry of Education of China(B08025)the Hi-Tech Key Project of Jiangsu Province(BG2005314)the project of Gene Bank Construction for Brassicaceae Vegetable Germplasm Resources of Jiangsu Province[sx(2007)g13].
文摘Radish(Raphanus sativus L.)is an important vegetable crop worldwide.High nutritional quality was critical in its genetic improvement and production.The nutritional quality of 42 Chinese radish cultivars was analyzed in this study.The contents of six nutritional facts,dry matter(DM),crude fiber(CF),total soluble sugar(TSS),vitamin C(Vc),protein,and nitrate,ranged from 29.7 to 88.2,4.507 to 18.546,2.233 to 15.457,0.1416 to 0.3341,0.34 to 1.15,and 1.81 to 5.89 g·kg^-1 fresh weight(FW),respectively.Significant differences among the 42 radish cultivars were detected in the contents of nutritional facts.The data were subjected to cross-correlation analysis and principal component analysis(PCA).It was found that DM content was positively correlated with the content of TSS(r=0.7104),Vc(r=0.4011)and protein(r=0.4120).Vitamin C(Vc)content of radish showed a positive correlation(r=0.3300)with the protein content.According to the principal component analysis,out of the 42 radish cultivars,Nau-17,Nau-28,Nau-6,Nau-11,Nau-10,Nau-27,and Nau-31 were detected with very high scores in comprehensive evaluation.It could be concluded that abundant diversity of nutritional fact content occurred in different radish genotypes,and PCA analysis was effective for selecting radish germplasm with high quality.The results could contribute useful knowledge of nutritional quality,and provide important germplasms for the elite cultivar development and the inheritance study of nutritional facts in radish.
